2017 SUMMER RESEARCH SCHOLARS
Developing a System to Measure Fish Abundance and Length Using Stereoscopic Video Danielle Baik1, Christopher Rillahan2 and Pingguo He2 1Manhattan
College, Bronx, New York 2School for Marine Science and Technology, UMass Dartmouth, New Bedford, Massachusetts
40 cm Image at 3m
40 35 30 25 20 15 10 5 0
1
10 cm
20 cm
30 cm
40 cm
2 3 4 5 Distance from Camera (m)
12 10 8 6 4 2 0
0
15
30 Angle (°)
45
Scup, Stenotomus chrysops3
Objectives
Stereo Camera • Used Tara stereo camera • Deployed for approximately three to four hours (n=10) • Squid used as bait and placed one meter away • System accuracy and water visibility were the two main factors in selecting bait location a.
b.
1. Develop a cost-effective system to capture underwater stereoscopic video of fish. 2. Develop a method to obtain length and abundance estimates of fish from a stereo camera. 3. Compare camera data to a traditional fish pot.
Figure 3. (a) The stereo camera underwater. (b) The set-up of the stereo camera.
Calibration
• Needed to turn the image data into three-dimensional spatial data • Estimates the cameras’ intrinsic (i.e. image distortion) and extrinsic (i.e. relative position of the cameras) characteristics. • Conducted with a checkerboard pattern image4 (fig. 1) • 34 pairs of images were taken at various angles and distances in the School for Marine Science and Technology (SMAST) test tank • The calibration was done in the SEBASTES program
Trap • Two traditional fish pots were set off the SMAST pier and allowed to soak for ~24 hours (n=10) • Squid used as bait • Each fish was measured by the fork length and then returned back to the water a.
a. b.
Figure 1. (a) Images used in the calibration. (b) Calibration being done in SEBASTES.
Figure 4. (a) The trap used. (b) Danielle measuring scup caught in a trap.
b.
Figure 5. A black sea bass being measured in SEBASTES.
Results
60
Field Deployments
Black sea bass, Centropristis striata2
• SEBASTES is a program developed by the National Marine Fisheries Services (NMFS) Alaska Science Center to process stereoscopic images.5 • The first frame of every minute of video was extracted and used in this analysis. • Within each frame, each fish was marked, identified and measured (if feasible).
Black Sea Bass
Frequency
Absolute Error at 0°
Analysis
20 18 16 14 12 10 8 6 4 2 0
Scup 120 100 Trap Video
Frequency
• To determine the accuracy of the stereo camera, four images of fish measuring 10 cm, 20 cm, 30 cm and 40 cm were used. • Video was taken with the stereo camera. • For each distance (1 m, 2 m, 3 m, 4 m and 5 m), 2 images were taken at each angle (0°, 15°, 30°, 45° and 60°) for a total of 10 images/distance. Average Absolute Error (cm)
• Baited remote underwater video (BRUV) is a non-extractive sampling method. • They are popular in complex habitats (e.g. rocky) unlike trawls and traps.1 • BRUV is appealing due to the advancements in hardware (i.e. sensors, batteries and storage) and software (i.e. computer vision and image analysis algorithms). • Stereo cameras have two cameras, which allows it to create three dimensional images. • Stereo cameras are useful because in addition to getting estimates of population abundance, they can be used to measure fish. • BRUV can be used to study local fish species, such as black sea bass and scup.
Length Measurement Validation
Average Absolute Error (cm)
Introduction
80 60 40 20
6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 Length (cm)
0
3 7 11 15 19 23 27 31 35 39 43 47 51 55 59 Length (cm)
Figure 6. The length and frequency of black sea bass (left) and scup (right) from traps and video Species Scup Black sea bass Puffer Striped bass Smooth dogfish Oyster toadfish Tautog Summer flounder
Trap 339 10 1 0 1 2 1 1
Video 1872 768 242 10 0 0 0 0
Table 1. The species composition of the traps and videos.
Conclusions
Stereo Camera Traps • Camera and SEBASTES were able • Only caught intermediate sized fish to measure abundance and lengths • Factors that may have affected of the fish capture: • Had a larger range of lengths, but o Location of traps maybe susceptible to measurement o Size of fish inaccuracies o Size of mesh • Accuracy of fish length decreases as the distance from the camera increases References 1. Mallet, D. and Pelletier, D., 2014. Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952–2012). Fish. Res. 154, 44-62. 2. Peebles, D. R. (n.d.). Black Sea Bass - female [Digital image]. Retrieved from http://www.naturalnorthflorida.com/black-sea-bass/ 3. Scup. Retrieved from https://thisfish.info/fishery/species/scup/ 4. Bouguet, J.Y., 2008. Camera calibration toolbox for Matlab [online]. [Available from http://www.vision.caltech.edu/bouguetj/calib_doc/index.html (accessed September 2008)]. 5. Williams, K., R. Towler, P. Goddard, R. Wilborn, and C. Rooper. 2016. Sebastes stereo image analysis software. AFSC Processed Rep. 2016-03, 42 p. Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv., 7600 Sand Point Way NE, Seattle WA 98115. doi:10.7289/V5/AFSCPR-2016-03. Acknowledgment This material is based upon work supported by the National Science Foundation under Grant No. DBI-1469080.
Predicting Rates of Bark Formation on Saguaro Cacti (Carnegiea gigantea) Mia Bertoli, Biology Department
Background
Green
Bark Formation
Death
Materials and Methods 600 individual cacti were sampled from Tucson Mountain Park in Tucson, Arizona. The percent bark was determined and entered into various computer programs. These programs predicted cactus mortality and rates of bark formation over time.
Saguaro cacti are native to southern Arizona. Each cactus has twelve surfaces that bark at different rates as a result of sun injury. This bark prevents adequate gas exchange, which results in premature death.
Purpose The purpose of this research was to determine if bark percentages from previous years can be used to predict cactus mortality and rates of bark formation over time.
Hypothesis 1
The sum of bark percentages on all twelve surfaces predicts cactus death.
Results The sum of bark percentages on all twelve surfaces predicts cactus death (WEKA program – 100% and 72% accuracy.)
Hypothesis 2
Hypothesis 3
Bark percentages on south facing surfaces will predict cactus death.
MATLAB was used to predict rates of bark formation over more than 4000 surfaces of cacti. Rates of bark formation for cacti slower or faster than average were compared to determine whether surfaces were shaded or unshaded.
Results
Results
Bark percentages on south facing surfaces predicts cactus death (WEKA program – 64% and 72% accuracy.)
Cactus outliers that had bark formation rates slower than average were more shaded than cactus outliers that had bark formation rates faster than average.
Image Recognition using Autoencoding in Multilayer Neural Networks and Multi-valued Neurons Manhattan College Summer Research Program, Research Student Niko Colon Research Colleague: Alexander Gonzalez, Research Advisor Dr. Igor Aizenberg
Autoencoding and Splitting the Network
MLMVN
Batch Learning
Introduction
Autoencoding is a process where a multilayer neural network reproduces its inputs through a learning process. Autoencoding makes it possible to extract some specific features from a dataset to be learned. These features, being accumulated in the weights of hidden neurons could then be used to classify (recognize) the input data. This is done by splitting the network where we keep all or some of the hidden layer weights while replacing the output layer by the new one with random weights. Then such a re-created network may learn how to classify.
A Multilayer Neural Network with Multi-Valued Neurons (MLMVN) is a neural network, that is an intelligent tool, which gathers experimental knowledge acquired through a learning process, in which each neuron forms a set of complex-valued weights corresponding to its inputs and produces an output using an activation function applied to the weighted sum of inputs.
Experimental Results
The 1024 network learned 1200 sample and when tested recognized 51% of the test sample.
With the batch learning approach, we are able to train the network using the entire learning set consisting of 60,000 learning samples through increments of 200 samples. This is done by having the network learn the first 200 samples, saving the weights and then using the weights gained from the first 200 samples for the next 200 samples, etc. The network will keep on repeating this process in till if finishes learning all 60,000 samples, while at the same time accumulating new weights for each batch of 200 learning samples. This process should be repeated iteratively.
Conclusion
The 1024 network learn the entire training set through the batch approach and recognize 71% of the test sample.
The 2048 network learned 1200 sample and when tested recognized 42% of the test sample.
Future Works
Acknowledgments
With the use of the school GPU network simulator we will be able to teach the network using bigger batch, which should give us significantly better results.
. Manhattan College School of Science for the summer grant and housing Dr. Igor Aizenberg for mentoring me throughout during the research •
The 1024x3 network and 2048x3 network were only able to learn 1200 samples from the learning set due to the limitations of the conventional computer memory. The 1024x3 network was still able to recognize 51% of the test set samples (there are 10,000 test samples in total). This is most likely due to the network size looks optimal for the size of the learning set. The 2048x3 network size might have been too big and wasn’t able to generalize as well as the 1024x3 network. The network with the best results is the 1024x3 network with the batch learning approach. This is due to the fact that this network was able to learn the entire learning set and therefore when tested was able to recognize correctly 71% of the test sample. Yet, because the size of the batch was only 200 it wasn’t able to maintain enough information from the entire learning set. But this tells us the if we were able to run a GPU network simulator on a computer with a powerful GPU, we should expect significantly better results then the ones received in this research. Such a simulator was designed, but we did not have an access to such a computer so far. So we will definitely continue our work and expect to have better results.
Reference •
•
Aizenberg, Igor. “Complex-Valued Neural Networks with Multi-Valued Neurons”, June 2011, pp. 2-10 http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/
Methylammonium lead iodide nanowires for perovskite solar cells Jacqueline DeLorenzo, Department of Chemistry and Biochemistry, Manhattan College, e-mail: jdelorenzo01@manhattan.edu Faculty Advisor: Dr. Alexander Santulli, Department of Chemistry and Biochemistry, Manhattan College
Introduction Methylammonim Lead Iodide absorbs a large amount of the solar spectrum and conducts electrons well, making it an excellent material to be used in solar cells. By changing the conditions under which the nanowires grow, we can grow the optimal MALI nanowires for cost-efficient solar energy production.
X-Ray Diffraction
A
Materials and Methods .1 M Potassium Iodide and .05 M Lead Nitrate were run in a U-tube reaction. Lead iodide nanowires were formed at the interface through a polycarbonate template with pores ranging from 15-200 nm. The filter was placed in .1 M Methylammonium and then dissolved in Methylene chloride to isolate the nanowires. MALI was then spraycoated onto Fluorinedoped Tin Oxide glass and coated with spiro-MeOTAD in chlorobenzene to make the final electrically conductive device.
B
X-Ray Diffraction measurements were taken for our synthesized Lead Iodide and MALI nanowires. As shown in graph A, our synthesized Lead Iodide was pure compared to the standard, purchased Lead Iodide. In graph B it can be observed that the Lead Iodide was completely converted to Methylammonium Lead Iodide.
SEM Imaging A
B
C
D
SEM images were taken of the synthesized Lead Iodide and MALI nanowires. Images A and B show 100 nm Lead Iodide and MALI nanowires, respectively. Images C and D show 200 nm Lead Iodide and MALI nanowires, respectively. Synthesized nanowires ranged from approximately 1.0-7.0 microns in length and on average 0.1-0.2 microns wide.
Conclusions Lead Iodide nanowires can be synthesized successfully and then converted to MALI nanowires. These nanowires can then be spray coated onto a device that can convert UV rays to an electrical current (shown to the right).
What’s in the Air? Using Mathematical Models to Predict Boston Air Quality Anthony DePinho, Tara Ippolito, Biyonka Liang, Kaela Nelson, Annamira O’Toole Institute of Applied Computational Science, Harvard University Sponsors: Gary Adamkiewicz, Jaime Hart, Pavlos Protopapas Advisor: Weiwei Pan
Motivation
Visualization Tool
Elongated exposure to air pollutants are a significant public health concern, especially for those living in large cities. In this study, we trained geospatial and spatio-temporal models for three EPA criteria pollutants - N O2, SO2, and P M2.5 - using data collected from 398 counties across the US and applied the models to predict intra-urban pollution concentration levels for a 107.495 square mile region covering the Greater Boston area. Our study addresses also the public health challenge of effectively and meaningfully communicating scientific findings in environmental science to the general public.
Our interface is an interactive map of the Greater Boston Area which our 50 by 50 grid was overlaid. The map has layers that can be toggled on and off such as transit maps, or county lines. The drop-down menu is fully functioning, as well as drag and zoom functions. Each pollutant can be visualized on its own or with other pollutants. There’s a link to download the cleaned and rastered data, as well as a take a tour option for those new to the site. The interface was designed using HTML, CSS style sheets and D3.js, a javascript library.
Figure 4: Variable selection for PM2.5
Figure 5: Variable selection for NO2
Problem Statement Limited Spatial Coverage. There are not enough Environmental Protection Agency (EPA) sensors to provide an adequate assessment of intra-urban spatial variations in air quality conditions (see Figure 1). Our main objective is to implement statistical modeling techniques to model air pollution in areas that existing sensors do not cover. Inaccessibility of Scientific Findings Another major goal of this project is to design an easy-to-read, interactive interface which aims to communicate the results of our statistical models meaningfully to anyone who uses it, a way to understand air quality conditions on both a city wide and granular (neighborhood) level.
Figure 2: Data sources and formats Land Use Regression (LUR) is a linear regression model commonly used to predict air pollutant concentration based on geospatial variation. Our LUR model was trained and tested on data collected from US sites outside of Boston and, thereafter, used to predict concentration levels for each grid cell covering the Greater Boston Area. In addition, We performed variable selection and analysis to reason about the impact of variables on the concentration of atmospheric pollutants. The form of our LUR models is as follows y = β0 + β1X1 + β2X2 + ... + βnXn + , where the dependent variable y is the pollutant concentration of a given area; and X1...Xn represent the set or subset of geospatial predictor variables. Prior to variable selection, these predictors consist of a total of 14 variables from two categories: land use and weather. Three different metrics were used for variable selection: p-value, R2 and AIC, and the results were compared. For R2 variable selection, 8-fold cross validation was implemented. For all metrics, variable selection reduced the predictor set to 8-12 variables. Using results from our variable selection, we analyzed predictors that appear most often in the final subsets (Figure 3)
Figure 1: Locations of EPA Air Quality sensors in Greater Boston with 50x50 grid.
To work with dynamic data and to provide more flexibility, we also used a Gaussian Process model. A Gaussian Process model uses a nonparametric representation of the underlying function relating predictor and response. We assume that any subset of our pollution concentration levels (both observed, y, and unknown, y∗) have a joint Gaussian distribution, (y, y∗) ∼ N (µ, Σ)
wherein the covariance matrix Σ is determined by some metric of similarity of the geospatial and dynamic characteristics of the locations of the corresponding observation sites. That is, each entry in the covariance matrix is computed by a function in terms of the predictors, this is the kernel function. In this study, the standard radial basis function (RBF) kernel is used: 2 −(x − x*) 2 ) + σ K(x, x*) = σf2 exp( nδ(x, x*) 2 2 where σf2 is the amplitude of the air quality approximation, is the length scale, and σn2 is the noise variance. In our models, we used a constant noise level of 0.001.
Figure 7: Evaluation of Gaussian Process Model for three pollutants
Methodology In our models, we include static geospatial data as well as dynamic data. Static geospatial data consists of land use, topography and bus routes while dynamic data consists of traffic and weather. The training data consists of data (Figure 2) collected from 398 counties throughout 16 US states, the majority of which where located on the East Coast. The trained models were applied to a 107.495 square mile region overing Greater Boston. This area was divided into a 50 by 50 grid. All data were rastered through proportioning or averaging processes so that each cell had it’s own weather, land use, and traffic attributes.
Figure 6: Variable selection for SO2
Analysis
Figure 3: Land Use type frequency in variable selection
A common theme in all three of our Gaussian Process models is that they did not perform significantly better than our LUR models, as we initially hypothesized (and as existing studies in literature would indicate). One possible explanation is that further feature engineering is required. Another compelling possibility explaining the poor performance of the Gaussian Process Model is that it mixes geospatial predictors extracted from the 1970s and 1980s with dynamic predictors gathered from 2017.
Figure 8: Screenshot of interface
Conclusion Air quality measurements, while not widely available or understandable, are crucial for understanding public health. While there were time and resource constraints limiting the quality of the data collected, our models can be expanded upon to create better predictions in a variety of places. Better data would allow for more accurate predictions to be made and conclusions to be drawn about the way pollution differs geographically. Our interface could also be generalized to fit a number of other cities and their data. Since Sulfur Dioxide, Nitrogen Dioxide, and Particulate Matter 2.5 can exacerbate cardiovascular and respiratory issues, it is crucial that the public have knowledge of areas to avoid and city-specific issues to be addressed.
References [1] David Hasenfratz, Olga Saukh, Christoph Walser, Christoph Hueglin, Martin Fierz, Tabita Arn, Jan Beutel, and Lothar Thiele. Deriving high-resolution urban air pollution maps using mobile sensor nodes. Pervasive and Mobile Computing, 16:268–285, 2015. [2] US Environmental Protection Agency. Air quality criteria for particulate matter, October 2004. [3] US Environmental Protection Agency. Risk and exposure assessment to support the review of the so2 primary national ambient air quality standards: Final report, July 2004. [4] US Environmental Protection Agency. Risk and exposure assessment to support the review of the no2 primary national ambient air quality standard, November 2008.
Characterizing in vivo Chromatin Remodeler Interactions at the Yeast Nucleosome Core Brian Evans Manhattan College School of Science, Chemistry & Biochemistry Department Introduction
Results and Discussion
Humans and yeast are separated by a billion years of evolution. Even still, their core histones have retained essential and fundamental roles in gene regulation. In humans and yeast, genetic information is stored as chromatin. Within the chromatin, DNA is wound around octameric units of histone proteins. The histone-DNA complex forms the nucleosome and represents the most basic level of DNA compaction. Depending on the cellular needs, this complex is directly modified and reorganized in order to control transcriptional activation or repression.
