SJS Issue 3, 2015-2016

BIOLOGY

MATH & STATISTICS

PHYSICS

SJS

Issue 03: 2015-2016

Swarthmore Journal of Science

ORGANIZED ANARCHY Ant Colonies and their Surprising Applications

CHEMISTRY

ENGINEERING

COMP SCIENCE

Letter from the Editor BOARD

2015-2016

Dear readers,

I’d like to welcome you to this issue, the culminating work of the sophomore year of the Swarthmore Journal of Science.

I’ve always remembered being a sophomore as hard, both in high school and at Swarthmore, and SJS’s sophomore year has been no exception. My year at the helm of SJS has definitely been full of challenges, but I have been lucky enough to have a very talented and very flexible group of writers and editors alongside me through every bump in the road that led to this issue. These many challenges were only survived because of some very special people that I would like to thank. First, I would like to thank my amazing Managing Editor, Christina Labows for all her help and support during this year. I am also very grateful to Taylor Chiang and John Sun for their exceptional work on the layout and graphic design.

Swarthmore Journal of Science was created to face the challenge of communicating science to the larger Swarthmore community, not just us scientists who live in Cornell or assorted labs during our time at Swarthmore. If you are a science-y type, there are still plenty of new topics for you to discover inside this issue. In its essence, however, SJS was created to foster dialogue between us all, so when you pick up this issue, share it with a friend and start a conversation.

I am hugely proud of the work that this year’s editorial board created and I hope you enjoy it.

EDITOR IN CHIEF Chrissy McGinn

MANAGING EDITOR Christina Labows

EDITORS Physics: Katherine Dunbar Biology: Zelu Sibanda & Helen Wang Math & Statistics: Meghana Ranganathan Chemistry: Aaron Holmes Engineering: Jimmy Shah Copy Editor: Thomas Ruan Graphic Design: John Sun Layout: Taylor Chiang

CONRTIBUTERS Physics: Luke Barbano Biology: Talia Borofsky & David Tian Math & Statistics: Nicole McNabb Chemistry: Jennifer Guo

Happy reading, Chrissy McGinn Editor-in-Chief 2015-2016

JOIN THE CONVERSATION swatjournalscience@gmail.com Connect with us on: https://issuu.com/swarthmorejournalofscience https://www.facebook.com/SwatJournalScience http://pintrest.com/sciencejournal

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CONTENTS 4 6 9 10 12 16 18 20 27 28 30 32 34

Evolution of Time

Synthesizing Rare Marine Molecules for Chemotherapy CPU FAQ: A Beginner’s Guide to Understanding Processors Ecosystem Engineers: Modifying out Surrounding Enviornments Regulating Missing Planes Friedrich Wohler vs. The “Vital Force” The Trouble with Girls: Gender Bias in the Sciences Organized Anarchy: Ant Colonies and their Surprising Applications The Miniature World of Exciting bubble Physics Transferase Enzymes: Chemistry with the Solution to Cancer Treatment Common Core Mathematics: The New Standard?

Beauty Without a Spine Jiadifenolide 3

Evolution of timE By lukE BarBano Time is a mystery, perhaps one of the most philosophically elusive characteristics of the universe.

It is a concept that has perplexed even humanity’s most notable thinkers, so it is only natural that our understanding of time has progressed to reflect a diversity of advancements in scientific thought. The aim of this article is to develop a basic understanding of that profound evolution.

irrespective to any external influences. Absolute time and space comprise the foundations of a background in which all physical events in the universe occur, and observers in this universe experience the passage of time at the same rate, regardless of their respective states of motion. Newton’s laws were developed to govern the motion of a body subject in accordance with these assertions, so absolute time and space constitute a theoretical basis for the mathematical development of Newtonian mechanics.

This depiction of universal time is seemingly consistent with the reality that we experience in our daily lives; however, our reality transpires at speeds significantly less than the speed of light. Since Newton’s laws well describe the motion of bodies in an everyday, sub-light speed context, the basis of these laws (specifically the supposition of absolute time) could not be properly challenged until the advent of

The Beginning of Time:

In 1687, Sir Isaac Newton formally introduced the concept of absolute time (along with absolute space) in his Philosophiæ Naturalis Principia Mathematica: “Absolute, true, and mathematical time, of itself, and from its own nature flows equably without regard to anything external…” (Newton 73). The implication is that time is an inherent characteristic of an objective reality that passes at a constant rate, 4

the 19th century when advancements in electrodynamics and electromagnetism revealed inconsistencies in applying Newton’s laws to light speed phenomena.

a moving reference frame. Conversely, a relativistic approach to time entails every observer experiences time in a unique manner. Only clocks in the same reference frame would measure the same readings, while clocks observed in any other frame of reference would appear to tick at a different rate. The structure of our reality is a spacetime continuum in which observers perceive space and time relatively.

Relativity:

19th century mathematical physicist James Maxwell is immortalized for demonstrating that electricity and magnetism are merely manifestations of the same phenomenon. Maxwell’s equations stipulate that electromagnetic radiation (also known as light) must move through space at the same rate in all frames of reference, meaning that the speed of light has a constant value with respect to any observer. In applying Newton’s classical laws to electromagnetic phenomena, the constancy of the speed of light could not be satisfied, so Albert Einstein proposed the famed special theory of relativity to reconcile this incompatibility.

The premise of Einstein’s theory involves two postulates: the constancy of the speed of light and the notion that the laws of physics hold true in any inertial reference frame. The combination of these suppositions reveals a profound underlying characteristic of the universe’s mathematical structure; space and time are interdependent and undivorceable.

Space and time constitute a continuum, and one has no meaning without the other. The term spacetime continuum arises from this concept, and any point (or event) in this spacetime continuum must be specified with information about time as well as position.

Time is Imaginary:

The formulation of a spacetime continuum demands the equal treatment of time and space, both conceptually and mathematically. In order to describe spacetime, it is necessary to invoke a four-dimensional manifold (or topological space) in which each point consists of one time coordinate and three position coordinates. In A Brief History of Time, Stephen Hawking introduces the concept of imaginary time, which differs from the real time we experience in that it is actually a mathematical construct involving imaginary numbers. If the flow of time were to have a direction, real time would have a “forward” direction while imaginary time could flow either “forwards” or “backwards.” By substituting this imaginary time in place of real time, the seemingly daunting four-dimensional spacetime manifold takes on the characteristics of a familiar Euclidian space. Time and space become indistinguishable, and calculations can be easily performed using imaginary time as a tool to gain insights into real spacetime.

Enfin:

Understanding Relativity:

Our understanding of time has evolved from Newton’s original proposal of absolute time to the relativistic time resulting from Einstein’s theory of relativity. This relative time transpires in a “forward” direction, but when considering spacetime in a mathematical context, the time component of an expression can be replaced with imaginary time (which can transpire in either the “forwards” or “backwards” directions) as a tool by which to simplify calculations.

Although we can describe this fundamental characteristic of the universe well, physics presses on in attempting to understand its underlying facets.

The effects of relativity most pronouncedly manifest themselves when the relative velocity between two frames of reference approaches the speed of light, in which case observers in both frames observe significant length contraction and time dilation.

Length contraction refers to the perceived shrinkage of objects in the direction of motion in another frame of reference, whereas time dilation denotes the perceived retardation of the rate at which time passes in another frame of reference.

A relativistic approach to time manifests itself quite differently from that of an absolute approach.

A universe where absolute time prevailed would be one in which an observer perceived no difference in the passage of time denoted by a clock at rest in their reference frame in comparison to that of a clock in

Literature Cited: Hawking, S (1988). A Brief History of Time. New York: Bantam Dell Publishing Group.

Newton, I (1687). Principia. Philosophiæ Naturalis Principia Mathematica

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Synthesizing Rare Marine Molecules for Chemotherapy

By Aaron Holmes

Angucyclines are a class of molecules taken from certain bacteria in the genus Streptomyces.

These molecules are widely used as antibiotics, but some of them have been used as anti-cancer agents in chemotherapy treatment. All well-studied anti-cancer angucyclines, like most chemotherapy drugs, destroy cancer cells by targeting DNA, which is present in all living cells (1). DNA-targeting drugs work by unwinding the tight double strands of DNA and binding onto it. This inhibits DNA from their normal enzymatic function, and it prevents cell division, a necessary process for all cells to grow. A significant problem with these types of chemotherapy drugs is that they actually attack all cell types, not only cancer cells.

To be clear, traditional chemotherapy drugs are still highly effective. While these drugs target healthy cells, most normal cells divide very slowly, and are not influenced strongly by the treatment.

Most healthy cells have a very long life cycle as well, and have enough time for their DNA-repair mechanisms to reverse the DNA damage done by the chemotherapy (2). Cancer cells, conversely, divide much faster and have a much shorter life cycle (Figure 1).

This short life cycle means that as the cancer cell cannot divide due to the chemotherapy drug, the cancer cell will “outlive” its cell cycle and undergo apoptosis, a programmed cell death. Similar to cancer cells, some normal cells, including hair, digestion, and blood cells divide rather quickly (3). Chemotherapy drugs target these normal cells and lead to the side effects commonly associated with the treatment, including hair loss, diarrhea, immune deficiency, and general fatigue. Not surprisingly, this normal-cell targeting problem is an important field of current research, and many researchers have pioneered molecules with more cancer-cell specificity in the last 15 years.

In December 2015, a French group set out to develop a one-pot synthesis of Marmycin A, an angucycline that was found in marine sediment in 2007 (4, 5). Marmycin can only be extracted in limited amounts in nature, so an effective, complete synthe-

sis of this compound could prove highly applicable in future chemotherapy drugs. The group was able to synthesize this molecule using classic organic reactions.

Marmycin A’s structure, while seemingly complex, is understandable with a deeper understanding of angucycline molecules. Angucyclines tend to be tetracylics, meaning they have four connected ring structures. These molecules must contain a fragment of anthraquinone, a compound not found independently in nature, but nonetheless present in 79 known biomolecules (6, Figure 2). With its five ring structures, Marmycin A is large for an angucycline, but its components can certainly be analyzed. The researchers thought backwards from the Marmycin A product, and proposed three reactions that could occur in succession (a one-pot synthesis), which eliminated the need for separations and purifications, which may worsen product yields. These reactions were a Diels-Alder cycloaddition first, an Ullmann cross-coupling second, and a Friedel-Crafts cyclization last.

While the “one-pot” aspect to the synthesis seems simple, the French group needed to perform various preparatory reactions to form three organic reactants needed in the one-pot synthesis. For the

Figure 1. The cell life cycle. Cancer cells have short lifespans, dividing very quickly during the mitotic phase, which is the final phase of the life cycle. Adapted from (14).

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distinct molecules, forming a tetracylic angucyline.

After this introduction of another ring, known as a cycloaddition, the researchers added basic reagents to create new sequential double bonds along the two right-most rings. Sequential double bonds in rings make a molecule “conjugated” and more stable. The researchers then performed an Ullmann coupling reaction between the angucylcline and their amine reactant, which connected the nitrogen to the carbon where the highly unstable triflate (-OTf) group once was (Figure 4). This reaction occurred because the amine is electron-rich, and can donate electrons to the conjugated angucycline; Ullman reactions seem to require at least one conjugated reactant. While the Ullmann reaction (discovered 1901) is actually older than the Diels-Alder (1928), the Ullmann has not been well-understood, and there is no consensus on its exact mechanism; so, its application here is very impressive (10). In the final step, the well-studied Friedel-Crafts reaction (1877), which requires a conjugated reactant, occurred under acidic conditions, where one of the conjugated double bonds linked up with a carbon of the amine ring, ejecting the unstable oxygen (methoxy) group in the process (Figure 5). The molecule was able to retain its conjugated nature, five rings were formed, and Marmycin A was synthesized!

While their approach lead only to small amounts of product, the researchers were still able to examine

Figure 2. Anthraquinone, angucyclines, and Marmycin A. The target compound, Marmycin A, is an angucycline, which contains an anthraquinone molecule in its structure.

Diels-Alder reaction, they synthesized two known but noncommercial molecules, a two-ringed molecule and a one-ringed molecule, following relatively quick reactions cited in relatively recent chemistry papers (7, 8, 9). In addition, they formed a new monocylic reactant needed for both the Ullmann and Friedel-Crafts reactions, using an advanced, modern elimination reaction, among their other steps (Figure 3).

