Drosophila Digest: Why DIP? A Fly’s Perspective

Drosophila Digest: Why DIP? A Fly’s Perspective

Located at the end of the hallway there is a place infested with a whole bunch of fruit flies, also known as the Hiraizumi lab.  We are using these little creatures to study a class of digestive enzymes called dipeptidase. This class of enzymes is known to break chemical bonds between two amino acids, and low dipeptidase activities have been correlated with human diseases such as Alzheimer’s, Crohn’s, Celiac’s, and Huntington’s.  Two different strains of fruit flies have been isolated that differ significantly in enzymatic activity of one of these dipeptidases, DIP-B.



NC25III shows about 10% of the DIP-B activity that Cl55 shows. 

Our mission is to figure out what causes this difference.  We are doing so by studying the regulation of the Dip-B gene in these two strains. This could help us to understand how dipeptidases in humans are regulated. One possible explanation for the lowered enzyme activity in NC25III is that there are differences in the Dip-B gene sequences (and corresponding amino acids) that affect the catalytic activity of DIP-B.  The diagram below is the whole Dip-B gene; the dark green boxes represent the coding sequence while the light green boxes represent the untranslated region. The thin lines are introns.

This summer we are working on isolating and analyzing the Dip-B coding sequence of these two strains.  In order to do so, we extracted and isolated DNA from adult male Drosophila melanogaster (fruit fly). Then using Polymerase Chain Reaction (PCR), many copies of coding sequence were generated in portions.  To do this we had to design many primers (short and specific DNA sequences, which are the arrows on the above diagram) for PCR. This is a very time consuming process.  Below is a picture of one of us on a typical day waiting for NCBI BLAST to complete.

We then did gel electrophoresis to check if PCR was successful.  Below is one image of an agarose gel that shows the darkly stained band of a PCR product which contains the 5’ untranslated region and coding sequence. 

Next, we sent the PCR product to a company for DNA sequencing of the coding region.  When the results arrived it was off to where we’d do the analysis. Here is a live look at the workspace for sequence analysis.

After analyzing the Dip-B coding sequences so far, we found four differences in amino acid sequence between the two strains.  Of these, only two were non-conservative missense mutations, which may alter the catalytic function of the enzyme.  We are waiting for more sequencing results to further analyze the potential differences between the two strains, both in coding portion of the gene and in the corresponding amino acid composition.

Some of the reactions did not
go our way and produced Images
we couldn’t explain….

The Science of Aggression

Here in the Aggression Research Lab, we are often asked what is aggression and how can it be measured subjectively. In its most basic form, aggression is doing harm to someone who intends to avoid that harm. Therefore, aggressive acts may be physical, such as hitting, kicking or pushing another person, verbal, such as yelling or taunting, or relational, where harm is done through damage to one’s social standing. In our lab we have been mainly focused on cyberbullying, which is a series of repeated aggressions committed via an online medium, as well as how individuals respond to provocation in the laboratory. Through both of these, it is important to have a series of paradigms by which we can measure aggression subjectively at a state level.

We would like to go through four more traditional measures of aggression as well as four measures of aggression that are much more interesting and unique. The Teacher/Learner paradigm is one of the traditional paradigms developed by Buss (1961). In this paradigm, the participant is assigned the role of the teacher and the confederate (a disguised researcher) is given the role of the learner. The learner aggresses against the teacher at the beginning of the experiment and the teacher is given a chance to retaliate by giving shocks to the learner for each incorrect answer. The cover story is that the teacher is helping the learner by punishing incorrect responses to a word matching task, but in reality the learner is not being shocked at all. This measures aggression by allowing the teacher to select both the intensity and duration of the shocks given.

The Milgram Experiment remains one of the most infamous uses of the Teacher/Learner paradigm, but measured obedience, not aggression

The Essay Evaluation Paradigm, developed by Berkowitz, Corwin, and Heironimus (1962) is similar to the Teacher/Learner Paradigm in that shocks are used to measure aggression. In this paradigm, the participant and a confederate are asked to write essays on a topic and then evaluate each other’s essays. The rating of the essays is performed by shocking the essay writer with anywhere from 1 (a good essay) to 10 (a bad essay) (If professors graded us with shocks, I think we’d all be much more motivated to do well). The participant received a random number of shocks between 1 and 7 and then was allowed to “shock” their partner to rate their essay. The level of aggression is measured by the number of shocks assigned to the partner. Both the Teacher/Learner Paradigm and the Essay Evaluation Paradigm possess similar shortcomings in that the nature of the cover story may result in participants believing they are engaging in prosocial behavior (helping the learner to learn through shocks or accurately evaluating an essay with shocks). Participants are also not given alternate options to the shocks (such as just not shocking the other) and therefore must aggress to some degree regardless of aggression level.

The third of the traditional paradigms allows for children to achieve one of their wildest dreams… beating up a clown!* The Bobo Modelling Paradigm (Bandura, 1967) is a situation in which a child observes an adult interacting with a Bobo clown doll. The adult will either be aggressing toward the doll or not and then a variety of variables can be altered from the type of aggression the adult is performing to whether the adult is rewarded or punished for their treatment of the Bobo doll. The child is then allowed into the room with the Bobo doll without any instruction. Often, children who viewed aggressive adults responded to the Bobo doll aggressively, while those who saw non-aggressive interactions did not usually aggress toward the doll. Aggression was measured in the children by their interactions with the Bobo doll. The problems with the Bobo Modelling Paradigm are more significant than the problems in the previously listed paradigms. It has been argued that this paradigm is not actually measuring aggression but is measuring either imitation or simply rough play because the child may not intend to do harm to the Bobo doll, instead believing the interaction to be of a playful nature.

Bandura’s Bobo Doll Study remains the most famous use of the Bobo Doll, arguing against its use as a toll for measuring aggression

*Many clown dolls were harmed in the making of this paradigm

The Competitive Reaction Time Task is one of the most well-known aggression tests, and was created by Taylor in 1967, and was originally called the Taylor Aggression Paradigm. People in the task were either provoked or not by their fellow participant (confederate), and then they played a game against each other. The game involved having a faster reaction time than your partner, and if you won, you get to assign a noise blast/shock and if you lose, you get the blast/shock. The Taylor Aggression Paradigm dealt with giving electrical shocks on a scale of 0 (low) to 10 (max), while the CRT was noise blasts on the same scales. The measure of aggression was how high you would go to hurt your partner with either the shocks or noise blasts. While effective in measuring aggression, it does not bring out the creative expertise of psychologists, like the next few paradigms have apparently done.

