When your research results glow

E. coli glowing: Agar plate of E. coli, some with the red fluorescent protein gene

Staring at our plate, my lab partner and I couldn’t help but smile, tears and sweat dripping down our faces.

We had actually done it.

Our teacher had told us this would be a long, hard journey, and that some of us would not be successful. After this week-long quest, we obtained the red fluorescent treasure at the end. We were successful.

Everyone surrounded our plate and us, staring at it in awe as well.

My lab partner looked at me and said, “Our E. coli are literally glowing red! We did it!”

I first realized my passion for microbiology and research when performing a week-long experiment in my A.P. Biology course as a junior in high school. We conducted a bacterial transformation using recombinant DNA technology to insert our gene of interest– a red fluorescent protein gene– in a bacterium, Escherichia coli. As seen in the picture above, this gene allowed the bacteria to, well, glow red! It was a long process that, if not you did not follow the procedure exactly as stated and if you were not paying attention to detail, you would not get the end result.

This short but sweet experiment is a small scale example of what research is like. The process to get to an end result might be long and daunting, but once you get the result, it is like eating that cake after a week of being on a “diet.” I am painting the process as being unenjoyable, but on the contrary, it is full of exciting moments as well and each step encourages you to keep going. Even on setbacks, you learn from them and find new ways to tackle what you were working on.

As I mentioned earlier, my passion for research really started when doing this experiment. I had done other experiments, such as what factors affect the rate of photosynthesis in leaves and understanding the physiology of roly-poly bugs, but none really caught my attention as the bacterial transformation experiment. There are many branches of research that people can go into. You can be in a lab with a microscope, 60 feet underwater in the middle of the Pacific, or in the valley with hiking boots and a large bottle of water. I prefer to be with a microscope and pipette, looking at organisms that are much smaller than I am and understanding their importance and complexity.

Phytoplankton: Different species of phytoplankton collected at the Sea Center

Since there is such a diverse range of research topics, you may not know what you want to go into or what you enjoy most. It is important to try different research areas first. I knew I enjoyed microbiology, but that is still a broad category. I first combined my interest in the ocean with the smallest creatures that live in it: phytoplankton. I was in a laboratory that researched how climate-induced changes affect the physiological and community composition of phytoplankton.

Even though I enjoyed studying phytoplankton, I was drawn more towards the biomedical side of research. I downsized in the organisms I was studying to viruses, which are even smaller and less complex than phytoplankton but have huge impacts on individual people and communities at large.

In general, research allows you to explore your interests while also teaching you patience, new skills, and how to follow a procedure, because trust me, you’ll need to pay attention to all the details in order for your end result to glow as our E. coli did.

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How my Research Perceptions Changed

When I first envisioned research, I thought of a white coat scientist in a laboratory setting. I would never have guessed the EUREKA program would expose me to so many different fields of research. Research projects range from analyzing biochemical processes to coding computer programs and working with participants to examining complex mathematical functions. Although the term “research” can be associated to the cliche white coat lab setting and specific areas of study, the beauty of research is that it can be applied interdisciplinarily with the same goal of advancing knowledge and bettering society. Aside from that, I perceived research as a lone activity. With the experience I’ve gained so far in my lab, I’ve realized that I am constantly communicating with lab members about data analysis and various machinery. Research is a cohesive, team-effort and even includes communicating with people outside the lab such as official personnel and other faculty advisors. In fact, communication and connections are one of the most crucial factors leading to success in a research project.

On the other hand, I completely expected to be surrounded by intelligent and inspiring people such as my project’s faculty advisor, post doctoral, graduate students, and research assistants. Each colleague is an opportunity to learn something completely new and deepen my understanding of the project at hand. Additionally, I knew that research rewards hard work and continuous effort. Whether you are running a participant, analyzing data, or keeping up with the literature, you are responsible to have the initiative to do those things. By doing such tasks efficiently, you gain more information and skills directly tied into the betterment of your future. On top of that, expressing your interests and initiative to the lab members allows them to find ways to make your experience as beneficial as possible.