DNA Compaction
DNA Helix
Nucleosome
Beads on a String
30nm Fiber
Active Chromosome with Scaffold Proteins
Metaphase Chromosome
Chromatin remodeler (CR) complexes play crucial roles in regulating chromosomal architectural and act to modify nucleosomal DNA contacts by repositioning nucleosomes (1). The mechanistic details of the CR family of proteins are of importance because each remodeler complex contributes to unique chromatin structural maintenance. CRs are comprised of an ATPase active subunit and numerous auxiliary components that are involved in elaborate protein-chromatin stabilizing interactions. This makes it a challenging task to resolve completely their structures and how each subunit may interact individually with the nucleosome.
Core histone H2A/H2B/H3/H4 Complex between nucleosome core and DNA (3).
Cartoon Representation of Nucleosomal Structure
Low Resolution RSC/Nucleosome Complex based on Cryo-EM
Subunit of Interest: Sth1
PCR Verification for Sth1
PCR products were run on 1% agarose gel
The remodeler, RSC, contains over 15 subunits. The enzymatic subunit, Sth1, is an ATPase responsible for catalytic control of nucleosomal translocation. To gain better structural and functional insight into this important subunit, yeast strains were produced to harbor sitespecifically encoded pBPA on histone H2A at position A61. This strain was then transformed to modify the Sth1 gene to add a short peptide fusion myctag to the C-terminal end of Sth1. Due to its larger size, the myc-tag acts to alter the electrophoretic migration rate of Sth1.
Western Blot of UV Crosslinks
Samples were run on 8% polyacrylamide gel
* * Figure 1
Figure 1: PCR verification of the Sth1 gene was done to ensure one of the strains produced was successfully mutated to express the desired myc-tag. The PCR verification product for Strain #2 had a similar electrophoretic migration rate as the control Wt strain. This strongly indicates that Strain #2 was unsuccessfully mutated. On the other hand, Strain #1 exhibited a clear electrophoretic shift. This shift indicates Strain #1 successfully incorporated the expected genetic mutation to express the myc-tag due to a larger PCR product from the insertion of the tag and a selectable marker.
Figure 2 Figure 2: Crosslinks to Sth1 from histone H2A were examined in wild type (Wt) and Sth1 tagged yeast strains by western blotting. A band shift was observed that was clearly UV dependent in response to a histone H2A interaction. The Sth1histone H2A interaction was also identified by an electrophoretic size shift relative to the myctagged Sth1 protein when compared to crosslinks in Wt cells.
Conclusion Previous research studies on chromatin remodelers often rely on the reconstitution of nucleosomal arrays in solution. These studies inherently fail to properly recapitulate the true nuclear environment. As a result, to date, there are no high-resolution structural data on CRs in complex with the nucleosome. Therefore, to enhance the understanding of CR behavior a technique is required that illuminates the molecular contacts that occur between the nucleosome and CR proteins inside the living nucleus.
Methodology To illuminate these contacts we use a method that allows for the covalent trapping of histone-protein interactions in the nucleus by employing site-specific, UV-inducible crosslinker amino acids that are genetically incorporated into the nucleosome (3).
UV-Activation of pBPA
The crosslinking approach requires a means of identifying the interaction partner following activation of the crosslink event. A known chromatin related protein is tagged and subsequently assayed for the correct electrophoretic shift in a western blot analysis.
Insertion of 3-myc Tag
By properly manipulating the endogenous system, a yeast strain was produced harboring a myc-tag on the Sth1 gene. This fusion tag combined with the pBPA crosslinking technology represents the first technique to assess structural and dynamic mapping of a chromosomal remodeler, in vivo. Therefore, this technique has the power to bridge the gap between in vitro versus in vivo experimentation of CRs. It will also help researchers to discover and assign biologically relevant structures in these large protein complexes. Using this new structural insight, CRs will become important medicinal targets for disease and developmental disorders. For the immediate future, our lab is interested in using this technique to define how histone posttranslational modification events influence CR binding and dynamics at the nucleosomal surface.
Placement of the crosslinker within a histone protein will provide insights into the nucleosomal surface contacts made by CR subunits. This technique has the potential to reveal the network of interactions within chromosomal pathways and identify the sequence of events involved in those mechanisms (4).
Incorporation of pBPA into Nucleosome
Acknowledgments I am grateful for the generous support of Dr. Constantine Theodosiou, Dean of the School of Science. Special thanks goes to Rani Roy and Elly Mons, directors of the Summer Research Scholars Program. Finally, a big thank you goes to my advisor, Dr. Bryan Wilkins, for his continued guidance and support.
References 1. Bartholomew, B. Regulating the chromatin landscape: Structural and mechanistic perspectives. Annu. Rev. Biochem. 83, 671–696 (2014). 2. Luger, K., et al. Crystal structure of the nucleosome core particle at 2.8 A resolution. Nature 389, 251–260 (1997). 3. Chin, J. W., et al. An expanded eukaryotic genetic code. Science 301, 964–967 (2003). 4. Wilkins, B. J., et al. A cascade of histone modifications induces chromatin condensation in mitosis. Science 343, 77–80 (2014).
Leaf venation patterns and interveinal area in tree leaves Jorge Gonzalez, Biology Department
Introduction Tree species have a variety of morphologies. Leaves on tree species differ in size, overall shape and venation pattern.
The aim of this study was to determine relationships between entire leaf areas with secondary, tertiary and quaternary areas in relation to the distribution of water in leaf veins throughout the leaves.
Results
Materials and Method 1. 2. 3.
Purpose
Seventeen percurrent leaves were chosen from the Manhattan College campus and Van Cortlandt Park. The leaves were photographed under and light box to make all veins visible. All leaf areas were outlined with Microsoft Paint and measured using Image J (National Institutes of Health).
**Secondary areas and number of secondary areas were not related with entire leaf areas.
Conclusion 1 Tertiary areas are related to entire leaf areas. Quaternary areas are not well scaled to entire leaf areas.
Conclusion 2
Table 1 :Percurrent leaf areas with various species.
Number of tertiary areas are not well scaled with entire leaf areas. Number of quaternary areas are well scaled with entire leaf areas.
The author is grateful to the Catherine and Robert Fenton Endowed Chair to Dr. L.S. Evans financial support for this research.
Conclusion 3 Quaternary areas are well scaled with tertiary leaf areas.
Table 2 : Characteristics of percurrent leaves of various species.
Conclusion 4
Number of quaternary areas are well scaled with tertiary leaf areas.
Remarkably Rapid Reduction of Toxic Chromium(VI) Levels Patsy Griffin Department of Chemistry and Biochemistry
BACKGROUND
While Chromium is regarded as the odorless metallic element which occurs naturally in things such as rocks, plants, and animals, its Chromium(VI) form is a known toxicant wreaking havoc on natural water sources around the world. Chromium(VI) is found in the environment both organically by being a product of the erosion of natural chromium deposits, as well as industrially through inadequate industrial waste disposal practices and run-off from dye or textile factories. When consumed its effects range from certain cancers to skin damage as well as respiratory problems. With this contaminated water being drank by Americans across 42 states, the growing problem of Chromium(VI) in drinking water has become a top priority.
STATIC CHROMIUM(VI) REDUCTION
DYNAMIC CHROMIUM(VI) REDUCTION (CON’D)
One of the Chromium(VI) reduction methods employed involved a static reaction with the GAC-AAK complex. Each trial was conducted using a 0.5g sample of the AAK loaded GAC added to various volumes of 200μM Potassium Chromate solution and left to sit statically covered using parafilm for particular times. After the time frame was completed, a 5mL aliquot of the chromate solution was taken out and tested using proper EPA concentration guidelines which includes DPC and a spectrophotometer reading at 540nm. A concentration determined to be less than 1μm is deemed to be safe to consume.
After the optimal stirring speed for Cr(VI) reduction, the next variable that was focused on was how quickly the reaction was actually taking place. To be able to analyze this a kinetics experiment was conducted where 5mL aliquots were taken out from a larger batch (75mL of the 200μM Chromate solution) and were tested for concentration after being stirred for 3, 6, 9, 12, 15, and 30 minutes.
Chromium has another common form, Chromium(III), which is actually an essential human dietary element and is found in many vegetables, fruits, meats, grains, and yeast. For this reason this project aimed to chemically reduce toxic Chromium(VI) into beneficial Chromium (III) through the use of an soluble absorbent, specifically Ascorbic Acid Ketal (5,6-Isopropylidene-L-ascorbic acid).
Ascorbic Acid Ketal + Chromate, Cr(VI) ⟹ Dehydroascorbic Acid Ketal + Cr(III)
WHAT IS GAC AND AAK? •
GAC is an abbreviation for Granulated Activated Carbon which is commonly used to adsorb natural organic compounds, taste and odor compounds, and synthetic organic chemicals in drinking water treatment processes.
•
AAK is short for Ascorbic Acid Ketal, a compound which is both commercially available as well as synthesized using Ascorbic Acid, Acetone, and TFA with heating.
Ascorbic Acid + Acetone + TFA ⟹ Ascorbic Acid Ketal
LOADING ASCORBIC ACID KETAL ONTO GAC
The optimal procedure for loading Ascorbic Acid Ketal on GAC begins by first dissolving the ketal compound in distilled water with use of a stir bar. During this period of time the GAC is to be soaked in distilled water as well. A 1:1 weight equivalent ratio of the ketal to GAC was found to be the most successful. After the ketal fully dissolved, the GAC was decanted into the solution of water and ketal, then left to load statically for 2 hours. It is then filtered and washed with about 2 liters of distilled water, then left to dry in an oven at about 60℃ overnight. This Ascorbic Acid Ketal was deliberately chosen in hopes of improving the results found by my fellow researcher Analisse Rosario who reported that out of the approximate 67 mg of ascorbic acid that was able to be loaded onto 1g of GAC, only about 3.1mg of it was responsible for reducing chromate. This low result suggested that there was ineffective binding occurring in GAC at the C2C3 site. Our hypothesis was that by using this ascorbic acid metal derivative would improve chromate reduction by producing more productive binding orientations. This would happen by eliminating the chances of hydrogen bonding at that site and therefore allow the greasy compound to force hydrophobic binding.
Hydrophobic Surface Map of Ascorbic Acid Ketal Molecule
CONCLUSION DYNAMIC CHROMIUM(VI) REDUCTION
The other method used for Chromium(VI) reduction involved a dynamic stirring reaction with the GAC-AAK complex. Each trial was conducted using a 0.5g sample of the AAK loaded GAC added to various volumes of 200μM Potassium Chromate solution and after being covered with parafilm was placed on the rotator for particular times. After the time was up, the EPA concentration tests explained in detail above were also conducted in order to determine if the water was safe to drink.
•
Ascorbic Acid Ketal is more effective at reducing Cr(VI) than Ascorbic Acid alone. AAK is able to load 99.5mg onto GAC and have 8.6mg of that be responsible for reducing chromium as compared to 67mg and 3.1mg consecutively in Rosario’s research.
•
18 Hour Static reductions are much more proficient than 1 Hour static reductions. Within an hour time frame GAC-AAK is not even capable of reducing 5mL of chromate to sub-micro molar while overnight 20mL is successfully reduced to EPA standard.
•
Dynamic Cr(VI) reduction is completed in 30 minutes with the most rapid reduction taking place within the first 6 minutes. The optimal stirring speed for the reduction of Cr(VI) is 150rpm.
•
Dynamic Cr(VI) reduction is way more proficient than Static. After 30 minutes of stirring 35mL of chromate solution is able to be brought to safe consumable concentration levels.
“Chromium in Drinking Water.” EPA, Environmental Protection Agency, 24 Apr. 2017, www.epa.gov/dwstandardsregulations/chromium-drinking-water. II. Rosario, Analisse. “Eliminating Aqueous Chromium(VI) with Renewable Technology.” The Manhattan Scientist, Volume 3, 175-183 (2016) I.
Manhattan College School of Science for Financial Support The Department of Chemistry and Biochemistry Dr. John Regan as Mentor
Monte Carlo Computer Investigation of Ideal Dendrimers in Two and Three Dimensions Tim Hamling, School of Science Summer Research Program 2017 Introduction
A
B
C
Extrapolated Results
D
• Dendrimers are polymers, consisting of repeating beads/units. • They are tree-shaped, with many branches. • Perfect dendrimers have rotational symmetry. • They have attracted attention because they can carry drugs into hard-to-reach areas of the body.
3D 9-Branches
Methods
• Each dendrimer is classified by the number of branches it contains.
0.604939(0)
0.486(1)
⟨A⟩
0.266(1)
0.26583(9)
0.245(1)
0.24560(1)
0.233(1)
0.116134(0)a
0.208(1)
0.10438(0)a
3D 21-Branches
Structures
Property
Two dimensional representations of the four different dendrimer structures investigated: 9-branch (A), 12-branch (B), 21-branch (C), and 39-branch (D)
N=55
N=181 N=442
N=892
3.76(1)
12.22(2)
29.73(5)
60.07(9)
90.27(14)
⟨2⟩
1.39(1)
4.40(1)
10.64(2)
21.38(3)
32.14(5)
⟨3⟩
0.57(1)
1.81(1)
4.33(1)
8.71(1)
13.08(2)
⟨A⟩
0.255(1) 0.262(1)
0.264(1)
0.266(1)
0.266(1)
⟨S2⟩
5.73(1)
18.43(2)
44.71(5)
90.17(11) 135.50(16)
⟨P⟩
0.215(1) 0.226(1)
0.230(1)
0.233(1)
3D 39-Branches
g-ratio
0.442(1)
⟨A⟩
0.211(1)
0.21076(9)
0.165(1)
0.16484(4)
0.154(1)
0.0763392(0)a
0.104(1)
0.0519346(0)a
N=1342
⟨1⟩
Extrapolated
Exact (Wei Method) 0.441529(0)
⟨P⟩
3D 9 Branch Properties
Exact (Wei Method)
Extrapolated
Property
This is a method that uses random numbers to determine growth directions.
a
Extrapolated
0.267(1)
Exact (Wei Method) 0.265868(0)
Extrapolated
Note that the Wei Method has an additional factor of ½
0.233(1)
3D
Properties • 100,000 independent samples of each structure were generated in both two and three dimensions. • Properties were calculated for each individual sample, and then averaged over the total number of samples. Properties studied include: ▪ Mean-Square Radius of Gyration, ⟨S2⟩ ▪ Asphericity, ⟨A⟩ ▪ ⟨⟩ for two dimensions ▪ ⟨P⟩ for three dimensions ▪ Eigenvalues of the Radius of Gyration Tensors, ’s ▪ Scattering Function, S(k)
30
Theory
Debye 9 Branch Exact 12 Branch Exact 21 Branch Exact 39 Branch Exact 3D Linear N=829 3D 9 Branch N=892 3D 12 Branch N=889 3D 21 Branch N=883 3D 39 Branch N=898
25
20
This graph shows our S(k) results (points) vs the theoretical values (lines) for each different dendrimer configuration.
1/S(k)
• To simplify the models, dendrimers were positioned on a square or cubic lattice coordinate system in two or three dimensions.
0.606(1)
⟨P⟩
Monte Carlo
• Models of dendrimers were created with programs in the C language, compiled using a Linux GCC compiler.
g-ratio
Exact (Wei Method) 0.486112(0)
Property
Raw Data
3D 12-Branches
15
10
5
0
Conclusion Many properties were calculated for various dendrimers, and all computer results held up with theoretical predictions.
Acknowledgements I would like to thank my mentor, Dr. Marvin Bishop, for all of his help throughout this project, as well as the Manhattan College Mathematics and Computer Science departments. Additionally, I would like to thank Dr. Rani Roy and Elly Mons for organizing and running the Summer Research Program at Manhattan College, as well as the Dean of the School of Science, Dr. Constantine Theodosiou, for selecting me for the School of Science’s Summer Research Program and providing financial support. Finally, I would like to thank each of my friends and all of my family for supporting me in this process.
0
5
10
15
20
x
Future Work These methods can be expanded to study properties of dendrimers with more branches and in higher dimensions.
iGEManhattan College
E. (lectro) coli and the GOxLDEN nANODE Students: Farzana Begum, Brian Evans, Amanda Lazkani, Dawud Abdur-Rashid, Syeda Rithu, Ashley Abid, Samuel Corby and Gregory Sanossian iGEManhattan College
iGEManhattan College
iGEManhattan College
iGEM - what is it?
Our iGEM Project
Results
Our goal is to design an environmentally friendly, and efficient bioanode that will maximize electron shuttling using various glucose oxidase mutants derived from aspergillus niger. Additionally, we will try to take MtrCAB operons from shewanella to make E.coli electric in the presence of an anode with nanowires adhered to it which will connect to the shewanella nanowires. Increasing the efficiency of the overall biofuel cell will allow for many advances in the field of medicine, and technology. Biofuel cells can be used as portable power sources for miniaturized electronics, as well as self power implanted medical devices to improve health. For future research we hope to make this biofuel cell solar, cost effective, and to make clean energy a reality for all. e−
Who are we?
College
The above figures show our test digest of the expression plasmids we created containing the GOx genes. The 4x mutant is the same coding sequence as the wild type with the following mutations: T56V, T132S, H469C, CV543V. The mutant is a published GOx variant that has shown increased stability and enzymatic rate. We then expressed our protein and verified its expression in whole cell lysates via western blot analysis of the 6xHIS-tag on the protein (see figure below). Our next step is to isolate the protein and conduct enzymatic catalysis on our anode.
GOx protein expression
Our project utilizes nanowires instead of plate electrodes as a way to maximize the surface area and minimize the diffusion distance for the electrons contact with the anode. Using a U-tube (see image below) we conducted the following chemical reaction to produce gold nanowires at ~ 200 nm in size. The reagents were added to separate chambers in the U-tube and allowed to meet and react inside the pores of a membrane at the center of the apparatus.