Seen in Figure 3, this third reactant is an amine, since the molecule contains a nitrogen atom connected to carbon. With all the pieces set, the researchers were ready to begin the one-pot synthesis of Marmycin A. In the Diels-Alder cycloaddition, the two known molecules come together in thermal conditions, or high temperature (Figure 4). The reason that the Diels-Alder reaction occurs can be explained by seeing carbon-carbon double bonds (seen as “=” in Figure 4) as containing stable electron pairs, called pi electrons. The one-ringed molecule has two double bonds, so it has more pi electrons than the tworinged molecule—as an aside, the ring with three double bonds can be ignored for now. Furthermore, the oxygen atoms in the two-ringed molecule can stably support negative charges, so they will withdraw electrons from the already electron-lacking double bond. Then, the electron-poor double bond interacts with the two, electron-rich double bonds, creating a new ring structure connecting the two previously

Figure 3. The preparatory reactions before the one-pot synthesis. The first two molecules were used in the Diels-Alder reaction, while the amine was used in the Ullmann cross-coupling. For the Friedel-Crafts cyclization, they used inorganic reagents.

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regimen fail to recognize, likely due to a mutation in some cancer sub-clones. Though CQ has dangerous side effects, it is unique for targeting only cancer cells through lysosome pH detection. However, it is unknown how aggressive or cancer-cell selective marymycin A is as a drug, only that it certainly does not detect pH, unlike CQ (5). Most certainly, more research into Marmycin A and other unique chemotherapy drugs must be done.

Cancer is a multifaceted disease, and diversifying anti-cancer drugs has been shown to be a very effective method to fight it. Organic synthesis is an amazingly useful way to target cancer through novel means. Marmarycin A has great potential in chemotherapy, but it represents only a portion of the effort needed to defeat cancer. Recent breakthroughs from bioengineering included algae selectively administering chemotherapy drugs to cancer cells through its porous silica (SiO2) membranes (13). Along with creative discoveries in chemotherapy, advances in genetic testing, counseling, and nutritional care represent the full toolbox needed to confront cancer, and to finally beat it.

Figure 4. The Diels-Alder and Ullmann coupling reactions. 1a was the Diels-Alder reaction, while 1b and 1c were simple transformations. 2a was the Ullmann coupling, while 2b turned the –OMOM group to an – OH group.

Marmycin A’s cellular activity. As well, they were able to utilize fascinating, classic organic chemistry reactions.

Once the chemists synthesized Marmycin A, they discovered something peculiar under the microscope. They saw that this molecule targets lysosomes, organelles containing enzymes that digest large macromolecules, in the breast cancer cell line MDAMB-231 (human stem cells). This was unexpected, as almost all anguyclines target DNA. As a bonus, the lysosome targeting was supported by a recent 2016 paper (11). Marmycin A’s unique cellular target may write-off some of angucylines’ terrible side-effects, including heart disease, given Marmycin’s known targeting mechanism (12). The drug may also work well alongside DNA-targeting drugs in chemotherapy regimens. Chloroquine (CQ), an antimalarial as well as a lysosome-targeting chemotherapy drug, has been shown to prevent the relapse of cancer (5).

It has been theorized that CQ does this by targeting cancer cells that DNA-targeting drugs within the same

Literature Cited: 1. Koskiniemi, H, et.al., 2007. Crystal Structures of Two Aromatic Hydroxylases Involved in the Early Tailoring Steps of Angucycline Biosynthesis. J. Mol. Biol., 372(3), 633-48.

2. Helleday, T., et. al., 2008. DNA repair pathways as targets for cancer therapy.

Nature Reviews Cancer, 8(3), 193-204.

3. Cooper, GM, 2013. The Cell: A Molecular Approach, 6th Ed., 693-695.

4. Martin, G., et. al., 2007. Marmycins A and B, Cytotoxic Pentacyclic C-Glycosides from a Marine Sediment-Derived Actinomycete Related to the Genus Streptomyces. J. Nat. Prod., 70(9), 1406–1409.

5. Cañeque, T., et. al., 2015. Synthesis of marmycin A and investigation into its cellular activity. Nature Chemistry, 7(9), 744-751.

6. Chien, S., et. al., 2015. Naturally Occurring Anthraquinones: Chemistry and Therapeutic Potential in Autoimmune Diabetes. Hindawi, Vol. 2015, Article ID 357357, 13 pages.

7. Heinzman, S., and Grunwell, J., 1980. Regiospecific synthesis of bromojuglone derivatives. Tetrahedron Letters, 21(45), 4305-4308.

8. Shih, C., and Swenton, J., 1982. Use of protected beta.-bromocyclopentenones and beta.-bromocyclohexenones as beta.-acylvinyl anion equivalents. J. Org. Chem, 47 (15), 2825–2832.

9. Carreño, M., et. al., 2000. Enantioselective Diels-Alder Approach to C-3-Oxygenated Angucyclinonesfrom (SS)-2-(p-Tolylsulfinyl)-1,4-naphthoquinone.

Chem. Eur. J., 6(5), 906-913.

10. Sperotto, E., et. al., 2010. The mechanism of the modified Ullmann reaction. Dalton Transactions, 39(43), 10338-10351.

11. Mariani, A., et. al., 2016. Iron-dependent lysosomal dysfunction mediated by a natural product hybrid. Chem. Commun., 52, 1358-1360.

12. Li, Y., et. al., 2010. Induction of apoptosis by the angucyclinone antibiotic chemomicin in human tumor cells. Oncology Reports, 23(2), 477-483.

13. Delalat, B., et. al., 2015. Targeted drug delivery using genetically engineered diatom biosilica. Nature Commun, 6, 8791-8791.

14. Pearson Education, Inc., publishing as Benjamin Cummings.

Figure 5. The Friedel-Crafts cyclization forming Marmycin A. Some arrow-pushing is shown, but the step involving methoxide (MeO-) reforming the double bond is not included. A summary of the one-pot synthesis reactions is also shown. Summary was adapted from (5).

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Another rising concern is power consumption. Simply said, a competent processor will consume more power. Finding the balance is essential for the growth of devices such as smartphones, smartwatches, tablets. and laptops.

CPU FAQ: A Beginner’s Guide to Understanding Processors

Literature Cited: Abazovic, F. (2013, May 30). Qualcomm aims at 2.5 to 3W TDP for Phones. Retrieved July 23, 2015, from http://www.fudzilla. com/31532-qualcomm-aims-at-25-to-3w-tdp-for-phones The Benefits of Multiple CPU Cores in Mobile Devices. (n.d.).

1-23. Retrieved July 24, 2015, from http://www.nvidia.com/content/PDF/tegra_white_papers/Benefits-of-Multi-core-CPUs-inMobile-Devices_Ver1.2.pdf Janeba, M. (1995). The Pentium Problem. Retrieved July 25, 2015, from http://www.willamette.edu/~mjaneba/pentprob.html

By Jimmy Shah

List of CPU power dissipation figures. (2015, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 00:16, July 28, 2015, from https://en.wikipedia.org/w/index.php?title=List_of_CPU_power_dissipation_figures&oldid=670310946

One of the most prominent causes of the modern “Information Age” stems from the advancements made in CPU architecture. Every single electronics device has a CPU, whether it is an alarm clock or a super computer. However, each processor varies in power consumption and performance.

Most phones today have a CPU made by Qualcomm while most computers use processors manufactured by Intel. A Central Processing Unit’s performance is measured by frequency, or the chip’s maximum output of calculations per second. For example, if you have a 2015 13 inch Macbook Pro, it may have an Intel i5 processor clocked at 2.7 Ghz, or 2.7 billion calculations per second.

Modern CPU’s also may have multiple cores.

This means that there may be multiple processing units on a single die. Using a multi-core processor can reduce power consumption and increase performance, when compared to an equivalent, single core processor (NVIDIA, pg. 3) However, the operating system and specific applications have to be designed to take advantage of this feature.

There is a famous law, called Moore’s law that claims that the number of transistors, or the power of the CPU, will double every two years, and this bold prediction made over 50 years ago has held to be true (Moore’s Law and Intel Innovation, 2015). However, this prediction is slowing down as Intel has said as of 2012, the ability to commit to this rule is being challenged by technological limits (Wikipedia, 2015).

Measuring Processor Power. (2011). 1-8. Retrieved July 25, 2015, from http://www.intel.com/content/dam/doc/white-paper/resources-xeon-measuring-processor-power-paper.pdf Moore’s law. (2015, August 24). In Wikipedia, The Free Encyclopedia. Retrieved 18:38, August 25, 2015, from https://en.wikipedia.org/w/index.php?title=Moore%27s_law&oldid=677617408 Moore’s Law and Intel Innovation. (n.d.). Retrieved July 23, 2015, from http://www.intel.com/content/www/us/en/history/museum-gordon-moore-law.html

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kelp forms dense kelp forests along coastal oceans.

As the kelp grows taller, it provides a habitat and food source for fish and a multitude of marine invertebrates, along with marine birds and mammals at higher trophic levels (Jones, Lawton & Shachak, 1994).

Perhaps the most well known prototype of an ecosystem engineer would be the beaver, an allogenic engineer. Beavers completely transform their habitats by felling live trees, cutting them down into smaller sizes, and using them to build dams and lodges that block streams and create ponds. The habitat engineering of beavers allows for increased biodiversity in the surrounding environment (Wright et al. 2002).

In fact, the distribution and population abundance of essentially all organisms, from plants and insects to reptiles and amphibians in the area, are altered. Researchers have discovered that beaver dams dramatically alter surrounding riparian zones, or areas along the banks of rivers, by supporting herbaceous plant species that can’t be found in other areas of the riparian zone (Wright & Jones, 2006). Species richness is enhanced as the number of herbaceous plant species increases by a third (Wright, Jones, & Flecker, 2002).

The effects of beaver ecosystem engineers remain long after the beavers move away. When ponds are abandoned and the dams are breached, sediments that have been trapped in the dams over years create meadows that offer a nutrient rich habitat (Terwilliger & Pastor, 1999). The number of systems and cycles that beavers alter is mind boggling. A few examples include hydrology, nutrient cycling, decomposition rates, and trophic structures dynamics, both upstream and downstream.

While many ecosystem engineers can have positive effects, introduced species can cause damage and negatively affect ecosystems. These species tend to be introduced due to accidental human transportation, and often become prolifically successful. In terms of plants, invasive species are often successful due to being able to tolerate less than ideal soil conditions and prodigious growth that crowds out native species that have not had sufficient time to adapt and co-evolve. The invasive plant kudzu is an excellent example of a harmful ecosystem engineer that has far reaching effects. Native plant species are crowded out and left without a habitat. As a result, insects that may have fed on the original native species are left without their normal food source and may decline in population, which affects birds and other higher-level predators that previously fed on the insects (Mäntylä,

Ecosystem Engineers: Modifying our Surrounding Environments

By David Tian Ecologists broadly investigate and analyze the interactions between organisms and their environments. Within ecosystems, a community of organisms collaborates with non-living environmental factors in a cycling system. All organisms have impacts, whether positive or negative, on their environments. However, these impacts are not always equal or proportional.

Ecosystem engineers are keystone species that disproportionately affect their ecosystems by creating, modifying, maintaining, or destroying habitats in significant ways. As a result, it is increasingly key to understand the contributions of ecosystem engineers to their environments in endeavors to protect or restore habitats that have been influenced by humans.

There are two categories of ecosystem engineers: allogenic and autogenic engineers. Allogenic engineers mechanistically modify the environment by changing living or nonliving materials from one form to another (Jones, Lawton & Shachak, 1994).

An example of this would be woodpeckers that bore holes in trees for nesting. After the woodpeckers are done and leave, other bird or small mammals are able to take their place and use the free housing to their advantage. Meanwhile, autogenic engineers modify environments by modifying themselves. For instance, 10

Figure 1. http://www.sciencedaily.com/releases/2011/01/110103110331.htm

Klemola & Laaksonen, 2010).