            The creativity of aggression researchers seems to be on full display with a series of more recent aggression measurements that attempt to solve the problems of the traditional aggression measures. The Hot Sauce Paradigm (Lieberman et al., 1999) has participants believe that they are doing a food taste test with a partner (a confederate), the confederate provokes the partner by assigning them to drink orange juice that has been diluted with vinegar (not a pleasant taste, trust me), or normal orange juice if they are not being provoked. The participant is then told that they are to assign their partner to eat an amount of hot sauce of their choosing, as well as that their partner does not like spicy things. The amount of hot sauce given to the partner measures the aggression of the participant. This paradigm mimics real-world aggression in which spicy foods are used as punishment as well as showing convergent validity with the Buss-Perry Aggression Questionnaire, which is the gold standard of measuring aggression.

One of the odder forms of measuring aggressions was the Bug Killing Paradigm. It was created in 2007 by Andy Martens as yet another way to measure how aggressive people are. Participants who are tested using the Bug Killing Paradigm, put a bug into a grinder and kill it (before any bug loving people get angry, there were no bugs hurt in the use of this aggression paradigm). There was a trapdoor that the bugs escaped through before they were killed. People were given several minutes to complete the task, and the number of bugs that they killed was the measure of aggression. Now, it isn’t the most accurate because sometimes people just don’t like bugs and would rather kill them than deal with them, but that doesn’t mean that they would put a person in a grinder (or be more aggressive than someone else)…

Bug-Lovers rejoice! This fly survived the ordeal

Now, one of the most interesting (and controversial in this day and age with all of the gun related issues) aggression tasks is the Bungled Paradigm. Participants were given a choice between several pellet guns that had varying intensities, and they were told to choose one gun, pump it as much as they wanted, and then shoot the participant across from them. The measure of aggression was the number of times the participants pumped the gun, and the choice of gun they made. Oh, and I forgot to mention that the gun “failed” (i.e. never worked) to fire every time, and that no people were harmed in the making of the aggression study. This paradigm has barely seen any testing because it is so controversial of giving even a bungled gun to a participant because of all the weapon related violence, so it has largely been put on the back burner.

The Experimental Graffiti and Tearing Paradigm is the most amusing (in our opinion). People are given a picture of “Adam and Eve in the Garden of Paradise” and told to essentially desecrate it and draw whatever they want on it. The “graffiti” was the amount of drawings put on the picture, and the amount of drawings of a sexual nature. Participants were then given another picture, this time of “Samson and the Lion”, in order to incite aggressive beliefs of them. People were then given an envelope and told to rip up the Adam and Eve picture however much they wanted to, and the measure of aggression was how much graffiti they put on the picture, and how much they tore up the picture after viewing the Samson picture. I have to give props to Norlander and co. for creating probably the most amusing aggression measure of them all, and for how creative it was as well.

While understanding how aggression is objectively measured, in our lab we do not only look at pure aggression, but instead aggression related behaviors and constructs. Our major focus is on understanding cyberbullying as a phenomenon above and separate from its close neighbor, traditional bullying. We look at the predictors of cyberbullying such as perceived online anonymity, belief in the irrelevance of muscularity for online behavior, and positive cyberbullying across contexts so that we can garner a broad understanding of cyberbullying in all of its forms. Our research exists within the bounds of the Barlett Gentile cyberbullying model which allows for us to build upon a plethora of theory and prior research to make further theoretical contributions by looking variables that have been shown to be related to cyberbullying such as media violence, culture, technology access, etc.

The Barlett-Gentile Cyberbullying Model

An example of how our research is often conducted within the lab is a recent study in which a participant and a confederate are brought into the lab to complete some tasks together. The participant is told that the confederate is either more or less aggressive than the participant. The participant then participates in the CRT for 25 trials. We were most interested in their first given noise blast and the reasons for it to see if there existed such a thing as a preemptive strike. That is the participant who believing their partner was more aggressive than them would assign a noise blast in order to strike the partner before the partner could strike them.

Saturation Overshoot in Two Phase Flow through a Porous Media

Mitchell Fenton and Elaine Chen

This summer, we are researching Saturation Overshoot in two liquid fluids flowing through 1mm glass beads. In our application, saturation overshoot is the process of water collecting in a higher concentration at the interface or junction between two immiscible fluids.

To measure this phenomenon, we are using a mixture of canola oil and blue water that is pumped into a glass tube full of glass beads and clear water. To track the position of the overshoot as well as the water content throughout the tube, we are using a camera to take pictures of the tube as the canola oil and water mixture is pumped in. Using Matlab, we can then produce a graph of the water saturation vs. position based on the blue dye that is in the displacing solution.

The image above depicts the tube with the infiltrating solution which is 66% blue water and 33% canola oil, on the far left. To the immediate right of this region is a thin lighter region that has a low water saturation because of conservation of mass. As the blue water rushes ahead, oil takes its place. After this region is a bright blue region; this is the saturation overshoot. This region then contacts the clear water that was initially in the tube before the displacing/infiltrating fluid was pumped in.

This image is the graph produced by MatLab using the image of the tube above. The x-axis represents an arbitrary position along the tube and the y-axis represents the percent water saturation at the given position in the tube, where 1 is 100% water and 0 is no water. Region 1 corresponds to the mixture of water and canola oil that is pumped in. Region 2, which dips to a lower water saturation, corresponds to the lighter region to the immediate right of the first zone. Region 3 corresponds to the saturation overshoot, which is the bright blue region in the tube where most of the water collects.

The images above show our experimental setup with the tube held vertically in a tube stand and our camera set up a fixed distance away. There is a lightbox behind the tube which illuminates the water and oil in the tube.

In our experiment, we performed all our measurements with water as the displaced fluid. We initially hoped to be able to measure each of the mixtures while displacing oil as well, however viscosity mismatch made the measurements less than desirable. Because the oil/water mixture has a higher viscosity than the water, displacing the water was very clean and smooth. With oil as the displaced fluid, the displacing fluid is now less viscous than the fluid it is displacing. This causes the displacing fluid to “tunnel” through the displaced fluid, rather than push it uniformly. This makes it very difficult for us to measure the water saturation, as well as to track the interface of the fluids, which now exists at the tip of each of the tunnels as opposed to one interface.

This image is an example of the fingers that form due to the
viscosity mismatch. Image taken from (R.L. Chuoke, P. van Meurs, C. van der Poel, The Instability of Slow, Immiscible, Viscous Liquid-Liquid Displacements in Permeable Media, AIME Volume 216, 1959).

This experiment is based on mathematical solutions that are generated by the modified Buckley-Leverett equation. This partial differential equation models one-dimensional two-phase flow in porous media. Now that we have collected the data for each of the oil/water mixtures, we plan to compare the experimental data to the simulated data that is generated from this model. One technique we will use to do this is the Dram algorithm. This algorithm incorporates Bayesian probabilities and a Markov chain to estimate unknown parameters in the model and propagate uncertainty in predictions. This has not been done in the decade or so that the model has been studied, so we believe this will be a new and exciting contribution. This should also be interesting because we have found that our experimental data has not adhered well to the solution profile produced by the Buckley-Leverett model.