4 HAuCl4 + 3 NaBH4 -> 4 Au + 6 H2 + 3 NaCl + 3 BCl3 + 4 HCl
e−
Anode
e
−
e− e−
2 H2 O
Glucose
e− e−
H+
e−
e−
e−
e− e− e−
H+
e−
e
iGEManhattan
e−
cas Lac
Samuel Corby - Chemical Engineering Amanda Lazkani - Chemical Engineering Syeda Rithu - Chemical Engineering Farzana Begum - Chemical Engineering Dawud Abdur-Rashid - Biology Ashley Abid - Chemistry Bright Shi - Biochemistry Gregory Sanossian - Chemical Engineer
e−
e−
Product H
+
Microbial Oxidation
Cathode
Dr. Bryan Wilkins and Dr. Alexander Santulli Project P.I.s
We obtained the gene coding region for A. Niger glucose oxidase (GOx) from the NCBI protein database. To the sequence to also possess an N-terminal 6xHIS-tag followed by a TEV recognition site for purification. We then modified the entire sequence for E. coli codon optimization and ordered the DNA fragment from Integrated DNA Technologies (IDT). Using the restriction sites EcoRI and PstI we digested the fragment and expression vector for ligation construction of our GOx plasmid.
V
se
Synthetic biology utilizes concepts in engineering and mathematical modeling to design ways to genetically manipulate biological systems in order to alter, or create de novo, unique physiological pathways. Cellular biochemistry is redesigned and “tuned” for highly specific outputs, essentially treating stimuli, genes and promoters as circuit timers, gates and switches. DNA serves as the functional units in the architecture of improved genetic blueprints of existing organisms. A synthetic biologist tries to manipulate an organism into serving as a biofactory for the production of biofuels, the bioremediation of hazardous materials, manufacturing useful pharmaceuticals, or even generating renewable energy. Scientists look to manipulate microorganisms in order to host their biofactory designs because they are small, easily controlled, and much of their biological machinery is well understood. Synthetic biologists aim to use these engineered biomachines to solve many of the most pressing issues in today’s world.
Bioelectric batteries and the generation of electricity using bacteria is currently inefficient and produces low power output. We hypothesize that increasing electron transfer to the anode will create a more efficient biobattery. We use nanowires to increase the surface area of the anode, design oxidase enzymes to bind directly to that anode, and engineer E. coli to produce bacterial nanowires in an effort to increase the efficiency of electron transfer. We attempt to enhance an already established biofuel system by increasing electron production, while at the same time decreasing the distance of electron transfer at the anode. Our engineered oxidizing enzymes, in combination with microbial cells that grow bacterial nanowires allows for a system that can directly adhere the electron production to the anode. We expect we will see more efficient transfer of electrons, thus generating more electricity.
se O xida
Synthetic Biology
IEnergy has become a necessity to sustain our society and to further its advancement. The depletion of fossil fuels and the need for clean energy production has called attention to biofuel cells which convert chemical energy into electrical energy via enzymatic reactions. This source of energy is sustainable, renewable, and does not emit CO2. Conventional fuel cells are generally cost-ineffective in regards to energy production. In addition, once one of the active masses in a conventional fuel cell is fully consumed, the current-producing reaction ceases. Scientists have shown glucose powered biofuel cells to hold much promise. As a resource, glucose is energy dense, cost-efficient, and abundant. It also represents a clean source of power.
Glu co
iGEM stands for International Genetically Engineered Machine. The iGEM competition is a yearly synthetic biology endeavor that utilizes standard DNA components to engineer biological devices. The iGEM foundation provides a library of standardized parts called BioBricks that are genetic elements with known functions. Using concepts in biology, mathematics, engineering, and beyond, students employ BioBricks to assemble novel biological circuits with predictable outputs. In other words, each team tries to engineer unique biological devices for useful purposes. Last year there were 300 undergraduate iGEM teams from all over the world. At the culmination of their summer research, each team is invited to the iGEM Jamboree, in Boston, to share and celebrate in their hard work with all of the international synthetic biology community. The iGEM competition presents its participants with the opportunity to network within a diverse and highly regarded scientific circle that is not generally offered to most undergraduate students.
e− e−
4 H+ + O2
Microbial Reduction + electrons
U-tube reaction
Gold nanowires
Currently working on: 1. Isolating the GOx enzymes to test cell potential with the more stable GOx mutant. 2. Creating GOx expression plasmids that have genetically modified cysteine tagged GOx. 3. Stable expression of the mtrCAB operon from Shewanella to build "electric" E. coli. In conclusion - we are using genetically engineered approaches to improving the efficiency of a biobattery. While we have much more work ahead of us we expect that we can increase the power output of a classical glucose fuel cell with our approach.
We will present our ideas and work at MIT in November of this year.
Predicting rates of mortality for saguaro cactus plants (Carnegiea gigantea) Cole R. Johnson, Biology Department Purpose Background
The purpose of this study was to examine barking characteristics on several surfaces of saguaro cacti. Using Machine Learning programs, these characteristics could be used to predict rates of cactus mortality.
A population of saguaro cacti in Tucson Mountain Park have been evaluated from 1994-2017 over 8 year periods. Cacti experience epidermal barking as a result of sun exposure. Barking prevents gas exchange from occurring at the surface, resulting in premature death.
Materials and Methods • 1100 cactus plants were evaluated in this study. • 4 evaluation periods: 1994, 2002, 2010, 2017. • 12 surfaces were evaluated on each cactus for the percentage of bark coverage. 3 surfaces were evaluates for north, east, west, and south. • Each cactus was assigned a degree of injury based on barking on south surfaces only (Table 1). • Data for cacti belonging to each health category were analyzed by WEKA to predict injury for the next evaluation period.
Hypothesis 1 South facing surfaces are the major predictors for predicting bark among healthy cacti.
Table 1. Criteria used to assign classes of barking injury to individual saguaro cacti (Carnegiea gigantea). Each criterion pertains to barking percentage on the south crest only. Health
Criteria
Healthy
Less than 20% bark during entire evaluation period.
Bark
Less than 20% bark during initial evaluation, but more than 20% bark during final evaluation.
Slight bark
Between 20% and 49% bark during initial evaluation.
Medium bark
Between 50% and 80% bark during initial evaluation.
Severe bark
Greater than 80% bark during entire evaluation period.
Dead
Dead.
Barking rates on north-facing surfaces are the major predictors of cactus death for cacti with varying levels of injury.
Severe Bark
A WEKA decision tree can predict death of cacti with varying levels of injury at a high accuracy.
Table 3. Predicting death of saguaro cacti (Carnegiea gigantea) with various predictive surfaces during each time interval using WEKA.
(Carnegiea gigantea) that became unhealthy using
Time interval Health Status Major predictor 1994-2002 Healthy North 1994-2002 Slight bark North 2002-2010 Healthy South 2002-2010 Severe bark North 2010-2017 Medium bark North 2010-2017 Severe bark South 1 n refers to the number of cactus plants analyzed.
1n Time Accuracy Interval 1994-2002 386 89.2 2002-2010 85 75.8 2010-2017 66 63.4 1n refers to the number of cactus plants analyzed.
Medium Bark
Hypothesis 3
Hypothesis 2
Table 2. Predicting bark of healthy saguaro cacti WEKA with south-facing surface data.
Healthy
n1 231 112 191 448 107 427
Accuracy (%) 89.2 84.7 83.5 76.1 92.5 72.6
Predicting death for healthy cacti from 1994-2002.
Predicting death for slightly barked cacti from 1994-2002.
Synthesis, Isolation, and Characterization of Organic Cyclic Cr(VI) Molecules! Christopher Dae-Hyun Kim! School of Science, Department of Chemistry and Biochemistry
Abstract
Methods for removal of chromium remain a prevalent field of research. Removal of chromium from water sources is feasible through absorption of the Cr(VI) by granulated activated charcoal (GAC).1 A study had also shown that the proposed formation of a chromate ester complex was more readily absorbed by GAC.2 A recent research suggested that formations of hypothetical cyclic organic chromium complexes contributed to higher absorption and removal rates.3 Current methods to synthesis, isolate, and characterize the hypothetical cyclic organic chromium complexes have identified the narrow opportunities for success. Observations from this research provide promising hypothetical ideas for further study.!
Introduction
[1] Hexavalent chromium (Cr(VI)) is a naturally occurring substance from chrome-iron deposits. [2] Chromium from industrial waste sites have also leaked into public water sources. [3] Hexavalent chromium = CrO42- & Cr2O72-. [4] Hexavalent chromium has been labeled as toxic due to its strongly oxidizing property. [5] Increase of lung cancer in chromium workers.4 [6] Health effects from study of villagers living near a chromium alloy plant: Oral ulcers, diarrhea, abdominal pain, indigestion, vomiting, leukocytosis, and immature neutrophils.5 [7] The Safe Drinking Water Act -> United States EPA -> Set maximum contaminant levels in public drinking waters to deter adverse health effects (0.1 mg/L). 6 [8] A recent study demonstrated the facilitation of chromate removal through the use of activated charcoal and formation of cyclic organic-Cr(VI) carbonates and ureas from diols and diamines respectively.3 The focus of this research was to synthesize, isolate, and characterize said novel molecules using various diols.
Figure 1. Scheme of diol reacting with chromic acid to form cyclic organic Cr(VI) molecule.
Materials and Methods
Organic reactants: Ethylene glycol, trans-1,2-cyclohexanediol, cis-1,2-cyclohexanediol, pinacol, 1,2dihydroxybenzene, cis-2-butene-1,4-diol, & 2-phenyl-1,3-propanediol. ! Chromium sources: K2CrO4 & Na2CrO4. ! Solvents: EtOH, isopropanol, heptanol, acetone, Et2O, n-hexane, MTBE, DCM, & DMF. Reagents: 15% HCl.! [1] Stock solutions of dilute and saturated K2CrO4 were prepared. Organic reactants were added into the solution using a 1:1 or 2:1 molar ratio. HCl was added dropwise until pH reached 3. The reaction solution was stirred for 30 minutes and separated into five equal components. A series of Et2O, isopropanol, or heptanol was prepared (1, 1/2, 1/4, 1/8, and 1/16) and added onto the separated solutions. Liquid-liquid extraction methods were conducted using MTBE or DCM as solvents. Products’ identities were assessed using UV/Visible, infrared, and NMR spectroscopy. [2] Organic reactants and Na2CrO4 were dissolved in DMF or anhydrous acetone using a 4:1, 3:1, 2:1, or 1:1 molar ratio. Solutions were left for various periods of time (<3 days) while capped. [2.1] Pinacol reaction solution was centrifuged and the resultant supernatant liquid was decanted. The supernatant liquid was extracted and rid of DMF with a mixture of Et2O and n-hexane. The solution was left to dry. [2.2] 1,2-Dihydroxybenzene-reaction solution was subject to liquid-liquid extraction methods via a separatory funnel. DCM was used to extract organic layer. The organic layer was then washed several times with water and brine and dried.
Table 1. Starting organic reactant with respective cyclic formation with addition of chromate.
Name
Structure
Hypothetical Cyclic Organic Cr(VI) Molecule
UV spectroscopic data of dilute and saturated K2CrO4 solutions with organic reactants treated with hydrochloric acid and added onto EtOH, isopropanol, or heptanol demonstrated peaks at 350 nm, indicating presence of Cr(VI). Infrared and NMR spectroscopic data of product solutions demonstrated exact properties of starter organic reactants and no dissimilarities.!
K2CrO4 + 2 HCl -> H2CrO4 + 2 KCl
Figure 5. Chemical equation of potassium chromate and hydrochloric acid.
[3] Chromic acid would react with organic start to form cyclic complex. [4] Cyclic complex could then be isolated using liquid-liquid extraction methods while the KCl salts could easily be removed. [5] Hydrochloric acid was instead forming dichromate since chromate and dichromate are in equilibrium in an aqueous solution. [6] Also, dilute chromate solutions with organic reactants treated with hydrochloric acid did not produce any viable crystals due to the formation of more dichromate ions and equilibrium rates not favoring product formation, [7] Therefore, to favor product formation and probability of obtaining crystals by shifting equilibrium, increased concentration of chromate by preparing a saturated solution and did not add hydrochloric acid. [8] However, only the potassium chromate recrystallized. Infrared and NMR analysis failed to indicate any conclusive results of any desirable products being formed.
Ethylene glycol Pinacol
Trans-1,2cyclohexanediol
Literature research of synthesizing cyclic organic chromium compounds yielded feasible experimental methods.7,8 Such methods included dissolving sodium dichromate and organic starter reactants in polar aprotic solvents, specifically DMF, on a micro-scale level.7,8 One published article demonstrated synthesizable and viable crystals for analytical purposes using cis-1,2-cyclohexanediol.7 The article also showed useful qualitative information; the reaction solution with cis-1,2-cyclohexanediol, sodium dichromate, and DMF turned from yellow to orange and finally to dark green, and at three days, green, needle-like crystals were formed.7 This reaction was repeated to verify feasibility with sodium chromate instead of sodium dichromate. The reaction was successful in producing the same green, needle-like crystals. Other organic reactants were chosen due to their hypothetical viabilities to form stable chromium compounds. Such properties that were thought to facilitate stability and crystal formation were bulky aromatic rings, stereochemistry, no free rotations of hydroxyl groups, and energetically favored products, as seen in the cis-1,2-cyclohexanediol. However, all products formed throughout the entirety of this research project were not isolatable in crystalline form.
Cis-1,2-cyclohexanediol
Cis-butene-1,4-diol
2-Phenyl-1,3-Propanediol
Conclusion
We were not able to isolate the hypothetical cyclic organic chromium compound, except the cyclic organic chromium compound using cis-1,2-cyclohexanediol. However, we have identified the properties akin to cis-1,2-cyclohexanediol that suggests viability for future research.
1,2-Dihydroxybenzene The products of the organic reactants dissolved in DMF or acetone with disodium chromate were not isolatable. The experiment using pinacol as a starter reactant was promising at first. The solution turned from yellow to orange and then orange to green. After centrifuging, decanting, extracting, and leaving the solution to dry, a blackish brown tacky material with imbedded, clear crystals were found. Infrared and NMR data analysis indicated that the clear crystals were pinacol. The experiment using 1,2-dihydroxybenzene was also promising. The reaction solution proceeded from yellow to orange and then from orange to purple. After the work-up to remove DMF was completed and the solution was left to dry, a sludgy, tarry substance was formed instead of crystals. !
Discussion
[1] Focus = Use oxidizing agent (chromic acid) on promising diols that would not undergo vicinal cleavage to isolate a stable and crystalline intermediary.
Figure 2. Primary and secondary alcohol oxidized by chromic acid to form carboxylic acid and ketone respectively.
Results
All methods conducted failed to produce any crystals of pure or impure products usable for analytical purposes. Furthermore, analytical and qualitative data for all experimental methods demonstrated that no expected hexavalent chromium compounds were formed.
[2] Treating K2CrO4 with hydrochloric acid forms chromic acid and KCl salt.
Acknowledgements
I would like to thank The Camille and Henry Dreyfus Foundation Senior Scientists Mentor Program and the Kakos Endowed Chair in Chemistry for financial support. I would also like to thank the School of Science Summer Research Scholars Program for providing an opportunity to pursue this research. I would finally like to thank Dr. Richard Kirchner and Dr. John Regan for providing insurmountable guidance, support, and advice throughout the entirety of this research project.
References
1. DeSilva, Frank. “Activated Carbon Filtration.” Water Quality Products Magazine (January 2000). 2. Nakajima, Akira, and Yoshinari Baba. “Mechanism of Hexavalent Chromium Adsorption by Persimmon Tannin Gel.” Water Research 38.12 (2004): 2859-864. ! 3. Regan, John, et al. “Bidentate Reagents Form Cyclic Organic-Cr(VI) Molecules For Aiding in the Removal of Cr(VI) from Water: Density Functional Theory and Experimental Results” Structural Chemistry (2017). ! 4. Gibb, H.J., et al. "Lung Cancer Among Workers in Chromium Chemical Production." American Journal of Industrial Medicine (AJIM) 38.2 (July 7, 2000): 115-126. ! 5. U.S. Environmental Protection Agency. (1998). Toxicological Review of Hexavalent Chromium: In Support of Summary Information on the Integrated Risk Information System (IRIS). Washington D.C.! 6. "Chromium In Drinking Water." US Environmental Protection Agency, 2017, https://www.epa.gov/ dwstandardsregulations/chromium-drinking-water.!
Figure 3. Vicinal diols undergo a cyclic intermediary complex before cleavage when periodic acid is used
Figure 4. Proposed reaction between 1,2-cyclohexanediol and chromic acid to form hypothetical cyclic organic Cr(VI) molecule. All reactants chosen would follow this proposed scheme.
7. Bartholomaus, Ruben, et al. “Synthesis and Characterization of a Chromium(V) cis-1,2-Cyclohexanediolato Complex: A Model of Reactive Intermediates in Chromium-Induced Cancers.” Inorganic Chemistry, 51(21), 11238-11240 (2012). ! 8. Gez, Swetlana, et al. “Chromium(V) Complexes of Hydroxamic Acids: Formation, Structures, and Reactivities.” Inorganic Chemistry, 44(8), 2934-2943 (2005).
Synthesis of Antimony Sulfide (Sb2S3) Nanowires in Aqueous Solution James Louis Ksander, Dr. Alexander Santulli
Introduction To meet the ever increasing power demands of the world’s population renewable energy sources must become more efficient, more practical, and cheaper. In this experiment we attempted to produce Antimony Sulfide nanowires in aqueous solution for use in solar cells. Antimony Sulfide is a promising solar material because of its stability, low cost, and ability to absorb a large portion of the solar spectrum.
