A Constraint Logic Programming Approach to Predicting the Three-Dimensional Yeast Genome

In order for all of a cell's genetic information to fit inside its nucleus, the chromosomes must undergo extensive folding and organization. Just like in origami where the same piece of paper folded in different ways allows the paper to take on different forms and potential functions, it is possible that different genomic organizations (or architectures) are related to various nuclear functions. Until recently, it has been impossible to comprehensively investigate this relationship due to the lack of high-resolution and high-throughput techniques for identifying genomic architectures. The recent development of a technique called Hi-C, which is a derivation of chromosome conformation capture, has made it possible to detect the complete set of interactions occurring within (intra-interactions) and between (inter-interactions) chromosomes in the nucleus. Many computational methods have been proposed that use these analytical results to infer the rough three-dimensional (3D) architecture of the genome. However, the genomic architecture also impacts additional types of nuclear interactions and techniques exist that are able to capture and measure these interactions. Unfortunately, it is difficult to incorporate these additional datasets into the existing tools. To overcome this, a novel application of constraint logic programming (CLP) was used to develop a new program for the prediction of the 3D genomic architecture. The unique representation used in this program lends itself well to the future incorporation of additional genomic datasets. This thesis investigates the most efficient way to date to represent and optimally solve the constraint satisfaction problem of the 3D genome. The developed program was used to predict a 3D logical model of the fission yeast genome and the results were visualized using Cytoscape. This model was then biologically validated through literature search which verified that the prediction was able to recapitulate key documented features of the yeast genome. Future work will utilize this tool as a computational framework and extend it to incorporate additional genomic datasets and information into the prediction and visualization of the 3D genomic architecture. The development of the CLP program described here is a step towards a better understanding of the elusive relationship between the 3D structure of the genome and various nuclear functions

Development of New Computational Tools for Analyzing Hi-C Data and Predicting Three-Dimensional Genome Organization

Background: The development of Hi-C (and related methods) has allowed for unprecedented sequence-level investigations into the structure-function relationship of the genome. There has been extensive effort in developing new tools to analyze this data in order to better understand the relationship between 3D genomic structure and function. While useful, the existing tools are far from maturity and (in some cases) lack the generalizability that would be required for application in a diverse set of organisms. This is problematic since the research community has proposed many cross-species "hallmarks" of 3D genome organization without confirming their existence in a variety of organisms. Research Objective: Develop new, generalizable computational tools for Hi-C analysis and 3D genome prediction. Results: Three new computational tools were developed for Hi-C analysis or 3D genome prediction: GrapHi-C (visualization), GeneRHi-C (3D prediction) and StoHi-C (3D prediction). Each tool has the potential to be used for 3D genome analysis in both model and non-model organisms since the underlying algorithms do not rely on any organism-specific constraints. A brief description of each tool follows. GrapHi-C is a graph-based visualization of Hi-C data. Unlike existing visualization methods, GrapHi-C allows for a more intuitive structural visualization of the underlying data. GeneRHi-C and StoHi-C are tools that can be used to predict 3D genome organizations from Hi-C data (the 3D-genome reconstruction problem). GeneRHi-C uses a combination of mixed integer programming and network layout algorithms to generate 3D coordinates from a ploidy-dependent subset of the Hi-C data. Alternatively, StoHi-C uses t-stochastic neighbour embedding with the complete set of Hi-C data to generate 3D coordinates of the genome. Each tool was applied to multiple, independent existing Hi-C datasets from fission yeast to demonstrate their utility. This is the first time 3D genome prediction has been successfully applied to these datasets. Overall, the tools developed here more clearly recapitulated documented features of fission yeast genomic organization when compared to existing techniques. Future work will focus on extending and applying these tools to analyze Hi-C datasets from other organisms. Additional Information: This thesis contains a collection of papers pertaining to the development of new tools for analyzing Hi-C data and predicting 3D genome organization. Each paper's publication status (as of January 2020) has been provided at the beginning of the corresponding chapter. For published papers, reprint permission was obtained and is available in the appendix

A guidebook for implementing a writer\u27s workshop

The purpose of this project is to provide beginning teachers or teachers new to writing instruction with a step-by-step guideline for implementing writer\u27s workshop in a K-3 classroom. The first eight weeks of writer\u27s workshop are outlined and defined complete with prompts and reflections teachers can use to make this strategy responsive to the needs of their students.19 440 0 Thesis (M.A.)--California State University, San Bernardino, 1998