Puckett Lab 2019

Statistical Mechanics of Granular Materials

Carlos Sanchez

For my research, I am doing statistical mechanics on granular materials. Granular materials are materials that are made of inelastic, athermal grains or particles (so equilibrium Statistical mechanics doesn’t apply). Granular materials are all around us; salt, gravel, snow, even oranges.

I’m interested in finding the entropy, so I focused on Microstates and Macrostates of Granular materials. Microstates are an arrangement of each molecule in a specific system, that is a single configuration. Macrostates are defined by the macroscopic properties, such as temperature, pressure, volume, etc. For each macrostate, there are many microstates which result in the same macrostate.

For instance, say someone’s roommate is a very messy person and their desk is a complete mess. If I were to come by and moved a pencil that was on the desk no one would be able to tell the difference at a glance because it looks like the same. In this analogy here, the messy room would be the macrostate and the room with a pencil and with the pencil moved are two microstates. In the view of our experiment is that the macrostate is defined by the pressure, the volume, the preparation protocol, etc. while the microstates are the different configurations for this controlled macrostate. By counting Microstates, I will gather enough data to come up with a hypothesis in regards to Macroscopic properties. For an example take a look at the picture below. Which one is more stable, the left or the right?

Screen Shot 2019-06-24 at 11.19.37 AM.png

If one said the right than that is correct. So, with that information one can conclude that the left one which is the unstable one will occur the least because it would be hard to get that same microstate with the system being unstable (there are fewer microstates associated with this macrostate). I am looking at how friction between the particles influences the number of microstates. Going back to the left picture, it would be safe to assume that this system is only stable if the particles have enough friction. While this has been done by simulation (which do not accurately simulate friction), this is the first time doing it this way experimentally.

Screen Shot 2019-06-24 at 11.20.15 AM.png

For my experiment, I use plastic circles that I cut out to idealize granular material.  To generate lots of configurations, I’m automating the experiment. Everything is controlled by the computer. There are many moving parts to this experiment.  First we generate a configuration, which is done using compressed air to ‘fluidize’ the system (controlled by a Raspberry Pi to activate the solenoid). The camera takes a photo of this ‘frictional’ configuration. Then we use a voice coil (essentially a speaker), to vibrate the particles into a frictionless state. The camera takes a second photo.  This process is repeated N times, where N is a large number. All the code is written in python. Those are all the moving parts of this experiment so far.

The two pictures below are an example of microstates that were created using 50 PSI from the compressor. The difference is visible and all this experiment is doing is counting these different microstates.

Screen Shot 2019-06-24 at 11.21.14 AM.png

Thus, far I have not taken solid data yet because I’ve built this entire apparatus. It takes time and patience to complete and get every moving part in order.

Statistical Mechanics of Collective Animal Behavior

Xiaoxiao Taoli

   People gathering together, birds flying in a group, fish schooling all exhibit collective behavior. But what is collective behavior?Screen Shot 2019-06-24 at 10.55.23 AM.png Figure 1. Examples of collective behaviors happening, ranging from different social creatures.

   Collective behaviors are social events and processes which emerge spontaneously. Recently, the collective field behavior has expanded into other fields, such as engineers to help design robots work better together and accomplish more complex tasks. Collective behaviors involve and can have many different forms and properties. They can be seen as having similar physical properties of solids, liquids, or gases. In collective behavioral terms, individuals experience milling, polarized flocking, and swarming. Figure 2. displays some of the examples of various collective behavioral groups that show different properties. Groups may display and experience and switch among different properties.

Screen Shot 2019-06-24 at 10.58.50 AM.pngFigure 2. Properties of collective behaviors.

   One model that was proposed for collective behaviors is called self-propelled particles (SPP), also known as self-driven particles, which involve individuals interacting with neighboring individuals. In physicists’ words, we use them to describe autonomous agents, which convert energy from the environment into directed or persistent motion. Many researchers developed computer simulations models using SPP to explore collective animal behavior. One of the best-known models on fish schools is proposed by Iain Couzin, where he suggested that the model in which individual animals follow three simple rules: (i) repulsion: move away from very nearby neighbors; (ii) alignment: adopt the same direction as those that are close by and (iii) attraction: avoid becoming isolated.

Screen Shot 2019-06-24 at 11.01.31 AM.png

Figure 3. The model proposed by Iain Couzin and his colleagues.

   The collective behavior models effectively portray and capture qualitative features of collective structures in animal behavior and capture. But this model is only a guess at the interactions of real biological elements.  Our approach is to generate data with which to test models, where we examine the material and thermodynamic properties of fish schools to provide a more robust and testable benchmark for modeling collective behavior. In simpler terms, what kind of thing is a school of fish? Are they more like a solid, liquid or a gas?

   Our specific focus this summer is to focus on measuring the surface tension of fish schools. We will use the phototaxic freshwater fish (rummy nose tetra, Hemigrammus bleheri) to examine the response of laboratory schools under project light fields using a high-speed camera and particle-tracking setup.

Screen Shot 2019-06-24 at 11.02.23 AMFigure 4. Rummy nose tetra.

Screen Shot 2019-06-24 at 11.05.08 AM.pngFigure 5. The three tanks of fish. 

   Our experiment involves putting the fish into an experimental quasi-2D tank and using the fish’s natural phototoxicity to apply non-invasive external stress on the group. The external stimuli are generated by a projector where we project two dark boxes.  Fish will be drawn to the dark spot created onto the experiment tank by projection, effectively experiencing an environmental ‘force.’ This creates some stress on the group, as individuals feel conflicting social (to be part of one big school) and environmental forces (to swim inside the two dark boxes). We use Psychtoolbox and Matlab to create and make a projection of two squares and adjust the distances and sizes of the boxes on to the experimental tank and we will have a high-speed camera that catches the motion of each individual fish. When we are taking data, we will pull the curtains that surround the tank so the fish will be in the dark environment and the only light source will be the projector, so the fish will not be influenced by the outside light source.

Screen Shot 2019-06-24 at 11.11.20 AM

Figure 6.  Apparatus.

Screen Shot 2019-06-24 at 11.11.30 AMFigure 7. The experimental tank.

Screen Shot 2019-06-24 at 11.11.39 AMFigure 8. The desired coded boxes.