Results and Discussion
Conclusions
● Sb2S3 readily oxidizes in the presence of water and heat ● Solutions of Na2S are very alkaline due to the presence of HS- and corresponding OH- ions H2O (l) + Na2S(s) ⇋ HS-(aq) + 2Na+(aq) +S2+(aq)+ OH-(aq) ● The alkaline conditions of the solution produce Antimony Hydroxide compounds Sb3+(aq) + 3OH-(aq) ⇋ Sb(OH)3 (s)
● Under specific pH conditions amorphous Sb2S3 can be easily synthesized at room temperature ● Sb2S3 nanowires can be obtained through the use of polycarbonate templates in aqueous solution ● The optimal conditions for Sb2S3 synthesis are in a neutral or slightly acidic solution with equal concentrations of reactants Sb2K2(C4H2O6)2 and Na2S
Sb(OH)3 (s) + OH-(aq)⇋ Sb(OH)4- (aq) ● Very pure Sb2S3 can be obtained by adding acid to solutions of “Used” Na2S which had dissolved the Antimony Hydroxide compounds HS-(aq) + H+(aq)⇋ H2S(aq)⇋ 2H2+(aq) + S2-(aq) 3H2S(aq) + 2Sb3+(aq) ⇋ Sb2S3 (s) + 6H+(aq)
References ● “Antimony Trisulfide.” Wikipedia, Wikimedia Foundation, 4 Sept. 2017, en.wikipedia.org/wiki/Antimony_trisulfide. ● Gödel, Karl C., et al. “Efficient Room Temperature Aqueous Sb2S3 synthesis for Inorganic - Organic Sensitized Solar Cells with 5.1% Efficiencies.” Chem. Commun., vol. 51, no. 41, 2015, pp. 8640–8643., doi:10.1039/c5cc01966d.
Figure 1. Diagram of Solar cell
Methods and Materials
● Solutions of Sb2K2(C4H2O6)2 Antimony Tartrate and Na2S Sodium Sulfide were made at varying concentrations from .01M to 1M ● Experiments were run in 100 mL or 200 mL U-tubes, separating the solutions were polycarbonate templates meant to foster the growth of certain sized nanowires ● Annealing of amorphous samples was done in a tube furnace at temperatures ranging from 200 ℃ to 500 ℃ in an atmosphere purged with nitrogen gas ● X ray diffraction was then used on the samples to ascertain the chemical makeup and crystal structure.
Figure 3. Polycarbonate template covered in Antimony Hydroxide Compounds and Antimony Sulfide
Figure 4. Bulk Amorphous Antimony Sulfide in Crucible before Annealing
Figure 6. XRD of Antimony Hydroxide Compound
Figure 7. XRD of Antimony Sulfide
FIgure 8. Antimony Hydroxide Compound
Figure 9. Annealed Antimony Sulfide on Silicone disc
Acknowledgments ● This project was made possible by the Chemistry and Biochemistry department at Manhattan College through the ● Special Thanks to Dr. Alexander Santulli and The Jasper Summer Scholars program.
Figure 2. U-tube of Sb2K2(C4H2O6)2 and Na2S
Figure 5. SEM picture of Antimony Sulfide Nanowire
DETERMINING THE CRYSTAL STRUCTURE OF ZEOLITE ZSM-18 Daisuke Kuroshima Department of Chemistry and Biochemistry Abstract
ZSM-18 the first aluminosilicate zeolite reported that has 3-member rings.[1] However, the structure of this zeolite has not been confirmed by refinement of X-ray crystallographic data. The focus of this research was to use the sophisticated crystallographic computer program Superflip[2] to determine the structure of the zeolite from synchrotron X-ray powder diffraction data. The graphics program ATOMS[3] was used to visualize and determine bond connections between atoms generated in a Superflip solution.
Introduction
Zeolites are hydrated aluminosilicate minerals that are microporous. In nature pores commonly contain water. When the zeolite is heated, loosely bonded water boils out from the zeolite. This is the origin of the name zeo-lite (boiling-stone in Greek). Zeolites are used for molecular sieving, ion exchange beds, and purification of water. ZSM-18 is one of hundreds of zeolites which have been synthesized in laboratories. ZSM-18 stands for ZeoliteSocony-Mobil preparation 18. Socony is an older name for Standard Oil Company of New York. ZSM-18 was synthesized using the organic templating agent triquat (Figure 2). The ZSM-18 model described in the literature was highly controversial because tetrahedra silicon atoms were in 3-member rings. Other models were also proposed for the structure of ZSM-18.[4]
Determining the ZSM-18 Structure
This research used synchrotron X-ray powder diffraction data to calculate atoms that reveal a three dimensional structure. The structure is correct when its calculated powder pattern matches the experimental one (Figure 1).
Therefore, a symmetry lower than P63/m is possible. However when P63/m is used in the ATOMS program (even when Superflip suggested P11m), the results were more organized. Typical results show mainly 3 and 4 membered ring. Figure 1: X-ray powder diffraction data for SUZ-9[5]
Superflip
Superflip is a computer program for application of the charge-flipping algorithm for structure solution of crystal structures from diffraction data.[2] The literature model of ZSM-18 has space group P63/m.[6] Superflip calculates 10 solutions each with their own space group. These solutions were then input into the graphics program ATOMS to display the all atom positions.
Results from Superflip Figures 3, 4, and 5 show various Superflip solutions.
Conclusion The structure of the ZSM-18 is not yet revealed. More effort extracting a framework topology (structure) from Superflip results is needed.
Acknowledgement
ATOMS
Atoms allows manipulation of the result from Superflip. False atoms can be deleted, the symmetry can be changed, distance limits can be set to reveal bonds between atoms, and rotating the display in three dimensions helps reveal the possible structure.
Figure 3: Superflip result displayed in ATOMS. Pink atoms represent Silicon and red atoms represent Oxygen.
The small picture on the lower right side of Fig. 3 suggest that a structure made up from Si in 4-member rings (shown above as three overlapping squares). Behind the 4-rings are 3 member rings (shown at far right). Because silicon is usually tetrahedral its presence in 3-rings is unusual.
Figure 2: Structure of Triquat[7]
Results
In 60 trials, Superflip never suggested space group P63/m. Most commonly, Superflip suggested P and P11m. P is somewhat similar to P63/m, but P11m has much less symmetry. Presumably P11m shows up because ZSM-18 has the organic template triquat in its pores. Triquat has symmetry lower than P63/m.
Figure 5: Superflip result displayed by ATOMS. Silicon atoms are shown in pink, oxygen atoms are shown in red. This model has 3-,4-,and 6-member rings. Most oxygens are between two silicon atoms, as expected.
Figure 4: Another Superflip result displayed by ATOMS. Oxygen atoms are shown in red and silicon atoms are shown in pink. This result has a huge 12 member ring and 3-,4-,and 6-member rings. This result also has disorganized squares.
Financial support from the Kakos Endowed Chair in Chemistry and the Summer Research Scholars progrm is gratefully acknowledged. Special thanks to Dr. Richard Kirchner, for acting as my faculty advisor for this research.
References
1. S. L. Lawton and J. Ciric, G. T. Kokotailo, Acta Crystallogr. Sect. C41, 1683 (1985) 2. L. Palatinus and G. Chapuis, SUPERFLIP, J. Appl. Cryst. 40, 786-790 (2007) 3. E. Dowty, ATOMS, Shape Software, 521 Hidden Valley Road, Kingsport TN 37663 4. J.D. Gale and A.K. Cheetham, Zeolites, 12:674-679 (1992) 5. K.D. Schmitt and D.J. Kennedy , Zeolites, 14:635-642 (1994) 6. Ch. Baerlocher and L.B. McCusker, Database of Zeolite Strucutres: http://www.iza-structure.org/databases/ 7. A. W. Burton and H. B. Vroman, US. Pat. 9,452,424(November 14, 2013)
Predicting rates of morbidity for saguaro cactus plants (Carnegiea gigantea) Marissa LoCastro, Biology Department Purpose The purpose of this research is to examine bark formation percentages and understand the progressive death of cacti. This takes into account bark coverage and the influence of surrounding vegetation.
No Bark
Background
Bark Formation Bark Coverage
Method 1 • Percent bark data was collected for 600 cacti in Tucson Mountain Park. • Percentages of bark on 12 cactus surfaces were determined in 1994, 2002, 2010 and 2017. • Data were entered into WEKA 3.8 program to predict bark percentages over 8-year intervals.
Saguaro cacti (Carnegiea gigantea) are native to Tucson, Arizona. Sun exposure results in bark formation on the ribbed surfaces of the cacti plants. Barking begins on the south facing surfaces and makes its way around the stem of the plant, increasing the destruction of the cactus’ health as bark accumulates. Bark coverage prevents gas exchange, resulting in premature death.
Data from 1994-2002 predicted 2002-2010: Unhealthy to unhealthier or dead.
Data from 1994-2002 predicted 2002-2010: Healthy to unhealthier or dead.
Conclusion 1 Bark percentages on south crest surfaces were predicted the above 91% accuracy. The author is grateful to the Catherine and Robert Fenton Endowed Chair to Dr. L.S. Evans financial support for this research.
Unhealthy
Method 2 • A portrait photo of each cacti was taken in the 2017 evaluation. • The photos were grouped based on if they had surrounding vegetation, if the vegetation provided shade, and Surrounding No surrounding to what cardinal direction the vegetation – no vegetation shade casted on the cactus. shade Average bark coverage of all cacti surfaces in 2017 based upon vegetation characteristics.
Predicting bark accumulation of saguaro cacti (Carnegiea gigantea) with various predictive surfaces over several interval periods using WEKA.
Healthy
Conclusion 2 Shading of cactus plants by neighboring vegetation decreased bark percentages on south crests by 13% (probability 0.003) form 2010 to 2017. Non-shading neighboring vegetation had no effect.
Surrounding vegetation – shaded
Average bark coverage of all cacti surfaces in 2017 based upon vegetation shading of South-facing surfaces.
Conclusion 3 Between 2010 and 2017, the presence of south surfaces shading by neighboring vegetation decreased changes in bark percentages on south crests by 15% (probability 0.01).
Magnetic/TiO2 Core-Shell Nanoparticles as Photocatalysts for Water Purification Arvind Damodara
Dept. of Chemistry &
INTRODUCTION Millions of people do not have direct access to clean water, which allows diseases to spread more easily and turns routine activities into a struggle. Water pollution affects humans and causes harm to aquatic systems. The pollutants in waste water from domestic and industrial sources are primarily pathogens and organic chemicals. These pollutants must be removed from waste water effluents before being discharged into water bodies. Several methods are in use to purify water but are either high in cost or generate harmful byproducts. Efficient, cost effective, and environmentally friendly water purification techniques can greatly improve this situation. Oxidation is the transfer of one or more electrons from the electron donor to the electron acceptor, which results in chemical transformation of both the electron donor and acceptor. Advanced oxidation processes (AOPs) are used to remove organic pollutants from water. AOPs involve the formation of reactive oxygen species ( OH) and the reaction of these oxidizing agents with organic pollutants in water. The benefit of using AOPs to purify water is that they are environmentally friendly.
1 Biochemistry ;
2 Kannan ,
Dept. of Civil & Environmental
T i O 2 – U V C ATA LY Z E D O X I D AT I O N Major Reaction (formation of OH) H2O2 + + 2e+− H2+O+ O2H + H 2 H O 2 O + 2h CB 2H 2
2
VB + + 2H O + 2h 2 H 2 O2 + + e−VB CB − O2 + H+ + e HO2 CB
H2O2 + HO O22− + 2 H2− O2++h O OH VB
OH− + h+VB
2
2
+ + H O 2H 2 2 HO2 + +2 O − HHO 2
+−+ O − H OH + O22+ OH −+ O + OH OH 2 OH
OH
h+VB : Valence-band holes e−CB : Conduction-band electrons § TiO2 is a semiconductor– it has a filled valence band and an empty conduction band which provides a small band gap § When TiO2 is illuminated by UV light (365 nm), the photon with threshold energy greater than or equal to the energy gap excites valence band electrons to the conduction band creating positive valence band holes § The recombination of charged holes and electrons can initiate several chemical reactions § Hydroxyl radical formation is the main mechanism of oxidation to degrade organic pollutants in contaminated water. Source: International Journal of Modern Engineering Research
Dr. Alexander
2 Engineering ,
C O R E - S H E L L N A N O PA R T I C L E S
1 Santulli
M ANHATTAN C OLLEGE
Manhattan College
D ATA & R E S U L T S
Methylene Blue Degradation: 30 mM MB solution was made and 100 mL was put into each of two evaporating dishes. 5 mg of the catalyst being tested was sonicated in the MB solution to disperse the nanoparticles. The samples were illuminated by UV light (365 nm) and the efficacy of the photo catalysts was tested by taking 2-3 mL samples of the solution at different time intervals and examining with UVVisible Spectroscopy the concentration of MB.
Methylene Blue Degradation
1.05
Slope of Nanomaterial Trend Line
1 0.95
Figure B: (above) SEM images of TiO2 coated Fe3O4 nanoparticles
Figure A: (above) SEM image of SiO2 coated Fe3O4 nanoparticles
Intensity
100 60
Figure C: (above) SEM images of Fe3O4 nanoparticles
0
50
Control
40 2 θ (degree)
50
60
TiO2 TiO2
100
150
Mean Length (nm)
Standard Deviation (nm)
Fe3O4
10.991
1.871
TiO2 coated Fe3O4
16.883
2.493
SiO2 coated Fe3O4
23.00
4.00
Fe3O4 Fe3O4
TiO2 TiO2 coated coated Fe3O4 Fe3O4
a) TiO2 coating:
§ A mixture of 10 mL hexane and 2 mL tetrabutyl orthotitanate (TBOT) was made § 600 mg of Fe3O4 nanoparticles was added to the TBOT & hexane mixture § Fe3O4 nanoparticles were left to sit for 20-30 minutes in mixture § The supernatant was decanted and the nanoparticles were washed 3-4 times with hexane § The coated core-shell nanoparticles were collected by use of a strong magnetic § The nanoparticles were then annealed for 30 minutes at 500°C b) SiO2 coating:
The same procedure was followed to coat the Fe3O4 nanoparticles in SiO2 with the substitution of tetraethyl orthosilicate (TEOS) instead of TBOT
SiO2 coated Fe3O4
-0.0002
0.01 0.001 0.0001
E X P E R I M E N TA L
Coating Fe3O4 Nanoparticles:
-0.0006
0.1
0.000001
Fe3O4 + 4H2O
SiO2 SiO2 coated coated Fe3O4 Fe3O4
TiO2 coated Fe3O4
E. Coli Disinfection
1
0.00001
§ Ferric chloride and ferrous chloride were mixed in a 2:1 molar ratio; this solution was heated to 50 °C for 10 minutes § Ammonia solution was added and black iron oxide particles precipitated from solution § The particles were separated from the solution by use of a strong magnet § The particles were washed several times with water and left in an oven to dry over night at 100°C. Overall reaction:
250
-0.0002
E. Coli Disinfection: 102 CFU/mL stock solution of E. coli was made. 5 mg of the photocatalyst being tested was added to 100 mL of the stock solution, 50 mL was added to a petri dish and illuminated by UV light. Samples were taken at several time intervals to test the efficacy of the catalyst.
Figure E: (above) measurements taken of the core-shell nanoparticles using ImageJ software
Synthesis of Fe3O4 Nanoparticles:
200
Time (min)
Figure D: (above) XRD of Fe3O4 nanoparticles
Nanomaterial
Fe2+ + 2Fe3+ + 8OH−
-0.0014
Fe3O4
0.75
140
30
TiO2
0.9
0.7
20
-0.0002
0.8
180
20
Control
0.85
Fe3O4 NPs XRD
220
OV E RV I E W § Various magnetic nanoparticles were synthesized through decomposition and precipitation reactions and then coated in a TiO2 shell § TiO2 acted as a photosensitizer– when illuminated by UV light (365nm), reactive oxygen species (ROS) are formed, which oxidize and degrade organic pollutants in water § The efficacy of the synthesized magnetic/TiO2 core-shell nanoparticles was tested through: § Disinfection experiments of E. coli from water § Degradation experiments of methylene blue, an organic dye used in textile, pharmaceutical and plastic industries, from water § The magnetic core allows the core-shell nanoparticles to be removed from the water by a strong permanent magnet after they have been used § This serves to minimize toxicity concerns of the nanocatalyst and could allow for reuse of the nanocatalyst
Dr. Hossain
2 Azam ,
Log (N/No)
1 Mabey ,
I/Io
Hannah
M ANHATTAN C OLLEGE
0.0000001
0
20
40
TiO2 Coated coated Fe TiO2 Fe3O4 3O4
60
80
Time (min) TiO2 TiO2
100 Fe3O4 Fe3O4
120
140
Control
CONCLUSION
§ Magnetic/TiO2 & SiO2 core-shell nanoparticles were successfully synthesized § Figure E shows that the mean length of the coated nanoparticles is larger than the length of the Fe3O4 nanoparticles by themselves, proving that there is a coating § Fe3O4 nanoparticles by themselves did not have any affect on the organic dye or microorganism § TiO2 coated Fe3O4 nanoparticles showed good results in disinfection of E. coli and degradation of methylene blue from solution, which shows again that the nanoparticles were successfully coated in a TiO2 shell § SiO2 coated Fe3O4 did not have any photocatalyst affect in either experiment.
ACKNOWLEDGEMENTS
I would like to thank the Jasper Summer Scholars Program of the office of the Provost for the opportunity to conduct research this summer. Additional thanks to Korean Institute of Science and Technology (KIST) for additional funding.
Automated Quantitative Analysis of Tree Branch Similarity Using 3D Point Cloud Registration Matthew Maniscalco, Mechanical Engineering The purpose of this project was to compare various tree branches using 3D Register a program written in Matlab. The program scales the imputed tree branches and uses computer vision technology to compute the similarity between the two inputted tree branches. Materials and Methods: STL files are received from scanner and imported into Cloud Compare, a software that allows the creation, editing and analyzing of point clouds.
