The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction [preprint]

Background: The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. Methods and Findings: In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR \u3e99% or antigen Conclusions: This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease

Global to local genetic diversity indicators of evolutionary potential in tree species within and outside forests

There is a general trend of biodiversity loss at global, regional, national and local levels. To monitor this trend, international policy processes have created a wealth of indicators over the last two decades. However, genetic diversity indicators are regrettably absent from comprehensive bio-monitoring schemes. Here, we provide a review and an assessment of the different attempts made to provide such indicators for tree genetic diversity from the global level down to the level of the management unit. So far, no generally accepted indicators have been provided as international standards, nor tested for their possible use in practice. We suggest that indicators for monitoring genetic diversity and dynamics should be based on ecological and demographic surrogates of adaptive diversity as well as genetic markers capable of identifying genetic erosion and gene flow. A comparison of past and present genecological distributions (patterns of genetic variation of key adaptive traits in the ecological space) of selected species is a realistic way of assessing the trend of intra-specific variation, and thus provides a state indicator of tree genetic diversity also able to reflect possible pressures threatening genetic diversity. Revealing benefits of genetic diversity related to ecosystem services is complex, but current trends in plantation performance offer the possibility of an indicator of benefit. Response indicators are generally much easier to define, because recognition and even quantification of, e.g., research, education, breeding, conservation, and regulation actions and programs are relatively straightforward. Only state indicators can reveal genetic patterns and processes, which are fundamental for maintaining genetic diversity. Indirect indicators of pressure, benefit, or response should therefore not be used independently of state indicators. A coherent set of indicators covering diversity–productivity–knowledge–management based on the genecological approach is proposed for application on appropriate groups of tree species in the wild and in cultivation worldwide. These indicators realistically reflect the state, trends and potentials of the world’s tree genetic resources to support sustainable growth. The state of the genetic diversity will be based on trends in population distributions and diversity patterns for selected species. The productivity of the genetic resource of trees in current use will reflect the possible potential of mobilizing the resource further. Trends in knowledge will underpin the potential capacity for development of the resource and current management of the genetic resource itself will reveal how well we are actually doing and where improvements are required

The role of adaptive evolution of phenotypic plasticity and historical population genetic processes in purple loosestrife (Lythrum salicaria L.) invasion in North America

The introduction and spread of non-native species has become a global ecological and environmental problem. We will be able to develop a deeper understanding on the background of invasiveness from the studies of evolutionary changes that invasive species undergo from their introduction to the establishment and aggressive spread. The ultimate goal of this dissertation is to contribute a deeper knowledge on the ability of invasive species to respond to the novel environment and prevail via evolutionary changes, using purple loosestrife (Lythrum salicaria L. Lythraceae) as a model system.;The dissertation is composed of several journal papers. Chapter 1 is a general introduction and Chapter 2 is a review paper summarizing recent studies on the leading hypotheses on the mechanisms for invasiveness: EICA (Evolution of Increased Competitive Ability), the evolution of phenotypic plasticity, local adaptation, allelopathy, hybridization/polyploidization. Chapter 3 tests the evolution of phenotypic plasticity hypothesis with an empirical study on the phenotypic plasticity in native vs. invasive populations subject to experimentally manipulated water and nutrient environments. This study has been published in Ecology and expanded into a larger international collaborative project that aims to detect the genetic difference in demography and phenotypic plasticity between native and invasive populations. Essentially this is a reciprocal transplant study with multiple common gardens involving collaborators from New Jersey and Germany. The results from the Iowa common garden have been presented in Chapter 4, employing structural equation modeling approach (path analysis) to describe causal relationships between fitness and traits that contribute to fitness (fitness-related traits) considering the ontogeny of purple loosestrife. Chapter 5 is a technical note on the development of a genomic DNA extraction protocol for plants that have a large amount of secondary metabolites including polysaccharides and polyphenols. Chapter 6 is a population genetic study on the same populations used in Chapter 3, to examine the effect of major evolutionary forces on invasive populations - whether the formation of invasive populations is purely due to stochastic events such as genetic drift and migration, or instead by disruptive and/or stabilizing selection leading to locally adapted populations in the North America

Scoping current and future genetic tools, their limitations and their applications for wild fisheries management

The overarching goal of this project was to prepare a document that summarises past, present and emerging ways in which research using genetic technology can assist the Australian fishing industry to maintain productive and sustainable harvests. The project achieved the following specific objectives: 1. Documented existing and prospective biotechnologies and genetic analysis tools that are relevant to wild fisheries management, and their availability and application at a national and international level; 2. Documented the FRDC’s past and current investment in biotechnology and genetic tools used in wild fisheries management research; 3. Documented the different biotechnology and genetic tools that are being used in wild fisheries management research in Australia, and the nature and location of key research groups; 4. Described what management question each tool has been used for (e.g. stock structure, biomass estimation, product provenance, disease monitoring); 5. Identified those tools and approaches (existing and future) most likely to deliver significant advances in fisheries management; 6. Identified the potential for collaborations which could improve the focus and impact of work in this area