   Using a high-speed camera, particle-tracking setup, Matlab, and Python codes, we will calculate the material properties and examine the effects by changing the sizes and distances of the boxes. Data will be collected and analyzed to describe schools in terms of state variables and response functions, where we can contrast with theories from the dynamic side. We will use a projected light field to test the material properties of the fish school and measure it’s bulk and shear modulus, viscosity, and surface tension. However, our theories may change once we have more data taken because it is possible that later, the data may imply that we need to examine the fish schools with different or additional theories.Screen Shot 2019-06-24 at 11.13.18 AM.png

Figure 9. The histogram of the data we collected on June 7th of 2018.

Screen Shot 2019-06-24 at 10.37.50 AMFigure 10. A snapshot of the data taken on June 19th of 2019.

Screen Shot 2019-06-24 at 11.29.52 AM.pngFigure 11. The histogram of the June 19th data.

   Figure 9. is one of the data we got from last year. We can see that the fish were in two boxes, but one box contained more fish than the other. However, the fish tended to be separated into two boxes, which is what we expected to see.  We are now using larger schools of fish and are taking lots of data. Figure 10. is a snapshot of the data video we took on June 19th. Even though this is raw data, we can visually see that the fish is swimming and exchanging between two boxes. Figure 11. is the histogram of the data we took on June 19th, and it was the 14th run of the experiment for the day. The histogram displays the stretching and exchanging between two groups of fish in two projected dark squares. It is obvious to see that the fish divided into two clusters. 











Welcome to the Splash Zone

Follow @snails_and_friends on Instagram for daily updates with our snails AND other friends!

The Aquatic Toxicology Trio and Their Fearless Leader

Sarahrose Jonik, the “Life of the Lab”

Sarahrose Jonik (a.k.a. Sayro, Troublemaker #1, or The Life of the Lab) ensures that the aquatic toxicology lab stays on its toes. When she’s not maneuvering crayfish like an absolute champ or rocking boot pants in the field, she entertains the lab by belting out the Krusty Krab pizza song and teaching her lab mates a variety of card games (we promise it’s not poker). No one will ever really know how she manages to keep her white kicks so crisp and clean, or how she can keep her energy up while being a dedicated biology student and softball star. If your mood doesn’t improve seeing her ride around campus on her bright yellow bike (carrying a bucket of crayfish, no doubt), then nothing will. Keep an eye out for her hot new mix-tape featuring the sounds of Donny Thornberry #EEGITYBOGITYBOOGITY (look it up).

 Courtney Ward, “The Cooking Prodigy

Courtney is the girl to go to if you ever need any life advice. Courtney brightens up the aquatic toxicology lab, both with her colorful leggings and sunshiny attitude. Her cooking skills are absolutely impeccable, establishing the best gourmet meals a college student could prepare. One look at her chickpea cookie dough or her chicken salad will leave your mouth-watering. Courtney also has a very active lifestyle, which is reflected in her job at the Den and her love of yoga and zumba. If you catch her at the right time, you can find her double-fisting crayfish with her bare hands #God-GivenNets. If you ask Courtney what the best outfit is for crayfish hunting in the field, she will undoubtedly tell you that its a little black dress and sparkly eye shadow, matched with the “haute couture” boot-pants #TheseBootsAreChanel.

Hayden Dubniczki, “The Certified Wilderness Explorer”

Hayden is the youngest member of the lab and arguably one of the most level-headed and “calm, cool, and collected” member of this trio. Don’t be fooled by her stoic demeanor, she truly is excited when she monotonously mutters, “Wow, I’m so excited.” She reads exhilarating novels 24/7, two of which are titled “Cod” and “Salt.” This scholarly individual can be found relaxing under trees or in nature, grounding her in adventurous GRAB Staff roots. She has an exotic taste for worldly foods, especially from her years spent living in China. We are so happy to have her as the new addition to this lab.

 Dr. Peter Fong, “The Fearless Leader”

As a pioneer in aquatic toxicology and dog enthusiast, Dr. Fong has been an absolute privilege to work with for the past two summers. He is extremely devoted and passionate about his work and all things invertebrate. You can almost always catch him with his best friend, Messi, a beautiful golden retriever, or meticulously stacking containers or glassware. He frequents the Covered Bridge as his prime fishing location and does anything he can to avoid mowing the lawn. This generous soul worries constantly about the safety and eating habits of his students, as shown through the many delicious meals he has prepared for us #ThxForTheRibs. It has been a wonderful summer in the Aquatic Toxicology Lab and we will never forget our Fearless Leader!

Check out his some of his recent work here.

A Day In The Life: So Many Assays!

The main focus of this toxicology lab is on the induced effects that pharmaceuticals & pesticides commonly found in the environment have on specific behaviors of aquatic animals such as crayfish, snails, and sea anemone.

Every day, the bright-eyed individuals of McCreary 212 and 214 adapted quickly to the variety of experiments that took place in this Aquatic Toxicology Lab. We began this summer of 2019 exactly how we ended last summer… with the feisty crayfish from Marsh Creek. #WhatTheFong

Big. Meaty. CLAWS. #Spongebob

If you can’t find us in our lab, be sure to drive by any part of Marsh Creek to find us hip-wader-deep in crayfish-infested waters. You wouldn’t believe that the four of us collected over 500 crayfish in just 4 weeks, even catching 100 in a single day! #LimitSurpassed.

Peep the Socks. #ThatFishCray

Not a day passed where our clothes weren’t soaked with pond water from these vigorously tail-flipping crayfish. However, we stood firm and made sure they knew who was boss. The crayfish were cozy in their laboratory homes during experimentation, and were safely returned to their natural habitats proceeding their days in our lab.

After our numerous crayfish experiments were completed, we ventured back to what we know best: SNAILS! This ongoing project proves to test our patience as we so anxiously await their breaching of the air-water interface… 4 hours later. #MoreSpongebobReferences

Another ongoing project was with sea anemones. This brilliant work, started by a previous snail lab member, displays the detrimental effects that anti-depressants, like Prozac and Sertraline, have on these fascinating creatures.

Important Life Lessons Learned in Snail Lab:

Who took this picture you ask? No one! #SelfieMode

  • How to siphon gasoline (handy)
  • How to short-sheet a bed (useful prank)
  • How to create a galaxy in coffee with cream (fun when bored)
  • How to properly season bland foods (yum).
  • And most importantly, how to properly stack dishes! #BoneDry #NoMoisture #TheyStick #:(

This is our Boy Band Album Cover. Please enjoy our songs detailing our endeavors on this incredible 8 week journey.

Sweet Escape – If you were one of the lucky few, you may have had the opportunity to see a scampering crayfish enter your lab. Thank you to everyone who has safely returned them to their rightful lab! xoxo

Toxic – … we work in a toxicology lab, you had to see this coming. #drugs

Jeopardy Theme Song – This is the song that played in our minds as we waited 4 hours for our snails to crawl upwards a mere 3 inches.