If we reject an anthropocentric perspective and embrace a biocentric one, we can see that humans are perhaps one of the most influential ecosystem engineers in the history of life, for better or worse. Humans have completely transformed ecosystems to suit their needs through urban development, agricultural expansion and resource exploitation, all in the name of continued progress and growth. These developments began to grow at an alarming rate following the onset of the industrial revolution, and have continued to do so ever since. Humans have overwhelmingly damaged the environment with increased pollution and extreme land use changes; this has had major impacts on biodiversity, to the extent of leading some species to extinction. However, it is still important to note that humans are still capable of being positive ecosystem engineers through restoration ecology and related projects. Human intervention is capable of restoring damaged habitats, with the use of proper invasive species management and sustainable practices. It is useful to think of humans as ecosystem engineers, as this framework may lead to new insights in how humans relate to and affect the environment (Wright & Jones, 2006).

The power of ecosystem engineers is often underestimated, as many of their impacts can be indirect and are often discovered further down the

cascade. All organisms affect the environment in which they live, but the effect of ecosystem engineers carries added weight. To a certain extent, the characterization of ecosystem engineers is subject to human bias. After all, we are setting the standard for whether or not species are considered ecosystem engineers.

Whether ecosystem engineers are allogenic by modifying their neighboring environment, or autogenic by modifying themselves, a complete understanding of the roles that different ecosystem engineers play is crucial for developing sustainable strategies to effectively protect and maintain the precious natural environments that we all share.

Literature Cited: Jones, C., Lawton, J., and M. Shachak. 1994. Organisms as ecosystem engineers. J OIKOS 69: 373-386 Mäntylä, E., Klemola, T., and T. Laaksonen. 2010. Birds help plants: a meta-analysis of top-down trophic cascades caused by avian predators.

J Oecologia 165(1): 143-151.

Terwilliger J, and J. Pastor. 1999. Small mammals, ectomycorrhizae, and conifer succession in beaver meadows. J Oikos 85:83–94 Wright, J. and C. Jones. 2006. The concept of organisms as ecosystem engineers ten years on: progress, limitations, and challenges. J Bioscience 56(3): 203-209.

Wright J., Jones C., and A. Flecker. 2002. An ecosystem engineer, the beaver, increases species richness at the landscape scale. J Oecologia 132: 96-101.

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Regulating Missing Planes

By Jimmy Shah The air travel industry has enjoyed illustrious last transmission was made by the pilot at 1:19 PM profits over the last few years. The International Air with the iconic phrase, “Good night Malaysian three Transport Association expected a record 19.7 billion seven zero”. (To see the full transcript- http://goo.gl/ dollars in industry profits for 2014, which is more than KTdL30). However, by 1:21, the plane did not check a 50% increase from the profits of 2013 (N.B., 2013). in with Ho Chi Minh City in Vietnam. In later investiHowever, the industry has also faced heavy criticism gations, the detail was revealed that the plane had against the security of flights. Although fatalities altered its route and decided to go west, instead of involving airplanes has decreased significantly comnorth. Satellite handshakes or traces showed that the pared to previous years, there are still far too many plane was still in flight but on an altered path. The last catastrophes. possible handshake or contact was at 8:19 (Malaysia In 2014, news sources amplified the events Airlines Flight 370, 2015). caused by flights MH370, 17, and QZ8501. Each catastrophe must be examined independently due to the innumerable differences, but engineers can be aided in one search by the information gained through another.

The first of these flights to go down was Malaysia Airlines Flight MH370.

The flight was traveling from Kuala Lumpur International Airport in Malaysia to Beijing International Airport in China, but was out of contact in less than one hour after takeoff. The plane departed at 12:41 AM. Planes typically send ACARS transmissions to computers that are on the ground. The expected 1:37 transmission Figure 1. History of Plane Crashes. Source – International Business Times was not received unlike the 1:07 transmission. The 12

On July 17, 2014, Malaysian Airlines Flight 17 was shot down. The flight was heading from Amsterdam to Kuala Lumpur when it was attacked by a surface-to-air missile, causing the plane to crash, leaving no survivors. Malaysian Airlines lost contact with the plane while it was 30 miles away from the border of Russia and Ukraine. The plane was crossing an area where there had been heavy turmoil in the past few days.

Separatist forces were the first to reach and had already located the Black boxes needed to retrieve vital data from the plane. Initial investigations suggest that the plane was attacked by the separatists that were training in Russia, whom had mistaken it for Ukrainian military plane. The black boxes were handed over by Russian-backed rebels to Malaysian government officials (Sonne, 2014).

On December 29, 2014, AirAsia Flight QZ5801 crashed. The flight was scheduled to go from Surabaya, Indonesia to Singapore, but crashed in the Java Sea due to bad weather. Later reports indicated that this was due to the engine stalling. There were many controversial decisions regarding this flight. For example, the route was approved to be only used on Sundays. While traveling along the flight path, the aircraft approached thunderstorms. The pilots asked to elevate 6,000 feet from 32,000 feet and to divert away from the flight path. Air traffic controllers approved the diversion, but did not accept elevation, since other aircraft were flying closely. Shortly after, the plane lost contact with the air traffic controllers (Campbell, 2014). Notably, it is widely known that Indonesia has had troubles updating their technology, and their safety conditions are need of great improvement. Shown below is the estimated location where Indonesia says they lost contact via radar.

Last year, many news outlets declared total outrage over the disasters of Malaysia Airlines MH370, Malaysia Airlines Flight 17, and AirAsia Flight QZ8501, but the truth is that these crashes have been occurring since the dawn of aircraft. However, what has changed is the technology that is has been engineered to aid in the investigations.

Modern commercial airplanes currently employ a number of technologies such as “Black Boxes”, ACARS, ADS-B, ADS-C, and radar, along with radio transmission to record flight data. Unfortunately, these esoteric names are used for technology that is immensely outdated.

Airplanes wrecks are scoured for the “black boxes”. Britannica defines such a device as an “instru-

Figure 2. Handshakes with Airplane Source BBC

ment that records the performance and condition of aircraft in flight.”(2015). These tools are required on all commercial aircraft so that flights can be analyzed in the case of fatal crashes or disputes.

These surprisingly orange flight recorders contain two critical parts, the Flight Data Recorder (FDR) and the Cockpit Data Recorder (CDR) Furthermore, as Britannica states,”The FDR records many variables, not only basic aircraft conditions such as airspeed, altitude, heading, vertical acceleration, and pitch but hundreds of individual instrument readings and internal environmental conditions.” The CVR records conversations within the crew members and radio transmissions. A flight recorder is often in the tail of an aircraft because in the event of a crash, it is typically impacted the least. The data is stored digitally and the boxes are able to withstand extreme conditions of damage and temperature. Flight recorders were required on all flights during the 1960’s and the 1990’s provided the advancement of using digital storage. Modern devices use solid state memory boards, which are similar to solid state drives for computers.

A solid state form has no moving parts and thus poses less risk for failure compared to the more prominent hard disk drives, which use rapidly spinning “platters”.

The speeds are vary from 5400 to 10,000 rotations per minute. Solid State drives work by employing an integrated circuit that writes to memory (Bonsor, 2015). Black Boxes range in price from ten to fifteen thousand dollars.

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ACARS or Aircraft Communications Addressing taneously as the pilot sees the data in his cockpit. This and Reporting System, is another tool that aided in would require GPS technologies to be enabled globthe investigations of these planes. ally, along with some superior method of data trans“It’s an automated communication system mission. Imposing such a technology would be very used by commercial planes to transmit and receive expensive and overwhelming. Furthermore, Earth is messages from ground facilities (airline, maintenance only 29.2% land and many third world countries have department, aircraft or system manufacturer, etc). not enabled such advanced technologies needed for Therefore, along with the general information about global GPS and data transmission. These countries the flight (callsign, speed, altitude, position, etc), have focused instead on lowering airfare to make it these messages may contain what we can consider accessible to the modern native of the country. As systems health checks.”

Fuller and Bradsher note about the AirAsia Flight 8501 ACARS is not a free service so different airlines passengers, “Many were among the first generation will receive different information depending on their in their family who could afford such a luxury, avatars financial commitment. Furthermore, ACARS can theof a demographic shift that has produced an airline oretically be turned off if two of the radios, VHF and boom in Indonesia.” (2014).

SATCOM are disabled (The Aviationist, 2015).

This past August, Indonesia suffered another There are also some communication methods that are tragedy when Trigana Air Service Flight 257 crashed, employed that are not killing all passenger The clear solution would be to enable a required. One such and crew. The flight example is ADS-B, relay system so that a plane is monitored by may have crashed which transmits a due to difficult an aircraft controller, who would be in the plane’s unique identifinature of the trip cation, along with three same position as a pilot, therefore receiving across Papua, spatial coordinates, at a there are a live stream of information simultaneously where specific radio frequency. regions of mounas the pilot sees the data in his cockpit.

According to Flighttains and tropical Aware, a website that climates (Quiano tracks aircraft in transit, and Mullen, 2015).

70% of airlines around Another possithe world employ this technology. The limitation of ble cause has been the lack of technology to assist this technology is that the aircraft needs to be in the pilots. As stated by the Ministry of Transportation, the line of sight of the receiver (2015). navigation system that some of t he pilots use for the Similarly, there exists ADS-C, which functions journey date back to almost 75 years. This weakness like ADS-B, but the data is not continuously transcombined with the challenges of the weather climate mitted. The data transfer is dependent on a contract can provide the conditions for a dangerous flight (Trigbetween the service provider and the aircraft. ana Air Service Flight 257, 2015).

These three incidents of tragedy have surfaced many Another argument that stems from this soluof the arcane problems of the airline industry, as they tion of expanded access is hacking a plane. Although continue to use antique methods of tracking. To this this idea sounds absurd, it was only recently that Jeep day, most air traffic controllers do not receive all of recalled 1.4 million vehicles due to a hacking issue. the information that the pilot receives, and this is (Greenburg, 2015). In April 2015, Chris Roberts was problematic. A pilot may see a warning that an air detained by the FBI after allegedly hacking into as traffic controller may not, or vice versa. Many govmany as 20 commercial flights between 2011 and erning bodies have pushed for such legislation, but 2014. He proclaimed the process was very simple and airlines have argued that the costs are too high and highlighted the lack of security provided by major airfrankly, accidents don’t happen very often. craft manufacturers such as Boeing and Airbus (Perez, The clear solution would be to enable a relay 2015). system so that a plane is monitored by an aircraft conThe immediate solution would be to expand troller, who would be in the same position as a pilot, upon the current technologies used today. Instead of therefore receiving a live stream of information simul- recording logs from only a few sources for only a few

“

”

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hours, every source should be recorded and stored on an external server after a flight is complete. This may also aid in troubleshooting mechanical failures if the Black Box can detect degrading parts beforehand.

With a more powerful processor and a larger hard drive, a black box should be able to complete this feat.

Since the events of these tragedies, some initiatives have been taken to improve the conditions. In June of 2014, the International Air Transport Association pushed for reform regarding airplane tracking. Although they don’t have any clear jurisdiction over the airlines, their proposals will be sent to the International Civil Aviation Organisation, a UN agency that is responsible for setting global aviation standards.

Unfortunately, the major roadblocks include the time taken to get such proposals passed, typically years, and airlines’ refusals to spend more than necessary on safety requirements (Parker, 2014).

More recently, other countries have held court cases to advance tracking by assigning more visibility to air traffic controllers. In March of this year, Australia, Indonesia, and Malaysia worked collaboratively to ensure that aircraft are required to check in every 15 minutes, a much shorter time frame compared to the current standard. This new technology could also reduce the area searched in the event of a missing aircraft in the future. This is still being tested, and although progress is being made, the progress is a work in progress (Trial To Improve Flight Tracking A Year After MH370, 2015).

