Biology and biomedicine · Fair entry · August 2026 Demonstration
dm_student1
Intertidal gastropods experience a steep thermal gradient across a few vertical meters of shore, and their upper thermal limits are known to shift with acclimation history. This study asked whether exposure logged directly in the field predicts thermal tolerance measured in the laboratory, using 240 animals collected across three shore bands on four dates, with continuous temperature logging at each band for the six weeks preceding collection. Survival at thirty two degrees fell from eighty eight percent at the high shore to forty one percent at the low shore, and logged exposure above twenty five degrees predicted the temperature of reattachment failure more strongly than shore height alone. Within band variation was large enough that height predicts a population mean well and an individual animal poorly. The design is correlational and cannot separate acclimation from selection, which a transplant experiment would.
Synopsys Silicon Valley Science and Technology Championship 2027, Second Award ·Ricoh Sustainable Development Award
Biology and biomedicine · Paper · June 2024
Tyler Rose, Nicolò Monti, Navvye Anand, Tianyu Shen
Predicting protein-ligand binding affinity is crucial for drug discovery, as it enables efficient identification of drug candidates. We introduce PLAPT, a novel model utilizing transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPT's superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.
Biology and biomedicine · Paper · June 2024
Rishab Perati
Objective: A leading challenge in aging research is measuring age accurately, as monitoring healthy individuals over decades to assess the effects of interventions for the aging process is time and cost-prohibitive. The goal of this work was to develop an accurate epigenetic skeletal muscle-based aging clock using high-dimensional DNA methylation patterns to calculate chronological age. Method: Genome-wide DNA methylation values from skeletal muscle tissue samples from 47 individuals with 200,000 methylation sites (CpGs) per sample were used for the study. Feature selection was used to narrow down CpGs. An Elastic Net regression model with a Leave-One-Sample-Out Cross-Validation (LO-SAMPLE-OCV) was used to evaluate feature relevance and model performance. I then implemented an ensemble approach where predictions from the LO-SAMPLE-OCV process served as inputs to another model using ElasticNet regression. This two-step approach leverages the strengths of ElasticNet for both feature selection and meta-learning, aiming to enhance predictive accuracy while also capturing the complex relationships of high-dimensional data. Results & Conclusion: The model outperformed the Voisin et. al. method against the performance metrics of median absolute age differences (6.5 years using the Voisin method versus 1.9 years and 2.31 years using the LO-SAMPLE-OCV and ensemble methods respectively) and mean age differences (1.9 years using the Voisin method versus 0.26 years and -0.17 years using the LO-SAMPLE-OCV and ensemble methods respectively). Impact: This tool can be used to accurately predict and understand aging to develop interventions for the prevention, early detection, diagnosis, and treatment of aging-related diseases, including cancer.
Biology and biomedicine · Paper · June 2024
Praneel Shah
This project aims to create biodegradable nanoparticles that deliver zinc ions to the soybean plant using a slow-release mechanism, allowing for accelerated seed germination without introducing synthetic materials into the environment and limiting overfertilization of agricultural land. Nanoparticles were formed using Ionic Gelation through an alginate pre-gel matrix. Once the nanoparticle solution was formed, the solution was then diluted to concentrations of 0.25, 0.5, and 1 mg/mL for further testing. Nanoparticle supplementation promoted shorter, thicker root growth in Glycine Max versus non-supplemented plants, suggesting improved zinc uptake as traditionally applied zinc fertilizers leach through the soil, requiring plants to develop longer roots to access it. Optimal growth was seen at 0.25 mg/mL, where plants doubled root quantity, increased root density by 74%, and shoot length by 25% under normal conditions. Under drought and nutrient deficient conditions, 0.25 mg/mL plants grew similarly to control conditions, suggesting nanoparticles improve water conservation within the plant and uptake of nutrients present in the soil ecosystem.
Biology and biomedicine · Paper · June 2022
Ethan Liu
Cataracts are a leading cause of blindness, especially in the developing world. With clean water not always accessible in many third-world countries, the poor quality of water may trigger oxidative stress, a major inducer of cataracts, as demonstrated by previous studies, and one-way oxidation can occur is through Fenton reactions reacting with iron. Perhaps the iron in the untreated water causes oxidation, and this could be a potential cause of cataracts. The objective of the study is to find if the presence of iron ions in drinkable water sources is a contributing factor to cataracts. Pig eyes were soaked in iron solutions and stored under a UV light for 24 hours, simulating human eyes in contact with iron water over a long period, building up oxidative damage. The lenses were then analyzed by comparing the color of the lenses and the change in luminance compared to the control. The results were found to show how iron in water can induce oxidation in the eye due to the Fenton Reaction, leading to cataracts.
Biology and biomedicine · Paper · June 2022
Christopher Sun, Jai Sharma, Milind Maiti
The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.
Biology and biomedicine · Paper · May 2021
Anika Nagavara
According to the Centers for Disease Control and Prevention (CDC), 1 in 13 people have asthma. Each day, ten Americans die from asthma (Asthma and Allergy Foundation of America 2018). A previous project that was conducted last year looked at the correlation between zinc deficiency and asthma and yielded positive results meaning that a correlation between additional zinc intake and a better control of asthma could be seen. This project observed whether there is a correlation between iodine deficiency and asthma since iodine has previously been used to treat inflammatory diseases since it has properties that can stabilize thyroid hormone levels as well as reduce bronchial secretions and mediate immune cell responses (Lake 2017). In order to supply the iodine, potassium iodide was used since iodine is more easily absorbed by the body when it is in the form of potassium iodide. A combination treatment of potassium iodide and zinc was also given.
Biology and biomedicine · Paper · March 2021
Rishi Pankhaniya
Tunicamycin is a commonly used drug to cause an unfolded protein response in multiple myeloma cells in order to treat the cancer. However, many multiple myeloma cell lines have slowly developed resistance to this treatment. The goal of my project is to find out the reasons in the RNA behind why this resistance is caused, in order to create a better, altered treatment that could possibly circumvent these problems. First, multiple myeloma cells were treated with tunicamycin repeatedly four times, such that the living cells would sufficiently have developed resistance to the treatment. Then a short-term treatment was performed before plating the cells, dividing the cells into a control and treated group to find differences between their RNA to find indicators that cause the resistance. After plating the cells and isolating the RNA, a mass transcriptome analysis returned exonic data to allow us to look at how the resistance was being developed through the creation of proteins and certain protein responses. Upon looking at the data, several markers were made clear, such as the suppression of VAPA and DDIT3 as examples. Through looking at all of these gene markers, the treatment can be slightly altered in order to prevent the suppression of certain responses that would cause the cancer cell to die, thus making the treatment apply to a wider range of cells.