The Crawdad Song – Grab a net and maybe a stick, meet us down at the crick!

Nanolab Nineteen

Nanolab 19

Welcome to Nanolab Nineteen, the game where you find out if you have what it takes to survive in the Nanolab. First, choose you fighter. Will you pick Shelby, the polymer film guru? Or will you choose Claire, the nanorod master? Or maybe you will select Vivian, the only one brave enough to tackle organic synthesis? Choose carefully, and good luck!

choose your fighter

Shelby 1


Choose your player, Shelby, a senior Chemistry major from West Virginia! This is her second summer in the Nano Lab so she’s got a few syntheses under her belt, but she’s not all work with no play. She loves all things superheroes, binge-watching TV shows, and enjoying a cider and burger at ABC! Pick Shelby to journey to the world of Trial-and-Error! Along the way you can learn about oxyphilic metal trends, nanoparticle synthesis and protocols, polymer trends, and film stability. Collect 5 coins for your golden star (one correct answer = 1 coin)! Scroll down for more!

Claire 1


Choose your player, Claire, is also returning for her second summer in the Nanolab. While she doesn’t have as much experience as Shelby, she is ready and raring to take on some nanorods. Claire is a senior Chemistry and Anthropology double major from Massachusetts. When she is not in the lab, Claire loves going on long walks and reading books. Armed with a pH probe and some sodium hydroxide, she will guide you through the world of Try-It-Again. Learn about gold nanorods, rod assembly, and conformational changes. If you get all 5 coins, you may achieve helical rod assembly! If not, you can always Try It Again!

Vivian 1 


Choose your player, Vivian, a sophomore Chemistry major, economics minor, and has Pre-Health professions concentration from New Oxford, PA.  This is her first summer in the Nano Lab, very fresh and new. She loves riding motorcycles, getting tattoos, hanging out with her friends, and being outdoors. Pick Vivian to escape to the world of Learning-As-You-Go. Come learn about extractions, rotovapping, columns, NMR, and TLC (Don’t worry I’ll be learning along with you)! Collect 5 coins for your golden star (one correct answer = 1 coin)! Scroll down for more.

choose your project


Shelby 2

1.)   Which metal is more oxyphilic?

  1.      Gold
  2.     Silver
  3.      Copper

Answer: 3: Copper is the most oxyphilic metal here, followed by silver, then gold! We are using this oxyphilic trend to predict ratios of polymers to nanoparticles needed for stable nanoparticle films based on our metallic nanoparticle/polymer compatibility.

2.)   What is the most common way to synthesize aqueous citrate capped gold nanoparticles?

  1.      Turkevich Method
  2.     Thompson Method
  3.      Faraday Method

Answer: 1: The Turkevich Method is the most common method used by scientists for making citrated capped AuNPs around 15-20 nm in size in aqueous solution (but Faraday did create AuNPs over 150 years ago – click the link to learn more!) https://www.rigb.org/our-history/iconic-objects/iconic-objects-list/faraday-gold-colloids

3.)   What is sodium citrate’s role in nanoparticle syntheses?

  1.      Capping agent
  2.     Reducing agent
  3.      Both A and B

Answer: 3: Depending on the synthesis, sodium citrate can act as either a reducing agent or capping agent, or sometimes both! Reducing agents are used to create metal atoms in order to grow particles, and capping agents are needed to control particle size and avoid aggregation.

4.)   What is the appropriate way to collect and clean your nanoparticles?

  1.      Centrifugation
  2.     Cell Filtration
  3.      Both A and B

Answer: 3: We can use both centrifugation and cell filtration in order to clean away excess capping and reducing agents and collect concentrated stocks of our nanoparticles! It can be a finicky process, depending on the size and type of particle you’re working with.

5.)   Which polymer has the lowest glass transition temperature?

  1.      Poly(methyl methacrylate)
  2.     Poly(vinyl acetate)
  3.      Poly(isobutyl methacrylate)

Answer: 2: Poly(vinyl acetate) is the polymer listed here with the lowest glass transition temperature of 30 ºC. The glass transition temperature (Tg) essentially tells us the rigidity of our polymer (it’s the temperature you heat the polymer to where it changes from a glassy and brittle state to something more flexible). Poly(methyl methacrylate) and poly(vinyl acetate) have Tg’s of 114 ºC and 47 ºC, respectively. The Tg is a polymer property that the Nano Lab is using to explain and predict nanoparticle-polymer film stability.

Congratulations! You have traveled through the worldShelby 3 of Trial-and-Error! Hopefully, you’ve earned 5 coins to receive your golden star! If not, don’t worry; that is what this world is all about. Try again!


Claire 2

  1. Which of the following chemicals can affect gold nanorod length?
    1. Cetyltrimethylammonium bromide
    2. Silver nitrate
    3. Dihydrogen monoxide

Answer: 2! During the synthesis process, the amount of silver nitrate added will affect the aspect ratio of the nanorods. Cetyltrimethylammonium bromide (CTAB), on the other hand, acts as the capping agent on the nanorods and gives the rods a positive charge.

  1. What color are gold nanoparticles?
    1. Gold
    2. Red
    3. Brown
    4. Any color

Answer: 4! The color of the nanoparticles depends on the size. In the lab, nanospheres tend to be red, while the nanorods can range from blue-green to brown. In water, oxidized gold (Au3+) is the typical yellow-gold color.

  1. In layer-by-layer deposition, nanoparticles can be coated with oppositely charged polymers. Which of the following has a positive charge and acts as the final coat?
    1. Poly-L-Lysine
    2. Polyanethol sulfonate
    3. Polyacrylic acid

Answer: 1! Poly-L-Lysine has a positive charge, and can form different shapes (conformations) based on the conditions. In normal conditions, PLL is in a random coil, but as you increase the pH it will shift to alpha-helix and an increase in temperature will result in beta-sheet conformation. Polyanethol sulfonate and polyacrylic acid, on the other hand, are both negatively charged and act as “middle layers.”

  1. Which of the following instruments is primarily used to detect rod assembly:
    1. Circular dichroism spectrophotometer
    2. Zetasizer
    3. UV-Vis Spectrometer

Answer: 3! UV-Vis can show which wavelengths of light are most absorbed by the nanorods. When nanorods clump together, the absorption changes dramatically. The zetasizer can also be used to approximate the size of the nanorod assemblies. The circular dichroism, though, is used to detect which conformation the PLL is in.

  1. You got some interesting UV-Vis results on the last pH test you ran. What do you do now?
    1. Try to repeat the results
    2. Consider your job done, and resign
    3. Sprain your ankle to get out of work early

Answer: 1! While we can neither confirm nor deny Answer 3, interesting results don’t mean anything unless they can be repeated. So get back to work, and get that data!

