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Articles 1 - 16 of 16
Full-Text Articles in Life Sciences
Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy
Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy
All Dissertations
Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these …
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan
Doctoral Dissertations
Carnivores are distributed widely and threatened by habitat loss, poaching, climate change, and disease. They are considered integral to ecosystem function through their direct and indirect interactions with species at different trophic levels. Given the importance of carnivores, it is of high conservation priority to understand the processes driving carnivore assemblages in different systems. It is thus essential to determine the abiotic and biotic drivers of carnivore community composition at different spatial scales and address the following questions: (i) What factors influence carnivore community composition and diversity? (ii) How do the factors influencing carnivore communities vary across spatial and temporal …
The Classification Of Basket Neural Cells In The Mammalian Neocortex, Sreya Pudi
The Classification Of Basket Neural Cells In The Mammalian Neocortex, Sreya Pudi
Senior Theses
Basket neuronal cells of the mammalian neocortex have been classically categorized into two or more groups. Originally, it was thought that the large and small types are the naturally occurring groups that emerge from reasons that relate to neurobiological function and anatomical position. Later, a study based on anatomical and physiological features of these neurons introduced a third type, the net basket cell which is intermediate in size as compared to the large and small types. In this study, multivariate analysis was used to test the hypothesis that the large and small types are morphologically distinct groups. The results of …
A Generative-Discriminative Approach To Human Brain Mapping, Deepanshu Wadhwa
A Generative-Discriminative Approach To Human Brain Mapping, Deepanshu Wadhwa
Electronic Thesis and Dissertation Repository
During everyday behaviours, the brain shows complex spatial patterns of activity. These activity maps are very replicable within an individual, but vary significantly across individuals, even though they are evoked by the same behaviour. It is unknown how differences in these spatial patterns relate to differences in behavior or function. More fundamentally, the structural, developmental, and genetic factors that determine the spatial organisation of these brain maps in each individual are unclear. Here we propose a new quantitative approach for uncovering the basic principles by which functional brain maps are organized. We propose to take an generative-discriminative approach to human …
Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano
Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano
Computer Science and Computer Engineering Undergraduate Honors Theses
Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …
Unsupervised And Supervised Learning For Rna-Protein Interactions And Annotations, Kateland Sipe
Unsupervised And Supervised Learning For Rna-Protein Interactions And Annotations, Kateland Sipe
Honors Projects
This project analyzed the base and amino acid interactions and annotations through the use of unsupervised and supervised learning techniques. For unsupervised learning, clustering found the data was not able to be distinguished into clear groups which matched the original annotations through kmeans clustering and hierarchical clustering. For supervised learning, the use of random forest, glmnet, and deep learning neural networks were successful in creating accurate predictions. However, machine learning likely will not be able to replace the original complex program, but could be used for possible simplification.
Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed
Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed
Theses and Dissertations
We contribute in saving the lives of cancer patients through early detection and diagnosis, since one of the major challenges in cancer treatment is that patients are diagnosed at very late stages when appropriate medical interventions become less effective and full curative treatment is no longer achievable. Cancer classification using gene expressions is extremely challenging given the complexity and high dimensionality of the data. Current classification methods typically rely on samples collected from a single tissue type and perform a prerequisite of gene feature selection to avoid processing the full set of genes. These methods fall short in taking advantage …
A Multi-Resolution Graph Convolution Network For Contiguous Epitope Prediction, Lisa Oh
A Multi-Resolution Graph Convolution Network For Contiguous Epitope Prediction, Lisa Oh
Dartmouth College Master’s Theses
Computational methods for predicting binding interfaces between antigens and antibodies (epitopes and paratopes) are faster and cheaper than traditional experimental structure determination methods. A sufficiently reliable computational predictor that could scale to large sets of available antibody sequence data could thus inform and expedite many biomedical pursuits, such as better understanding immune responses to vaccination and natural infection and developing better drugs and vaccines. However, current state-of-the-art predictors produce discontiguous predictions, e.g., predicting the epitope in many different spots on an antigen, even though in reality they typically comprise a single localized region. We seek to produce contiguous predicted epitopes, …
A Bayesian Hierarchical Mixture Model With Continuous-Time Markov Chains To Capture Bumblebee Foraging Behavior, Max Thrush Hukill
A Bayesian Hierarchical Mixture Model With Continuous-Time Markov Chains To Capture Bumblebee Foraging Behavior, Max Thrush Hukill
Honors Projects
The standard statistical methodology for analyzing complex case-control studies in ethology is often limited by approaches that force researchers to model distinct aspects of biological processes in a piecemeal, disjointed fashion. By developing a hierarchical Bayesian model, this work demonstrates that statistical inference in this context can be done using a single coherent framework. To do this, we construct a continuous-time Markov chain (CTMC) to model bumblebee foraging behavior. To connect the experimental design with the CTMC, we employ a mixture model controlled by a logistic regression on the two-factor design matrix. We then show how to infer these model …
