Neuroscience · Paper · July 2024
Aryan Singhal, Hursh Shah
Magnetic resonance imaging (MRI) is widely used by neurologists to detect brain abnormalities such as strokes, tumors, and various forms of dementia, including Alzheimer's disease. However, accurately diagnosing the different stages of Alzheimer's disease remains a challenge, with nearly one in five patients misdiagnosed due to symptom overlap with other conditions. This paper introduces QViSTA, a novel hybrid quantum vision transformer (QViT) model that exploits quantum parallelism to improve early diagnosis and differentiation of Alzheimer's disease stages. By integrating quantum variational circuits (VQCs) with vision transformers (ViTs), QViSTA addresses the data scalability and computational efficiency limitations of classical machine learning models. Using a balanced, multi-class dataset of 40,000 MRI images, QViSTA achieved a validation area under the receiver operating characteristic (AUC) of 87.86% and a test AUC of 86.67%, closely matching the performance of a benchmarked classical ViT while reducing feature space by 3.18%. Early and accurate detection of Alzheimer's disease is critical, as it allows for timely interventions that can significantly improve the quality of life for patients and their caregivers. As more hospitals adopt AI for biomedical imaging, QViSTA's innovative approach could dramatically reduce misdiagnosis rates, improve patient outcomes, and reduce costs.
Earth and climate · Paper · June 2024
Navvye Anand, George Cheng, Tyler Rose
240 million people are affected by floods each year, reflecting the urgent need for accessible flood prediction and detection. WaterGate is a computational model that uses geographic elevation data and the rational method to predict flooding patterns, generating an interactive 3D model for user accessibility. Computational hydrology applies numerical methods, machine learning algorithms, and computational simulations to understand, predict, and manage water resources, including floods. Our project employs computational hydrology by analyzing the structure of river tributaries in 2D through polygon clustering, satellite imaging, and various cleaning protocols. We developed respective tributary tree graphs, morphological graphs, and nodes to create a comprehensive tree and 3D model. Afterward, we examine the morphology of flood plains in 3D space, implementing the rational method (Q = CiA) framework with curated relief plots to predict, model, and visualize flooding elevation. Then, we constructed our stream order analysis, waterline delineation, and statistical analysis to validate our data. Lastly, we modeled different river systems and developed further extensions to increase the applicability of WaterGate to communities around the world.
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.
Earth and climate · Paper · June 2024
Aaryan Doshi
700 million people are in danger of being displaced due to inept drought prediction and prevention systems. Current research on drought assessment focuses solely on factors such as soil moisture and rainfall, which require painstaking measurements and lab samples, and can often be misleading. This research eliminates this requirement by proposing an end-to-end pipeline to detect and prevent droughts in at-risk areas using satellite images and vision transformers. The dataset is comprised of over 86,000 satellite images labeled by pastoralists and divided with an 80-20 ratio for training and validation. First, using feature filtering, normalization, and a Gaussian filter, the images in the dataset are modified to yield a better performance. Next, a deep vision transformer model with multi-headed attention is constructed, consisting of four heads, three transformer layers, and a patch size of five. The final MLP head produces logits for drought severity prediction level. Overall, the best transformer model achieves 78.3% accuracy in predicting drought conditions on a validation set of 10,000, unseen satellite images. In addition, this method outperforms state-of-the-art convolutional neural networks on this classification task, as compared to VGG-16, ResNet-50 and DenseNet-121 models. The model harnesses AWS cloud computing, deep vision transformers, and specific image augmentation to achieve state-of-the-art results in drought prediction and prevention. With this research, scientists have the potential to assess droughts quickly and accurately, revolutionizing our ability to provide resources and care to those affected by the increasingly common droughts caused by the climate crisis worldwide.
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.
Earth and climate · Paper · June 2024
Advaith Anand
In the context of escalating climate change impacts, precise flood mapping has become crucial for effective disaster response and management. We address this challenge by developing a deep learning model based on the U-Net architecture, specifically tailored for rapid and accurate flood extent mapping using Synthetic Aperture Radar (SAR) data from the Copernicus Sentinel-1 mission published as part of IEEE 2024 GRSS data fusion challenge. Our approach leverages the unique capabilities of U-Net for detailed image segmentation, combined with SAR imagery, digital elevation models, land-use patterns, and historical water presence data to differentiate between water and non-water surfaces accurately. The model was rigorously trained and validated across a comprehensive dataset representing a variety of geographic conditions and flood events. It demonstrated significant advancements over traditional flood mapping techniques, achieving an overall accuracy of 95% and an F1-score of 0.7. These metrics underscore the model's effectiveness in accurately classifying flooded areas. Furthermore, the model exhibits excellent scalability and adaptability, offering potential applications beyond the initial flood mapping scope to other environmental and disaster management scenarios. Our findings not only contribute to the improvement of flood response strategies but also underscore the potential of advanced machine learning techniques in enhancing the accuracy and efficiency of environmental monitoring in the face of global climate challenges. This research represents a significant step forward in the application of deep learning to hydrological extremes, providing a robust tool for emergency response planners and climate scientists alike.
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.
Neuroscience · Paper · June 2024
Tanisha Sood
The gravity of seizures in pediatric populations cannot be overstated. From the youngest infants to adolescents on the brink of adulthood, seizures disrupt not only neurological functions but also the fabric of daily life. According to the Centers for Disease Control and Prevention, about 470,000 children in the United States have epilepsy, which is a neurological disorder characterized by recurrent seizures. Drawing from personal encounters with seizures, I recognize the critical need for effective seizure detection mechanisms. This study addresses the challenge of detecting seizures in children, especially in environments where constant supervision may not be feasible. Utilizing CAD, 3D design, and machine learning algorithms, this prototype predicts a seizure onset by analyzing bio data from a simulation of possible data sets carefully overviewed by my pediatrician Sara MachMan. The prototype integrates insights from a literature search, including the CDC publication on seizure detection, highlighting brain activity, fever, and body movements as triggers. Based on these triggers and thorough data analysis with a pediatrician, I was able to discover a breakthrough. Despite encountering a 15% error rate from my simulated data analysis, the prototype effectively alerts parents and medical professionals, demonstrating its potential to reduce the unpredictability of seizures and positively impact lives.