Claire 3

If you collected all 5 coins, congratulations! You get a gold star. Did you achieve helical assembly? Nanorods can be difficult sometimes, and it is unclear with your current set of data. Whatever your results are, feel free to go back and Try It Again!



  1. What molecule is this?
    1. 11-Bromoundecanol
    2. Bromoundecylmethacrylate
    3. Bromoundecylacrylate

Answer: 3: This molecule is bromoundecylacrylate. The difference between these three choices is what is on the right end of the molecule. As shown above, bromoundecylacrylate has only a single hydrogen bound to the one end. If this was bromoundecylmethacrylate, the hydrogen would be replaced with a methyl group. If this was 11-bromoundecanol, there would only be a hydroxyl group after the carbon chain.

  1. When doing an extraction, which layer do you take out using the separatory funnel?
    1. Organic layer
    2. Aqueous layer
    3. Intermediate layer

Answer: 2: You take out the aqueous layer, because your syntheticVivian 2 material is less dense above the aqueous layer, which is the object you want to keep so that you can continue with your synthesis.



  1. When rotovapping, what liquid are you putting your dry ice into in the condenser?
    1. 2-propanol
    2. Methanol
    3. Ethanol

Answer: 1: The correct answer is 2-propanol gets colder faster since it has a higher freezing point.

  1. What do peak integrations stand for in H NMR?
    1. Number of protons at that specific part of the molecule
    2. The concentration of your sample
    3. Polarity of your moleculeVIvian 3

Answer: 1: The peak integration indicates the number of protons at a specific part of your molecule based off your first integration of a known peak of your molecule.


  1. Why are columns used in organic chemistry?
    1. To combine materials
    2. For purification
    3. For titrations

Answer: 2: Columns are done to separate a product from its impurities through a filtration technique, in which fractions are taken from the column with the idea that the product will come through the column faster than its unreacted materials and impurities.

Great Job! You have wandered through the world of Learning-As-You-Go! If some topics are still fuzzy to you, just keep on moving forward because you’ll learn more as you go!


Did you collect all three stars? If you did, congratulations!


You have the skills to survive the Nanolab! Now it is time to meet the Game Master, Dr. Thompson…



An avid coffee drinker, Dr. Thompson will decide your fate in the Nanolab. He holds the answers to all questions, but he can be elusive at times. Solve his quests, and you can level up in the Nanolab!

Moving at a Glacial Pace

Hi! I am Ilana Sobel and I am working in Dr. Principato’s glacial geology lab this summer! I am a rising senior Environmental Studies and International Affairs double major.

I am doing two different things in the lab this summer. The first thing that I am doing in the lab this summer is identifying and analyzing all of the ice scour lakes in Iceland.

You may ask, “What is an ice scour lake?”

Ice scour lakes are specific type of lake formed by erosive glacial processes. A river or stream running beneath a glacier will create the correct erosive conditions for ice scour lakes to form. The freezing, melting, and refreezing of the subglacial water will create a type of glacial erosion called “plucking” in which the water freezes in cracks in the rock and forces the rock to break and go up into the glacier. This rock is then carried away by the glacier. When plucking and other glacial erosive processes happen repetitively, ice scour lakes can form under the ice sheet. 

Since I am not in Iceland, I had to do my project remotely on ArcGIS. I first gathered shape files that I would need.

The National Land Survey of Iceland was kind enough to send me almost all of the shape files that I needed. Specifically, they sent me shape files concerning the geology and hydrology of Iceland.


Unfortunately for me, all of the information contained in the files was in abbreviated Icelandic, so I was unable to translate it. I ended up using a lot of trial and error and trying different functions until I separated the geology layers the way that I knew that they were supposed to be from the previous research that I had read. I then used the previously published research to decide which geology layers to delete in the map I was creating. I had to delete parts of the geology of Iceland because of the very unique conditions that Iceland is subject to.

Iceland’s geology is very complicated because it sits upon both a mantle hotspot, and the spreading rift zone of the mid Atlantic ridge. Basically this means that much of current day Iceland did not exist during the Last Glacial Maximum (LGM) in Iceland, 10,000 years ago, when the country was almost completely covered in an Ice sheet, the period when the ice scour lakes were formed. The lakes that were not in the geology which was present during the LGM, were all deleted.

In the hydrology shape file, I had to separate out the lakes from the rivers and glaciers. In addition, I then had to delete lakes which were human dammed, or otherwise tampered with. In addition, I also deleted crater lakes, rift lakes, and glacial meltwater lakes from the data set.

Eventually, I was left with just lakes, about 20,000 of them. When zoomed in, the shape file looks like this.

There is another common type of lake in Iceland which I had to remove from the data set.

Isolation basins are lakes which, during the LGM were below sea level but are now above sea level due to glacial isostasy. Basically, glacial isostasy is the weight of the ice sheet causing the actual crust of the earth to be depressed to a lower elevation in the mantle.

Since these areas were below sea level during the LGM, the lakes on them could not be ice scour lakes from the LGM. The isolation basins then had to be deleted based upon elevation. I used the ArcticDEM combined with the lake map to determine which lakes needed to be deleted.

I read many papers of prior research to determine the elevation of the old shorelines around Iceland, and then cut the DEM into pieces according to different levels of depression by the weight of the ice.

I then created contours, like a topographic map, and overlaid them with the lakes in order to delete the lakes.

I am now using various evidence such as density and packing of lakes, along with the lakes’ elongation and shape to try and understand what my data means.

In the beginning of the study, Dr. Principato and I hypothesized that there were several paleo ice streams in Iceland during the LGM which have left a high number of ice scour lakes as a record.

Ice streams are sites of fast moving iced bordered by areas of slow moving ice in an ice sheet, perhaps giving new meaning to a “glacial pace”.

Ice streams are important to watch when tracking global climate change and sea level rise. In our study we hypothesize that ice streams are the site of heavy erosion. A large number of ice scour lakes may be indicative of an ancient ice stream which no longer exists. 

The first project I am doing in the lab this summer is heavily tied to the second project I am doing in the lab this summer, or rather not in the lab because we are doing field work in the Faroe Islands!


The Faroe Islands have many examples of erosive glacial processes similar to the processes which I am studying currently in Iceland. I will be examining these erosive glacial land forms and processes in the Faroe Islands!

This is a cirque basin which is what we will be focused on studying in the Faroe Islands. Basically, that circular depression used to have a small round glacier in it which has since melted away.

To prepare for this expedition, Dr. Principato and I have begun hiking to get in shape before we go. Clearly, I still am not ready. We will also be going with Kiki Wallick who is still in Australia!

Which member of the Funk lab are you?