Results Species
RMSE Values
Species 1:
3.80
Species 2:
0.18
Species 3:
9.14
Species 4:
9.62
The STL files are repositioned in Cloud Compare and then meshed using the sample point option, this creates a point cloud.
Before
The point cloud is exported from Cloud Compare as a PCD file and loaded into the â&#x20AC;&#x153;3D Registerâ&#x20AC;? GUI, the program then analyzes the similarity of the branches and produces a RMSE value
After
Conclusion #1 When creating point clouds in Cloud Compare, 5000 points were used as it was the best ratio between computational time and accuracy.
Conclusion #2 Terminal tree branches can be scanned with a commercial scanners. Images can be saved as STL files and later imported into Cloud Compare. Cloud Compare was used to determine similarities of geometries of paired terminals of trees. To date, data from one branch pair shows high similarity. More terminals will be tested over time. Clearly, the method operates well and many pairs can be tested.
The author is grateful to the Catherine and Robert Fenton Endowed Chair and to Dr. L.S. Evans for financial support for this research.
Building a Mathematical Model for Lacrosse Analytics Samantha K. Morrison (smorrison01.student@manhattan.edu) Faculty Advisor: Dr. Helene Tyler (helene.tyler@manhattan.edu) Clears Analysis
Introduction
Shots on Goal Analysis
We created a mathematical model to describe the playstyle of the Manhattan College Women’s Lacrosse Team in the 2017 season, focusing on questions posed by student athletes, Jordyn DiCostanzo and Nicole Quivelli. We examined shots on goal and clears, aiming to evaluate the team’s midfield transitions. This analysis employed a variety of mathematical tools in order to identify, quantify, and compare patterns in players’ behavior during games.
Mathematical Tools Probability: We computed the following: • Success of a clear given its length, • Player’s position given that she was one of the last four players in possession of the ball before a shot on goal. Network Theory: We considered the Jaspers as a weighted, directed passing network. The passing matrix, A = [Aji ], records the number of passes from player j to player i. The betweenness score of player i, CB (i), measures the extent to which she lies on the shortest paths between other players. If nijk denotes the number of paths from j to k through i,and gjk gives the number of paths from j to k, then The PageRank of player i, xi , is a recursive notion of "popularity" out = or importance. We define L j k Ajk as the total number of passes made by player j.
The eccentricity score of player i, CE (i), is the inverse of the distance from the player who is farthest from her. If dji is the distance from players i to j, then
CB (i) =
xi = p
1 121
Centrality Measures and Matrix Distances Player A1 A2 A3 A4 M1 M2 M3 D1 D2 D3 D4 G
nijk j =k =i gjk
Aji xj j =i Lout j
+q
CB (i) 0 0.058 0.017 0 0.099 0.322 0.066 0 0 0.083 0.033 0.088
xi 0.1 0.15 0.11 0.11 0.13 0.12 0.15 0.03 0.03 0.02 0.03 0.02
CE (i) 0.632 D1 0.705 D1 0.637 D1 0.632 D1 0.682 D1 0.718 D1 0.75 D1 0.374 A1 0.461 A1 0.335 A1 0.419 A1 0.247 A1
Table 1: The Betweenness, PageRank, and Eccentricy scores of each position in an average game. The highest value is highlighted.
Data CE (i) =
1 max{dji }
Play-by-play data was collected from game tapes by Jordyn DiCostanzo and Nicole Quivelli. We then used Excel to sort the data into the categories of passing, clears, and shots on goal using Excel.
Matrix Norms: Calculated "distance" between games.
We used the programs Matlab, Excel, and Python, specifically NetworkX, to compute our results and create our graphics.
The infinity norm of a matrix, A ∞ , is the maximum of the absolute row sums. It yields the max number of passes sent by a single player.
Acknowledgments
The 1 norm of a matrix, A 1 , is the maximum of the absolute column sums. It yields the max number of passes received by a single player. The Euclidean norm of A, A F , is the square root of the sum of the squares of its entries. It gives a general summary of the team’s play.
I would like to thank Dr. Rani Roy and Elen Mons, the directors of the Jasper Summer Research Scholars program. I would also like to acknowledge Nicole Quivelli and Jordyn DiCostanzo for their help in collecting the Jaspers’ game data, and their expertise in lacrosse. I would like to thank Andre Oliveira for his help with Python. Finally, thank you to Dr. Helene Tyler for all of her guidance.
Future Work We hope to evaluate more network centrality measures and matrix norms, and explore the ways in which they can be applied to the game of lacrosse. We also hope to analyze the team by player rather than by position, and to analyze the game data of the Jaspers’ opponents.
References [1] Oliveira, A. P., Tyler, H. R. (2015). Measurement and Comparison of Passing Networks in Collegiate Soccer. The Minnesota Journal of Undergraduate Mathematics, 1, 1. [2] Clemente, F. M., Martins, F. M. L., Mendes, R. S. (2016). Social Network Analysis Applied to Team Sports Analysis. SpringerBriefs in Applied Sciences and technology.(ISBN 2191-5318) [3] Devore, J. N., Berk, K. N. (2012). Modern Mathematical Statistics with Applications. Springer Texts in Statistics.(ISBN 978-1-4614-0390-6) [4] Trefethen, L. N., Bau, D. (1997). Numerical Linear Algebra. SIAM. (ISBN 0898713617) [5] Newman, M. E. J. Networks : an introduction. Oxford New York: Oxford University Press, 2010.
Evidence of Giardia lamblia Oocyst Stage in Bivalves Collected in the Bronx, NY Bivalves are important bio-indicators due to their sensitivity to pollution, their ability to trap pollutants, and their widespread distribution. Bivalve are filter feeders, therefore may retain parasitic oocysts, such as Giardia lamblia (G. lamblia) oocysts in their tissue. Infections by G. lamblia lead to diarrhea, and cause giardiasis. This project focuses on three bivalve species that were collected at Orchard Beach (OB) and Clason Point Soundview (SV): Mya arenaria (55 at OB), Crassostrea virginica (17 at OB; 26 at SV), and Geukensia demissa (39 at OB). The bivalves were previously tested for the presence of β-giardin DNA, which determines exposure to G. lamblia. The goal of this project was to find evidence of hard-shelled, weather, and chemical resistant oocysts in bivalves collected from Orchard Beach and Clason Point Soundview. This goal was achieved by detecting the oocyst cell wall protein, CWP2, by using the polymerase chain reaction. We found that CWP2 was not detected in Geukensia demissa samples collected at Orchard Beach. In contrast, we observed a prevalence of 60% in Mya arenaria collected at Orchard Beach, while a prevalence of 24% was observed in Crassostrea virginica collected at Orchard Beach, and a prevalence of 11.5% was observed in Crassostrea virginica collected from Clason Point Soundview.
Results
58 bp
Figure 3. Presence of Giardia lamblia oocyst DNA in Mya arenaria. Top Row: Lane 1: 100 bp marker; Lane 3-19: Giardia DNA from Mya arenaria; Middle Row: Lane 1: 100 bp marker; Lane 3-10: DNA from Mya arenaria; Lane 11-15: DNA from Mya arenaria; Bottom Row: Lane 1: 100 bp marker; Lane 3-9: DNA from Mya arenaria; Lane 12: positive control.
On Sept. 15, 2016, bivalves were collected at low tide at Orchard Beach (OB) and Clason Point Soundview (SV) Mya arenaria: 55 at OB; 1 at SV Crassostrea virginica :17 at OB; 26 at SV Geukensia demissa: 39 at OB; 37 at SV Mytilus edulis: 61 at OB; 0 at SV DNA and RNA extracted using Qiagen Dneasy Blood & Tissue Kit.
CWP2 forward primer: 5’CTCTTCGACCTGCCTTACATGAT-3’ CWP2 reverse primer 5’CAAACGAGATCGGTGTTGCA-3’
Presence of CWP2 gene was detected by PCR PCR product was detected in 2.5% agarose gel.
50%
40%
30%
20%
10%
0%
Mollusks
Mantle Digestive
Mya arenaria
21.8% (12/55)
23.6% (13/55)
14.5% (8/55)
14.5% (8/55)
0%
Geukensia demissa
0%
0%
0%
0%
0%
0%
0%
Crassostrea 11.76% virginica(2/17) OB
0%
11.76% (2/17)
11.76% (2/17)
5.88% (1/17)
0%
0%
Crassostrea 11.54% virginica(3/26) SV
7.69% (2/26)
0%
3.85% (1/26)
3.85% (1/26)
0%
Total
17.35%
18.52%
Gills
13.89%
Abductor Hemolymph
11.22%
4.65%
Figure 4. G. lamblia oocyst distribution among bivalve species. Summary The prevalence of G. lamblia oocysts in Mya arenaria was 60%. No oocyst DNA detected in Geukensia demissa collected from Orchard Beach. The prevalence of G. lamblia oocysts in Crassostrea virginica collected at Orchard Beach was 24%. The prevalence of G. lamblia oocysts in Crassostrea virginica collected at Clason Point Soundview was 11.5%. Relatively equal distribution of oocysts was observed between different bivalve tissues. Data suggests bivalves collected at Orchard Beach and Clason Point Soundview may be infectious.
Discussion & Conclusions
Table 1. Prevalence of Giardia lamblia in Bivalve Tissue
Materials and Methods
60%
Species Name
Figure 2. Life Cycle of G. lamblia
Figure 1. Collection Sites
Total Prevalence
Department of Biology, Manhattan College, Riverdale, NY 10471
Oocyst Prevalence
Abstract
Monique Ng, Joseph Annabi, Jordan Decade, and Ghislaine Mayer
Foot
Siphon
18.2% 20% (10/55) (11/55)
18.18%
0%
20%
• Mya arenaria and Crassostrea virginica are good bio-sentinels to track fecal contamination of oocysts in aquatic environments. • Due to the high prevalence of infectious oocysts found in bivalves, this is a concern to the New York City beaches and the health of the environment. • Prevalence of oocysts at Orchard Beach may be higher, because Soundview’s landfill operations. Acknowledgements: I would like to express my gratitude to Dr. Ghislaine Mayer for mentoring me throughout this research. I am grateful to Dean Constantine Theodosiou and the Department of Biology for financial support. References 1. Radunovic, M., Klotz, C., Saghaug, C. S., Brattbakk, H., Aebischer, T., Langeland, N., & Hanevik, K. (2017). Genetic variation in potential Giardia vaccine candidates cyst wall protein 2 and α1-giardin. Parasitology Research, 116, 2151-2158. doi:10.1007/s00436-017-5516-9. 2. DNeasy Blood & Tissue Kits. (n.d.). Retrieved July 15, 2017, from https://www.qiagen.com/us/shop/sample-technologies/dna/genomic-dna/dneasy-blood-andtissue-kit/#orderinginformation. 3.. Tei, F., Kowalyk, S., Reid, J., Presta, M., Yesudas, R., & Mayer, D. (2016). Assessment and Molecular Characterization of Human Intestinal Parasites in Bivalves from Orchard Beach, NY, USA. International Journal of Environmental Research and Public Health, 13, 381. doi:10.3390/ijerph13040381 4. https://www.nycgovparks.org/parks/soundview-park
SEARCH FOR LORENTZ INVARIANCE VIOLATION FROM GAMMA RAY BURST Linh Nguyen
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24.04.17
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100
200
300
400
500
600
700
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According to Einstein, the speed of light in vacuum has the maximum possible speed and has the same value c in all inertial frames of reference. Therefore, c is a Lorentz Invariance quantity. Lorentz Invariance means a quantity that does not change due to a Lorentz transformation; a quantity that is independent of the inertial frame. Lorentz transformation means we have the primed frame moves with velocity v in the x direction with respect to the fixed reference frame. % &'(
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What are Gamma Ray Bursts? Gamma Ray Bursts (GRBs) are short lived bursts of high energy gamma photons. They are associated with extremely energetic explosion that have been observes in distant galaxies. Long GRBs (>2s) come from hypernovae. Short GRBs (<2s) come from merging neutron starts.
Reference 1. "Gamma-ray Bursts." NASA. NASA, Mar. 2013. Web. 2. Fermi-LAT Collaboration. "The First Fermi LAT GammaRay Burst Catalog." N.p., 12 Mar. 2013. Web.
After the Nuclear Test Ban Treaty in 1963, the US launched a series of statellites called Vela. I n 1969, Klebesadel and Olsen found an event has flashes of radiation that were not from nuclear explosions seeming to come from random directions in space.
Name and refrences of author : Font Arial 18 pt
Project proposal in collaboration with: J. Ellis and N. Mavromatos (King's College London, CERN); A. Sakharov and R. Konoplich (NYU and Manhattan College, CERN); E. Sarkisyan (University of Texas at
Arlington, CERN).
Xylem conductivities from stems to leaves for grass plants Humberto Ortega, Biology Department Plants need water to grow. Xylem cells conduct water from the roots to stems and then to leaves. Xylem conductivity (McCulloh, et al. 2003) is a measure of a plant tissue’s ability to transport water.
Purpose What percentage of water in stems is conducted to the leaves? Is water conductivity the same in C3 and C4 grasses? How much water do grass stems give to each leaf?
Vascular bundles are unevenly distributed among the stem tissues and leaf tissues as seen in the images to the left and below.
Grass stems must feed water to their leaves. LEAF
Results
Methods
Background
Water conducted in cell in vascular bundles.
27 grass species were obtained from locations such as New York, Ireland, the South Pacific, and Hawaii. Tissue samples were prepared for histology and viewed under a microscope. Stem xylem conductivities were compared to leaf xylem conductivities, which were provided with water from the stem. The number of bundles in the both stems and leaves were determined. Grass species used in this study C3 species Agropyron junceiforme Á.Löve & D.Löve Alopecurus pratensis fo. brachyglossus (Peterm.) Şerb. & Nyár. Arundo donax ssp. plinii (Turra) Mateo & Figuerola Bromus rigidus ssp. ambigens (Jord.) Pignatti Calamagrostis × acutiflora (Schrad.) DC. Dactylis glomerata ssp. nestorii Rossello & L. Saez Festuca rubra ssp. villosa (Mert. ex Koch) S.L. Lu Hordeum vulgare var. abdulbasirovii Omarov Koelaria glauca ssp. pohleana (Domin) Tzvelev Lolium multiflorum fo. submuticum (Mutel) Anghel & Beldie Phragmites australis ssp. berlandieri (E. Fourn.) Saltonstall & Hauber Poa nemoralis var. popovii Tzvelev Poa pratensis ssp. stenachyra (Keng ex Keng f. & G.Q. Song) Soreng & G.H. Zhu Sphenopholis intermedia var. macrantha B. Boivin C4 species Andropogon virginicus var. decipiens C.S. Campb. Axonopus fissifolius var. polystachyus (G.A. Black) L.B. Sm. & Wassh. Cenchrus agrimonioides var. laysanensis F. Br Chloris gayana fo. oligostachys (Barratte & Murb.) Maire & Weiller Digitaria fuscescens (J. Presl) Henrard Digitaria insularis (L.) Fedde Digitaria setigera var. calliblepharata (Henrard) Veldkamp Miscanthus sinensis ssp. condensatus (Hack.) T. Koyama Pennisetum alopecuroides ssp. sordidum (Koidz.) T. Koyama Saccharum officinarum ssp. sinense (Roxb.) Burkill Sacciolepis indica ssp. oryzetorum (Makino) T. Koyama Sorghum halepense var. propinquum (Kunth) Ohwi Zea mays ssp. huehuetenangensis (Iltis & Doebley) Doebley
Results Species
Stem diameter (mm)
Number of bundles
Bundle conductivity (g cm MPa-1 s-1)
Stem conductivity (g cm MPa-1 s-1)
C3 species (14) Mean S.D.
2.66 2.78
49.6 85.7
0.0233 0.039
3.32 9.93
C4 species (13) Mean S.D. Probability
4.48 4.76 0.25
94.7 115.5 0.26
0.0398 0.0603 0.41
9.32 25.2 0.43
Each point represents a species.
Species with larger stems have more bundles for water conduction.
More vascular bundles in stems - more vascular bundles in leaves.
Larger stem bundle xylem (water) conductivity – larger leaf bundle xylem conductivity.
Larger stem xylem conductivity – larger leaf xylem conductivity.
For smaller species, larger stem xylem conductivity – larger leaf xylem conductivity.
C4 plants are supposed to be more efficient than C3 plants. However, for water conductivity, both C3 and C4 plants are similar. STEM
Reference: McCulloh, K. A., J. S. Sperry, and F. R. Adler. 2003. Water transport in plants obeys Murray’s law. Nature 421: 939-942.
Quantification of Eccentric Growth in Stems of Artemisia tridentata Nutt. ssp. Wyomingensis and A. tridentata spp. Tridentata Ismael PeĂąa, Biology Department
Introduction
Eccentric growth occurs when the secondary growth in parts of stems does not occur while normal growth occurs in other areas. Previous studies have shown that sagebrush stems have extensive eccentric growth. The Purpose of the present study is to quantitative document the extent of eccentric growth
Experiment 2
The second series of experiments was aimed at understanding how eccentric growth changed along stems. Stem of A. tridentata spp. Wyomingensis, 550mm, 520mm, and 334mm were used.
Materials and Methods 1. Three stem segments(550mm,520mm,370mm) of A.tridentata spp Wyomingensis were sent from Fremont Canyon, Utah. 2. Multiple samples were cut from each stem along the entire stem. 3. 10 transects were drawn(0,36,72,etc) and data was taken using Microsoft Paint.
Results ______________________________________________________________ Main Stem sample number
Experiment 1 This first experiment is aimed to determine the prevalence of eccentric growth of similar stem diameters for two subspecies of sagebrush.