8. Flight Recorder. (n.d.). Retrieved June 26, 2015, from http:// academic.eb.com/EBchecked/topic/210220/flight-recorder?sections=210220TOC%2C210220main%2C210220Citations&cit=mla&view=print 9. Fuller, T., & Bradsher, K. (2014, December 31). Crash of AirAsia Flight 8501 Spotlights Indonesia’s Poor Air Safety Record. Retrieved June 28, 2015, from http://www.nytimes. com/2015/01/01/world/asia/airasia-flight-8501-indonesia-airline-safety.html 10. Greenburg, Andy. “After Jeep Hack, Chrysler Recalls 1.4M Vehicles for Bug Fix.” WIRED. 24 July 2015. Web. 1 Aug. 2015 11. How Many Planes Crash Every Year, And How Many People Die In Plane Crashes? [CHART]. (2014, March 10). Retrieved July 19, 2015, from http://www.ibtimes.com/how-manyplanes-crash-every-year-how-many-people-die-plane-crasheschart-1560554 12. Jeffries, S. (2014, March 31). Secrets of the black box: How does MH370’s flight recorder work? Retrieved July 19, 2015, from http%3A%2F%2Fwww.theguardian.com%2Fworld%2F2014%2Fmar%2F31%2Fairplane-black-box-flight-recorders-investigators%23img-1 13. Malaysia Airlines Flight 370. (2015, August 25). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, August 25, 2015, from https://en.wikipedia.org/w/index.php?title=Malaysia_Airlines_ Flight_370&oldid=677745712 14. Parker, A. (2014, June 3). Push to improve aircraft tracking after MH370 disappearance - FT.com. Retrieved July 25, 2015, from http://www.ft.com/cms/s/0/bd1ed53c-eb00-11e3-9c8b00144feabdc0.html#axzz3h2vd3u32 15. Perez, E. (2015, May 18). FBI: Hacker Chris Roberts claimed to hack into flights. Retrieved August 29, 2015.

16. PILOT-ATC RADIOTELEPHONY TRANSCRIPT. (2014). 1-2.

Retrieved June 28, 2015, from http://www.bbc.co.uk/news/special/2014/newsspec_7440/transcript.pdf 17. Quiano, K., & Mullen, J. (2015, August 18). No survivors in Indonesia Trigana Air plane crash. Retrieved August 29, 2015 18. Sonne, P., Cullison, A., & Barnes, J. E. (2015, July 17). U.S. Says Missile Downed Malaysia Airlines Plane Over Ukraine.

Retrieved June 28, 2015, from http://www.wsj.com/articles/ malaysia-airlines-loses-contact-with-plane-over-ukrainian-airspace-1405612373 19. T. (2015, March 18). Indonesia Calls Off Search for Remaining AirAsia Victims. Retrieved June 28, 2015, from http://www. nytimes.com/aponline/2015/03/18/world/asia/ap-as-indonesiaplane-crash-search.html 20. Trial To Improve Flight Tracking A Year After MH370. (2015, March 2). Retrieved July 27, 2015, from http://news.airwise.com/ story/view/1425299168.html 21. Trigana Air Service Flight 257. (2015, August 27). In Wikipedia, The Free Encyclopedia. Retrieved 05:53, August 29, 2015, from https://en.wikipedia.org/w/index.php?title=Trigana_Air_ Service_Flight_257&oldid=678059577 22. What Happened to Malaysia Airlines Flight 17. (2014, July 17). Retrieved July 28, 2015, from http://www.nytimes.com/ interactive/2014/07/18/world/europe/malaysia-airlines-flightmh17-q-a.html 23. Where AirAsia Flight 8501 Was Lost and Debris Found. (2014, December 30). Retrieved June 28, 2015, from http://www. nytimes.com/interactive/2014/12/28/world/asia/airasia-flightqz8501-map.html

Literature Cited: 1. ADS-B. (n.d.). Retrieved July 23, 2015, from http%3A%2F%2Ftr. flightaware.com%2Fadsb%2F 2. Aircraft Crashes. (n.d.). Retrieved July 19, 2015, from http:// www.infoplease.com/ipa/A0001449.html 3. B., N. (2013, December 27). Good times for the airline industry.

Retrieved July 18, 2015, from http://www.economist.com/blogs/ gulliver/2013/12/airline-profits 4. Bachman, J. (2014, December 30). Robust Airplane Tracking Technology Still Isn’t Widely Used. Retrieved June 28, 2015, from http://www.bloomberg.com/news/articles/2014-12-30/robust-airplane-tracking-technology-still-isnt-widely-used 5. Bonsor, K., & Chandler, N. (n.d.). How Black Boxes Work. Retrieved June 28, 2015, from http://science.howstuffworks.com/ transport/flight/modern/black-box.htm/printable 6. Campbell, C. (2014, December 27). AirAsia Plane Missing With 162 Aboard. Retrieved June 28, 2015, from http://time. com/3647835/airasia-qz-8501-missing-plane-indonesia-singapore/ 7. Cenciotti, D. (2014, March 16). What SATCOM, ACARS and Pings tell us about the missing Malaysia Airlines MH370. Retrieved July 25, 2015, from http://theaviationist. com/2014/03/16/satcom-acars-explained/ 15

Organic chemistry entails the study of organic compounds. But what exactly does ‘organic’ mean? An organic compound is simply a compound containing carbon, one of the most common chemical elements in biological systems and the natural world. While there are many exceptions- compounds that contain carbon but remain inorganic- the general definition contends that organic compounds are found in living systems while inorganic compounds are not.

Western scientists in the early 19th century had a similar definition, but maintained a significantly different understanding of the origins and reactions of these compounds. While the study of chemical elements and inorganic compounds had developed briskly by this point, a lingering question for many chemists was the characterization of organic compounds. Scientists had encountered these compounds in living systems and found that they behaved differently from inorganic materials like metals. For example, inorganic compounds did not lose their structure when heated and could be recovered, while organic compounds were destroyed after heating (Weisstein).

In order to explain the problematic difference between these classifications of matter, many scientists agreed on the concept of vitalism. As the esteemed chemist Jöns Jacob Berzelius explained in his 1827 edition of Textbook of Chemistry, “The being of the living body is…based not in its inorganic elements but in something else…the something we call the vital force” (Bawden, 73). Vitalism was a theory that living systems contained some unique mysterious “force” that pervaded in its organic compounds and their resulting reactions. While today we are more likely to find such a theory in the plot of Star Wars, many scientists in the early 19th century accepted it, not necessarily due to a strong conviction but because few alternative explanations existed. One of the primary tenants of vitalism claimed that organic compounds could not be synthesized from inorganic materials. There was no way to replicate this “vital force” from matter that was already considered “dead” (Lipman, 453).

One of the earliest and now well-known experiments to contradict this theory came in 1828, when the physician and chemist Friedrich Wöhler (one of Berzelius’s students) synthesized urea from inorganic reagents.

Like many significant scientific discoveries, Wöhler’s synthesis emerged serendipitously (Myers, xx). He originally wanted to synthesize the inorganic compound ammonium cyanate, but his experimental results showed the presence of a different compound: urea. Urea is found in mammal urine, and the excretion of urea is an essential component of the human renal system. In fact, ammonium cyanate and urea have the same molecular formula, but their atoms are arranged in different ways (this is called structural isomerism- something that Wöhler also discovered in this synthesis. This experiment was actually one of the earliest demonstrations of the existence of isomers).

Friedrich Wöhler vs.

The “Vital Force”

By Jennifer Guo

Figure 1. Structures of ammonium cyanate and urea (Image source: Purdue University)

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Wöhler’s original publication describes a variety of reagent combinations that generated ammonium cyanate, which then transformed into the final product urea. One combination that produced a relative pure product for analysis was lead cyanate and ammonium hydroxide:

Figure 2. Formatino of ammonium cyanate (Source: Kauffman, 199)

The combination first produced aqueous ammonium cyanate as well as a lead oxide precipitate. He then heated the reaction in order to evaporate the solution and obtain solid crystals (Kauffman, 198).

Figure 3. Formation of urea (Source: Kauffman, 199)

By heating this solution, Wöhler dissociated the ammonium cyanate solution into ammonia and cyanic acid, which combined to create urea, an organic compound. Through this process, Wöhler inadvertently isomerized (rearranged the structure of) ammonium cyanate into urea, obtaining white crystals that performed similarly in chemical tests as urea derived from living organisms (Kauffman, 199).

At the time, Wöhler’s publication did not completely debunk the concept of vitalism (Cohen, 885). Even Wohler himself wondered if some of the reagents he used could have had some lingering “vital force” from their preparation from organic material. This “force” could have altered the reaction and allowed the production of urea (McKie, 608). While this reasoning sounds a bit ridiculous today, many scientists continued their vitalist beliefs despite Wöhler’s publication. While he did not single-handedly take down vitalism, his synthesis of urea was the first of many discoveries by other scientists who would prove that organic compounds could certainly be artificially prepared. These experiments include the synthesis of acetic acid by Hermann Kolbe (one of Wöhler’s students) in 1845, and the synthesis of various fats and sugars

by Marcellin Berthelot in the late 19th century (Myers, xxi).

These discoveries would sow the beginnings of the field of modern organic chemistry, a subject that would likely not exist had vitalism prevailed.

Literature cited: Cohen, Paul S. “Wöhler’s Synthesis of Urea: How Do the Textbooks Report It?” Journal of Chemical Education 73, no. 9 (September 1996): 883-86.

Friedmann, Herbert C. “From Friedrich Wohler’s Urine to Eduard Buchner’s Alcohol.”

In New Beer in an Old Bottle: Eduard Buchner and the Growth of Biochemical Knowledge, edited by Athel Cornish-Bawden, 67-123. N.p.: Universitat de Valenica, 1997.

Kauffman, George B., and Steven H. Chooljian. “Wohler’s Urea Synthesis: Modern Version.” Journal of Chemical Education 56, no. 3 (March 1979): 197-200.

Lipman, Timothy O. “Wohler’s Preparation of Urea and the Fate of Vitalism.” Journal of Chemical Education 41, no. 8 (August 1964): 452-58.

McKie, Douglas. “Wohler’s ‘Synthetic’ Urea and the Rejection of Vitalism: A Chemical Legend.”

Nature 153 (May 20, 1944): 608-10.

Meyers, Richard L. The 100 Most Important Chemical Compounds. N.p.: Greenwood Press, 1997.

Figure 4. https://en.wikipedia.org/wiki/Friedrich_Wöhler

Weisstein, Eric W. “Vitalism Theory.” Eric Weisstein’s World of Chemistry. http://scienceworld.wolfram.com/chemistry/VitalismTheory.html.

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At Swarthmore, everyone is given an equal opportunity to contribute to scientific advancements on campus. We then hope that the scientists our community produces will go on to find equal support in their endeavors at hospitals, research laboratories, engineering firms, or wherever they choose to bring their talents. Unfortunately, the attitude of equal encouragement does not yet permeate the extended academic and scientific community.

In early June of this past summer, Nobel laureate Tim Hunt made a rather distasteful joke at a conference, saying “Let me tell you about my trouble with girls. Three things happen when they are in the lab … You fall in love with them, they fall in love with you, and when you criticize them, they cry” (Bilefsky, 2015). Hunt claims that these comments were simply meant for laughs, but women in all branches of scientific study have rightfully taken offense upon reading his statements. Women at the conference tweeted their responses, extending Hunt’s audience.

The audience’s outrage took the internet by storm, producing a backlash that made headlines. One notable Twitter post by Dr. Kate Devlin, of the University of London, reads, “Dear department: please note I will be unable to chair the 10am meeting this morning because I am too busy swooning and crying.”

(Bilefsky, 2015). Hunt’s careless comments cost him his position at University College, London, and on the European Research Council (Miller, 2015). Hunt’s wife, Dr. Mary Collins, stated that Hunt’s superiors gave him the ultimatum of stepping down or being fired (Miller, 2015). Certain voices in the subsequent debate on social media have questioned whether he deserved this extreme consequence. After all, Hunt has contributed groundbreaking work the study of cell division, is married to a highly regarded immunologist, and

The Trouble with Girls: Gender Bias in the Sciences

By Christina Labows

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has many female students working for him in his lab.

Beyond his controversial comments, he seems to treat all scientists equally. However, digging deeper into the systemic sexism of the scientific world shows that the attitudes of some elite male scientists and even some female scientists are a major setback to young women in science. Perhaps this harsh discipline against Hunt will serve to set a standard across academia and industry.

A recent survey shows that male faculty members at prestigious academic laboratories hire significantly fewer female graduate students than do female faculty members (Sheltzer & Smith, 2014). The difference is even more pronounced when observing “elite” male faculty, such as those funded by the Howard Hughes Medical Institute (HHMI), elected to the National Academy of Sciences, or those that have received a major scientific career award. Female career development faces cumulative disadvantages that begin with being hired to less prestigious labs. This hiring practice leads to fewer females being appointed to assistant professorships, and thus fewer female scientists eventually becoming primary investigators in a laboratory (Sheltzer & Smith, 2014). Tim Hunt is a prime example of an elite male primary investigator who perpetuates discrimination against women, whether intentionally or as a joke.

