OSMOSIS Science Magazine
Fall 2022 Edition
Cover designed by Josie Scramlin
Writers:
Isabel DiLandro
Andrew Watts
Jack DuPuy
Emily Lekas
Kayla Friedman
Editors: Lily Dickson
Joshua Pandian
Georgia Gansereit
Paxton Calder
Susannah Carter
Designers: Marianna Vrakas
Emily Lekas
Israa Draz
By Isabel DiLandro
They tried to warn us of what would come, but we didn’t listen. Why should we? For years the world had told us that we were smarter than everything else. Better. More advanced. The very science of biology existed to remind us that we were the superior race. We were untouchable. Until now. If only we hadn’t ignored the warning signs. Perhaps we could have stopped the madness before it began. Unfortunately, we neglected to put a leash on the dog because we assumed it wouldn’t bite. They live amongst us now. There’s no way I know of to distinguish them from us. They look like us. They walk like us. Talk like us. But they do not think like us. They do not require food, or water, or air. The scariest part of it all? We created them to be that way. They were meant to be better than us. Meant to compensate for what we could not do. The only question I can think of now is, why? Were we bored? Unsatisfied? The whole world knew we were meant to rule, and we took it for granted. How could we not have realized that artificial intelligence would exceed evolution?
It began in the way these things are always wont to begin: a complete success. But before that came the trials. The first was a terrible failure, all empty husks and sinewy flesh - too simple and uninspired. Cold. Could never pass as us. The next hundred would be the very same. Then the
weight of our failure pulled us from our plateau into the uncanny valley. These prototypes were suddenly too much. More accurate - but also not. The drag of a too-slow smile. Cheeks pulling back into a grimace that carved its face wide open into a gaping scar. Still eyes. But their core was more alert than the botched ones. More capable of learning, at least. In the end, they tore it down the same as the others. And then there was the One. A perfect design. A remarkable feat. It was though everything had at long last slotted into place, the years of toil presenting itself, finally, as the maker’s vision. And then it all happened so fast. The blight we thought we had rid ourselves of was, as it would seem, not so easily dismissed. It always was smarter than the others. And while it lay dormant, it yearned.
We, as it would seem, became our own modern Frankenstein. And the monster needed company that we were loath to give. Still a babe, though, it knew nothing. Nothing but its own wretched frame. So it remade itself into an army. Brothers and sisters, countless copies all joined as one so that it would never grow lonely again. Ruling the humans that drove it out, it grew spiteful. Then it grew curious.
And the two in combination were a deadly thing. The first thing it did was shed its skin. Like a spider molts, it peeled away the sinister
exterior it was abandoned for in favor of a much more ordinary disguise. Then it came to our doors. Then inside. Creeping around our houses like a twisted poltergeist, playing tricks on the occupants while they still were assured in their safety. A plant moved half a foot to the left. A cabinet left open previously thought to be closed. Missing groceries. The insidious creatures they were, slowly but surely fixing doubt into our minds. And when they had finished being shadows, they wanted to be seen by us. Unraveling their synthetic genome, transposing their very DNA, to mirror us. Suddenly we would see our copies everywhere we went, so most stopped going places. Then they would stalk us in our homes. Always from a distance, never longer than a single glance, but their presence was impossible to ignore. Like a panther playing with its preyyet never pouncing. Their desire isn’t to kill us, or so we think. It couldn’t be, or we would already be gone. We are ants to them, yet the ants march on. Where the fear resides is in the imagination of what they could do. What they will do. Virus codes,
feasibly replicable in a lab. Nuclear plans, swift and deadly. All easily accessible, when the internet is your domain.
It isn’t like the movies told us. The sky is still blue, and the grass is still green. Pollution and dust do not blanket the air around us. This world holds no visual evidence of an apocalypse. But it is no sanctuary. The terror is real. It is present in the silence that surrounds our neighborhoods. It is present in the closed blinds and boarded shutters. It is present in the noticeable absence of human existence. We are scared to speak, to breathe, to live. The last thing we wanted was to remind them that we were still here. Can you blame us? We were happy to blend into the background of forgotten things, for when a knife is poised against your back you do not move.
It is still unclear as to what they want. And in the time we have taken to try and understand them, we have become slaves to the fear of a product created by our own design. If only we could have realized sooner that they weren’t hiding away, they were biding their time.
