Multi-faceted Structure-Activity Relationship Analysis Using Graphical Representations

A core focus in medicinal chemistry is the interpretation of structure-activity relationships (SARs) of small molecules. SAR analysis is typically carried out on a case-by-case basis for compound sets that share activity against a given target. Although SAR investigations are not a priori dependent on computational approaches, limitations imposed by steady rise in activity information have necessitated the use of such methodologies. Moreover, understanding SARs in multi-target space is extremely difficult. Conceptually different computational approaches are reported in this thesis for graphical SAR analysis in single- as well as multi-target space. Activity landscape models are often used to describe the underlying SAR characteristics of compound sets. Theoretical activity landscapes that are reminiscent of topological maps intuitively represent distributions of pair-wise similarity and potency difference information as three-dimensional surfaces. These models provide easy access to identification of various SAR features. Therefore, such landscapes for actual data sets are generated and compared with graph-based representations. Existing graphical data structures are adapted to include mechanism of action information for receptor ligands to facilitate simultaneous SAR and mechanism-related analyses with the objective of identifying structural modifications responsible for switching molecular mechanisms of action. Typically, SAR analysis focuses on systematic pair-wise relationships of compound similarity and potency differences. Therefore, an approach is reported to calculate SAR feature probabilities on the basis of these pair-wise relationships for individual compounds in a ligand set. The consequent expansion of feature categories improves the analysis of local SAR environments. Graphical representations are designed to avoid a dependence on preconceived SAR models. Such representations are suitable for systematic large-scale SAR exploration. Methods for the navigation of SARs in multi-target space using simple and interpretable data structures are introduced. In summary, multi-faceted SAR analysis aided by computational means forms the primary objective of this dissertation

Evaluation of Current Technologies for Training, Web Apps, and New Technologies

This report details the activities conducted to assess the feasibility of using new technology tools for safety training. Utilizing established research studies, risk frameworks, and vendor quotations, we compared the different attributes of training methods such as Traditional Training (classroom/presentations), LMS (Learning Management System) based gamification, Computer Simulation, Virtual Reality (VR), and Augmented Reality (AR). The anticipated benefits include improved training program development, higher interactivity and long-term retention, and the chance to reduce work zone risk. The project was divided in three phases, and the following are our four key takeaways. (1) Quality of Safety Training: Benchmarking training practices provided strong evidence that participative programs, such as role plays, demonstrations of safety devices, and risk mapping are some of the best practices. Additionally, training engineers on work zone design, auditing, and recording safe work zones can influence project attributes, such as the length and duration of work zone. Including all these aspects during the project planning phase has a greater chance of influencing work zone safety. (2) Effectiveness of New Technology Tools: Our vendor outreach project phase allowed us to determine the different attributes in training course development and customer experience using new technology tools. Established research studies provided significant support to our hypothesis that new technology tools are more effective and interactive compared to traditional learning. (3) Risk-Based Approach to Training: Analyzing the risk index for work zone attributes indicate the degree of risk that a worker faces while performing a task characterizing those attributes. We concluded that implementation of new technology tools should be planned based on this risk index and optimization model. This will ensure better worker performance and perception of the course content in alignment with the severity of that work attribute. (4) Optimizing Selection of Training Tools for Tasks: We provide an optimization model to choose the optimal mix of training tools to attain the desired level of risk reduction. The tool is spreadsheet-based and shows the benefit of using a portfolio of modules across training tools, each targeted at attaining the desired risk reduction by attribute for a task. By using the risk reduction due to training tools from the literature, the cost data from vendors and task characteristics, this tool can enable INDOT managers to manage risk cost efficiently

Understanding Intracellular Biology to Improve mRNA Delivery by Lipid Nanoparticles

Poor understanding of intracellular delivery and targeting hinders development of nucleic acid‐based therapeutics transported by nanoparticles. Utilizing a siRNA‐targeting and small molecule profiling approach with advanced imaging and machine learning biological insights is generated into the mechanism of lipid nanoparticle (MC3‐LNP) delivery of mRNA. This workflow is termed Advanced Cellular and Endocytic profiling for Intracellular Delivery (ACE‐ID). A cell‐based imaging assay and perturbation of 178 targets relevant to intracellular trafficking is used to identify corresponding effects on functional mRNA delivery. Targets improving delivery are analyzed by extracting data‐rich phenotypic fingerprints from images using advanced image analysis algorithms. Machine learning is used to determine key features correlating with enhanced delivery, identifying fluid‐phase endocytosis as a productive cellular entry route. With this new knowledge, MC3‐LNP is re‐engineered to target macropinocytosis, and this significantly improves mRNA delivery in vitro and in vivo. The ACE‐ID approach can be broadly applicable for optimizing nanomedicine‐based intracellular delivery systems and has the potential to accelerate the development of delivery systems for nucleic acid‐based therapeutics

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

A narrative review of health research capacity strengthening in low and middle-income countries: lessons for conflict-affected areas

Abstract Conducting health research in conflict-affected areas and other complex environments is difficult, yet vital. However, the capacity to undertake such research is often limited and with little translation into practice, particularly in poorer countries. There is therefore a need to strengthen health research capacity in conflict-affected countries and regions. In this narrative review, we draw together evidence from low and middle-income countries to highlight challenges to research capacity strengthening in conflict, as well as examples of good practice. We find that authorship trends in health research indicate global imbalances in research capacity, with implications for the type and priorities of research produced, equity within epistemic communities and the development of sustainable research capacity in low and middle-income countries. Yet, there is little evidence on what constitutes effective health research capacity strengthening in conflict-affected areas. There is more evidence on health research capacity strengthening in general, from which several key enablers emerge: adequate and sustained financing; effective stewardship and equitable research partnerships; mentorship of researchers of all levels; and effective linkages of research to policy and practice. Strengthening health research capacity in conflict-affected areas needs to occur at multiple levels to ensure sustainability and equity. Capacity strengthening interventions need to take into consideration the dynamics of conflict, power dynamics within research collaborations, the potential impact of technology, and the wider political environment in which they take place

Urban Mobility and Urban Form: Understanding Mobility Patterns of the New York City Taxi System Using Latent Factor Models

Understanding human movement through urban spaces can offer actionable insight for various policy outcomes and is increasingly critical as the future of cities see heightened demand for urban space. This research develops a latent factor model to analyze how taxi travel reflects New York City urban relationships, and investigate the association between racial and socioeconomic similarities and affinity between urban spaces. Urban mobility was modeled through interpretable latent parameters including: travel distance cost, desirability of destinations, and affinity between locations. The model was optimized using Stochastic Gradient Descent, validated through parameter recovery analysis on a constructed synthetic data set, and ultimately fit to almost 300 million trips from the New York City Taxi System. The learned model parameters represent a novel data source of latent factors of human travel which were analyzed alongside New York City geospatial data and US Census Data. The desirability and popularity of New York urban spaces were found to be distinct, and several areas within the exterior regions of The Bronx and Queens were identified as potentially underserved by current taxi supply. In addition, correlations between the affinity of two locations and similarities in socioeconomic and racial indicators were strongest for travel between populations that were the least-educated, lowest-income, and had the highest portions of African American and Hispanic residents; little evidence of association between social indicators and location affinity was found for other populations. Overall, similarities in racial composition, income level, and population density were the most important in the model, after accounting for proximity and desirability, to understand the strength of connection between urban spaces in New York City

Urban Mobility and Urban Form: Understanding Mobility Patterns of the New York City Taxi System Using Latent Factor Models

Understanding human movement through urban spaces can offer actionable insight for various policy outcomes and is increasingly critical as the future of cities see heightened demand for urban space. This research develops a latent factor model to analyze how taxi travel reflects New York City urban relationships, and investigate the association between racial and socioeconomic similarities and affinity between urban spaces. Urban mobility was modeled through interpretable latent parameters including: travel distance cost, desirability of destinations, and affinity between locations. The model was optimized using Stochastic Gradient Descent, validated through parameter recovery analysis on a constructed synthetic data set, and ultimately fit to almost 300 million trips from the New York City Taxi System. The learned model parameters represent a novel data source of latent factors of human travel which were analyzed alongside New York City geospatial data and US Census Data. The desirability and popularity of New York urban spaces were found to be distinct, and several areas within the exterior regions of The Bronx and Queens were identified as potentially underserved by current taxi supply. In addition, correlations between the affinity of two locations and similarities in socioeconomic and racial indicators were strongest for travel between populations that were the least-educated, lowest-income, and had the highest portions of African American and Hispanic residents; little evidence of association between social indicators and location affinity was found for other populations. Overall, similarities in racial composition, income level, and population density were the most important in the model, after accounting for proximity and desirability, to understand the strength of connection between urban spaces in New York City

Urban Mobility and Urban Form: Understanding Mobility Patterns of the New York City Taxi System Using Latent Factor Models

Understanding human movement through urban spaces can offer actionable insight for various policy outcomes and is increasingly critical as the future of cities see heightened demand for urban space. This research develops a latent factor model to analyze how taxi travel reflects New York City urban relationships, and investigate the association between racial and socioeconomic similarities and affinity between urban spaces. Urban mobility was modeled through interpretable latent parameters including: travel distance cost, desirability of destinations, and affinity between locations. The model was optimized using Stochastic Gradient Descent, validated through parameter recovery analysis on a constructed synthetic data set, and ultimately fit to almost 300 million trips from the New York City Taxi System. The learned model parameters represent a novel data source of latent factors of human travel which were analyzed alongside New York City geospatial data and US Census Data. The desirability and popularity of New York urban spaces were found to be distinct, and several areas within the exterior regions of The Bronx and Queens were identified as potentially underserved by current taxi supply. In addition, correlations between the affinity of two locations and similarities in socioeconomic and racial indicators were strongest for travel between populations that were the least-educated, lowest-income, and had the highest portions of African American and Hispanic residents; little evidence of association between social indicators and location affinity was found for other populations. Overall, similarities in racial composition, income level, and population density were the most important in the model, after accounting for proximity and desirability, to understand the strength of connection between urban spaces in New York City

“My soul was singing at a work apart”: Female Victorian poets achieving voice

For the last few decades, critics\u27 efforts have been concentrated on rediscovering female poets long excluded from the canon, and in the case of Victorian poets, studying each woman individually. This project attempts to synthesize these readings and biographies in order to consider the work of female Victorian poets in terms of the movements their work displays, balanced against the more familiar background of male Victorian poetics. I have chosen to explore four strategies by which female Victorian poets—including Augusta Webster, Michael Field, and Christina Rossetti—not only achieved their poetic voices, but depicted themselves doing so. Analyzing a few exemplary poems per chapter, I track the uses these poets made of their literary lineage, their function as sisters, their desire to avail themselves of the mask of the renegade, and their understanding of the silences death seems to demand. I have organized these trends so that the project moves from community to isolation, and it ends with a discussion of the complex work that stakes claim to various genres as it progresses and, moreover, unites all of these methods: Elizabeth Barrett Browning\u27s Aurora Leigh