This year, there hasn’t been a shortage of moments that made us proud of our students. As CGPS breaks for summer, we would like to highlight just a few of these moments and celebrate our students’ creativity and outstanding achievements.
Advanced Science Research
The goal of the ASR is to immerse students into original college-level STEM research. It is aimed at students who wish to pursue excellence and progress into advanced areas of original research. Emphasis is on both laboratory and bibliographic research. The course will develop and foster students' commitment to long-term focused research that demonstrates initiative, perseverance, and creativity.
This program affords students the opportunity to participate in authentic, advanced scientific research and scholarship as part of their high school experience. It furthers excellence in performance and achievement while drawing from and developing scientific capabilities. Students taking the course accomplish the following:
- They choose and explore a topic of interest. It may come from any area of basic or applied science, mathematics, medicine, or engineering. They develop researching skills using professional databases and other research tools.
- They find and study numerous journal articles, using textbooks and other articles to fill in their gaps in understanding so that they are able to explain every detail of each article and its significance.
- Once they have read a critical mass of literature on their narrowly-defined topic, they use it to write a review article that outlines the background of the topic, the cutting edge of our understanding of it, and the outstanding problems.
- Students contact a scientist who has completed research in the field they wish to study and ask the scientist to serve as a mentor to assist them in carrying out a research project in their area of interest. Students will learn how to do this themselves.
- Students then engage in an original piece of research under the supervision of their external mentor and their ASR teacher. This may be the student’s own project, or the student may assist the mentor in some meaningful manner. If the student works on the mentor’s research, it is the student's responsibility to acquire sufficient knowledge and skills to become a genuine asset to their mentor. Many students eventually know more about their highly focused topic than their teachers.
- Students will be taught fundamental inferential statistics which they will competently use in their research, including tests such as the t-test, ANOVA, and Pearson’s r. Most students complete their own data analyses or actively assist the mentor with theirs.
Current and former ASR MENTORS
Dr. Alice Giani, Weill Cornell Medicine Laboratory of Shuibing Chen, New York City, New York
Dr. Catherine Roe, Washington University School of Medicine, St. Louis, Missouri
Dr. Minkui Luo, The Minkui Luo Lab at Memorial Sloan Kettering, New York City, New York
Dr. Prathap Ramamurthy, City College of New York Department of Engineering & NOAA-Crest Affiliate, New York City, New York
Dr. Alberto Rossi, University of Florence, Florence, Italy
Dr. John Cijiang He, Icahn School of Medicine at Mount Sinai, New York City, New York
Dr. Sanjay Basu, Harvard Medical School
Dr. Tyler Carson, project engineer at Arcadis
Dr. Gulnihal Ozbay, Delaware State University
Dr. Paul Corlies, Massachusetts Institute of Technology
Dr. Terry Ruas, University of Wuppertal, Germany
Dr. Antonia Ypsilanti, Sheffield Hallam University, England
Dr. Terence R. Flotte, University of Massachusetts Medical School
Dr. Baneshwar Singh, Virginia Commonwealth University
If you are excited by the prospect of discovering a tiny sliver of the universe that nobody has ever known before or making something nobody else has, no matter how much time and toil it will take you, we want you. If you have the heart to keep that end goal before your eyes and be brutally honest in the process, and the guts to do an incredible amount of high-level work to get where you want to get, we want you.
ilya yashin, ASR coordinator
Alexander L. '21
- Presented at the International Symposium on Visual Computing, 2020
- First author of a published, peer-reviewed paper co-authored with his mentor
- 3rd place, Computer Science category at the NYC Metro Area section of the Junior Science and Humanities Symposium
- Finalist, Terra NYC STEM Fair; one of 13 students in NYC to advance to the International Science and Engineering Fair
- Invited to present to members of the Medical Imaging team at General Electric, and to do research with them in the future
Alexander S. '21
- Honorable mention, American Statistical Association’s 2020 Virtual Science Fair
“Researching a cutting-edge field of science that has the potential to save lives has truly been one of the peaks of my life."Akshay S. '22
The Modeling of Neurological Disorders Using Induced Pluripotent Stem Cells
Abstract: Human induced pluripotent stem cells (IPSCs) have opened up new possibilities in the study of neurological disorders, as they have the potential to form every type of cell in the body. IPSCs are derived from an already specialized cell, like a skin or blood cell, and are reprogrammed into a fetal-like state of pluripotency through the introduction of reprogramming genes. One of the most promising uses of IPSCs is the formation of organoids. Organoids are simplified, miniature versions of the body’s organs grown in vitro. Organoids can be used to model disorders, allowing for the study of the behavior of these disorders. This method of studying neurological disorders may enable the discovery of novel pharmaceuticals and genes that may be able to inhibit the effects of these disorders. This can be most effectively done through a process called high-throughput screening. This type of analysis tool is an automated method of testing thousands of pharmaceuticals on cell cultures until a chemical compound is found that is effective against a neurological disorder. Akshay and his mentor, Dr. Alice Giani, plan to model Zika in iPSC-derived neurons whose genome contains a “gene of interest” to test whether this gene will inhibit the virus more effectively than iPSC-derived neurons modeling Zika that do not contain this gene.