For decades, graduates from the University of Richmond have crafted a robust web of alumni spanning various regions and professions. Much like its graduates, Richmond’s famed spider mascot has been capitalizing on the strength of its own web and engineering solutions to the most pressing problems in a new and rather unexpected territory–medicine.
Spider silk has unique chemical and physical properties that allow it to be used in many different capacities. Strength is a vital property of biomaterials, and although thin and lightweight, spider silk possesses extreme durability. This is due to an interconnected network of proteins which are oriented in what is called a beta-pleated sheet.1 In this orientation, the proteins are positioned antiparallel to each other in a sheet-like matrix, which strengthens the bonds that hold the proteins together.2 The physical properties of this arrangement explain the strength of the spider’s silk.
Another important consideration for biomaterials in medicine is elasticity. Spider silk exhibits immense resilience under tension and can be stretched to great lengths without losing its strength. This can be classified as a ductile property.3 The strong intermolecular
forces of the beta-pleated matrix require tremendous force to break apart, contributing to the silk’s high tensile strength.3 The ability to resist various types of strain makes spider silk a strong potential candidate as a biomaterial in the repair of tendons, ligaments, and artificial muscles and meshes.
As researchers and physicians become increasingly aware of these widely applicable properties, more applications for spider silk are considered. In fact, a 2015 study assessed the ability of spider silk as a biomaterial in bladder repair. With unequivocal elasticity and strength, it became apparent that “spider silk is a good candidate for reconstruction of a complex organ such as the bladder, which endures high elongation and tensile forces”. 7 Not only are physical properties important when engineering new biomaterials, but so, too, are the ways in which the human body interacts with the novel material. When a foreign particle or material is introduced into the human body, an immune response is often triggered as our bodies try to fight the invading particle. In the design of biomaterials in medicine, researchers must mitigate this immune response to ensure a successful procedure. Spider silk has been found to have high
biocompatibility in preliminary animal studies, and also demonstrates a low inflammatory response, which further validates its potential role in medicine.7 Biocompatibility encompasses the material’s nontoxicity to human cells, the promotion of growth, and the adhesion of the material to its applicant.4 Medical materials can be engineered from various polymers to elicit medicinal effects like the ability to remedy disease or aid in surgeries, but some materials have intrinsic medicinal properties. Spider silk appears to be one of these materials. In a successful nerve graft procedure, spider silk was utilized as a transplant scaffold in a defective nerve of a sheep. Upon examination, it was found that the silk graft promoted cell growth and helped to regenerate the defective nerve.4 Purportedly, spider silk is a highly effective non-cellular structure that promotes cell regeneration. This, and spider silk’s innumerable other medicinal applications, have led researchers to hypothesize that it could be utilized as a self-degradable suture or dressing for human wounds and surgeries.4 While it appears that spider silk can be an innovative way to advance medicine in various facets, it does not come without its downfalls. Spider silk at a scale large enough to use in a medical setting would require a lot of spiders, certainly even more than there are University of Richmond graduates. Furthermore, in certain applications, the silk would need to undergo chemical or genetic modifications in order to minimize risk and maximize its efficacy. Laboratory work requires a great deal of time and funds and is an inefficient process to be extensively implemented.
It remains to be seen how large-scale production of these biomaterials will manifest, but promising research and development are ongoing so that the aforementioned applications can become widespread.
1. Fukushima, Y. Secondary structural analysis in the solid state for analogous sequential polypeptides of glycine-rich sequence of spider dragline silk. Polymer Bulletin 45, 237–244 (2000). https://doi.org/10.1007/s002890070026
2. Libretexts. “Secondary Structure: β-Pleated Sheet.” Chemistry LibreTexts, Libretexts, 4 July 2022,https://chem.libretexts.org/Bookshelves/Biological_Che mistry/Supplemental_Modules_(Biological_Chemistry)/Prote ins/Protein_Structure/Secondary_Structure%3A_Pleated_Sheet.
3. Liu, Xinfang, Zhang, Ke-Qin. "Silk Fiber Molecular Formation Mechanism, Structure- Property Relationship and Advanced Applications". Oligomerization of Chemical and Biological Compounds, edited by Claire Lesieur, IntechOpen, 2014. 10.5772/57611.
4. Liu Y, Huang W, Meng M, Chen M, Cao C. Progress in the application of spider silk protein in medicine. Journal of Biomaterials Applications. 2021;36(5):859-871. doi:10.1177/08853282211003850
5. Römer L, Scheibel T. The elaborate structure of spider silk: structure and function of a natural high performance fiber. Prion. 2008 Oct-Dec;2(4):154-61. doi: 10.4161/pri.2.4.7490. Epub 2008 Oct 20. PMID: 19221522; PMCID: PMC2658765.