Women have taken incredible steps forward in the sciences, now earning 52% of PhDs in life sciences and half of all MD degrees. Math intensive fields now award around 8.8-15.8% of tenured positions to women, and 71% psychology PhDs go to women (Ceci et. al, 2011). Despite these gains, women are still seeing great obstacles further down the career path. The discrepancies in hiring practices explained above cannot be attributed to the capabilities of women. A study at Cornell University in 2011 compared scientific manuscripts by men and women with comparable resources and found there to be no difference in the quality of work. The main problem is that women often lack these essential resources, such as training under elite mentors, sufficient funding, and positions in prestigious labs. Studies show that both male and female science faculty members exhibit bias, even subconsciously, by judging female students

as less competent and therefore deserving a lower starting salary and fewer essential resources (Moss-Racusin et. al, 2012).

In addition to general gender bias, women face more workhome balance issues than their male counterparts due to obvious child-rearing biological constraints. There are many suggestions for resolving issues of gender bias including educating faculty on the presence of the problem, creating programs to provide female science students with role models, and directing hiring committees to ignore family-related gaps on women’s CVs. At UC Berkeley, there is funding in place to support female scientists as they navigate motherhood. This funding provides quality child care and hires postdocs to keep a lab’s research in motion during maternity leave. Additional ideas already in practice in other scientific institutions include allowing a part-time tenure track option and promoting the accomplishments of female scientists by increasing speaker selection to include esteemed women in various science fields. Other options to standardize treatment across genders may include giving equal paternity and maternity leave, so that men give up just as much time in the lab as women. These policies and others will set the framework for an environment with zero tolerance for detrimental comments such as those by Tim Hunt.

Literature Cited: 1.

Bever, Lindsey. “Sexism and the Nobel prize scientist: A backlash to the backlash.” The Washington Post. The Washington Post, 12 June 2015.

2.

Bilefsky, Dan. “Women respond to Nobel Laureate’s ‘Trouble with Girls’.” The New York Times. The New York Times, 11 June 2015.

3.

Ceci, Stephen J., Williams, Wendy M., Thompson, Richard F. Understanding current causes of women’s underrepresentation in science. Proceedings of the National Academy of Sciences.

2011;108(8):3157-3162.

4.

Miller, Michael E. “Nobel laureate Tim Hunt says he was forced to resign: ‘I have been hung out to dry by academic institutes’.” The Washington Post. The Washington Post, 15 June 2015.

5.

Moss-Racusin, Corinne A., Dovido, John F., Brescoll, Victoria L., Graham, Mark J., and Handelsman, Jo. Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences. 2012;109(41):16474-16479.

6.

Sheltzer, Jason M., Smith, Joan C. Elite male faculty in the life sciences employ fewer women. Proceedings of the National Academy of Sciences. 2014;111(28):10107-10112.

7. https://www.iconfinder.com/icons/425923/analysis_chemical_analysis_chemistry_experiment_laboratory_scientific_technology_icon#size=512 19

Organized Anarchy: Ant Colonies and their Surprising Applications On Ants, Interaction Networks, and their Surprising Connections to Brains and Computer Science

By Talia Borofsky and Nicole McNabb

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female workers, and fertile females and males which are sent out once a year to mate so that then fertilized females can go start new colonies. Different ants complete different tasks, such as caring for the young, building tunnels, and foraging (Gordon, 2010).

Importantly, individual ants are not aware of the colony functions or behaviors. They only have the ability to sense and respond to local cues (Couzin, 2009). Since they can only differentiate, at most, between light and dark, ants rely on touch and smell rather than sight (Gordon 2010). Their tactile interactions consist of bumping into things. Their olfactory interactions happen through antennae, and most interactions between ants involve smelling pheromones, or secreted chemicals that activate a social response from a member of the same species (Hölldobler and Wilson 1990). Edward O. Wilson found some of the first concrete evidence of these chemicals in 1959. He made a solution from fire ant glands and used it to create a trail outside of a nest. The fire ants burst from the nest to follow the trail. However, as Professor Deborah Gordon (2015) explained to me, ant species that use chemical trails are “the exception rather than the rule.” They rely on many chemical cues, particularly cuticular hydrocarbon coatings on each others’ bodies, which can indicate both colony identity and the task being performed (Greene and Gordon, 2003).

There is a discrepancy between the behavior of individuals and colonies. An ant is limited in its ability to change, sense, and respond to its environment, but when many ants come together, they form an organized and arguably intelligent being – a “superorganism” -- without any blueprint or instructions (Wheeler 1910). These superorganisms exhibit collective decision making, choosing between possible courses of actions to appropriately respond to dynamic environmental conditions, all without any individual being aware that it is making a decision (Couzin 2009).

Another term relevant to the study of social insects is emergence. However, instead of describing how the ant colonies actually work – their ontology – it describes the epistemology, or how we think about the ant colonies. Emergence occurs when what we know about the parts of a system does not explain

D

During elementary school, my neighbor and I raided an ant colony. Wielding plastic spoons, we scooped ants and dirt into my tiny terrarium. Within a few days, tunnels permeated the soil. When an ant died, another took it out of the nest to a pile of dead bodies. Food was carefully stored underground. At the time, I figured that my neighbor and I must have managed to dig up the queen ant. Thanks to movies such as Ants, we figured that the queen gave the orders to a hierarchy of ants in the colony. In retrospect, those plastic spoons could not have dug deep enough into the nest to catch the queen. Without a leader, a bunch of randomly collected ants built their own nest and performed the basic tasks of a colony.

What if an anarchist society made up of thousands (Gordon, 2010), even billions of members (Giraud et al. 2002), could, instead of falling into mayhem, actually be highly organized? Ant colonies fit that description. Though ants are extremely diverse, with over 11,000 species, some characteristics universally apply. (Gordon, 2010). Their colonies can be described as eusocial, since multiple generations live together, adults care for the young, and there are reproductive castes (Hölldobler and Wilson, 1990).

The reproductive castes are the queen(s), the sterile

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our observations of the whole system (O’Connor & Wong, 2015). If scientists can find a unifying theory for an emergent property, one that describes the whole in relation to its parts, then the system ceases to qualify as emergent.

Biologists, computer scientists, and particle physicists have used models to chip away at the emergence of colony behavior (Couzin 2009). These models have confirmed that repetitive, local interactions determine group behavior, and thus control of behavior happens through a distributed system, or a system where each node can do different things based on environmental conditions (Gordon 2014a).

Explorations of the nature of these interactions have found surprising connections with neuroscience and computer science. By examining similarities with ants, scientists can gain new insight into both brain function and efficient network search algorithms.

Neurons, Ants, and Feedback Loops

Dr. Deborah Gordon, an Ecologist at Stanford, is at the forefront of research into ant interaction networks. She has extensively studied task allocation, a term she coined for the process by which colonies get the right number of ants to the right task under changing conditions (2014b). She focuses on one species particularly for task allocation studies: the desert-dwelling harvester ants called Pogonomyrmex barbatus. P. barbatus have three characteristics that make them ideal for her research. First, they do not use pheromone trails, and thus don’t provide spatial information when recruiting others to food. Seeds, their main food, are scattered by the wind, so there is no need to lead ants to particular locations with food.

Second, they lose water when they forage but only gain water by metabolizing the fat in seeds. Foraging has to be regulated so as to maximize returns while minimizing desiccation. Third, all ants are of the same size, so they can switch tasks, and be seen readily as a distributed system (Gordon, 2014b).

The task groups that work outside the nest are the patrollers, foragers, and midden workers.

Patrollers leave early in the morning, and their return signals foragers to leave the nest. Midden workers remove trash, and nest maintenance maintain tunnels and nest entrances. These groups only represent a

quarter of the colony, and much of the rest sit inside the nest, waiting. These play an important part in regulating foraging because if there’s a lot of food, the other task groups can become foragers, and the waiting ants can replace nest maintenance and patrollers.

Foragers, however, cannot switch back to their old tasks (Gordon, 2014b).

Foraging costs the colony both worker ants and water. Regulation of foraging is necessary. Professor Gordon has done a series of experiments testing the correlation between P. barbatus ant interactions and how many foragers leave the nest. Her team used beads coated with cuticular hydrocarbons characteristic of a given task to imitate ants. She found that foragers would only start leaving the nest if patrollers returned at a rate of one per 10 seconds. When controlling for how many foragers were allowed to return to the nest, she found that the rate at which foragers left the nest was highly correlated with rate of returning foragers. Then, she used the bead method to establish that higher rates of returning foragers increased the likelihood that waiting ants would leave the nest. If she added more beads smelling like foragers holding food, after a certain threshold number of interactions, ants were likely to leave the nest, with

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travel along the neuron and it will fire. However, neurons are leaky, and ions steadily diffuse through the membrane (Vecsey, 2014). Therefore, each signal has a decay rate, or how quickly the ion concentrations will return to normal. Neurotransmitters must arrive at least at a certain rate so a neuron can reach its threshold and fire (Goldman et al., 2009).

To understand the importance of the processes exhibited by neurons and harvester ants, we refer to an explanation of feedback. Positive feedback occurs when the effect of a process also causes the process to occur, forming a loop. In negative feedback, a process inhibits itself from recurring. In groups of organisms that exhibit collective behavior, positive feedback amplifies a stimulus, allowing many organisms to quickly respond, but can spiral out of control (Couzin, 2008). For instance, within groups of organisms that act through collective behavior, there are chance fluctuations in interactions. Even without experiencing an interaction, once in a while a neuron will randomly fire (Goldman et al., 2009) or a forager or two will leave and then return (Gordon, 2010). Furthermore, the environments ants and neurons respond to are complicated, with arbitrary changes in any number of variables. Negative feedback prevents both dramatic responses to random elements and cascading, snowball-effect responses to significant stimuli. However, it also compromises speed of reaction (Couzin, 2009).

A threshold process can balance positive and negative feedback. In this process, organisms at first have a low probability of a certain action, but once they’ve reached a threshold number of interactions with others, their probability of acting increases (Couzin, 2009). Here, positive feedback is reflected by interactions causing more interactions. Negative feedback happens through the leak of neurotransmitters in a neuron and through both the short memory of a harvester ant and the limited supply of potential foragers in a colony (Gordon, 2015).

The leak of a neuron and short memory of an ant also allow the colony or brain as a whole to make more accurate decisions. In both cases, decay rate allows organisms to not just add up signals, but also respond to the rate of those signals, which communicates the importance of a stimuli. If ants return faster, then more food is easily available, and if neurotrans-

one stipulation: beads had to be added at least once every ten seconds (Gordon 2010).

To further explain Deborah Gordon’s results, we turn to another emergent system: the brain.

Unlike ant colonies, parts of the neural networks in brains do demonstrate hierarchal organization (Couzin, 2009). Like ant colonies, neurons participate in a network where no one neuron is aware of the thoughts or consciousness to which it is contributing.

We, on our part, cannot yet make the connection between consciousness and the firings of millions of neurons (Deborah Gordon Interview).

Certain neurons use time integration, or the accumulation of inputs over time such that the output is related to the number of inputs (Goldman et al., 2009).

Harvester ants also use time integration when deciding whether to go forage. However, they are leaky.

They have the ability to remember an interaction with an incoming ant for 10 seconds, and if more time has passed since an interaction, they forget it ever occurred; they leak the signal, and go back to their own resting state, which is doing nothing (Gordon, 2010) (Figure 1). An equivalent process occurs in neurons.

Neural signals, called neurotransmitters, cause changes in ion concentration, and thus, electric voltage. If membrane voltage exceeds a threshold, current will

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mitters are travelling through a synapse (the space between two neurons) faster, then that means there is a reaction-worthy stimulus in the environment. The arguable defects in neurons and ants lead to a better ability to respond for the system as a whole.

