Physical Sciences and Mathematics Commons^™

Ungrading: Reflections Through A Feminist Pedagogical Lens, Erin M. Eggleston, Shelby Kimmel

Feminist Pedagogy

Ungrading is a pedagogical approach in which no grades are given on any assignments. Instead, students are provided with many opportunities to submit work and gain feedback. The goal is to shift student focus from achieving a grade to growth as a learner and a person. As instructors, our ungrading approach utilized personalized learning plans, checkpoint reflections, and student-professor learning conferences to put agency in the hands of our students. We employed this method in upper-level biology and computer science courses and provide critical reflections here regarding our experiences and the connections between this approach and feminist STEM pedagogy tenets. …

Making Sense Of Big (Kinematic) Data: A Comprehensive Analysis Of Movement Parameters In A Diverse Population, Naomi Wilma Nunis

University of the Pacific Theses and Dissertations

OBJECTIVE

The purpose of this study was to determine how kinematic, big data can be evaluated using computational, comprehensive analysis of movement parameters in a diverse population.

METHODS

Retrospective data was collected, cleaned, and reviewed for further analysis of biomechanical movement in an active population using 3D collinear resistance loads. The active sample of the population involved in the study ranged from age 7 to 82 years old and respectively identified as active in 13 different sports. Moreover, a series of exercises were conducted by each participant across multiple sessions. Exercises were measured and recorded based on 6 distinct biometric …

Simulating Polistes Dominulus Nest-Building Heuristics With Deterministic And Markovian Properties, Benjamin Pottinger

Undergraduate Honors Theses

European Paper Wasps (Polistes dominula) are social insects that build round, symmetrical nests. Current models indicate that these wasps develop colonies by following simple heuristics based on nest stimuli. Computer simulations can model wasp behavior to imitate natural nest building. This research investigated various building heuristics through a novel Markov-based simulation. The simulation used a hexagonal grid to build cells based on the building rule supplied to the agent. Nest data was compared with natural data and through visual inspection. Larger nests were found to be less compact for the rules simulated.

Analysis Of Subtelomeric Rextal Assemblies Using Quast, Tunazzina Islam, Desh Ranjan, Mohammad Zubair, Eleanor Young, Ming Xiao, Harold Riethman

Computer Science Faculty Publications

Genomic regions of high segmental duplication content and/or structural variation have led to gaps and misassemblies in the human reference sequence, and are refractory to assembly from whole-genome short-read datasets. Human subtelomere regions are highly enriched in both segmental duplication content and structural variations, and as a consequence are both impossible to assemble accurately and highly variable from individual to individual. Recently, we developed a pipeline for improved region-specific assembly called Regional Extension of Assemblies Using Linked-Reads (REXTAL). In this study, we evaluate REXTAL and genome-wide assembly (Supernova) approaches on 10X Genomics linked-reads data sets partitioned and barcoded using the …

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler

Graduate Student Theses, Dissertations, & Professional Papers

Biological sequence datasets are increasing at a prodigious rate. The volume of data in these datasets surpasses what is observed in many other fields of science. New developments wherein metagenomic DNA from complex bacterial communities is recovered and sequenced are producing a new kind of data known as metagenomic data, which is comprised of DNA fragments from many genomes. Developing a utility to analyze such metagenomic data and predict the sample class from which it originated has many possible implications for ecological and medical applications. Within this document is a description of a series of analytical techniques used to process …

2016-01-A3dsrinp-Csc-Sta-Cmb-522-Bps-542, Raymond Pulver, Neal Buxton, Xiaodong Wang, John Lucci, Jean Yves Hervé, Lenore Martin

Bioinformatics Software Design Projects

Cholesterol is carried and transported through bloodstream by lipoproteins. There are two types of lipoproteins: low density lipoprotein, or LDL, and high density lipoprotein, or HDL. LDL cholesterol is considered “bad” cholesterol because it can form plaque and hard deposit leading to arteries clog and make them less flexible. Heart attack or stroke will happen if the hard deposit blocks a narrowed artery. HDL cholesterol helps to remove LDL from the artery back to the liver.

Traditionally, particle counts of LDL and HDL plays an important role to understanding and prediction of heart disease risk. But recently research suggested that …

Computational Modeling Of Biological Neural Networks On Gpus: Strategies And Performance, Byron Galbraith

Master's Theses (2009 -)

Simulating biological neural networks is an important task for computational neuroscientists attempting to model and analyze brain activity and function. As these networks become larger and more complex, the computational power required grows significantly, often requiring the use of supercomputers or compute clusters. An emerging low-cost, highly accessible alternative to many of these resources is the Graphics Processing Unit (GPU) - specialized massively-parallel graphics hardware that has seen increasing use as a general purpose computational accelerator thanks largely due to NVIDIA's CUDA programming interface. We evaluated the relative benefits and limitations of GPU-based tools for large-scale neural network simulation and …

Abstracts Of Papers, 84th Annual Meeting Of The Virginia Academy Of Science

Virginia Journal of Science

Full abstracts of papers for the 84th Annual Meeting of the Virginia Academy of Science, May 25-26, 2006, Virginia Polytechnic Institute and State University, Blacksburg, VA