Materials and Methods 1. Stem samples of A.tridentata spp Wyomingensis and spp. Tridentata from Milford, Utah and Thistle, Utah were sent to us. 2. Each stem was cut to create a cross sectional sample. 3. 10 transects were drawn(0,36,72,etc) and data was taken using Microsoft Paint.
1
2
3
Number Of Stem sections Tip-most sample
62
37
44
Stem area (mm2)
9.6
13.4
6.4
Number of rings
8
8
6
Stem area (mm2)
760
603
727
Number of rings
35
38
38
Total branch length (mm) 550
334
520
n
Base-most sample
Results _____ Subspecies
Eccentricity___________
Mean Mean Number Diameter number samples Mean Standard Largest Smallest (mm) of rings deviation ______________________________________________________________________________ Artemisia tridentata spp. 8.4 14 22 111 50.8 184 16.8 Wyomingensis Artemisia tridentata spp. 8.3
Tridentata
5.1
24
50.7
28.6
131
14.5
Conclusion Data show that for more than 20 stem samples of the two subspecies of sagebrush, more than 95% of all samples showed eccentric growth
Conclusions Data show that eccentricity occurs in more than 90% of all stem samples. The images above show that eccentricity occurs in a random fashion along stems. To our knowledge this study is the first to quantitatively document changes in eccentric growth along stems of sagebrush plants. The author is grateful to the Catherine and Robert Fenton Endowed Chair to Dr. L.S. Evans financial support for this research
Study of Simulated Particle Data and Practical Applications Presenter: Danielle Rabadi
Background Located in Geneva, Switzerland, Cern is the world’s largest nuclear research facility and is home to the Large Hadron Collider (LHC) – a high energy particle accelerator. In 2014, scientists from Cern released particle collision data colleceted from the LHC to the public called ATLAS Open Data. With this platform, anyone with a computer can learn and explore the building blocks of the universe.
❖ MadGraph - a Fortran-based software that analyses simulated particle collision. MadGraph5 was used to view every possible particle collision outcome of initial particles chosen by program user. These possibilities are viewed using Feynman Diagrams.
MadGraph Results
Materials and Methods ❖ Virtual Machine - an operating system that acts as a virtual computer on which files of the datasets can be accessed and viewed. VM used ROOT software to analyze these datasets. ❖ ATLAS VM ROOTbooks- provide a guide to download, use, and explore ATLAS data sets. ❖ PGS - a type of detector simulator used when analyzing particle collision. PGS was used in MadGraph when producing collision events. ❖ Pythia - a program that simulates events of the final states of chosen particles of the extent at which the simulated outcome is most likely to occur in real life. Each event displays final states of particles, the remaining particles, and their momenta. Pythia8 was used in MadGraph to apply showering to the datasets.
Summary and Discussion ❖ ATLAS VM ROOTbooks take the user step by step through the process of setting up computer environment and producing graphs of selected particles and comparing multiple variables from one or more datasets. ❖ MadGraph generates all possible results for a collision, making it easy to understand the different particle combinations. ❖ Results with showering applied to data sets of the SM, BSM, and Mixture were provided to my colleague, Tyler Reese
Conclusion ❖ This software helps scientists at Cern to distinguish the different properties of real life data collected by ATLAS. ❖ Using ATLAS VM as a tool in high school and undergraduate physics classes will help students understand to concepts of nuclear physics at a basic level without going into the advanced aspects of the field. ❖ Implementing the program in classes would encourage students to consider physics as a major. ❖ Useful for invited students from different universities to use ATLAS VM in their research.
Acknowledgements I would like to thank those supporting the School of Science Research Scholars Program. This research was supported by the School of Science Dean's Office Summer Program in Manhattan College under the direction of Dr. Constantine Theosodiou, Dr. Rani Roy, and Elen Mons. I would like to thank the Department of Physics at Manhattan College for providing materials for the research, and Dr. Konoplich and Dr. Zhitnitskiy for their guidance and support. I also thank Tyler Reese for his collaboration.
References Webber, Bryan. “Parton shower Monte Carlo event generators.”Scholarpedia, www.scholarpedia.org/article/Parton_shower_Monte_ Carlo_event_generators. Accessed 2 Aug. 2017.
Stem Growth Dynamics of Artemisia tridentata Claudia S. Ramirez, Biology Department
Purpose
Background
The purpose of this study was to document the annual growth pattern of sagebrush plants. Focused on stem length, diameter size, distances, and branch lengths, a model of annual growth could be created.
Hypothesis 3
Hypothesis 1
For the current-year growth, the branch lengths increase markedly during reproductive growth.
For current-year growth, terminal stem diameter increases linearly throughout the entire growth season.
Flowering Phase
Vegetative Phase
For current-year growth of stems, terminal stem lengths increase linearly during the vegetative portion of the growth cycle.
Hypothesis 2
For current-year growth, the number of branches per stem increase linearly during the vegetative portion of the growth cycle.
Hypothesis 4
Importance
Materials and Methods 1. Stem Samples of Artemisia tridentata were received from Thistle, Utah. 2. Samples were organized and analyzed. The stem branches were classified as vegetative or flowering. 3. Images were taken and measurements were completed using Image J. 4. A model of each sample was created using data from terminal stems.
Sagebrush plants play a critical role in the hydrologic cycle of the arid West. They also serve as a habitat for large number of animals including migratory bird species. It also provides protection for a multitude of wildlife species.
The author is grateful to the Catherine and Robert Fenton Endowed Chair to Dr. L.S. Evans financial support for this research.
Reconstruction of the Higgs Boson Using Computational Methods Tyler Reese Manhattan College Physics Department
Background
Methods
In the summer of 2012, CERN announced the discovery of the Higgs Boson. The particle was found to have a mass of 125 GeV. The Higgs field grants mass to the particles in the Weak Interaction. The path to Higgs production was found through the gluon fusion process and subsequent decay into gauge bosons: WW, ZZ, γγ. For the purposes of this project only fusions in Higgs bosons which decayed into ZZ pairs were considered. The Weak Interaction is the exchange of the intermediate vector bosons: đ?&#x2018;žđ?&#x2018;ž+ , đ?&#x2018;žđ?&#x2018;žâ&#x2C6;&#x2019; , đ?&#x2019; đ?&#x2019; đ?&#x;&#x17D;đ?&#x;&#x17D; . The đ?&#x2019; đ?&#x2019; đ?&#x;&#x17D;đ?&#x;&#x17D; decays into a charged lepton/antilepton pair The đ?&#x2019; đ?&#x2019; đ?&#x;&#x17D;đ?&#x;&#x17D; bosonâ&#x20AC;&#x2122;s decay is a crucial component of the reconstruction process
In order to find a theoretical mass for the Higgs Boson we only consider events in which decay results in four leptons. Those leptons being only electrons and muons either in events of standard electron/positron muon/anti-muon pairs or one electron/positron pair with another muon/anti-muon pair. Electron/Positron pairs Muon/Anti-Muon pairs are combined to reconstruct the Invariant Mass of the Z Boson from which they originated. The pair of Z Bosons are combined back into the Higgs in the same way.
Objectives Replicate ATLASâ&#x20AC;&#x2122;s results for the mass Higgs Boson, 125 GeV, through the use of tools provided by ATLAS Outreach. Simulate Higgs Production using Monte Carlo statistics and create an accurate theoretical model of such an event. Attempt to apply our model(s) to the experimental data given by ATLASâ&#x20AC;&#x2122;s Outreach.
Tools 2012 ATLAS Collison Data MadGraph5 â&#x20AC;&#x201C; Used to create simulations of particle collisions through Monte Carlo technique. Pythia8 â&#x20AC;&#x201C; Mimics theoretical quark showering during particles collisions which can be applied to MadGraph simulations. PGS â&#x20AC;&#x201C; Replicates the detector effects of an ATLAS style particle detector in order to provide a more accurate simulation from MadGraph and Pythia. ROOT â&#x20AC;&#x201C; Software created for data analysis which was used in this project to evaluate theoretical and experimental collisions.
Higgs in Experimental Data The Experimental datasets includes 7,917,590 events which means there is a lot of background. In order to eliminate the background, cuts were made in order to select events likely to result from Higgs production. Low momentum lepton were excluded early as they were unlikely to have originate from the Higgs. While, lepton pairs +1 with momentum of ~đ?&#x;&#x2013;đ?&#x;&#x2013;đ?&#x;&#x2013;đ?&#x;&#x2013;+ 1 GeV and ~đ?&#x;?đ?&#x;?đ?&#x;?đ?&#x;? â&#x2C6;&#x2019; â&#x2C6;&#x2019; GeV were selected. â&#x20AC;˘
â&#x20AC;˘
đ?&#x;?đ?&#x;? đ?&#x2018;´đ?&#x2018;´đ?&#x2018;Żđ?&#x2018;Ż
=
đ?&#x;?đ?&#x;? (đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x;?đ?&#x;? )
đ?&#x;?đ?&#x;? + (đ?&#x2019;&#x17D;đ?&#x2019;&#x17D;đ?&#x;?đ?&#x;? )
+ 2(đ?&#x2018;Źđ?&#x2018;Źđ?&#x;?đ?&#x;? â&#x2C6;&#x2122; đ?&#x2018;Źđ?&#x2018;Źđ?&#x;?đ?&#x;? â&#x2C6;&#x2019; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x;?đ?&#x;? â&#x2C6;&#x2122; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x;?đ?&#x;? )
â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘
Theoretical Simulations
The Standard Model was simulated where the Higgs had spin 0 and positive CP parity, this is CP-even (0+) state. The Beyond the Standard Model simulation, the Higgs was again given a spin of 0 but negative CP parity, CP-odd (0- ) state. The third, Mixed Model was done in which the Higgs could be a mixture of states, 0+ or 0-.
â&#x20AC;˘ â&#x20AC;˘
+ 5GeV â&#x2C6;&#x2019; + Yellow 10GeV â&#x2C6;&#x2019; + Green 20GeV â&#x2C6;&#x2019; + Red 40GeV â&#x2C6;&#x2019; + Black 60GeV â&#x2C6;&#x2019; + Violet 80Gev â&#x2C6;&#x2019; Teal â&#x20AC;&#x201C; complete background dominance (no cuts) Blue
The Higgs Background
Results After events which did not resemble the model were excluded the mass of the Higgs Boson could be found in the same way as was done in my model. The Z Bosons masses could be combined back into their previous state and the Higgs mass becomes very clear. The most precise result produced had 20 events and still included some background. The Experiment al Higgs signal, centered around ~125 GeV.
Z Boson mass distribution
Conclusion Computers can be used as powerful tools for physical analysis. Along with some elementary understanding of the physics, such tools can be used for ambitious research. Simulating an accurate theoretical model is a crucial step in research. Without it, any experimental data is difficult to comprehend and accurate conclusions cannot be made. It is possible to isolate a signal for the Higgs from the background through the exclusion of lepton pairs. This project made rejections based around pair momentum but perhaps another measurement could be used as grounds and achieve a similar result. Most importantly, for the Manhattan College Physics Department, a functioning computational system is now in place for undergraduate research. Further experiments and simulations now can be done by students in order to provide them an elementary understanding of the physics at play regardless of their level of mathematical understanding.
References The ATLAS Collaboration. "Observation of a New Particle in the Search for the Standard Model Higgs Boson with the ATLAS Detector at the LHC." Physics Letters B 716.1 (2012): 1-29. 17 Sept. 2012. 28 June 2017. The ATLAS Collaboration. "Evidence for the spin-0 nature of the Higgs boson using ATLAS data." Physics Letters B 726.1-3 (2013): 120-44. 7 Oct. 2013. 28 June 2017. ATLAS Outreach. "Data and simulated data." Data and simulated data ¡ Software Book. CERN, n.d. . 21 July 2017. Belyaev, N., R. Konoplich, L. Egholm Pedersen, and K. Prokofiev. "Angular Asymmetries as a Probe for Anomalous Contributions To HZZ vertex at the LHC." Physical Review D 91.11 (2015): 2. 28 June 2017. The CMS Collaboration. "Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC." Physics Letters B716.1 (2012): 30-61. 2 Aug. 2017. Advisors: Dr. Konoplich Dr. Zhitnitskiy
From Solution to Adsorption: Innovative Method for Removing Toxic Chromate Dominick Rendina Department of Chemistry and Biochemistry Manhattan College, Riverdale, NY 10471-4098
Methods Cont.
Figure 1. General procedure to test hydroxamic acid’s chromate removal capabilities
Solution Based Removal O
OH
Cr
O
O
O
O
O
R
O
N
O
N
GAC
GAC Based Removal O
R
HO
GAC
R
N
N HO
HO
H
GAC O HO
OH
O
Cr O
OH
R O
+
O
O
N
R
Cr N
HO GAC
2.
O
1.
N
OH
N
OH
H3C
N
OH
HO
O N
H N
OH
H O H CSC was utilized for the more polar molecule SHA while GAC was utilized for the greasier molecules BHA, AHA, and NPBHA. Loading of the hydroxamic acids onto GAC was preformed in different environments; static soaking for 1 hour, static soaking for 18+hours, and kinetic soaking for one hour at different rotational speeds. The successfully loaded GAC samples were tested extensively to determine breakthrough values, static removal vs kinetic removal, and reliability at lower pH values.
GAC
Results SHA and BHA worked in solution while AHA and NPBHA did not, thus AHA and NPBHA were eliminated. BHA and SHA were left overnight in chromate solution to determine which was the better choice, SHA preformed optimally (table 1). Results include chromate removal at different rotational speeds, chromate removal with different hydroxamic acid loading conditions, and chromate removal at low pH values of CSC-SHA 1:1.
Concentration
20mL
<1µM
40mL
6.5µM
50mL
8.3µM
±1.56
GAC-BHA 1:1
13.6µM
±2.83
CSC
197.4µM
±3.1
60mL
21.6µM
CSC-SHA 1:1
1.6µM
±0.002
70mL
31.4µM
Figure 4. Stirring vs Static loading, chromate removal difference
Figure 3. Concentration vs Time of rotating Cr(VI) solutions with CSC-SHA 1:1
4.
Volume
22.4µM
H
O
Table 2. Breakthrough values of CGAC-SHA 1:1 in 200µM Cr(VI) solution, 19 hour soak, 1g equivalent
GAC
O
The efficacy of the Hydroxamic/GAC samples were determined through UV/VIS spectroscopy using Method 7196A, the EPA’s standard method for determining chromate concentration in water. [2]
R
Cr
O
GAC O
N
H
Cr
O
R
Cr
N HO
O
O
O
+
OH
O
R
Figure 2. Hydroxamic Acid samples: BHA (1), NPBHA (2), AHA (3), SHA (4)
3.
Hydroxamic acid samples were dissolved in solution with chromate and then removed through granulated activated carbon (GAC) or coconut granulated activated carbon (CSC) to test effectiveness of chromate removal, then the steps are reversed and the hydroxamic acid is loaded onto GAC/CSC first then added to chromate to see if the same removal properties are observed.
Table 1. Concentration of 20mL of 200µM Cr(VI) solution, 19 hour static soak Standard Concentration Material (averaged) Deviation
200 150
50RPM 100RPM
100
150RPM
50 0 0
30
60
Time (Minutes)
Stock Solution 30 minutes 60 minutes 6.0pH CSC
6.0pH CSC-SHA 1:1
4.0pH CSC
4.0pH CSC-SHA 1:1
2.0pH CSC
2.0pH CSC-SHA 1:1
Future Projects Hydroxamic acids adhering Figure 6. Future to the structural benefits of testing chemical O SHA, that being hydrogen O bonding and symmetry, can HO be synthesized and NH examined using the same HN experimentation with more hydroxamic acids attached. OH
NH NH
O
200
Static loading
150 100
100rpm loading
50 0 0
30
60
Time (Minutes)
• SHA preformed optimally over all other molecules tested, symmetry and hydrogen bonding the most probable reason • CSC-SHA 1:1 was able to remove 2.72mg of chromate per gram of sample • CSC-SHA 1:1 was able to adsorb more chromate rotating at 150rpm • CSC-SHA 1:1 works well in at pH 6 and increases its efficacy at lower pH values • Loading SHA onto CSC is more effective while the solution is stirring
Sources
OH
O
250
Conclusion
Figure 5. Chromate Removal Comparison at Different pH Values
200 180 160 140 120 100 80 60 40 20 0
Concentration (µM)
Methods
Four hydroxamic acid samples were tested ranging from polar to non-polar samples: • Benzohydroxamic acid (BHA) • Acetohydroxamic acid (AHA) • N-phenylbenzohydroxamic acid (NPBHA) • Dihydroxamic Acid Succinate (SHA)
Concentration (µM)
Many industries utilize the use of chromiumtype oxidants in their production, ranging from the tanning of leather, manufacturing stainless steels, various mining operations, and pigment/dye productions which all produce toxic Chromium (VI) waste water [1]. Activated carbon is the most studied adsorbent used in the removal of chromium (VI) [1], the purpose of this research is to utilize this information to make different variants of activated carbon more efficient in removing chromate by adsorption of organic molecules containing a functional group known as a hydroxamic acid.
Results Cont.