Connections between neuron and ant interactions could in the future add insight into currently emergent behaviors demonstrated in the brain. Ants are easily observable, and as Deborah Gordon explained, “it’s easier to describe ant behavior as the outcome of interactions among ants and the world around them than consciousness by the interaction of neurons.” The threshold process has been used to explain how neurons in primate brains integrate conflicting stimuli, in the form of firing rates, from different groups of neurons (Couzin, 2008). Dr. Mark Goldman, a computational neuroscientist at UC Davis, and

proposed the first Ant Colony Optimization algorithm (Dorigo and Stutzle 2003). This algorithm aims to use observed ant behaviors to find the shortest path from a starting point, such as an ant colony entrance, to a destination, such as a nearby food source (Stutzle and Dorigo 1999). Methods that try to find the shortest path to a destination are called search algorithms.

Computer scientists usually model path trajectories in a search algorithm using a graph. A graph is a structure with n nodes, or locations, that an ant may stop to search for food, and m edges, or straight paths between any two nodes. Figure 1 shows a small graph with five nodes and five connecting edges. In this section, we will discuss Dr. Dorigo’s algorithm and others in its family, and a later application of this family of algorithm to telecommunication networks.

Dr. Dorigo’s Ant Colony Optimization (ACO) model is based on a standard type of search algorithm, called a probabilistic algorithm. In the ACO algorithm, an ant begins at a root node, say the entrance of the colony. Each edge, or path from the root node to a node connected to it, has a “weight” value associated to it. This value is consistently updated based on the intensity of pheromones other ants have left on the path. The ant will sense the pheromones left by previous ants and will choose, with probability p, the path it senses to be shortest to the food source.

While complex probabilistic functions exist to more accurately model ant trajectories for the majority of species that do not rely primarily on pheromone detection (Dorigo and Stutzle 2003), this single-variable model demonstrates an efficient algorithm that is applicable to many problems in computer science.

When an ant successfully reaches the food source, in Dorigo’s model the ant will return via the same path. As the ant retraces the path, it releases pheromones on each edge that will indicate to other ants that the path is a good one to take (Stutzle and Dorigo 1999). If an ant makes a mistake and takes a longer, less traveled path to the food source, its pheromones will evaporate over time and other ants will sense that the path is less optimal than others.

Because of the “forgetting” effect of evaporating pheromones, this model favors new path searches over existing sub-optimal paths (Montemanni, Gambardella, Rizzoli, and Donati 2005).

Professor Gordon are starting a collaboration studying harvester ants and vesicle recycling in occulomotor neurons (Arnold, 2014). Others have been comparing the rhythms in movement of swarms of ants to the periodicity of neural network activity (Couzin, 2008).

Ant Applications in Computer Science

Recent research on the structure and behavior of ant colonies has led to a novel field of computer science research that attempts to use behavioral models in order to solve complex algorithmic problems.

The founding of this field of research can be attributed to Dr. Marco Dorigo, whose Ph.D. thesis in 1992

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This type of algorithm has been recently applied to another important problem in computer science: the management of telecommunication networks. Telecommunication networks, such as internet services, have many users. Consequently, many users will want to simultaneously send data to other devices as quickly as possible. Internet service companies want users’ internet to run quickly, in other words, to optimize the path that each package of data takes from its host to its destination so it gets to its destination in the shortest time possible. ACO algorithms have proven to be very useful for this problem.

We can model each device on the network as a node in a specific location on a very complicated graph, such as the one in figure 2. The devices will be the start and end points between which a data package travels. Each device can search and connect to others by its unique IP (Internet Protocol) address. Each device also has a routing table, a table that lists the IP addresses of desired destinations for data packages the user would like to send. The remaining nodes in the graph represent intermediate points through which data passes before reaching its destination.

These could be routers, firewalls, or other network hardware devices. Sometimes, the routing table of a host device may include information about the distance to and traffic level of nearby intermediate nodes. With high network traffic, a data package may have to wait in a queue at a node until other packages at that node are processed and sent, which may deem this path inefficient. From its routing table, each device will decide to which nearby intermediate node to send the data package. The device at each node of the network in turn will do this until the package arrives at its destination (Di Caro and Dorigo 1998).

While this problem may represent the ant foraging problem, there is one key difference. A traveling ant continually gains information about the “efficiency” of possible nearby paths by sensing deposited pheromone levels on each path. It is each network device however, not the traveling data package, that is able to gain information by the traffic on nearby paths.

Therefore, a device that has sent a data package through a different part of the network is not able to gain information about network traffic in that area.

This inhibits the “total” communication among the

ACO algorithms aim to find the shortest possible path in a graph in the way many ant colonies find optimal paths to a food source. There exist two main types of ACO algorithms today: construction algorithms and local search algorithms. Construction algorithms assume that the ants have already chosen the optimal first n paths or edges, so we assign this path as a partial solution to the problem and attempt to find a complete solution using the nth edge as a starting point. Local search algorithms assume no partial solutions, but instead take a random path and begin changing individual edges followed until a more direct path cannot be found by continuing to switch edges. As a result, only a small part of the entire graph is searched. However, a good solution can still be found with this approach. Local search is a very similar approach to that which patroller ants choose.

They have no “experience”, pheromones or other ants, from which to base their path decisions, so they must pick a random path by which to travel. Forager ants will then begin updating the path to “search” for an optimal path to the food from previously traveled paths. While any individual ant will probably not find a very short path to food, as a whole, the colony will almost surely find an optimal path by the agglomeration of individual searches (Dorigo and Stutzle 2003).

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network that exists among an ant colony. Since the ACO model in this situation must maximize the efficiency of data transfer over the entire network, the probability function for the network model will look more complicated than the pheromone model I introduced. By adding more variables to the model such as assigning priority to data packages from users paying more for the service, telecommunication networks have found workable solutions for their “search” problem.

While other types of search algorithms may also provide good solutions to the network problem, the ACO model works effectively because it is simple but widely applicable. By creating a model based on an emergent system like an ant colony, computer scientists are able to ignore pesky factors such as long-term memory storage—the algorithm doesn’t require the traveling “ant” to remember each path choice. The flexibility of this probabilistic model also means scientists can change factors such as the “pheromones” to suit their particular problem (Di Caro 2004). The variability of the ACO model gives hope to researchers: maybe studying biological systems can help answer more questions in computer science than we think.

Applying models to and between systems is tricky business. Just because a model fits a biological system in an experiment does not mean it reflects the system in reality. Any number of other possible models could also describe the same behavior. When discussing applying mathematical models to biology in a personal interview, Dr. Gordon explained that it’s much more informative when a model does not fit your observations.

What she said initially felt disappointing. Living in a world of theory and formulas is tempting. It’s exciting to think that you can easily predict an entire network’s complex behavior by describing simple, repeated interactions. The value of models then comes when we try to elucidate a small pie slice of system behavior at a time. Applications happen when we take a model that works for a particular experiment

on ants and apply it to another purpose, whether in neuroscience or computer science. And regardless of our conceived models and interpretations of them, the ants are still out there doing their thing: building tunnels and amazing spoon-wielding 4th graders, all without any awareness of what they’re doing.

Literature Cited: Gordon, D. (2015, August 10). [Personal interview].

Couzin, I. (2008). Collective cognition in animal groups. Trends in Cognitive Sciences, 13(1), 36-43.

Di Caro, G., & Dorigo, M. (1998). AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9, 317-365.

Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In The Handbook of metaheuristics (pp. 250-285). Springer US. Giraud, T., Pedersen, J., & Keller, L. (2002). Evolution of supercolonies: The Argentine ants of southern Europe. Proceedings of the National Academy of Sciences, 99(9), 6075-6079.

Goldman, M., Compte, A., Wang, X., & Squire, L. (2009). Neural integrator models. In Encyclopedia of Neuroscience (Vol. 6, pp. 165 - 178).

Oxford: Academic Press.

Gordon, D. (2010). Ant encounters: Interactions networks and colony behavior. Princeton, N.J.: Princeton University Press.

Gordon, D. M. (2014). The ecology of collective behavior. PLoS Biol, 12(3), e1001805.

Gordon, D. M. (2014, September 16) Deborah Gordon: Local interactions determine collective behavior. Retrieved August 18, 2015, from http:// www.ibiology.org/ibioseminars/evolution-ecology/deborah-m-gordon-part-1.html Greene, M., & Gordon, D. (2003). Social insects: Cuticular hydrocarbons inform task decisions. Nature, 423(6935), 32-32.

Hölldobler, B., & Wilson, E. (1990). The Colony Life Cycle. In The Ants.

Cambridge, Massachusetts: Belknap Press of Harvard UP. Montemanni, R., Gambardella, L. M., Rizzoli, A. E., & Donati, A. V. (2005). Ant colony system for a dynamic vehicle routing problem. Journal of Combinatorial Optimization, 10(4), 327-343.

O’Connor, T., Wong, H., & Zalta, E. (2015). Emergent Properties. In The Stanford Encyclopedia of Philosophy (Summer 2015 ed.). The Metaphysics Research Lab. Stützle, T., & Dorigo, M. (1999). ACO algorithms for the traveling salesman problem. Evolutionary Algorithms in Engineering and Computer Science, 163-183.

models to and between sys“Applying tems is tricky business. Just because

Vecsey, C. (2014, October 1). Neural signaling - action potential. Biology 001: Cellular & Molecular Biology. Lecture conducted from Swarthmore College, Swarthmore, PA.

a model fits a biological system in an experiment does not mean it reflects the system in reality.

Wilson, E. O. (1959). Source and possible nature of the odor trail of fire ants. Science, 129(3349), 643-644.

”

Wheeler, W. M. (1910). Ants: their structure, development and behavior (Vol. 9). Columbia University Press.

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The Miniature World of Exciting Bubble Physics

By Luke Barbano

glass, bubbles initially form due to the mechanical agitation associated with the beer sloshing into the glass. Once the liquid settles, however, impurities such as small scratches in the glass’s smooth surface act as nucleation sites, allowing gas molecules to coalesce and form bubbles (Leighton, 2006). Once a bubble forms, the force of buoyancy compels it to rise to the liquid’s surface and collect as the familiar, delectable, fizzy layer of foam at the top.

Let us now more closely examine the physical characteristics of just one of these bubbles. Bubble behavior is governed by three main properties: surface tension across the bubble wall, the bubble’s internal pressure, and the pressure of the bubble’s environment, all of which must maintain a delicate balance to preserve the integrity of the bubble (hyperphysics.phy-astr.gsu.edu). These bubble properties are seemingly unimportant to the average beer drinker, but modifying them allows scientist and engineers to control a bubble’s dynamic behavior.

Although bubble manipulation techniques are somewhat inaccessible to the average beer-drinking college student, understanding bubble characteristics allows us to appreciate, and perhaps even marvel at, bubbles in a new way. Unbeknownst to many people, our cherished alcoholic beverage hosts a miniature world of exciting physics.

Bubble behavior is a fascinating subcategory of physics with increasingly frequent applications in many disciplines of science and engineering. For example, medicine-coated bubbles make use of the human circulatory system to treat normally inaccessible regions of the body (Liu, 2006), while underwater explosions can be understood by modeling their resulting vacuous cavities as radially oscillating bubbles (Keil, 1961).

For these reasons, the scientific community has allocated many resources towards extensively analyzing these commonly underestimated physical structures.

To begin to understand and appreciate bubble characteristics, let us discuss a familiar environment in which bubble formation is commonplace: a glass of beer.

Once opened, a carbonated beverage is essentially a supersaturated liquid, meaning that by some mechanism, more gas than capacity has been forced into it. However, this gas has not yet nucleated, and is eager to form the spheroidal packets of gas that we call bubbles. In order to do so, the gas must physically separate itself from the liquid either by some form of agitation or with the aid of nucleation sites (Leighton, 2006). When a carbonated beverage is poured into a

Literature Cited: Liu, Y., Miyoshi, H., & Nakamura, M. (2006). Encapsulated ultrasound microbubbles: therapeutic application in drug/gene delivery. Journal of controlled release, 114(1), 89-99.

Keil, A. H. (1961). The response of ships to underwater explosions (No. DTMB-1576). DAVID TAYLOR MODEL BASIN WASHINGTON DC. Leighton, T. (2012). The Acoustic Bubble. Academic press.

Zhang, Y., & Xu, Z. (2008). “Fizzics” of bubble growth in beer and champagne. Elements, 4(1), 47-49. http://hyperphysics.phy-astr.gsu.edu/hbase/surten2.html

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transcription in two ways. First, the negative charge in the acetyl groups decreases the histone’s electrostatic attraction to DNA, since DNA itself is negatively charged with its phosphate groups (PO3-). Since the histones are now less attracted to the DNA, the DNA becomes unpacked, and proteins, known as transcription factors, can access the DNA for transcription.