Keep track of your A’s, B’s, C’s and D’s to see if you are most like Kim McCaskey, Evan Bertonazzi, Melanie Hempel, or Dr. Funk!

How long have you been in the Funk Lab?

A: Since this Fall

B: Since Last Spring

C: Also Since Last Spring

D: Since 2007


Its brown bag lunch, what are you bringing?

A: Peanut Butter and Nutella sandwich

B: A salad but I complain about it

C: I threw anything I could find into a bread bag

D: Nothing, eating is a waste of time


What are you working on in lab today:

A: It depends on the day

B: Working on multiple oxidation reactions

C: Starting over on a synthesis

D: Solving urgent Chemistry department problems


How many kids do you have?

A: 0

B: 0

C: 0

D: 2


What is your favorite chemical?

A: 1-phenylethanol

B: Furfural (distilled):

C: This thing:

D: epichlorohydrin, mainly for its versatility.


What are you doing this weekend?

A: Operating rides at an amusement park

B: Going to the farmers market

C: Sleeping

D: Wishing you were sleeping


What is your favorite element?

A: Zinc

B: Carbon

C: Xenon
D: Iron


What is your favorite TV show?

A: Star Trek

B: Grey’s Anatomy

C: Game of Thrones

D: The Good Place


What is your major?

A: Chemistry and Music

B: Biochemistry

C: Chemistry, Math minor

D: Chemistry, Math minor – but I graduated 19 years ago


Do you have any pets?

A: No, but I have plants

B: 3 cats

C: A dog

D: No, too much responsibility


What is your favorite class you have taken at Gettysburg?

A: Organic, duh!

B: Molecular Genetics

C: Advanced Inorganic

D: A philosophy class about logic and the JFK assassination


On average, how much hexanes do you use in a day?

A: 3 mL

B: 300 mL

C: 1.5 L

D: 0.5 mL


What is your favorite video game?

A: Mario and Sonic at the Olympic Games

B: Breath of the Wild

C: Red Dead Redemption I and II

D: Rygar


If you picked:


Mostly A’s: You’re Kim McCaskey!


You may be the newest member of the Funk lab, but you’re still excited to dive deeper into your research. You’ve done a few reactions involving diol synthesis and lactonizations, but your primary focus is on selective oxidations and reductions. Recently, you’ve been testing the selective oxidation of secondary alcohols in the presence of primary alcohols with the iron catalysts and tracking the percent conversions by gas chromatography. You have discovered some selectivity toward the oxidation of secondary alcohols, which could prevent the need for protecting groups in oxidation reactions involving molecules with multiple alcohol groups. Later in the summer, you hope to obtain more data regarding the alcohol oxidations as well as look at the selective reduction of ketones over aldehydes.


Mostly B’s: You’re Melanie Hempel!


You work on improving oxidation reactions with the iron cyclopentadienone catalysts we make. Last summer, you discovered furfural to be an excellent hydrogen acceptor, and have been finding conversions and isolated yields with various primary and secondary alcohol substrates with it ever since. This summer, you are also working on competition reactions to see which alcohol will be oxidized when both are present in a reaction. You hope to find selectivity of one alcohol over another, and for this to be applicable to molecules with multiple alcohols present. This would be beneficial because it would prevent the need for the addition and cleavage of protecting groups when synthesizing molecules with both primary and secondary alcohols. You are also doing these oxidation reactions with variations of our catalyst to see which ones work the best for different goals, from complete conversion to selectivity.


Mostly C’s: You’re Evan Bertonazzi!


You build new molecules to use as ligands for the iron catalysts! Most of your time is spent at your fume hood setting up and working up reactions. You run chromatography columns like it’s nobody’s business (even though 50% of the time something goes wrong). Last summer, you helped to discover the trend that having an electron-rich cyclopentadienone (CPD) ligand correlates to a more active catalyst; yielding higher rates of reaction and higher percent conversion. This summer you are focused on building catalysts with very electron-rich CPD’s. You will also be running preliminary tests to gauge the reactivity of these new catalysts. Your favorite/most useful instrument is the NMR. On the homefront, you’re the son of a farmer from South Jersey (which is better than North Jersey) and you will spend the time after X-SIG tilling the land and growing crops to feed many hungry mouths. You have a 2 year old Havanese dog named Jäger, who is your feisty sidekick. In your spare time, you watch many pointless YouTube videos and play video games.


Mostly D’s: You’re our fearless leader, Dr. Funk!


You spend most of your time sending and responding to emails, but you’re interested in chemical synthesis and homogeneous catalysis, too. You focus your catalysis efforts on developing processes using sustainable metals and reagents. For the last 10 years you have been exploring the reactivity of (cyclopentadienone)iron carbonyl compounds as catalysts in transfer hydrogenation and dehydrogenation reactions. More recently you became interested in understanding how different substitution patterns on the cyclopentadienone affect catalyst activity, and you (and your research students) have synthesized compounds that allow you to probe for activity differences. The main instruments you use are NMR spectroscopy and gas chromatography (GC), and you enjoy the hands-on aspects of chemical synthesis. You also enjoy retro video games.

Timmy, you’re grounded!

Meet the 2019 ‘GettysBirb’ crew!


Andy Wilson

Andy is the principal investigator of a now five year project which aims to develop new techniques for counting birds–using drones. Andy is basically the British big bird and he is often found outside snapping pictures for iNaturalist or keeping track of birds for eBird. Andy is a huge advocate for citizen science and has been arrested for birding on three continents.

Fun fact: He used to knit sweaters for his G.I. Joes.

McKenzie Somers

Often the architect of late night bird memes in the crew’s group chat, McKenzie is a rising senior ES major and biology minor with a biology teaching certification from Littlestown, PA. She’s usually seen at Quarry Pond, camera in hand, snapping pictures of the endlessly growing goslings or snuggling with her adorable French bulldog puppy, Levi. McKenzie also runs a nature instagram account called @birds4enviro.

Fun Fact: She’ll happily pull over while driving to take pictures of birds (with no fear!)

Precious Ozoh

An avid soccer player and sports enthusiast, Precious is a rising senior ES major and business minor from Plymouth, New Hampshire. When not in the lab, he is often playing video games with friends at OME, practicing for the upcoming soccer season or eating at Subway during lunch breaks. He also really likes Root Beer!

Fun Fact: He’s fluent in two languages and seven dialects!

Marisa Immordino

Nicknamed the ‘Alamo’ due to her shortened tenure with the birb lab, Marisa is a rising senior ES major and biology minor from Lawrenceville, New Jersey. Her passion for nature and the environment is just as strong as her ridiculous expertise of Pokémon, often seen around campus capturing creatures and conquering gyms. She is also the curator of the bird crew’s Spotify playlist, Bird Lab Bops, found here!

Fun fact: She has a twin brother who is four minutes younger than her!

Lauren Sherman

The youngest member of the lab, Lauren is a rising junior ES major from Erie, PA. Her knowledge and enthusiasm for birds is unmatched by (almost) everyone in the birb crew. Besides her love for our feathered friends, Lauren’s other passions include making timely ‘Dad’ jokes out in the field and partaking in sometimes hours-long napping escapades once our work is finished.

Fun Fact: She can’t straighten her right arm!

Timmy ‘The Drone’ Turner

While not technically a living member of the crew, Timmy, also known as ‘Prodigal Son’, ‘Problematic Child’ and ‘Rebel’, is a DJI Mavic Pro drone and  the crew’s primary research instrument out in the field. With the help of Cosmo and Wanda, our two recorders, he surveys the study site from high above, collecting important data during missions.

Fun fact: He has a tendency to not listen to orders and instead, fly towards the ground!

A Day in the Life

The Bird Crew is currently working on revolutionizing the way that scientists survey birds. How are we doing that exactly? We are taking one of the most popular new technologies, drones, and turning them into our ‘ears’ in the sky. One of the most common bird survey techniques is called point transects. This involves an expert going to a specific spot in a study site, listening for 3-5 minutes, and recording all of the birds that are seen or heard within a certain radius. We are replicating these point counts using a drone and recorders, which increases accessibility of difficult terrain and bias reduced. Currently, surveys are often conducted in areas that are easily accessible and don’t cause much disturbance to access. For example, the Breeding Bird Survey involves thousands of citizen scientists conducting point counts. This method is very efficient and it gathers a lot of data, however, it is highly biased towards roadsides. Using drones can allow scientists access to habitat that has rarely been traversed before. This means that rare species could be surveyed and monitored much more accurately. This technique also has the potential to have a substantial influence on future environmental policy.

Our research is based in State Game Lands 249, approximately 10 miles north of Gettysburg, Pennsylvania.

This is an image of State Game Lands 249. The different habitat types is also clearly visible

In order for the Bird Crew to get prime data, we often arrive at the study site at approximately 6:30am which is peak time for bird activity. We are usually in Andy’s gray Mazda for a luxury drive to the site. When we get to the site, the Bird Crew gets Timmy set up with the fishing line that holds the recorders while Andy treks into the wilderness to get his expert point counts. When he comes back, we are ready to fly the drone for our point counts. We can’t fly the drone ourselves because we haven’t gotten our pilot’s licenses yet, but the Bird Crew is currently hard at work studying for their FAA Part 107 Remote Pilot’s exam. The missions last about 15 minutes and if all goes well, then we will fly another mission. There was one occasion where Timmy decided to fall from the sky (about 20 meters) instead of landing smoothly. After that he went rogue a few times, including trying to fly away from us. Luckily, Andy was there to catch Timmy by his fishing line and reel him back in before he could get too far.

This is an example of a territory map of the Field Sparrow which is compiled after many spot mapping visits to one site

Once we complete our mission(s), we break off into pairs (McKenzie and Precious, Marisa and Lauren) and trek into the wilderness to spot map. We decided to focus on 10 different bird species that can be found around the field site, including the Robin, Field Sparrow, Song Sparrow, Eastern Towhee, Northern Cardinal, Common Yellowthroat, Indigo Bunting, Yellow Warbler, Willow Flycatcher and House Wren. In preparation for our studies, we were tasked with memorizing the songs and calls of these species in order to identify them out in the field. Spot mapping is where we look and listen for birds and plot where they are on a map. Each study species is given a unique symbol to help us determine where their territories are. Spot mapping mainly involves walking through environments that are both rough in terrain and difficult to navigate in due to tall and thick vegetation. The crew has managed to battle countless insect attacks, fight through what seemed like layers of thorny bushes as well as conquer the heat … all for the sake of science!

When field work is complete, the crew returns to the lab to analyze the data. The spot mapping data is placed on larger species maps that correspond to each bird species by Precious. Meanwhile, recorder data from the drone flights is analyzed by McKenzie and Marisa in Audacity and Raven in order to determine the distance of the recorded bird calls from the device.

This is a spectrogram of a Song Sparrow being analyzed in Audacity

Additionally, Lauren compiles point count data recorded by Andy in the field into an excel document. Eventually, we will have enough data to compare bird abundance estimates from the drone surveys with the two ground-based techniques (spot mapping, and point counts) Even when research data is finished, the lab is still hard at work! For the last five weeks, we have been studying for our Drone Pilot exam and researching similar studies of other prominent researchers. We will spend the next few months working on a scientific journal article in which we will outline our radically new approach for count birds.

Thanks for reading! Here are some fun bird memes for your enjoyment! (and ours)

Simulations for finding the neutron lifetime!

Hey there! My name is Jose Negron and I am a rising senior and physics major. I work with Professor Crawford in finding the precision of a neutron lifetime to 0.1% or better! One might say that this is a perfectionist’s goal, but in reality we need the uncertainty of the neutron life time to be this good if we want to fully explore the properties of a neutron and the important theories in particle physics involving it. This requires that we take tons of data and then analyze this data to make note of trends and comparisons between data taken in the past. There are three tools that I use to work towards this task: GEANT4, ROOT, and Comsol. Geant and Root help me gather and analyze data and both require understanding the use of C++ and Comsol is a comprehensive physics simulation software. Using certain commands, I can create simulations of the experiment and recreate the apparatus in a 3D space! Essentially, I am gathering and analyzing new data while comparing them to the data taken from last year.  


This a 3D Geometry model created within COMSOL that represents the apparatus of the neutron beam. 

Image result for NIST proton trap


This is the trap of the 3D model above in more detail and contributes to a majority of the data that I gather. In simple terms, we are using a neutron beam to shoot neutrons through the trap and count the protons that are produced from the free neutron’s beta decay. When the proton is produced, the magnetic field (shown as B =4.6 T) directs it towards the proton detector where we can count them. There are some parameters that I vary to obtain certain pieces of data. For example, with the trap electrodes, I vary the potential through them so that they are incremented equally to form a 40 volt ramp. I may also vary this ramp to be 30 volts so that each electrode is incremented by 3 rather than 4 compared to the 40 volt ramp. By varying parameters such as the voltages within the trap, we can see how the protons we are trying to count are being affected within the trap. 

Sometimes there is questioning data that I receive after analyzing it and I ask myself. Did I make a mistake? Was there a problem in the code? Is there something I am not seeing? Why is this data so different from what I expected? As scientists, we are prone to observe errors and receive data that we do not expect to see, but in my opinion, working on understanding these errors and correcting them is what makes our research interesting! As Einstein once said, “If we knew what we were doing, it wouldn’t be called research”.