Concentration (µM)
Abstract
OH
[1] Owlad, Mojdeh, Mohamed Aroua, Wan Daud, and Sacid Baroutian. “Removal of Hexavalent ChromiumContaminated Water and Wastewater: A Review.” SpringerLink. Springer Science, 20 Nov. 2008. Web 02 Sept. 2016. [2] EPA, Environmental Protection Agency, “Method 7196A: Chromium, Hexavalent.” , 27 Jan. 2017. www.epa.gov/hw-sw846/sw-846-test-method-7196achromium-hexavalent-colorimetric
Exploring Chromatin Dynamics Within the DNA Damage Response Pathway in Living Cells Bright Shi and Bryan J. Wilkins, Manhattan College, Department of Chemistry and Biochemistry
Abstract Genetic information is stored in the form of chromatin, consisting of DNA, histones and other essential proteins. Histone proteins mediate all aspects of chromatin function and are regulated by sets of posttranslational modifications (PTMs). Modification patterns dictate differential pathways dependent upon cellular queues. This dynamic behavior is at the heart of all chromatin related processes, such as replication, transcription and repair. Unfortunately, DNA is inherently susceptible to damage. There are numerous forms of damaging factors, where several DNA damage pathways collectively protect the genome from life-threatening mutations that have direct links to both cancer and aging. Therefore it is crucial that methods are developed that allow for us to study chromatin processes to better understand DNA damage pathways. We are using a synthetic biology approach that can trap histone-protein interactions in living cells, using unnatural amino acids. Comparing histone-protein interactions that are altered, due to DNA damage, will help us resolve the mechanisms that reshape chromatin structure under damaging stress. Many factors recognize and repair different types of damage but the orchestration of their function is still largely unknown. DNA damage signaling promotes broad changes in histone PTMs, and how the modifications control interactions at the nucleosomal interface during the response pathway is elusive. We can monitor histone PTMs across the cell cycle and correlate their influence on histone-protein interactions during damage pathways. We aim to expose nucleosomal repair protein-protein interactions and the mechanistic details of repair dynamics in yeast.
Introduction
Methods
Chromosomal stability is contingent upon localized chromatin domain reorganization in order to allow access to impaired nucleotides by cellular repair machinery. There remains an insufficient amount of data regarding the complicated molecular interactions that occur on the nucleosomal surface, particularly in the context of the living cell, during the DNA damage response. It is very difficult to assess chromatin dynamics in living cells. There do exist methods for studying chromatin in vivo, however the biochemical resolution of these techniques cannot completely clarify mechanistic details. Most chromatin related studies rely on the reconstitution of nucleosomal arrays in solution that cannot fully recapitulate real physiological conditions. In order to enhance our understanding of chromatin behavior in cells we require a technique that illuminates the molecular contacts that occur between the nucleosome and chromatin associated proteins in their native environment.
Our approach uses methyl methanesulfonate (MMS) as the DNA damaging agents. MMS is an alkylating drug that specifically methylates guanine and adenine DNA bases, mutations that cause DNA double-strand breaks as well as causing replication problems. We used MMS to monitor how UV-crosslinks from histone H2A changed when cells were stressed with DNA damage. We used plasmid borne histone H2A genes with amber mutations at the codon of interest for the site-specific installation of pBPA. The coding sequence also contained a region for a short peptide fusion tag (human influenza hemagglutin, “HA” tag) for antibody detection and visualization. The pBPA-containing H2A protein was expressed and allowed to incorporate itself into the native chromatin landscape. Cells were then exposed to UV-light and the cell lysates were analyzed by western blotting techniques and the crosslinked visualized via anti-HA antibodies. Control cells were not treated with DNA damaging agents.
The advent of an expanded genetic code has made it possible to express full-length protein harboring site-specific incorporation of unnatural amino acids (UAA). These amino acids are synthetic and possess side chain chemistries that can act as unique chemical handles. The power of this system resides in the ability to manipulate the endogenous translational machinery to read a stop codon as a sense codon. This requires the directed evolution of an aminoacyl-tRNA synthetase (aaRS) that is paired to a suppressor tRNA that recognizes the UAG (amber) stop codon. The substrate recognition site of the aaRS is evolved to accept only an UAA of interest. The aaRS/tRNA pair acts orthogonal to the host system creating a mechanism by which the UAA can be delivered to the ribosome and properly added to a growing chain of peptides. By introducing plasmid borne expression vectors containing the amber mutated coding sequence of a protein of interest plus the evolved aaRS/tRNA pair, live cells suppress the genetically installed stop codon with an UAA.
Approach
Grow yeast cells; add H2O2 and Methyl methanesulfonate (MMS) as DNA damaging agent.
1. Grow yeast cells to logarithmic phase, normalize the cell count, and then add MMS. • 0.025% and 0.05% MMS 2. Cell cultures diluted to .8A grown for 2 or 4 hours in presence of MMS. 3. Cells collected and then exposed to UV-light (365 nm) for 45 min. 4. Whole cell protein lysates prepared via trichloroacetic acid (TCA) precipitation. 5. Proteins separated by electrophoresis using SDS-PAGE gel.
General scheme for pBPA installation into the chromatin landscape and crosslinking approach.
Results
6. Proteins transferred to blotting membrane and then crosslinked proteins detected antibodies.
Discussion Generally as DNA gets damaged, most cell functions were reduced. This were expressed by a decrease in the signal strength of the band. Interestingly, for position H3 S22 there was a specific protein that begin to interact at the position only 4 hours after cells grew in .025% MMS solution. This suggests that this specific protein interact with H3 S22 to repair the DNA damage, while .05% MMS deals too much damage for the cell to repair with that specific protein. Figure 5. H4K16 acetylation analysis
Figure 4. Western Blot for H3 S22 General scheme for the introduction of UAA into yeast protein of interest.
We use the amino acid p-benzoylphenylalanine (pBPA) because it contains a benzophenone moiety that can form a diradical with low energy UV-exposure (~365 nm) allowing for hydrogen abstraction and radical recombination with neighboring proteins within a distance of 0.4 nm. We install pBPA into histone proteins and capture proteinprotein interacting crosslinks via exposure of living cells to UV-light.
The lowest band on Figure 4 represent the concentration of histone molecules alone. The denser the mark, the stronger the signal released by the histone or the histone-protein complex. Strong signal is equivalent to higher quantity of histone or histone-protein complex. From low to high, the size of histone-protein complex increases. Differ from most other histone positions, H3 S22 bind to a protein that expresses stronger signal 4 hours after adding MMS, while generally signal strength decrease as cell culture grew longer in DNA damaging agent. This band noted with asterisk only exist in the cell culture with lower MMS concentration. Figure 5 shows how the levels of H4K16 acetylation, a post translational modification, were affected by .05% MMS. For both H2A A61, H3 T11 and H3 R52, the wild type cell cultures express stronger signal than cultures grown with MMS. The densitometry graph on the right side of the figure was an average of 3 experiment, showing 40-50% signal decrease after adding MMS for 2 or 4 hours.
H4K16 acetylation blotting was designed to show opening and closing of chromatin. Increased acetylation means increased accessibility of chromatin for binding proteins. H4K16 ac western blot for H2A A61, H3 T11 and H3 R52 shows that .05% of DNA damaging agent significantly decrease acetylation, suggesting that normal cell functions were minimized to prevent synthesizing proteins with damaged DNA. Comparing to the 2 hour culture, H4 K16 acetylation increase in the 4 hour culture. This suggests that some DNA damage have been repaired, so some cell’s normal function can be resumed. There are other logical explanation for this phenomenal, such that the normal cell functions had paused for too long and the chromatin must became more active for the cell to survive. To proceed toward our initial goal of this experiment, such to reveal the mechanisms that shape chromatin structure under DNA damage. This experiment must be repeated for many more times at other significant positions surrounding the histone octamer. Also mass spectrometry is necessary to reveal the actual proteins that crosslink with the histone on the western blots. Only when the structures and the functions of these proteins are known, the mechanisms can be understood.
Reference pBPA molecule and mechanism for radical formation and recombination.
• Tsabar, M. and Haber, J. E. Chromatin modifications and chromatin remodeling during DNA repair in budding yeast. Curr. Opin. Genetics Dev. 23, 166–173 (2013). • Dorman, G. and Prestwich, G. D. Benzophenone photophores in biochemistry. Biochemistry 33, 5661–5673 (1994). • Chin, J. W., Cropp, T. A., Anderson, J. C., Mukherji, M., Zhang, Z. and Schultz, P. G. An expanded eukaryotic genetic code. Science 301, 964–967 (2003).
Variations of the A Cation in Organic/Inorganic Perovskite Materials Melissa Skuriat, Dr. Alexander Santulli Department of Chemistry and Biochemistry Manhattan College Abstract: Hybrid perovskite materials, particularly Methylammonium Lead Iodide (MALI), have been proven to perform effectively in solar cells. These materials are able to absorb a broad spectrum of light and convert it into electricity with minimal energy loss. These materials have the potential to create a cleaner, cheaper, and powerful source of energy. The goal of this research project was to synthesize perovskite materials in the form of nanowires to eventually be used in solar cells. The main focus was synthesizing different combinations and ratios of organic cations within the perovskite structure. These samples were tested for absorbance and stability to determine which perovskite materials would be optimal for making solar cells.
Methods:
Discussion:
Solutions of .88 M Acetamidinium Lead Iodide, Formamidinium Lead Iodide, Guanidinium Lead Iodide, Imidazolium Lead Iodide, and Methylammonium Lead Iodide in DMF were made. The same organic cations were used to make .88 M lead chloride solutions, but the data wasn’t optimized. Methylammonium Lead Iodide was used as a standard solution, and it was used to make 25/75%, 50/50%, and 75/25% mixtures with the four other organic cation/lead iodide solutions to make 12 more samples. Glass plates were made from microscope slides, and they were cleaned and sonicated with acetone and isopropyl alcohol. Each sample was spotted onto a glass slide, and a spin coater was used to cover each slide with its sample. The slides were then heated on a hot plate at 150˚C for 5 minutes in order to activate the sample and dry it on the plate. An Agilent UV-Vis-NIR Spectrophotometer was used to test each sample’s ability to absorb light at different wavelengths. The samples were all left out for about 24 hours to test how their absorbance capabilities were affected upon prolonged exposure to air.
Results:
Introduction: Perovskite is a mineral with the chemical formula CaTiO3. A perovskite structure is any material that has the same crystalline structure as the mineral and adheres to the same ABX3 composition. “A” represents an organic cation, “B” is an inorganic metal cation, and “X” is a Halogen anion.
MALI is used as a standard perovskite material because it can absorb a broad spectrum of light. Formamidinium Lead Iodide (FLI) also absorbs this broad spectrum. Guanidinium Lead Iodide (GLI) strongly absorbs blue light, and Acetamidinium Lead Iodide (ALI) and Imidazolium Lead Iodide (ILI) absorb both longer and shorter wavelengths of light. After about 24 hours of exposure to air, all samples faded, and their ability to absorb light diminished. The MALI and ALI mixes displayed a broad range of absorption, particularly the 50% MALI/50% ALI and 75% MALI/25% ALI samples. The MALI and FLI samples also absorbed a broad range of light, specifically the 50% MALI/50% FLI and 75% MALI/25% FLI samples. The MALI and ILI mixes were found to be more stable, and the MALI and GLI mixes primarily absorbed blue light. The next step of this project is to test the samples using an X-ray Diffractometer to determine their crystal structures and see how their structures affect the samples’ properties. Finally, the perovskite materials will be placed in solar cells and tested for their ability to conduct electricity.
Acknowledgements: Perovskite materials that are used in solar cells have the ability to convert photons into electricity effectively, and very little energy is lost in the process.1 The production of perovskite materials involves a precipitation reaction, which is easier and more cost-efficient compared to the multistep process of making traditional silicon-based solar cells.2 The use of powerful perovskite materials in solar cells provides a cleaner source of energy for society to utilize.
I would like to thank Dr. Santulli for his guidance as mentor. I would also like to thank the School of Science and the Jasper Summer Scholars for financial support.
References: Samples of Lead Iodide with Various Organic Cations. Samples were photographed after preparation.
Samples of Lead Iodide with Various Organic Cations Exposed to Air for 23 Hours
(1) “Perovskites and Perovskite Solar Cells: An Introduction.” Ossila, www.ossila.com/pages/perovskites-and-perovskite-solar-cells-anintroduction. (2) “Atomic Movies May Help Explain Why Perovskite Solar Cells Are More Efficient.” Phys.org - News and Articles on Science and Technology, 26 July 2017, phys.org/news/2017-07-atomic-moviesperovskite-solar-cells.html.
Effects of Chemical Exposure on Tadpole Behavior and Personality Cassidy Stranzl, Dr. Maria Maust-Mohl, and Dr. Gerardo Carfagno Manhattan College, Dept. of Biology
Results:
Introduction:
40%
• 15 tadpoles (5 control, 5 previously exposed to lemon grass oil pesticide, and 5 previously exposed to carbaryl pesticide) were video taped in a “home” habitat for 15 minutes before being transferred to a “novel” habitat for an additional 15 minutes. • Trials were run on each tadpole on two consecutive days. • Individual tadpole positions, number of movements, and time spent active were tracked and summarized in Excel. • T-tests were used to determine the significance of differences between individuals on different days (correlated) and between different groups (independent).
Proportion of Time
• There was a significant increase between Day 1 vs. Day 2 novel activity, but not home activity (Figure 1). 25% • This could be because tadpoles are more reserved on Day 1, but become 20% 15% more confident by Day 2, increasing their activity levels. These results 10% provide us with evidence of personality. 5% • There were significantly more boxes crossed by the carbaryl group in the 0% Home environment then the lemon grass oil group (Figure 2). Home Novel • There was significantly more time spent at the surface by the carbaryl Figure 1: Average proportion of time out of 15 minutes all tadpoles group compared to the other two groups (Figure 3). spent active in Home and Novel tanks during Day 1 and Day 2 • Both of these results indicate that tadpoles subjected to carbaryl are Home Tank Number of Boxes Visited T-test 18 df= 8 more active than the control group and the group subjected to lemon 16 p= 0.05 grass oil, meaning carbaryl may have an effect on tadpole personality. 14 • Tadpoles in all groups spent the vast majority of time avoiding boxes in 12 10 the center of the novel environment, indicating a hesitancy to explore the 8 new environment. 6 • Our results were consistent with the results of previous studies, which 4 2 found that tadpoles do exhibit evidence of personalities, and this can be 0 shown by their activity levels and how much they explored a novel Lemon Grass Oil Control Carbaryl 3 environment. Figure 2: Average number of boxes visited within 15 minutes for • The next step is to determine if individuals within groups show evidence each of the three treatment groups in their home tanks of unique personalities. Day 1
30%
9%
Day 2
8%
Percent of Time Spent at Surface
7%
T-test df= 8 p= 0.05
I would like to thank Dr. Theodosiou and The School of Science Summer Research Scholar’s Program for the opportunity, Dr. Maust-Mohl and Taylor Maher for help with video analysis, and Dr. Carfagno for being the best advisor!
5% 4% 3% 2%
Literature Cited:
1% 0%
Control
Carbaryl
Figure 3: Average proportion of time out of 15 minutes each of the three treatment groups spent at the surface in their home tanks
Side-view
Acknowledgments:
6%
Lemon Grass Oil
Home
Discussion:
Number of Boxes Visited
Materials and Methods:
35%
Proportion of Time
The idea that individual animals have personalities is a relatively new concept that has just recently received attention by the scientific community. Studies are especially limited in amphibian species and there are no known studies on the effect of chemical exposure on amphibian behavioral types.1 We studied the differences in the personality axes of 2 activity and exploration of Bullfrog tadpoles. We also studied the effect that lemon grass oil (eco-friendly, plant based) and carbaryl (industrial, toxic) insecticides have on an individual’s behavior. This is an important topic because altering an individual’s behavior could potentially affect its fitness. Our hypothesis was that there would be evidence of personality differences and that the insecticides may have an effect on these individual personalities.
Proportion of Time Spent Active
T-test df= 14 p= 0.05
Novel
1. 2. 3.
Conrad, J. L., K. L. Wienersmith, T. Brodin, J. B. Saltz, and A. Sih. 2011. Behavioural Syndromes in Fishes: A Review with Implications for Ecology and Fisheries Management. Journal of Fish Biology. 78:395-435. Carlson, Bardley E. and Tracy Langkilde. 2013. Personality Traits Are Expressed in Bullfrog Tadpoles During Open-Field Trials. Journal of Herpetology. 47.2:378-383. Urszan, Tamas Janos, Janos Yorok, Attila Hettyey, Laszlo Zsolt Garamszegi, and Gabor Herezeg. 2015. Behavioural Consistency and Life History of Rana dalmatina Tadpoles. Oecologia. 178:129-140.
Spectroscopic Analysis of the Interaction between Mefenamic Acid and Human Serum Albumin (HSA) Ewa Swiechowska and Dr. Jianwei Fan Department of Chemistry and Biochemistry, Manhattan College
Abstract
II. Quenching the fluorescence intensity of HSA by mefenamic acid tris-HCl buffer
In this study, the binding interaction between human serum albumin (HSA) and mefenamic acid, an anti-inflammatory drug, was investigated using UV/visible absorption and fluorescent spectroscopy. The parameters of binding between HSA and mefenamic acid such as binding constant, number of binding sites, the nature of binding force as well as thermodynamic parameters associated with binding process were determined.
Hepes buffer
8.00 7.00 6.00
Log (Ka)
Mefenamic Acid Added
Mefenamic Acid Added
5.00 4.00 3.00 2.00
Introduction
1.00 0.00 320
325
330
335
340
345
350
1/T *10^-5 (K-1)
Most drugs are transported to the target cell in the circulatory system with an albumin; therefore, the drug binding with albumin makes an important factor in the pharmacokinetic behavior, affecting active concentration of the drug. Human serum albumin (HSA) is a single chain globular protein of 585 amino acid residue with a high concentration in blood plasma and the carrier for many drugs to different molecular targets. HSA contains three homologous domains (named I, II and III), where each domain is made up by two separate helical subdomains (named A and B) connected by random coil. The two specific drug binding sites on HSA are located in the subdomain IIA (Sudlow site 1) and subdomain IIIA (Sudlow site 2) (Figure 1). The fluorescence properties of HSA are mainly due to the aromatic acids, mainly tryptophan located in the subdomain IIA, which can be selectively measured by exciting at 279nm. Tryptophan is highly sensitive to its environment and the changes in the emission intensity from tryptophan often occur in the response to the substrate binding or conformational changes within HSA.1 When HSA binds to a drug, it diminishes the drug’s ability to execute its biological functions. Therefore, the stronger the drug binds to HSA, the lower the concentration of the active form of that drug.2,3 The aim of this research is to study the interaction between HSA and mefenamic acid, a non-steroidal anti-inflammatory drug which is used to treat painful conditions such as arthritis, pain associated with heavy menstrual bleeding, and pain after surgical operations.4
a
b
Figure 7. Log (Ka) vs. 1/T at three different temperatures: 288K, 298K, and 308K. The ΔS and ΔH were determined from the y-intercept and the slope, respectively.