Machine Learning And Bioinformatic Insights Into Key Enzymes For A Bio-Based Circular Economy, Japheth E. Gado
Machine Learning And Bioinformatic Insights Into Key Enzymes For A Bio-Based Circular Economy, Japheth E. Gado
Theses and Dissertations--Chemical and Materials Engineering
The world is presently faced with a sustainability crisis; it is becoming increasingly difficult to meet the energy and material needs of a growing global population without depleting and polluting our planet. Greenhouse gases released from the continuous combustion of fossil fuels engender accelerated climate change, and plastic waste accumulates in the environment. There is need for a circular economy, where energy and materials are renewably derived from waste items, rather than by consuming limited resources. Deconstruction of the recalcitrant linkages in natural and synthetic polymers is crucial for a circular economy, as deconstructed monomers can be used to manufacture …
Of Biodiversity, Boundaries, And Distribution: The Myxomycetes Of The Philippines And Beyond, Sittie Aisha Bustamante Macabago
Of Biodiversity, Boundaries, And Distribution: The Myxomycetes Of The Philippines And Beyond, Sittie Aisha Bustamante Macabago
Graduate Theses and Dissertations
This dissertation contains a compilation of independently performed studies primarily focusing on the myxomycetes (plasmodial slime molds) from the Philippines and integrating local and worldwide data to demonstrate regional and global trends. The major themes include the following: (I) a review of the diverse group of spore-producing amoeboid protists, including the myxomycetes; (II-IV) diversity assessments in three different groups of islands in the Philippine archipelago; (V) mapping the myxomycetes found in the Philippines for databasing and analyzing the geocoded data; (VI) a study on regional boundaries, including the Philippines, using myxomycete species composition; and, (VII) creating a global species distribution …
Ensemble Protein Inference Evaluation, Kyle Lee Lucke
Ensemble Protein Inference Evaluation, Kyle Lee Lucke
Graduate Student Theses, Dissertations, & Professional Papers
The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …
Use Of Lymesim 2.0 To Assess The Potential For Single And Integrated Management Methods To Control Blacklegged Ticks (Ixodes Scapularis; Acari: Ixodidae) And Transmission Of Lyme Disease Spirochetes, Shravani Chitineni, Elizabeth R. Gleim, Holly D. Gaff
Use Of Lymesim 2.0 To Assess The Potential For Single And Integrated Management Methods To Control Blacklegged Ticks (Ixodes Scapularis; Acari: Ixodidae) And Transmission Of Lyme Disease Spirochetes, Shravani Chitineni, Elizabeth R. Gleim, Holly D. Gaff
Undergraduate Honors Theses
Annual Lyme disease cases continue to rise in the U.S. making it the most reported vector-borne illness in the country. The pathogen (Borrelia burgdorferi) and primary vector (Ixodes scapularis; blacklegged tick) dynamics of Lyme disease are complicated by the multitude of vertebrate hosts and varying environmental factors, making models an ideal tool for exploring disease dynamics in a time- and cost-effective way. In the current study, LYMESIM 2.0, a mechanistic model, was used to explore the effectiveness of three commonly used tick control methods: habitat-targeted acaricide (spraying), rodent-targeted acaricide (bait boxes), and white-tailed deer targeted acaricide (4-poster …
Plant Species Identification In The Wild Based On Images Of Organs, Meghana Kovur
Plant Species Identification In The Wild Based On Images Of Organs, Meghana Kovur
Graduate Theses, Dissertations, and Problem Reports
Image-based plant species identification in the wild is a difficult problem for several reasons. First, the input data is subject to a very high degree of variability because it is captured under fully unconstrained conditions. The same plant species may look very different in different images, while different species can often appear very similar, challenging even the recognition skills of human experts in the field. The large intra-class and small inter-class image variability makes this a fine-grained visual classification problem. One way to cope with this variability and to reduce image background noise is to predict species based on the …
Neither “Post-War” Nor Post-Pregnancy Paranoia: How America’S War On Drugs Continues To Perpetuate Disparate Incarceration Outcomes For Pregnant, Substance-Involved Offenders, Becca S. Zimmerman
Neither “Post-War” Nor Post-Pregnancy Paranoia: How America’S War On Drugs Continues To Perpetuate Disparate Incarceration Outcomes For Pregnant, Substance-Involved Offenders, Becca S. Zimmerman
Pitzer Senior Theses
This thesis investigates the unique interactions between pregnancy, substance involvement, and race as they relate to the War on Drugs and the hyper-incarceration of women. Using ordinary least square regression analyses and data from the Bureau of Justice Statistics’ 2016 Survey of Prison Inmates, I examine if (and how) pregnancy status, drug use, race, and their interactions influence two length of incarceration outcomes: sentence length and amount of time spent in jail between arrest and imprisonment. The results collectively indicate that pregnancy decreases length of incarceration outcomes for those offenders who are not substance-involved but not evenhandedly -- benefitting white …
Regional Impacts Of Invasive Species And Climate Change On Black Ash Wetlands, Joseph Shannon
Regional Impacts Of Invasive Species And Climate Change On Black Ash Wetlands, Joseph Shannon
Dissertations, Master's Theses and Master's Reports
For more than a decade intensive research on the ecohydrology of black ash wetland ecosystems has been performed to understand these systems before they are drastically altered by the invasive species, emerald ash borer (EAB). In that time there has been little research aimed at the scale and persistence of the alterations. Three distinct but related research articles will be presented to demonstrate a method for moderate resolution mapping of black ash across its entire range, understand the relative impacts of EAB and climate change on probable future wetland conditions, and develop an experimental and modeling approach to quantify and …