The prevalence of femoroacetabular impingement anatomy in Division 1 aquatic athletes who tread water

Abstract Femoroacetabular impingement (FAI) is a disorder that causes hip pain and disability in young patients, particularly athletes. Increased stress on the hip during development has been associated with increased risk of cam morphology. The specific forces involved are unclear, but may be due to continued rotational motion, like the eggbeater kick. The goal of this prospective cohort study was to use magnetic resonance imaging (MRI) to identify the prevalence of FAI anatomy in athletes who tread water and compare it to the literature on other sports. With university IRB approval, 20 Division 1 water polo players and synchronized swimmers (15 female, 5 male), ages 18–23 years (mean age 20.7 ± 1.4), completed the 33-item International Hip Outcome Tool and underwent non-contrast MRI scans of both hips using a 3 Tesla scanner. Recruitment was based on sport, with both symptomatic and asymptomatic individuals included. Cam and pincer morphology were identified. The Wilcoxon Signed-Rank/Rank Sum tests were used to assess outcomes. Seventy per cent (14/20) of subjects reported pain in their hips yet only 15% (3/20) sought clinical evaluation. Cam morphology was present in 67.5% (27/40) of hips, while 22.5% (9/40) demonstrated pincer morphology. The prevalence of cam morphology in water polo players and synchronized swimmers is greater than that reported for the general population and at a similar level as some other sports. From a clinical perspective, acknowledgment of the high prevalence of cam morphology in water polo players and synchronized swimmers should be considered when these athletes present with hip pain

Cell Lineages and the Logic of Proliferative Control

It is widely accepted that the growth and regeneration of tissues and organs is tightly controlled. Although experimental studies are beginning to reveal molecular mechanisms underlying such control, there is still very little known about the control strategies themselves. Here, we consider how secreted negative feedback factors (“chalones”) may be used to control the output of multistage cell lineages, as exemplified by the actions of GDF11 and activin in a self-renewing neural tissue, the mammalian olfactory epithelium (OE). We begin by specifying performance objectives—what, precisely, is being controlled, and to what degree—and go on to calculate how well different types of feedback configurations, feedback sensitivities, and tissue architectures achieve control. Ultimately, we show that many features of the OE—the number of feedback loops, the cellular processes targeted by feedback, even the location of progenitor cells within the tissue—fit with expectations for the best possible control. In so doing, we also show that certain distinctions that are commonly drawn among cells and molecules—such as whether a cell is a stem cell or transit-amplifying cell, or whether a molecule is a growth inhibitor or stimulator—may be the consequences of control, and not a reflection of intrinsic differences in cellular or molecular character

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Coping with Uncertainty: Achieving Cognitive Resolve through Self-Reinforcing Hope

This paper, drawing upon literature from psychology and philosophy, provides a thorough analysis of hope (what it is, how we hope, etc.), as a means of furthering our understanding of cognitive resolve ¿ a sense of mental stability and clarity that guides one's decisions and actions. Substantial hope, hope with cognitive resolve, does not fluctuate as "belief waxes and wanes with incoming evidence" (Pettit, 2014, p. 155), but instead remains aligned with an adopted assumption. While this paper focuses mainly on how cognitive resolve manifests in hope, other examples of cognitive resolve are discussed, such as cognitive resolve in precaution (which parallels hope in interesting ways). The discussion about cognitive resolve, in return, allows us to further our understanding of what hope is, what experiencing hope is like, how one comes to settle on a hopeful stance, and how hope influences motivation. I argue unwavering, substantial hope is self-reinforcing and in most cases has dispositional and motivational benefits for the person hoping

GeneRHi-C : 3D GENomE Reconstruction from Hi-C data

Background: Many computational methods have been developedthat leverage the results from biological experiments (such as Hi-C)to infer the 3D organization of the genome. Formally, this is referredto as the 3D genome reconstruction problem (3D-GRP). Hi-C datais now being generated at increasingly high resolutions. As thisresolution increases, it has become computationally infeasible topredict a 3D genome organization with the majority of existingmethods. None of the existing solution methods have utilized a nonproceduralprogramming approach (such as integer programming)despite the established advantages and successful applications ofsuch approaches for predicting high-resolution 3D structures ofother biomolecules. Our objective was to develop a new solution tothe 3D-GRP that utilizes non-procedural programming to realizethe same advantages