Table 2. The Calculated Thermodynamic Parameters for three different temperatures: 288K, 298K, 308K.
Figure 4. The emission spectra of HSA with increasing mefenamic acid concentration a) tris-HCl buffer and b) hepes buffer The addition of increasing amounts of mefenamic acid from 50uL to 120uL resulted in decrease of the fluorescent intensity of HSA at 333 nm (Fig.4). These results show that there is an interaction between the HSA and the mefenamic acid. The peak at 305 nm was due to the water Raman scattering.
Temp (K)
Hepes Buffer Tris Buffer △S △G △H △H (kJ/mol) △S (J/Kmol) △G (kJ/mol) (J/Kmol) (kJ/mol) (kJ/mol) 288 -‐158.1 -‐403.7 -‐41.8 -‐47.7 -‐39.2 -‐36.4 298 -‐37.8 -‐36.0 308 -‐33.8 -‐35.6
A. Stern-Volmer Equation F0/FCorr = 1+ Ksv[Q]
The calculated thermodynamic parameters determined that the reaction was exothermic and spontaneous. The results also depict that both ΔH and ΔS were both negative and therefore Van der Waals and hydrogen bonding are dominant5.
[2]
The Stern- Volmer equation [2] was used to determine the Stern-Volmer quenching constant, Ksv. Fo is the fluorescence intensity in the absence of the mefenamic acid, the quencher. Fcorr is the fluoroscence intensity in the presence of the mefenamic acid obtained using Eq.[1] to correct for the inner filter effect. The [Q] is the concentration of the mefenamic acid. The Stern-Volmer plots are shown in Fig. 5.
Discussion
An important aspect of this experiment was to determine the quenching mechanism by the drug molecules. Fluorescence quenching refers to 2.5 any process that decreases fluorescence intensity of a certain fluorophore such as tryptophan residue on human serum albumin (HSA). There 2.5 2 are two general types of quenching mechanism: dynamic and static. Dynamic quenching is due to collisions between quenching agents and 2 1.5 fluorophores during excited state, while static quenching is the result of the formation of a non-fluorescent complex between the fluorophore 1.5 1 1 and the quencher in the ground state. 0.5 0.5 For dynamic quenching, an increase in temperature will increase the collision frequency, and therefore the quenching constant Ksv increases 0 0 with an increase in temperature. In contrast, for static quenching the degree of complex formation tends to be inversely proportional to 0 .0 0 0 .5 0 1 .0 0 1 .5 0 2 .0 0 2 .5 0 3 .0 0 3 .5 0 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 MEFENAMIC ACID (UM) Figure 1.The three-dimensional structure of Human Serum Albumin (HSA) Figure 2: Structure of Mefenamic Acid temperature, and therefore quenching constant Ksv for static quenching tends to be lower at higher temperatures. Since the quenching constant [MEFENAMIC ACID] (UM) a b ( "Characterization of Subdomain IIA Binding Site of Human Serum Albumin (http://www.sigmaaldrich.com/catalog/substance/ measured in this experiment decreased with an increase in temperature, it was concluded that quenching process was most likely static. in its Native, Unfolded and Refolded State Using Small Molecular Probes”) mefenamicacid241296168711?lang=en&region=US) Furthermore, for dynamic quenching, Ksv= kqτ, where kq is the bimolecular quenching rate constant and τ is the life time of excited state of HSA without mefenamic acid, which equals 1.0 x10-8s. The maximum value of kq is kd , the diffusion rate constant in aqueous solutions which Figure 5: Stern-Volmer plots of HSA and mefenamic acid in a) tris-HCl buffer and b) hepes buffer at three different temperatures: 288K, is equal to 1010 M-1 s-1 . The kq calculated from the obtained Ksv value and the τ of HSA was equal to 1x1013M-1s-1, which was greater than the 298K, and 308K maximum kd indicating that the quenching process could not be dynamic. Therefore, both experimental evidence indicated that the quenching Experimental mechanism is static. The binding constant, Ka, is an equilibrium constant of the association between HSA and mefenamic acid. The results showed that the HSA and mefenamic acid were purchased from Sigma Aldrich and Fisher, respectively. The 0.05 M tris-HCl buffer (pH 7.4) and 0.05 binding constant, Ka decreases with an increase in temperature resulting in the higher concentrations of the free HSA and its fluorescent M hepes buffer (pH 7.4) prepared by using analytical reagent grade were used to closely resemble the pH of the human blood, which is B. Determination of the association constant Ka and number of binding sites properties. The number of binding sites was determined from the slope of the double log equation. The n values obtained were very close to in the range of 7.35 and 7.45. The fluorescence spectra of all solutions were measured using Photon Technology International (PTI) one indicating only one binding site on HSA. HSA contains several residues containing tryptophan, an amino acid with fluorescence properties spectrofluometer equipped with a 1.0 cm quartz cell connected to a thermostat bath. The absorption spectrum was recorded with an log [(F0-Fcorr)/Fcorr] = logKa + nlog[Q] [3] in the subdomain IIA and this suggested that the binding site is located in the subdomain IIA due to the same location of the tryptophan. Agilent 8453 UV/visible photodiode array spectrophotometer. The Van’t Hoff equation is related to the change in the equilibrium constant as a reaction to the change in temperature. The negative values The double log equation [3] allowed for the determination of the association constant, Ka, by observing the y-intercept of the graph, shown in Fig. of ΔG and ΔH indicate that the reaction between mefenamic acid and HSA was spontaneous and exothermic. The ΔΗ and ΔS values are also The solutions containing 1mL of 1.00 x 10-5 M HSA and different volumes (0, 50, 80, and 120 uL, respectively) of 1.27 x 10-4 M 6. The number of binding sites, n, was obtained from the slope of the graphs at the three different temperatures. negative and therefore, the interaction forces between the HSA and mefenamic acid are mainly Van der Waals forces and Hydrogen bonding, mefenamic acid were diluted to 5mL total volume with hepes or tri-HCl buffers, and incubated for 20 minutes. The emission spectra of 288 K 308 K 298 K 288 K 298 K 308 K however electrostatic forces are not excluded.5 these solutions were measured from 290-500 nm with an excitation wavelength at 279 nm. The bandwidths of excitation and emission LOG (F -F ./F ) LOG [F -F ./F ] This study also found that HSA is more stable in tris-HCl buffer than in hepes buffer. The emission intensity of HSA is very stable in tris monochromators were set at 1.50 mm and 1.00 mm, respectively. -6 .4 -6 .2 -6 -5 .8 -5 .6 -5 .4 -6 -5 .9 -5 .8 -5 .7 -5 .6 -5 .5 -5 .4 0.4 0.1 buffer until mefenamic acid is added. On the other hand, the emission intensity of HSA decreased slightly even in the absence of mefenamic 0.2 0 acid. When mefenamic acid was added to HSA in hepes buffer, the emission intensity decreased substantially, but hardly stopped decreasing. It -0.1 0 Results -0.2 -0.2 seems that hepes buffer itself has some weak interaction with HSA, and may compete with mefenamic acid since its concentration is much -0.3 -0.4 -0.4 larger than mefenamic acid. This observation is confirmed by the calculated thermodynamic parameters. In tris-HCl buffer both entropy (ΔS) -0.5 -0.6 I. The UV/visible absorption spectra of HSA and mefenamic acid -0.6 and enthalpy (ΔH) are more negative than those in hepes buffer, indicating that the disorder of an isolated system has decreased and the -0.8 -0.7 -0.8 complex formation between mefenamic acid and HSA is more stable. b -1 a 288 K
308 K
298 K
308 K
3
FO/FCORR.
298 K
F0/FCORR .
288 K
0
CORR
CORR
CORR
LOG [MEFENAMIC ACID]]
CORR
LOG [MEFENAMIC ACID]
0
Figure 6. Log (F0-Fcorr/Fcorr) vs. Log [mefenamic acid] in a) tris-HCl buffer and b) hepes buffer at three temperatures: 288K, 298K, and 308K. The Stern-Volmer constants, association constants and the number of binding sites obtained at three temperatures are summarized in Table 1. Table 1. Ksv, Ka, and n values at 288K, 298K, and 308K.
b
Tris Buffer
a
Temp (K)
Ka (M-‐1)
K sv
x 105
(M-‐1)
Hepes Buffer Ka x 106
n
(M-‐1)
Ksv x 105 (M-‐1)
n
288
3.73E+07
3.06
1.33
4.04
4.59
1.15
298
3.87E+06
1.65
1.21
1.96
4.13
1.12
308
5.16E+05
2.75
1.002
1.11
4.10
1.07
To correct the measured fluorescence for the inner filter effect, the following equation was used: 2 Fcorr= Fobs x eA/2
[1]
1) Jan M. Antosiewicz, D. Shugar. Biophys Rev 8 (2016) 163-177 2) M.Maciazek-Jurczyk, M. Maliszewska, J. Pozycka, J. Pownicka-Zubik, A. Gora, A. Sulkowska, Journal of Molecular Structure 1044 (2013) 194-200 3) Mullah Muhaiminul Islam, Vikash K. Sonu, Pynsakhiat Miki Gashna, N. Shaemninway Moyon, Sivaprasad Mitra, Spectrochimica Acta Part A: Mol and Biomol Spectroscopy 152 (2016) 22-33 4) J. Clin Pathol, 1987 Oct; 40(10): 1261–1262 6) T.Sanjoy Singh, Sivaprasad Mitra, Spectrochimica Acta Part A: Mol and Biomol Spectroscopy 78 (2011) 942-948 7) Philip D. Ross, S. Subramanian, Biochemistry 1981, (2011), pp 3096-3102
Where Fcorr and Fobs are the fluorescence intensities corrected and observed, respectively. A is the absorbance of mefenamic acid at 279 nm at the same concentrations as present in the mixture of mefenamic acid and HSA.
III. Thermodynamic Parameters Van’t Hoff Equation The thermodynamic parameters were calculated using the Van’t Hoff equation (4): log (Ka)= ΔS/2.303R - ΔH/2.303RT
This experiment indicates that mefenamic acid does quench the fluorescence of HSA by affecting the microenvironment of the tryptophan located in the Domain II of HSA. The quenching mechanism is static since both Ka and Ksv decrease with an increase in temperature. The temperature-dependent measurements indicate that the intermolecular forces between HSA and mefenamic acid are mainly Van der Waals forces and hydrogen bonding.6
References
Figure 3. The absorption spectra of a) 1.00 x 10-5 M HSA (λmax at 279nm) and b) 1.27 x 10-4 M mefenamic acid (λmax at 286nm) As shown in Fig. 3, the maximum absorption wavelengths (λmax) of mefenamic acid and HSA have an overlap of the spectra. This indicates the presence of an inner filter effect, which is the competition for the photons between the HSA and mefenamic acid. As the result, the fluorescence intensity of the HSA apparently decreases.
Conclusion
[4]
Acknowledgments
where Ka is the binding constant; ΔS is the change in entropy; and ΔH is the change in enthalpy. Equation (5) used ΔS and ΔH to determine Gibb’s • free energy, ΔG: • ΔG=ΔH – TΔS [5]
Dr. John Regan for providing chemicals and useful discussion throughout the experiment. Dean of the School of Science for Summer 2017 Research Fellowship.
Examining Nucleosomal Dynamics in Living Saccharomyces Cerevisiae (Yeast) Cells Amanda Zimnoch and Dr. Bryan Wilkins Department of Chemistry and Biochemistry, Manhattan College
Introduction In just one cell in your body, there are 2 meters of DNA. If you laid the DNA from all the 50 trillion cells in your body end to end, it would reach the Sun and back over 300 times. How does all this DNA fit inside of us? The strands of DNA, which have a negative charge, wrap around histones, which are positively charged proteins. When the DNA wraps around the histone 1.65 times, it is then called a nucleosome. At this point, the DNA and histones resemble “beads on a string”. These beads on a string condense into chromosomes. The combination of DNA and proteins (histones) which make up chromosomes is generalized as “chromatin”. The aim of my research was to examine the physical mechanisms of nucleosomes in living cells – an area of biology which is still unexplored – and to extend the research previously completed by Gabriela Bukanowska. © 2013 Nature Education Adapted from Pierce, Benjamin. Genetics: A Conceptual Approach, 2nd ed.
Summary Through Polymerase Chain Reaction, select proteins which have been known to have chromatin functionality were tagged genomically. The proper tagging of these genes was verified via protein expression, western blotting, and antibody recognition. The tagged proteins were then assayed for nucleosomal interaction in vivo by crosslinking from histone proteins that contained the photo-crosslinking unnatural amino acid p-Benzophenylalanine (pBpa).
Methods Agarose gel •Made of 50 mL 1X TBE Buffer, 0.5g Agarose, and 5µL CYBR® Safe •Loaded with 5µL Ladder and mixtures of 2µL sample + 2µL Load Dye •Run at 100 V for 45 minutes
Western Blotting
Western Blot •MOPS Buffer •50mM MOPS •50 mM Tris, pH 7.5 •3% SDS •1 mM EDTA •Layers of filter paper, protein gel, membrane, filter paper •Constant 250 mA for 2-3 hours
Methods (cont.)
Results
The generalized procedure for the overall experiment was as follows: 1. Polymerase Chain Reaction (PCR) 2. Run PCR results on Agarose gel 3. Ethanol Precipitation PCR 4. Grow cells •35.5 µL pure H2O 5. DNA Purification •10 µL 5X HF Buffer 6. PCR Verification •1µL plasma (PYM5) 7. Run PCR Verification results on Agarose gel •1µL dNTPs 8. Grow cells •1 µL Forward 9. TCA Precipitation Marker (S3) 10. Run TCA precipitated samples on 8% protein gel •1 µL Reverse 11. Run Western Blot Marker (S2) 12. Incubate membrane in antibodies and take photo •0.5 µL Polymerase 13. Yeast Transformation 14. Grow cells on plates 15. Attempt crosslinking Polymerase Chain Reaction is a procedure which can produce millions of copies of a targeted segment of DNA from just one original. We modified the Polymerase Chain Reaction by using Primers (S2 and S3) that were specially designed so that they contained 3’-homology to the amplification target from a plasmid (tag and marker) and also contained 5’-homology regions specific for the genomic sequences that flanked the intended genomic insertion site of the tag. The PCR product therefore yields the tag and selected marker with 5’-homologous regions that will bind to the genome of yeast. The myc-tag that was inserted would allow for the unnatural amino acid to bind with it later on, and the marker chosen was the HIS3M6X gene, which would allow cells to produce their own Histidine. The samples were then run on an agarose gel at 100V for 45 minutes. The PCR products were transformed into cells to initiate genomic tagging. The cells were then plated on agar plates deficient of Histidine because proper recombination also inserts the HIS3 gene, allowing the cells to produce their own Histidine. We then looked for our tagged proteins in whole cell lysates via western blotting and antibody visualization. After preparing the cellular lysates, each of the proteins were separated by size through gel electrophoresis on an 8% Tris-acetate gel. The proteins were transferred from the gel onto a membrane to provide solid support for antibody binding. The membrane was washed and then incubated in antibodies specific to the myc-tag of interest. A photo was then taken using a CCD imager. PCR Verification •15.5 µL pure H2O •5 µL 5X HF Buffer •1 µL DNA (diluted to 100 ng/µL) •1 µL dNTPs •1 µL Forward Primer •1 µL Reverse Primer •0.5 µL Polymerase
The initial PCR for RSC1 appears to have worked, because the bands are at 1500 b.p. as opposed to ~750 b.p. for wild type DNA. After troubleshooting (growing new cells and using new primers), the 3rd RSC1 Verification PCR worked for all 6 clones . There is a large jump from the wild type signal (below 750 b.p.) and all the RSC1 signals. The initial PCR for SNF2 was previously performed successfully by Gabriela Bukanowska. The Verification PCR for SNF2 +1 appears to have worked due to the large jump between the signal compared to the wild type. When we ran Snf2 +1 and two arbitrary Rsc1 samples on a western blot, along with positive control Bdf1, there is definitely a signal for Snf2, and there may possibly be a signal for Rsc1 #12.
Genes/proteins tagged Name
kDa (Mol. Weight)
Function
SNF2
194
Transcription regulatory protein. It changes chromatin structure by altering DNA-histone contacts within a nucleosome, leading eventually to a change in nucleosome position, thus facilitating or repressing binding of gene-specific transcription factors.
RSC1
90
Chromatin structure-remodeling complex. The complexes interact with histone and histone variant components of centromeric chromatin.
Conclusion & Future Research As seen in the SNF2 Verification PCR, SNF2 +1 has been successfully tagged with the myc-tag and HIS3X gene. We ran SNF2 +1 on an 8% gel and performed a western blot, which showed a signal. The next step for SNF2 in the future is to attempt crosslinking. As evidenced by the RSC1 Verification PCR #3, RSC1 has also been successfully tagged with the myc-tag and HIS3X gene. We were not able to obtain a clear Western Blot for RSC1, so another one of our future goals is to re-run this western blot in hopes of obtaining a clearer picture to prove that the experiment worked. Once we get these to work, the next step in this research is to attempt crosslinking with these genes with the photoreceptive unnatural amnio acid, pBenzophenylalanine (pBpa). In the future, this experiment can be duplicated with any other genes or proteins in order to expand the knowledge in this field.
Acknowledgments I would like to thank the Dean of the School of Science for giving me the opportunity to conduct research. I would also like to thank Dr. Wilkins for his invaluable guidance this summer.