"Most times, things don't go your way, but when they do, it's pretty amazing."Fabiha R. '21
The Impact of COVID-19 on the Driving Patterns of Individuals with Preclinical Alzheimer’s Disease
Abstract: Alzheimer’s Disease (AD) is a progressive brain disorder in which brain cells and brain cell connections degenerate and eventually die, causing a deterioration of memory and various mental functions. AD presents itself in both preclinical, asymptomatic, and symptomatic stages. Underlying AD pathology, such as amyloid plaques, which are aggregates of misfolded protein, are present during the preclinical stage, but cognitive symptoms are not; cognitive symptoms, such as memory loss, confusion, and difficulty with language, are present in the symptomatic stage of AD. Currently, there are over 35 million licensed drivers who are above 65 years of age in the United States, and as this number increases, so will the number of motor vehicle crashes and fatalities, as older adults are at a much higher risk of dying in a crash than younger ones. Therefore, it is important to be able to identify those individuals who will be at most risk of driving skill decline and when that decline will occur, so that safety measures can be implemented early on. Preclinical AD, during which symptoms of dementia are not present, has been associated with driving impairment, and persons with preclinical AD exhibit different driving behaviors than those without it. However, the extent to which preclinical AD affects daily driving is still unknown. Fabiha will be conducting a study on the impact of the presence of preclinical AD on driving behaviors from before and after the acceleration of the spread of COVID-19 in the US, from March 2020 to April 2020.
“Lunch and research really go together. They both need to be thoroughly digested.” yuqiao Z. '22
Chemoenzymatic Approach to the Synthesis of Human Milk Oligosaccharides
Abstract: Glycans are polysaccharides, meaning they consist of monosaccharide sugar molecules joined together, and are found within the human body. Glycans can regulate intracellular recognition, provide structural stability, and function as a source of energy for cells. Human milk oligosaccharides (HMOs) are a key class of glycans found within human milk. HMOs are able to regulate an infant’s gut microbiome as well as the development of the infant immune system. Currently, the compound lacto-N-neotetraose (LNnT), which can be further modified into other HMOs, has been made readily available. More complex HMOs, however, cannot be produced on a large scale, making them difficult to research. In addition, their lack of accessibility is preventing them from being used as potential supplements in infant formula. This is mainly because of the asymmetrically branched structures of many HMOs, requiring monosaccharide units to be added at one branch but not the other. Yuqiao aims to utilize substrate selective enzymes, which are enzymes that only catalyze the reaction between two specific reactants, to selectively install monosaccharide units at a specific branch of the molecule. By automating the use of substrate selective enzymes, Yuqiao hopes to enable the large-scale production of complex HMOs.
“Learning and contributing to a field I am so passionate about has been one of the most rewarding experiences of my life.”
alex s. '21
Using Urban Climate Modeling to Support Climate Change Adaptation in Urban Environments: A Case Study for New York City with Broader Implications
Abstract: In recent years, because of poor urban planning and design, the peaks and averages of temperature in our cities have risen and cities have not been able to adapt to this climate change. Furthermore, the temperature of cities is quickly outpacing that of surrounding areas. This phenomenon is known as the urban heat island effect. As a result, heat waves are more frequent and more intense, which leads to an increase in heat-related mortalities, one of the largest causes of death in the United States. In a time where 75% of the human population is expected to live in cities by 2050, research in this area is more important than ever. While much broad research has been done on this topic, little research is up to date about New York City. Alex’s research in collaboration with his mentor, Dr. Prathap Ramamurthy, looks to provide us with an up-to-date analysis of New York City’s urban climate, looking at multiple variables including air temperature, relative humidity, precipitation, and soil moisture. Specifically, Alex will look at land-atmospheric interactions, interregional differences in climate variables, and will aim to create a current heat index for the general public. This will all be done using computer modeling in an effort to direct NYC’s plans for climate adaptation in the future.
“It feels really accomplishing to know that I am doing research that I thought I wouldn’t be doing until college.”
alex L. '21
Prostate MRI Registration Using Siamese Metric Learning
Abstract: Medical image registration is the alignment of one or more medical images with another image. The process of aligning two images involves adjusting one image so that it appears as if it were taken in the same place, at the same time, and with the same equipment as the other image. One example of image registration is the alignment of an MRI (magnetic resonance image) captured before a patient’s surgery to an MRI of the same subject during surgery. This could aid the surgeon in monitoring the progress of surgery by eliminating the differences between the two MRIs that were caused by differences in how the images were captured. Since manual image registration, done by specialists, takes a long time to complete, computer scientists are working to create more efficient image registration methods using machine learning. A machine learning model is a coding algorithm that analyzes patterns within data and learns to output accurate results when presented with new data. One machine learning model, a Siamese neural network, can learn to break down two separate pieces of data into simpler patterns. Then, the model enters the collected patterns into a similarity metric, which is a system for measuring the similarity of data using the similarity of patterns within the data. Alexander and his mentor, Dr. Alberto Rossi, aim to create a Siamese neural network that can select the pre prostate biopsy MRI (out of numerous transformed versions of the MRI) that is most accurately registered to an MRI taken during the biopsy.