All the experiences I have gained from research were beyond what I imagined for myself and it’s a journey that every researcher takes. Research takes you down a road you never expect, and from there you decide what you can do with those skills. Ultimately, everyone’s research experience will be different and your perception of research will change over time in hopes to gain a better grasp of being a more valuable researcher.

Female Alcohol Use Disorders are on the rise—and we don’t know how to treat them

Over the past ten years, alcohol use disorders (AUDs) have increased significantly—particularly among females. Although AUDs are predominantly male, the prognosis is much worse for females. They are much more susceptible to the negative consequences of drinking, such as liver disease and breast cancer.

This rapid increase brought forth an inconvenient truth.

Almost all of the studies that tested the safety of drug treatment for AUDs were conducted primarily on males. So, how are we supposed to treat these females with drugs that are not proven to be effective​ or​ safe? And how on earth do we not have that data?

Until 2016, almost all biomedical research was conducted mostly on male mice. Researchers use mice as models for humans, because their mammalian brain are similar to ours. A long time ago, however, scientists concluded that female mice were too hormonal to produce clean data. They have a 5 day cycle—like a mini human menstrual cycle—which results in hormonal variations that influence their reactions in various behavioral tests.

These hormonal fluctuations, however, are not limited to females.

Recent findings proved that when male mice are housed in groups with other males —which they are for almost all studies—their testosterone levels sky-rocket. These variable hormone levels also influence behavior, but have never been regarded as an issue or blockade to reliable data.

Have sexist biases have gotten in the way of objective science? Scientists completely disregarded females because they were hyper-hormonal and messy, without giving male hormones a second thought.

Recognizing this issue, the NIH issued a mandate in 2016, stating that they will only fund preclinical studies if they include female subjects.

This summer, I am working on a project that examines sex differences on the effect of adolescent binge-drinking. In order to develop an effective pharmacological treatment for female AUDs, we must first understand the neurobiology of their addiction.

There are years of research to catch up on, but we are headed in the right direction. Hopefully, we gain some insight into the nature of female AUDs, and begin the process of safely treating them.

How to Teach Yourself Image Processing

1. Context

This summer I have been asked to write a program that will take the following picture of transistors…

…and measure lengths and angles of the gray shapes inside of each box. The scope of this writing is image processing so we’re not going to talk about what a transistor is or where the gray shapes come from. Just accept the fact that they exist and I’m trying to measure it. On to the image processing where the program I am using is MATLAB.

2. Binary Image

The entire process starts with edge detection: letting the computer decide where lines are. Googling edge detection tells you to start with converting the image into a binary image. Binary Images are the foundation of edge detection because there is a white pixel were a line is and a black pixel where there is not a line. To make a binary image simply type edge(image) into MATLAB and-

 

So it’s the first step and I have already come across an issue. The images I am working with are too noisy and produce real nasty looking pictures. So step 1 a) is to filter an image to reduce noise. There are multiple filters that can be applied and multiple methods for creating binary images and photos of these multiple iterations are below:

The best and most consistent thing that worked was the use if the ‘canny’ method for binary image creation and applying one filter.

So after all that, step one is completed on to step two.

3. Line detection

MATLAB has a built-in method for detecting lines called the Hough transform. It’s a popular method for line detection used in most computer vision programs today.

 

The graph is in terms of the length of the line (rho) and the angle it is at (theta). The brightest points are where the computer believes there is a line. Applying this to the current binary images I have yields…

 

An incomplete method for finding lines. Turns out the images I have are too pixelated to give clean lines to the program so MATLAB only finds the lines it is only 100% exist and anything else is left unnoticed. This led to a rabbit hole for finding other methods of line detection such as the Radon transform, and nothing worked. So eventually, I decided to tell the computer of multiple pixels are in a vertical or horizontal line then say it is a line.

