A Review of Mathematical Models for the Formation of\ud Vascular Networks

Mainly two mechanisms are involved in the formation of blood vasculature: vasculogenesis and angiogenesis. The former consists of the formation of a capillary-like network from either a dispersed or a monolayered population of endothelial cells, reproducible also in vitro by specific experimental assays. The latter consists of the sprouting of new vessels from an existing capillary or post-capillary venule. Similar phenomena are also involved in the formation of the lymphatic system through a process generally called lymphangiogenesis.\ud \ud A number of mathematical approaches have analysed these phenomena. This paper reviews the different modelling procedures, with a special emphasis on their ability to reproduce the biological system and to predict measured quantities which describe the overall processes. A comparison between the different methods is also made, highlighting their specific features

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 218, April 1981

This bibliography lists 161 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1981

AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models

Due to the large search space and conflicting objectives, drug design and discovery is a difficult problem for which new machine learning (ML) approaches are required. Here, the problem is to invent a method by which new, therapeutically useful, compounds can be discovered; and to simultaneously avoid compounds which will fail clinical trials or pass unwanted effects onto the end patient. By extending current technologies as well as adding new ones, more design criteria can be included, and more promising novel drugs can be discovered. This work advances the field of computational drug design by (1) developing MOEA-DT, a non-deep learning application for multi-objective molecular optimization, which generates new molecules with high performance in a variety of design criteria; and (2) developing SEMTL-BERT, a side effect prediction algorithm which leverages the latest ML techniques and datasets to accomplish its task. Experiments performed show that MOEA-DT either matches or outperforms other similar methods, and that SEMTL-BERT can enhance predictive ability

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

2013 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

URC Schedule and Abstract Book for the Fifth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: November 16-17, 2013Plenary Speaker: Mariel Vazquez, Associate Professor of Mathematics at San Francisco State UniversityFeatured Speaker: Andrew Liebhold, Research Entomologist for the USDA Forest Servic

The Impact of Anthropologically Motivated Human Social Networks on the Transmission Dynamics of Infectious Disease

abstract: Understanding the consequences of changes in social networks is an important an- thropological research goal. This dissertation looks at the role of data-driven social networks on infectious disease transmission and evolution. The dissertation has two projects. The first project is an examination of the effects of the superspreading phenomenon, wherein a relatively few individuals are responsible for a dispropor- tionate number of secondary cases, on the patterns of an infectious disease. The second project examines the timing of the initial introduction of tuberculosis (TB) to the human population. The results suggest that TB has a long evolutionary history with hunter-gatherers. Both of these projects demonstrate the consequences of social networks for infectious disease transmission and evolution. The introductory chapter provides a review of social network-based studies in an- thropology and epidemiology. Particular emphasis is paid to the concept and models of superspreading and why to consider it, as this is central to the discussion in chapter 2. The introductory chapter also reviews relevant epidemic mathematical modeling studies. In chapter 2, social networks are connected with superspreading events, followed by an investigation of how social networks can provide greater understanding of in- fectious disease transmission through mathematical models. Using the example of SARS, the research shows how heterogeneity in transmission rate impacts super- spreading which, in turn, can change epidemiological inference on model parameters for an epidemic. Chapter 3 uses a different mathematical model to investigate the evolution of TB in hunter-gatherers. The underlying question is the timing of the introduction of TB to the human population. Chapter 3 finds that TB’s long latent period is consistent with the evolutionary pressure which would be exerted by transmission on a hunter- igatherer social network. Evidence of a long coevolution with humans indicates an early introduction of TB to the human population. Both of the projects in this dissertation are demonstrations of the impact of var- ious characteristics and types of social networks on infectious disease transmission dynamics. The projects together force epidemiologists to think about networks and their context in nontraditional ways.Dissertation/ThesisDoctoral Dissertation Anthropology 201

USSR Space Life Sciences Digest, issue 1

The first issue of the bimonthly digest of USSR Space Life Sciences is presented. Abstracts are included for 49 Soviet periodical articles in 19 areas of aerospace medicine and space biology, published in Russian during the first quarter of 1985. Translated introductions and table of contents for nine Russian books on topics related to NASA's life science concerns are presented. Areas covered include: botany, cardiovascular and respiratory systems, cybernetics and biomedical data processing, endocrinology, gastrointestinal system, genetics, group dynamics, habitability and environmental effects, health and medicine, hematology, immunology, life support systems, man machine systems, metabolism, musculoskeletal system, neurophysiology, perception, personnel selection, psychology, radiobiology, reproductive system, and space biology. This issue concentrates on aerospace medicine and space biology