Second, the acetyl groups in the histones can increase transcription by aiding the recruitment of positively-charged transcription factors next to DNA.

ransferase Enzymes:

Chemistry With the Solution to Cancer Treatment

By Aaron Holmes

In comparison to acetyltransferases, methyltransferases have been studied more extensively in the field of genetics, as the latter enzyme requires less advanced techniques for its analysis. There are many types of methyltransferases, including ones that work on histones, but the most distinctive type is the DNA methyltransferase, which directly reacts with DNA bases (3). This type of enzyme not only has high implications for molecular medicine, but also has unique chemical activity. DNA methyltransferases use a substrate called SAM (S-Adenosyl methionine), which is formed when the amino acid methionine reacts with ATP (Fig. 2). The enzyme then catalyzes an SN2 reaction, a very common type of substitution reaction. In this case, the DNA base cytosine “attacks” the methionine in the SAM molecule, and a methyl group is substituted into the DNA (Fig. 3). The methyl group changes the normal structure of cytosine, so that enzymes can no longer work on the DNA. Typically, RNA polymerase and transcription factors, the proteins of transcription, catalyze reactions at DNA, so DNA methylation is widely regarded as a method of silencing gene expression. But in reality, DNA methylation has functions beyond gene silencing, and is especially relevant to the evolution of defense against viruses.

Bacteria have evolved to methylate their own circular DNA, known as plasmids, after detecting invading viruses. Bacteria methylate their own DNA to protect themselves from viruses, which would otherwise damage bacterial DNA and effectively hijack the

Transferases are enzymes that serve a vital function in living organisms by initiating a seemingly simple reaction. These enzymes catalyze the transfer of functional groups to other macromolecules. The most wellknown transferases are peptidyl transferases, which transfer amino acids from specialized RNA, known as transfer RNAs, to peptide chains needed to make proteins. Glucosyltransferases serve vital roles in the human body by transferring different carbohydrates to blood antigens in order to establish someone’s blood type (1). Transferase enzymes that transfer molecules to DNA, however, are arguably the most instrumental transferases in modern biomedical study.

The transferases involved with DNA, methyltransferases and acetyltransferases, regulate gene expression. They serve opposite roles, with methyltransferases inhibiting access to DNA, and acetyltransferase increasing the transcription of DNA into RNA, which later codes for a specific protein. As their names imply, the enzymes respectively transfer methyl groups, which consist of carbons attached to three hydrogens (CH3), and transfer acetyl groups, which are made up of carbons with a double bond to an oxygen atom and a single bond to a methyl group (O=C-CH3). These enzymes share many similarities in their activity, but their key chemical differences lead to their noticeably distinct molecular effects (Fig. 1).

Histone acetyltransferases are the type of acetyltransferases that work on DNA. They catalyze the transfer of acetyl groups from acetyl-CoA to lysine amino acids in histones, the proteins that pack DNA tightly into chromosomes (2). The oxygen of the acetyl group carries a negative charge, and neutralizes the net positive charge of lysine after the transfer reaction. These new acetyl-lysine bonds give histones a more negative overall charge, and enhance DNA

Figure 1. A methyl group (left) and acetyl group (right; 6).

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oncogenes from creating tumors. If histones in oncogenes become acetylated, or tumor-suppressor genes become methylated, the resulting unregulated cell growth can promote the start of cancer.

A paper published by German researchers in 2014 demonstrated the importance of examining transferase activity. They were able to reverse DNA methylation of a tumor-suppressor gene in stem cell cultures. They used a methyltransferase inhibitor, known as ADc (5-Aza-2’-deoxycytidine), to reactivate a tumor-suppressor gene and prevent further cancer development (4). Their results may show how transferase inhibitors can be used to combat cancer growth, but they do not mean that foolproof cancer prevention is here, yet. ADc and many other methyltransferase inhibitors work by reacting with SAM molecules, leaving the cytosine in DNA unmethylated.

However, these inhibitors are quite similar in structure to the cytosine in DNA itself, and can form toxic, nonfunctional proteins, because enzymes mistake the inhibitors for cytosine. Currently, the FDA only approves low doses of methyltransferase inhibitors for two very specific types of cancer (5). The issue surrounding these inhibitors highlights the need for an increased understanding of transferase enzymes.

Discovering how to best combat problems with transferases has widespread significance in preventative medicine. Genetic diseases often involve symptoms that cannot be detected until the disease has deeply progressed, when it may be much more difficult to treat. Controlling transferase activity can vastly improve the early treatment of many genetic diseases, demonstrating once again how chemistry plays an instrumental role in our lives.

Figure 2. The formation of SAM from ATP (7)

bacterial cell. Methylation also allows the bacteria to produce restriction enzymes that cut viral DNA at specific sites without targeting those same DNA sites in their own plasmids. Bacteria showcase DNA methylation in action, and provide the basis for working with transferases today. In order to determine whether genes of interest are methylated, researchers use restriction enzymes. If the enzymes can cut the DNA of those genes, then the DNA is active and not methylated. If the DNA is uncut, then the gene is methylated. However, the neat use of bacteria does not speak true for all transferases.

The methods for determining acetyltransferase activity are more tricky. Since acetylation increases enzyme activity, a test for histone acetyltransferases needs to find DNA with high enzymatic activity. For this reason, researchers look at how much RNA polymerase works on a gene, and compare its RNA levels to “normal” RNA levels to determine whether the gene’s DNA is acetylated. Given all that can be analyzed with transferases, their serious value for biomedical research is no surprise.

Current research in molecular genetics focuses on the functions of transferases implicated in genetic diseases, especially cancer. Tumors and cancer are caused by errors in gene regulation, which certainly includes activity by DNA transferases. The genes related to cancer are known as oncogenes and tumor-suppressor genes. Oncogenes promote cell growth, and tumor-suppressor genes are often needed to keep

Literature Cited: 1. Goodsell, David. 2012. “ABO Blood Type Glycosyltransferases”. Protein Data Bank. http://www.rcsb.org/pdb/101/motm.do?momID=156 2. Poux, A., et. al, 2002. Structure of the GCN5 histone acetyltransferase bound to a bisubstrate inhibitor. Pr. Natl. Acad. Of Sci, 99(22), 14065–14070.

3. Rose, N. and Klose, R., 2014. Understanding the relationship between DNA methylation and histone lysine methylation. Biochimica et Biophysica Acta, 1839(12), 1362-1372.

4. Buchholtz, et. al, 2014. Epigenetic silencing of the LDOC1 tumor suppressor gene in ovarian cancer cells. Arch. Gynecol. Obstet, 290(1), 149-154.

5. Johns Hopkins Medicine, 2012. “Scientists reprogram cancer cells with Epigenetic Drugs”. http://www.hopkinsmedicine.org/news/media/releases/ scientists_reprogram_cancer_cells_with_epigenetic_drugs 6. UCLA. “Illustrated Glossary of Organic Chemistry”. http://www.chem.ucla. edu/harding/IGOC/IGOC.html 7. UC Davis Chemwiki, 2014. http://chemwiki.ucdavis.edu/@api/deki/ files/6666/image459.png?revision=1 8. Wikimedia, 2014. SN2 Mechanism of Methyltransferases. https://commons.wikimedia.org/wiki/File:SN2_Mechanism_of_Methyltransferases.png

Figure 3. The Sn2 reaction between SAM and cytosine, which results in methylated DNA (8).

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ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) = ∏ (u + uk) G0(u) ƒ(z) = (π/2)(S1 + S2) p(x) = -G(-x2) / [xH (-x2)] ƒ’(x) dx = ƒ(b) - ƒ(a) d/dx f(t) dt = f(x) y = √x2 + 9 P = (1/3t3/2 - 1/3 + √2)2 y = ln | 1/2x2 + C | G(u) =

Common Core Mathematics: The New Standard?

By Nicole McNabb

When we think about the Common Core standards of K-12 mathematics, many of us cringe.

We are aware of the extensive testing and unfair teacher evaluations that have resulted from No Child Left Behind and the new “college-readiness” requirements for schools. In 2013, then potential presidential candidate Marco Rubio even criticized the Common Core for “turn[ing] the Department of Education into what is effectively a national school board.”However, the Department of Education and proponents of the new national curriculum claim that it has its share of benefits. While the Core has proven difficult to implement effectively across the country, it was developed with the noble goal of bringing American students to a level of knowledge and career-readiness to compete

on the international scale.

In 2009, American leaders in education from 48 states, two territories, and the District of Columbia came together to draft new standards for K-12 mathematics and English language education that would better prepare students for college and careers post-graduation. Professionals in higher education and K-12 teachers worked together to develop standards they thought each student should meet at the end of each grade level. The standards were published in June 2010. Although the Common Core is not mandated across the country, 43 states have voluntarily adopted the new standard since its publication.

The Common Core standards in mathemat30

ics are based on one simple goal: to teach useful mathematics that will prepare students for college and careers. K-12 teachers often note that many students complain they never use math in everyday life, and that they learn many mathematical concepts and techniques in school that they deem completely useless. The Common Core aims to address this problem by stating that students must understand not only how to do math, but why they are doing it. The Core wants teachers to focus more in depth on fewer concepts. For example, according to the Core, grades K-2 should only teach “concepts, skills, and problem solving related to addition and subtraction”. Similarly, grades 3-5 should solely focus on basic multiplication and division. This system allows for teachers to discuss concepts more in depth, and to provide real-world examples for each concept in order to connect K-12 mathematics to students’ everyday life.

The board of educators that designed the Common Core created two distinct assessment consortiums, called the PARCC and the Smarter Balanced.

Each of the 43 states that have adopted the Core has changed their K-12 standardized testing to one of these two tests. Most states switched their testing systems during the 2014-2015 academic year. The resulting average proficiency rates of students in mathematics across the United States on the new Common Core assessments reveal a gloomy reality. In Mississippi, the state ranked 50th in education in 2014, 76% of Algebra I students scored proficient on Mississippi’s standardized test in 2014. However, in 2015, Mississippi adopted PARCC, and only 27% of Algebra I students passed. In New Jersey, which was ranked 1st in education in 2014, results were not much better. In 2014, 72% of students scored proficient in 8th grade

math, which the Common Core dictates should include Algebra I. However, when New Jersey switched to PARCC in 2015, only 34% of students scored proficient in 8th grade mathematics. It is obvious that American K-12 students are not learning mathematics at an international standard, but why?

It seems that the difficulty of implementation of the Common Core seems to be the standard’s greatest weakness. According to the National Educators Association, for many school districts, especially those with little state funding, providing adequate resources in order to raise test scores to a proficient level is a huge barrier. In order to teach to the level of the new Core assessments, teachers must receive professional training and develop much more complex and comprehensive lesson plans than were previously required. In addition, advanced technology is often needed to teach to the Core more effectively.

However, many districts do not have the state funding to provide teachers and students with these resources. Districts representing underprivileged neighborhoods especially suffer from lack of state funds. As a result, minorities and students from low-income communities are less likely to perform at a proficient level on the new assessments, and are likely to be less prepared for college. While no clear solution has been proposed to equalize school districts’ access to funding and resources to improve their classrooms, one will need to be proposed soon if proponents of the Common Core expect to keep the new standard.

Literature Cited: 1http://www.politifact.com/florida/statements/2013/oct/22/marco-rubio/common-core-obama-administration-national-school-b/ 2http://www.corestandards.org/other-resources/key-shifts-in-mathematics/ 3http://www.pbs.org/parents/expert-tips-advice/2015/04/math-common-core-for-parents/ 4http://www.corestandards.org/Math/Practice/ 5http://www.corestandards.org/about-the-standards/frequently-asked-questions/ 6http://www.huffingtonpost.com/2014/08/04/wallethub-education-rankings_n_5648067.html 7http://www.edweek.org/ew/section/multimedia/map-common-core2015-test-results.html 8http://www.huffingtonpost.com/ 9http://www.edweek.org/ 10http://neatoday.org/2012/01/29/states-struggling-with-commoncore-transition-2/ 31

Beauty Without a Spine

By Talia Borofsky

To understand an organism, we have to explore and appreciate it through multiple lenses -- how did it evolve? What ecological factors influenced its evolution and its physiology, and how does it affect its environment? How does physiology connect to behavior? In biology courses, the difficulty lies in developing assignments and class activities which challenge students to think about life on multiple levels.