Now I can find lots more lines. My next step was to make an outline of the transistor using the furthest outward line. As you may have guessed, there is a lot of room for error using this method.

This is an error I plan to fix later in the project because this does work on most samples I have. From this point I moved on to my next Step.

4. Meaningful Measurements

I currently have measurements in pixels, but pixels mean nothing to the Lab, so I decided to implement a method for recording the measurements in nm. There is a scale bar on every picture I receive so the easy approach is to use the scale bar to create a pixel to nm conversion.

Ahh yes, clearly defined lines in this scale will make this step trivial compared to everything I have done up to this poi-

Yeah this is an issue, being unable to consistently measure the scale bar leads to many issues as you may have guessed. So on to plan B. I noticed by this point that all photos gathered are in exactly two resolutions, and the magnification is consistent across each photo. Below are data points that show the best fit curve to describe the relationship between magnification and pixel to nm conversions.

MATLAB has a built-in word detector called the OCR which is a simple tool to use. I can find the phrase ‘mag’ and read the number underneath to find the magnification of the photo and use the trendlines from above to find the approximate conversion for pixels to nm. So now I have a consistent form of real -world measurements and my next step from here takes an exciting turn.

5. Object Detection

My next step is to use object detection to take a full photo of transistors and locate each one individually and perform all the steps described above without the need of the user to locate each one at a time. This is the meat and potatoes of computer vison you brag to everyone about and feel real cool about it. So how hard is it? Step 1: type imagelabeler into MATLAB. Step 2: draw boxes around what you want to find like so.

Step 3: repeat step 2 until mouse breaks. Step 4: run program and the result is…

Easy. All it takes is a premade algorithm and your sanity to do object detection. This is the progress I have made so far in my task but there is still more to do. So how do you learn image processing? The answer is to google everything and see what works for you. Hint: if your subject is really small then most things are not going to work on the first try.

Experiment, try something new: but most importantly learn when to move on

The wide variety of majors available at UC Santa Barbara gives every student a chance to study whatever it is they are most interested in. This study can be applied by performing research and other activities that enhance the learning process. It is important, however, that students are able to balance experimenting and learning when to move on.

Being a second year Computer Engineering student, I knew that my interests for a variety of fields were in development. When I talked to my digital design professor about his research, I found out that he does research on improved drug delivery systems. In other words, his team was trying to target cancer cells and attack them directly instead of destroying other healthy cells in the process of treating the bad ones. This, to me, seemed like an incredible opportunity. A chance to apply my Electrical Engineering and Computer Science background to something more than computers. But I also learned the variety of research that goes in under his lab, and this really motivated me to be involved as I saw myself being able to explore different opportunities.

So, when I first started my Gorman Scholar internship, I started by researching how proteins interact with osmolytes under different conditions. I first learned how to use programs such as VMD, which is used to build the protein structure, and NAMD which performs the simulations. After working on these programs and reading research papers I found myself not being very productive. Knowing myself, I knew this was because my interest in the subject has dulled. After spending two weeks, I decided that it was more beneficial and more appropriate for my future to work on different project. That is when I first learned about the Two-Photon Microscopy that my lab works to build in collaboration with other departments.

This device will be unique in such that they want to enhance it with a wireless technology that is able to conduct real life experiments. And my part will be to help write a code that is able to process and reconstruct an image from the data flowing from the device. So, for the past week I have been experimenting with CUDA programming. This is an NVidia technology that uses the computer’s graphics card instead of the CPU to execute the code. It has been really interesting and an interactive process, and I hope to accomplish something by the end of these next 5 weeks.

I had kept thinking that I wasted time, and that I didn’t learn anything. But I was wrong. By spending time doing something I didn’t like I was able to learn something about myself. I was also able to make a constructive decision at a critical time which I felt took a lot out of me to do. Either way, my advice for anyone reading this post, pretty much the only thing I recommend you take out of this is to Experiment, try something new, but know when to move on and that will come with practice.