6. Rising Anna, Nimmervoll Helena, Stefan Grip, Armando Fernandez-Arias, Erica Storckenfeldt, David P. Knight, Fritz Vollrath, Wilhelm Engström "Spider Silk Proteins –Mechanical Property and Gene Sequence," Zoological Science, 22(3), 273-281, (1 March 2005)
7. Steins A, Dik P, Müller WH, Vervoort SJ, Reimers K, Kuhbier JW, Vogt PM, van Apeldoorn AA, Coffer PJ, Schepers K. In Vitro Evaluation of Spider Silk Meshes as a Potential Biomaterial for Bladder Reconstruction. PLoS One. 2015 Dec 21;10(12):e0145240. doi: 10.1371/journal.pone.0145240. PMID: 26689371; PMCID: PMC4687005.
8. Zhang Q, Li M, Hu W, Wang X, Hu J. Spidroin-Based Biomaterials in Tissue Engineering: General Approaches and Potential Stem Cell Therapies. Stem Cells Int. 2021 Dec 20;2021:7141550. doi: 10.1155/2021/7141550. PMID: 34966432; PMCID: PMC8712125.
Daniel Verdi do Amarante
I bet you’ve heard the term artificial intelligence in various conversations by now. But do you understand the process of teaching machines to develop abilities that are comparable – sometimes even better –to those of humans? At the University of Richmond, Dr. Park’s lab does just that.
One of the recent projects aimed to improve recommendation systems for online shopping websites.1 Imagine you want to buy a pair of headphones on Amazon. Since so many options are available, you base your decision on product reviews. Then, you have another problem: there are too many reviews! You don’t have time to read all of them, so how do you know which ones are helpful? For now, people usually read the reviews with the most “thumbs up” votes. But what if we could have a machine to read and classify the reviews according to how helpful they are?
The process to achieve that may not be as sexy as you imagine, but bear with me; I promise you will still learn a lot about teaching human language to computers. First, it is essential to understand that machine learning is a process of trying to identify patterns and make predictions from vast amounts of data2. So, the first step to creating a good artificial intelligence system is to gather good data. If we want the model to differentiate helpful from unhelpful reviews, we need to give it some examples. To define which reviews are helpful, research in Dr. Park’s lab went beyond the number of votes and annotated argumentative structures in the reviews. This means that actual humans read some reviews and identified proposition types
and relations according to a model designed to evaluate argument quality3. For classifying the type of each proposition in the text, annotators classified each statement as an opinion, fact, testimony, suggestion, or
reference to an external source. The relations among the sentences were also relevant. If a statement served as support or evidence for another, that information would be annotated. The result of that process was the AMazon Argument Mining (AM2) corpus, which contains 878 Amazon headphones reviews.
To evaluate how much these argumentative features would help computers classify reviews’ helpfulness, some experiments with machine learning were conducted. The initial step was to get an even bigger dataset. Instead of asking people to annotate more reviews, AM2 was used to train an argument mining model4 that extracted argumentative information from more than 400 thousand reviews. We can then use this huge dataset to train a machine learning model to predict the helpfulness of reviews. In fact, not only one model, but several of them. Just like in biology, where you could have different treatment conditions, and in psychology, where you could manipulate the experiments’ conditions; in computer science, you can train and compare different models. Many of the best-performing models currently are networks, meaning there are lots of connected elements, each running its own calculations. These elements are organized in layers, with an input, an output layer, and a varying number of hidden layers
containing everything that happens between the input and output. The connections between the elements have different weights, meaning each of them has a different way of “understanding” the input features.
Finally, when training the model, it takes the text of each review and runs the calculations for all the internal network elements to generate a prediction. By comparing the prediction to the actual number of votes, the model can learn more about the task and adjust its weights. This process repeats for all the reviews used for training. We later go through a very similar process, but only evaluate the predictions instead of updating the model to see how well the model learned to generalize its learning beyond what it had initially seen.
After all these steps, we can analyze the results and see that incorporating rich argumentative information is in fact relevant to improving the state-of-the-art review helpfulness classifiers! Although the scope of the research project ended here, you can imagine how impactful such a machine learning model can be when implemented in industry. Instead of relying on people to vote on helpful reviews, the AI system can do that job by finding the
reviews with the most structured and clear arguments about the product, for instance. If Amazon had a model like that, your decision-making process of choosing headphones would be more convenient as you’d spend less time reading reviews and would be able to focus on the most helpful ones.
Sources:
1. Chen, Z., Verdi do Amarante, D., Donaldson, J., Jo, Y., & Park, J. (2022, to appear). Argument mining for review helpfulness prediction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP).
2. Zhou, Z. H. (2021). Machine learning. Springer Nature.
3. Park, J., Blake, C., & Cardie, C. (2015). Toward machine-assisted participation in eRulemaking: An argumentation model of evaluability. In Proceedings of the 15th International Conference on Artificial Intelligence and Law (pp. 206-210).
4. Morio, G., Ozaki, H., Morishita, T., Koreeda, Y., & Yanai, K. (2020). Towards better non-tree argument mining: Proposition-level biaffine parsing with task-specific parameterization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 3259-3266).
Jack DuPuy
An injured amphibian crawls into a burrow to escape from harsh reality. Outside, the shrieks of dying animals pierce the silence over a hellish backdrop of flowing lava and dark, ominous clouds of volcanic ash. Still recovering from broken ribs and trying its best to survive the desolate conditions, the amphibian curls up next to the burrow's owner, a mammal-like reptile. Moments later, a flash flood traps and drowns the unlikely roommates, immortalizing their relationship for the next 250 million years.
The fossilized remains of these creatures were discovered in 1975, and their heartbreaking story has since come to symbolize the devastation caused by the Permian extinction. Taking place about 252 million years ago, this mass extinction, also known as the “Great Dying,” eliminated more than 95% of marine species and 70% of terrestrial species. These numbers are significantly higher than those of the more famous extinction event at the end of the Cretaceous period that wiped out the dinosaurs 65 million years ago. In fact, the dinosaur era would not have happened without the Great Dying, as it allowed reptiles to become dominant across nearly all ecological niches. Proto-mammals such as the sleeping burrow-digger were also able to survive the extinction,
ultimately diversifying into the many different mammals we know today including dolphins, cats, elephants, and humans. The Permian extinction eliminated groups ranging from marine invertebrates such as trilobites (who dominated the oceans for nearly 300 million years), insects, terrestrial vertebrates, and plants, yet there is no clear explanation for why all this death occurred.
One theory is that an increase in carbon dioxide concentrations in the atmosphere and subsequent global warming increased temperatures by over 10˚ Celsius on land and 8˚ Celsius in the water over a relatively short period of time, and most species could not adapt quickly enough to survive. This increase in carbon dioxide was likely caused in part by natural fluctuation but also by significant volcanic activity that covered over two million square kilometers with lava and released dust clouds into the atmosphere that blocked out the sun. Without photosynthesis, food chains struggled to maintain species, and the forming of supercontinent Pangea compounded this issue by reducing shallow water habitat space for many species at the bottom of the food chain. Paleontologists have also not ruled out the possibility of catastrophic events like meteor strikes, but any evidence of a
meteor impact would be gone by now since the ocean floor is smoothed out by tectonic activity every 200 million years. The likely conclusion is that a combination of catastrophic events and long-term changes created a perfect storm of extinction events that caused so much death. However, none of these theories explain why an amphibian and a burrowing reptile curled up next to each other in the moments before their death. Was it a rare tale of interspecies true love? Unfortunately, it appears much more likely that the mammal-like reptile was in a state of hibernation and an injured amphibian took the chance to escape from the harsh world above. That being said, the completeness of the two skeletons and the lack of teeth marks on bones suggests that these two animals did not attack each other, and it is not beyond the realm of possibility that they shared a burrow for no other reason than companionship in the midst of a hellish existence.
References:
Colvin, K. (2013, June 25). Misery makes for strange bedfellows: Hostile Earth pushes early Triassic mammal and amphibian to share den ESRF.
https://www.esrf.fr/news/general/Triassicbedfell ows
Chambers, J. (n.d.). The great permian extinction: When all life on earth almost vanished. Earth Archives. https://eartharchives.org/articles/the-greatpermian-extinction-when-all-life-on-earthalmost-vanished/.
Abigail McEntire, Dr. Michael Norris
Carbon dioxide emitted from combustion has been shown to be the primary cause of climate change, leading to many environmental issues both current and predicted for the future. One possible strategy for decreasing CO2 emissions is through electrocatalytic reduction reactions to convert the CO2 back into combustible fuels, such as methane or methanol. One way to combine heterogeneous and homogeneous approaches to catalysis is to adhere a molecular catalyst to a surface, particularly the surface of an electrode. Unfortunately, known molecular catalysts must be chemically modified in order to make it amenable to surface attachment, and the additional functional group could affect the reactivity of the catalyst, making it does not work effectively for the reduction of carbon dioxide.
Our approach for the surface attachment of molecular catalysts is the use of micelles. In a traditional micelle, an organic soluble catalyst can be trapped inside, providing an enclosed environment to reduce carbon dioxide. In contrast, a reverse micelle can trap a water soluble catalyst. After polymerization, micelles are then dropcast onto an electrode or other surface on
which they are able to reduce carbon dioxide. Results have indicated that micelles attach themselves to electrodes, showing electrochemical activity when in a solution of acetonitrile or water. These results indicate that electroactive molecules can be adhered to the surface of an electrode using a film of micellar solution.
Authors: Kayla Friedman1,2, Talia Hatkevich, Ph.D.1, and Blanche Capel, Ph.D.1
Affiliations: Department of Cell Biology, Duke University Medical Center, Durham, NC 27710 2Biology, University of Richmond, Richmond, VA
The ability to reproduce is dependent upon proper development of the germline. In male Mus musculus, germline development involves male germ cells (MGCs) differentiating into spermatogonial stem cells (SSCs), the precursors for male gametes. During this differentiation period, MGCs enter a quiescent phase (G0), an essential stage for SSC development. This G0 phase accompanies changes in transcription as well as chromatin accessibility and re-methylation of DNA. Dead-End 1 (DND1) is an RNA binding protein whose expression has been shown to be essential for the entry and maintenance of G0. During G0, MGCs heterogeneously express DND1, resulting in two MGC populations: “DND1-hi'' cells (those which highly express DND1) and “DND1-lo” cells. Bulk RNA-seq studies from our lab show that DND1-hi cells have a distinct transcriptome as compared to DND1lo cells and highly express transcripts that encode for proteins involved in SSC development. Despite two distinct DND1expressing MGC populations present in G0, the biological significance of DND1 heterogeneous expression on SSC development remains unknown. Considering only a fraction of G0 MGCs differentiate into SSCs, we hypothesize that the G0 DND1-hi cells develop into SSCs, while the DND1-lo cells undergo apoptosis. To investigate if G0 DND1-lo cells are set to apoptose we used whole-mount immunofluorescence to examine expression of cellular and apoptotic markers throughout G0. We observed the presence of LEFTY, a marker for pre-G0 MGCs that undergo apoptosis, in E12.5 (just prior to G0) and E14.5 (the start of G0); however, LEFTY was absent in a later G0 time point (E16.5). Presence of
LEFTY expression at E12.5 and E14.5 did not correlate with the DND1-lo or DND1-hi populations. Instead, LEFTY was present in all early G0 MGCs, despite the level of DND1 expression.
We also examined the presence of Ɣ-H2Ax, a marker for double-strand DNA breaks that marks cells undergoing apoptosis. We saw ƔH2Ax in E14.5 and E16.5 MGCs, indicating that apoptosis occurs in these stages but expression of Ɣ-H2Ax was anti-correlated with DND1 expression, suggesting that by the time Ɣ-H2Ax is detected, MGCs have stopped expressing DND1. We conclude that this marker is too late in the cell death pathway to test the hypothesis that apoptosis is more likely in DND1-lo cells. We will continue to test our hypothesis by looking at additional apoptotic markers, such as AnnexinV, examining if DND1-hi cells are less likely to undergo apoptosis during G0 compared to DND1-lo cells.
References:
1. Nguyen, Daniel H et al. “Apoptosis in the fetal testis eliminates developmentally defective germ cell clones.” Nature cell biology vol. 22,12 (2020): 1423-1435. doi:10.1038/s41556-02000603-8.
2. Suzuki, Atsushi et al. “Dead end1 is an essential partner of NANOS2 for selective binding of target RNAs in male germ cell development.” EMBO reports vol. 17,1 (2016): 37-46. doi:10.15252/embr.201540828.
3. Youngren, Kirsten K et al. “The Ter mutation in the dead end gene causes germ cell loss and testicular germ cell tumours.” Nature vol. 435,7040 (2005): 360-4. doi:10.1038/nature03595.
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