In the Invertebrate Biology course taught by Professor Rachel Merz last fall, we had an incredible assignment called “The Challenge of Displaying Amazing Invertebrates.” Rachel asked us to produce images of an invertebrate that showcased its beauty.

This project stood out against the backdrop of my other problem sets, labs, and readings as a unique assignment – one that gave the students a chance to delve into the shapes, colors, and patterns present in invertebrate biology. In high school, I took Studio Art classes straight through senior year. I developed a predilection for chalk pastel, because of its bright colors, and narrative art focusing on biotic elements, such as plants and landscapes. I took one semester in College, and then stopped. Drawing seemed incompatible with my pursuit of math and biology.

I jumped on the opportunity to make time to use a skill that I had abandoned for so long. I have been fascinated with the collective behavior of eusocial insects since last year (I enjoyed co-authoring an article on this topic in the last issue), and will research honey bees this upcoming summer, so I decided to paint a honey bee with my trusty chalk pastels. I first drew the full body, them honed in on its stinger, and finally illustrated an example of how honeybees use their stinger for colony defense.

As I looked through the microscope at a honeybee collected by Professor Chris Mayack, I identified three distinct parts in the honey bee’s body: the head,

thorax, and abdomen. In my first drawing (Figure 1), notice the hairs all over the bee’s body--I accentuated them, since prior to this first drawing session, I hadn’t realized that hairs cover the bees entire body, including its legs and eyes. I was inspired to look further into the function of the hairs. Honeybee hair provides insulation, since these honeybees have to survive both cold nights and winters (Southwick et al. 1987).

On the eyes, a tuft of hair senses wind flow, allowing the bee to sense whether it has to fly faster in order to move forward when in windy conditions (Winston 1987). Hair on the legs act as a brush to clean material off the head, and also to transfer material, such as pollen, to the pouches on the hind legs (Winston 1991).

As I looked over my painting and then discussed it with Rachel Merz, another part of the bee caught my attention; small, pointy, and barely sticking out from the rear of the thorax: the sting (Figure 2). I focused a microscope on the posterior ventral of the bee, and noticed the lancet protruding from the body and the plates of exoskeleton around it. The sting is made up of the lancet connected to a poison sack inside the abdomen. Uncommon among stinging insects (Breet et al. 2007), the poison sack is deposited into the honeybee’s victim along with the sting. The loss of the sting comes with the loss of part of the bee’s abdomen, so the honeybee dies soon after its attack (Winston 1991). Why engage in such costly defense Figure 1. Honey bee 32

by depositing the poison sack? One possible explanation is that after leaving the body, the poison sack continues to pump, thus delivering extra venom into the unlucky victim.

The purpose of bee stings is not necFigure 2. The sting essarily to terrorize children or send picnic-goers into anaphylactic shock.

In fact, it is used as a self-defense mechanism. This defense, as Breed et al. explained (2004), is part of a multistep process. Since the colony is packed with individuals, multiple bees might detect an invader with their antennae. These bees then release alarm pheromones, which triggers a mass attack by soldier bees in addition to the recruitment of more guard bees.

I decided to draw an example of a bee using its sting for defense against a non-human animal. Luckily for me, but not so much for the bees, the observation hive was infected with small hive beetles, which parasitize honeybee colonies. As shown in my final drawing (Figure 3), honey bees defend the colony by trying to climb on top of the parasite and kill it with a sting. I chose the dark colors of my painting to capture the life-and-death aspects of the situation--if the beetle doesn’t escape, it will die, and the honeybee is sacrificing itself for the hive. I incorporated complementary colors to accentuate and even exaggerate the bee’s bright colors, while making sure the viewer can see abdomen, thorax, and head, the fur, exoskeleton plates, and of course, the sting.

The small hive beetle is an invasive species from Africa. There, honey bees and beetles have evolved a balance such that the beetles do not significantly damage colonies (Ellis and Hepburn 2006). African honey bees have co-evolved to remove beetle eggs from the comb and use guards to imprison beetles in Figure 3. Honey bee climbing on top of the cracks, while the parasite

beetles have adapted to trick guard bees into feeding them through trophallaxis, the mouth-to-mouth exchange of fluids among members in a colony (Ellis and Hepburn 2006).

However, as Torto et al. (2007) discovered, European honey bees do not effectively defend themselves from small hive beetles; the process of alarm pheromones and mass attack loses potency against this parasite. Due to domestication, A. mellifera are more docile and less sensitive to alarm volatiles than their African counterparts, while small hive beetles can detect volatiles and alarm pheromones at levels lower than can the European honey bees, and are actually attracted to these chemicals (Torto et al. 2007).

By the time alarm pheromone levels are high enough for the colony to detect and respond with a mass attack, it is already infested with beetles. If the colony is already stressed, beetles can evade attack, and feed on the colony’s food stores. When eating, the beetles deposit yeast, which also generates the alarm pheromones, attracting yet more beetles. In the end, the nest can be so infested that the colony has to find a new the nest (Torto et al. 2007).

The time I spent drawing and observing bees increased my awareness of their beauty, which piqued my interest in learning more about their ecology and behavior, inspiring me to spend even more time drawing. My experience revealed that science pedagogy should involve a greater variety of activities, beyond the typical readings, experiments, problem sets, and papers. Incorporating art into my study of biology helped me to holistically understand my subject of study. Art can help biologists remember why they chose to study biology-- because the way living things appear, function, and interact with their environment is amazing.

Literature Cited: Breed, Michael D., Ernesto Guzmán-Novoa, and Greg J. E. Hunt. “Defensive behavior of honey bees: organization, genetics, and comparisons with other bees.” Annual Reviews in Entomology 49.1 (2004): 271-298.

Ellis, J. D., and H. R. Hepburn. “An ecological digest of the small hive beetle (Aethina tumida), a symbiont in honey bee colonies (Apis mellifera).”

Insectes sociaux 53.1 (2006): 8-19.

Southwick, Edward E., and Gerhard Heldmaier. “Temperature control in honey bee colonies.” Bioscience 37.6 (1987): 395-399.

Torto, Baldwyn, et al. “Multitrophic interaction facilitates parasite–host relationship between an invasive beetle and the honey bee.” Proceedings of the National Academy of Sciences 104.20 (2007): 8374-8378.

Winston, Mark L. The biology of the honey bee. Harvard University Press, 1991.

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iadifenolide

By Jennifer Guo Jiadifenolide. Its name may sound like a mouthful, but this compound has gained a high profile since its discovery in 2009. It has an unusual geometry composed of a polycyclic, almost cage-like structure, but its rockstar quality lies in its ability to promote neuron growth. Unfortunately, it can only be extracted in minute quantities from a Chinese plant, limiting experimentation on the promising compound.

Only recently has a scalable, efficient total synthesis for the compound surfaced, overshadowing previous attempts and opening the door for extensive study on the compound.

Scientists originally extracted jiadifenolide as part of a family of compounds derived from the plant Illicium jiadifengpi, found in southwestern China. Initially, researchers noted how jiadifenolide significantly extended neurite outgrowth, the growth of axons and dendrites, when applied to cultured rat neurons at concentrations as low as 10 nM (Kubo, 2009). This result boosted jiadifenolide’s profile as a potential drug for neurodegenerative diseases such as Alzheimer’s or Parkinson’s.

These diseases remain difficult to treat with traditional drug therapies. Present treatments which include dopamine often address the symptoms of these disorders, but do not stop or repair neurodegeneration. Neurotrophins, which are large proteins that encourage the growth and survival of neurons, have been studied as potential treatments for these diseases, but their bulky structure cannot pass through the blood-brain barrier, which separates fluid in the brain from the rest of the body (Price, 2007). Thus, the discovery of small molecules with neurotrophic properties, such as jiadifenolide, has become a promising area of research for neuroscientists. These molecules not only mimic the function of

neurotrophins, but can also slip past the blood-brain barrier and enter brain fluid.

Despite jiadifenolide’s utility, the shortage of useable compound has limited further study beyond cell culture experiments. The process of extracting from the Illicium jiadifengpi plant has a paltry yield of 0.000008%, requiring 117 kg of plant material to generate just 1 g of jiadifenolide (Lu, 2015). Without a sufficient amount of the compound, there was no feasible way to expand jiadifenolide experiments to animal models or potential clinical trials. As a result, this compound has received much attention from synthesis groups since its discovery. Synthesizing jiadifenolide from chemical building blocks rather than by plant extraction would provide a scalable and efficient means to obtain the compound for broader experimentation.

Since 2009, three independent groups have published syntheses for jiadifenolide, each one increasing in yield or efficiency. The Theodorakis group at the University of California, San Diego published the first method in 2011. Their synthesis was enantiospecific and required 25 steps, with a 1.5% yield (Xu, 2011). In 2014, the Sorenson group at Princeton University established a shorter enantiospecific synthesis with 18 steps, but only 0.9% yield (Siler, 2014).

A month later, the Paterson group at Cambridge University published a racemic total synthesis with 23 steps, but the highest yield of the other methods: 2.3% (Paterson, 2014).

A few months ago, the Shenvi group at the Scripps Research Institute published the most efficient synthesis of jiadifenolide, with fewer than ten steps and a greater than 20% yield. This method allows for gram-scale production of jiadifenolide from relatively accessible chemicals. To break down this synthesis, I 34

will take you through each step: 1.

First, a solution of (R)-(+)-Citronellal reacts with BTTP, a strong base. This step converts the carbonyl group of the original compound into a a C≥C triple bond, forming an alkyne.

2.

Next, the alkyne reacts with ozone to cleave the double bond and form an aldehyde.

3.

Then, aldehyde reacts with molybdenum hexacarbonyl in a hetero-Pauson-Khand reaction, generating a bicyclic butenolide. This is one of two building blocks for the main reaction.

4.

Hydroxyacetone and 2,2,6-trimethyl-4H-1,3-dioxin-4-one, combined with the base triethylamine, reacts with HCl to generate the second building block of the main reaction.

5.

The two building blocks combine with lithium di-isopropyl amide, a strong base, in a double Michael reaction to form a precursor structure to jiadifenolide.

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6.

This precursor combines with the Lewis acid Ti(OiPr)4 to rearrange the compound to the desired conformation. Then, it undergoes a Rubottom oxidation with mCPBA, hydroxylating the compound.

7.

The hydroxylated compound undergoes a Saksena-Evans reduction to convert one of its ketone groups into an alcohol. The new compound then reacts with carbon tetrabromide and LDA to generate an alkyl bromide.

8.

The alkyl bromide reacts with Davis oxaziridine to generate an epoxide and form the final desired product: (-)-jiadifenolide.

A few drawbacks of this method include its strong stereoselectivity, which means that obtaining other configurations would be difficult. In addition, some of the steps require reagents, such as (+)-citronellal, that are pricey or hard to find (Lu, 2015). Regardless, the Shenvi group has established the most efficient and high-yielding synthesis of jiadifenolide to date. This method can likely be adapted for other compounds found in the Illicium plant family, and will help accelerate studies on a valuable neurotrophic molecule.

Literature Cited: Kubo, M., et al. (2009). “Novel pentacyclic seco-prezizaane-type sesquiterpenoids with neurotrophic properties from Illicium jiadifengpi.” Org Lett 11(22): 5190-5193.

Lu, H. H., et al. (2015). “An eight-step gram-scale synthesis of (-)-jiadifenolide.” Nat Chem 7(7): 604-607.

Paterson, I., et al. (2014). “Total synthesis of jiadifenolide.” Angew Chem Int Ed Engl 53(28): 7286-7289.

Price, R.D., et al. (2007). “Advances in small molecules promoting neurotrophic function.” Pharmacology & Therapeutics 115(2): 292-306.

Siler, D.A., et al. (2014). “An Enantiospecific Synthesis of Jiadifenolide.” Angew Chem Int Ed Engl 53(21): 5333-5335.

Xu, J., et al. (2011). “Enantioselective Total Synthesis of (−)-Jiadifenolide.” Angew Chem Int Ed Engl 50(16): 3672-3676.

All images are from the the Lu, H.H.,et al. publication.

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