Sugar Bush Management in Ontario: Identification of Resilient Adaptation Strategies for a Changing Climate

The purpose of this research is to gain a better understanding of human-facilitated silvicultural, biodiversity and genetic forest management in Ontario sugar bushes through a biogeographical lens to determine the social and ecological resilience to climate change impacts. Because sugar bushes are highly managed and therefore ecologically different than unmanaged sugar maple forests, a literature review combined with primary farm-level research will help to determine best management practices and adaptation decision-making to increase the resilience of these stands. The overarching conclusion is that maple syrup producers in Ontario need to entrench climate change objectives within forest management

Developing a Decision-Making Framework for Assisted Migration: Applying this to the American Pika and Whitebark Pine

ABSTRACT Hedayat-Zadeh, Mai Kimya, Degree, May 2020 Environmental Studies Developing a Decision-Making Framework for Assisted Migration: Applying this to the American Pika and White Bark Pine This paper analyzes a novel conservation strategy: assisted migration (AM). AM is the practice of moving a highly vulnerable (i.e., endangered) species impacted by climate change and other exacerbating factors (e.g., land use), out of its historic range to a recipient site. Ideally, this site would be one where the species would have migrated, were there no barriers to dispersal or anthropogenic stressors. The paper is concerned with the readiness of the conservation community—particularly land managers—to implement a decision-making framework for AM. As such, the author reviews existing frameworks, both conceptual and statistical, and presents a step-by-step qualitative framework of her own that encompasses the ecological, legal, social and ethical dimensions of the AM strategy. The framework acknowledges that AM will likely occur in concert with other interventions of varying intensity (e.g., maintenance of habitat quality, restoration, and genetic rescue). The author acknowledges the limits of species distribution models (SDMs), a key tool to assess species vulnerability, which is the prerequisite of candidacy for AM. The author concludes that ecological theory— particularly concepts such as ‘adaptive capacity’ and the ‘evolutionary niche’—must better inform the design of models. An overview of modelling pitfalls, including the coarseness of climate data, data surveyed at differing resolutions or scales, gaps in data, the robustness of varying weighting and sampling techniques, and the need for development of community assemblage forecasts reveals the amount of work to be done, in terms of making confident assessments at finer scales into the future for this conservation strategy, which will likely be more prevalent with time. Finally, the author uses the holistic decision-making framework to assess two vulnerable species: the American pika and the whitebark pine. These case studies provide insights into the orientations of research and management communities to AM currently, and reveals the interplay of values that influence how we prioritize the survival of species, which can often be surrogates for the protection of a host of other species and environments

Scientific Workflows for Metabolic Flux Analysis

Metabolic engineering is a highly interdisciplinary research domain that interfaces biology, mathematics, computer science, and engineering. Metabolic flux analysis with carbon tracer experiments (13 C-MFA) is a particularly challenging metabolic engineering application that consists of several tightly interwoven building blocks such as modeling, simulation, and experimental design. While several general-purpose workflow solutions have emerged in recent years to support the realization of complex scientific applications, the transferability of these approaches are only partially applicable to 13C-MFA workflows. While problems in other research fields (e.g., bioinformatics) are primarily centered around scientific data processing, 13C-MFA workflows have more in common with business workflows. For instance, many bioinformatics workflows are designed to identify, compare, and annotate genomic sequences by "pipelining" them through standard tools like BLAST. Typically, the next workflow task in the pipeline can be automatically determined by the outcome of the previous step. Five computational challenges have been identified in the endeavor of conducting 13 C-MFA studies: organization of heterogeneous data, standardization of processes and the unification of tools and data, interactive workflow steering, distributed computing, and service orientation. The outcome of this thesis is a scientific workflow framework (SWF) that is custom-tailored for the specific requirements of 13 C-MFA applications. The proposed approach – namely, designing the SWF as a collection of loosely-coupled modules that are glued together with web services – alleviates the realization of 13C-MFA workflows by offering several features. By design, existing tools are integrated into the SWF using web service interfaces and foreign programming language bindings (e.g., Java or Python). Although the attributes "easy-to-use" and "general-purpose" are rarely associated with distributed computing software, the presented use cases show that the proposed Hadoop MapReduce framework eases the deployment of computationally demanding simulations on cloud and cluster computing resources. An important building block for allowing interactive researcher-driven workflows is the ability to track all data that is needed to understand and reproduce a workflow. The standardization of 13 C-MFA studies using a folder structure template and the corresponding services and web interfaces improves the exchange of information for a group of researchers. Finally, several auxiliary tools are developed in the course of this work to complement the SWF modules, i.e., ranging from simple helper scripts to visualization or data conversion programs. This solution distinguishes itself from other scientific workflow approaches by offering a system of loosely-coupled components that are flexibly arranged to match the typical requirements in the metabolic engineering domain. Being a modern and service-oriented software framework, new applications are easily composed by reusing existing components

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership