The Oyster River Culvert Analysis Project

Studies have already detected intensification of precipitation events consistent with climate change projections. Communities may have a window of opportunity to prepare, but information sufficiently quantified and localized to support adaptation programs is sparse: published literature is typically characterized by general resilience building or regional vulnerability studies. The Fourth Assessment Report of the IPCC observed that adaptation can no longer be postponed pending the effective elimination of uncertainty. Methods must be developed that manage residual uncertainty, providing community leaders with decision-support information sufficient for implementing infrastructure adaptation programs. This study developed a local-scale and actionable protocol for maintaining historical risk levels for communities facing significant impacts from climate change and population growth. For a coastal watershed, the study assessed the capacity of the present stormwater infrastructure capacity for conveying expected peak flow resulting from climate change and population growth. The project transferred coupled-climate model projections to the culvert system, in a form understandable to planners, resource managers and decision-makers; applied standard civil engineering methods to reverse-engineer culverts to determine existing and required capacities; modeled the potential for LID methods to manage peak flow in lieu of, or combination with, drainage system upsizing; and estimated replacement costs using local and national construction cost data. The mid-21st century, most likely 25-year, 24-hour precipitation is estimated to be 35% greater than the TP-40 precipitation for the SRES A1b trajectory, and 64% greater than the TP-40 value for the SRES A1fi trajectory. 5% of culverts are already undersized for the TP-40 event to which they should have been designed. Under the most likely A1b trajectory, an additional 12% of culverts likely will be undersized, while under the most likely A1fi scenario, an additional 19% likely will be undersized. These conditions place people and property at greater risk than that historically acceptable from the TP-4025-year design storm. This risk level may be maintained by a long-term upgrade program, utilizing existing strategies to manage uncertainty and costs. At the upper-95% confidence limit for the A1fi 25-year event, 65% of culverts are adequately sized, and building the remaining 35%, and planned, culverts to thrice the cross-sectional area specified from TP-40 should provide adequate capacity through this event. Realizable LID methods can mitigate significant impacts from climate change and population growth, however effectiveness is limited for the more pessimistic climate change projections. Results indicate that uncertainty in coupled-climate model projections is not an impediment to adaptation. This study makes a significant contribution toward the generation of reliable and specific estimates of impacts from climate change, in support of programs to adapt civil infrastructures. This study promotes a solution to today\u27s arguably most significant challenge in civil infrastructure adaptation: translating the extensive corpus of adaptation theory and regional-scale impacts analyses into localscale action

Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data

Purpose This study explores ongoing research into self-mapped learning pathways that students utilize to move through a course when given two modalities to choose from: one that is instructor-led and one that is student-directed. Design/methodology/approach Process mining analysis was utilized to examine and cluster clickstream data from an online college-level History course designed with dual modality choices. This paper examines some of the results from different approaches to clustering the available data. Findings By examining how often students interacted with others, whether they were more internal or external facing with their pathway choices, and whether or not they completed a learning pathway, this study identified five general tactics from the data: Individualistic Internal; Non-completing Internal; Completing, Interactive Internal; Completing, Interactive, and Reflective and Completing External. Further analysis of when students used each tactic led to the identification of four different strategies that learners utilized during class sessions. Practical implications The results of this analysis could potentially lead to the creation of customizable design models that can assist learners as they navigate modality choices in learner-centered or less-structured learning design methodologies. Originality/value Few courses are designed to give the learners the options to follow the instructor or create their own learning pathway. Knowing how to identify what choices a learner might take in these scenarios is even less explored. Preliminary data for this paper was originally presented as a poster session at the Learning Analytics and Knowledge conference in 2019

Copy number variation analysis in the context of electronic medical records and large-scale genomics consortium efforts

The goal of this paper is to review recent research on copy number variations (CNVs) and their association with complex and rare diseases. In the latter part of this paper, we focus on how large biorepositories such as the electronic medical record and genomics (eMERGE) consortium may be best leveraged to systematically mine for potentially pathogenic CNVs, and we end with a discussion of how such variants might be reported back for inclusion in electronic medical records as part of medical history

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants

Diversity and evolution of potato mop-top virus

Nearly complete sequences of RNA-CP and 3'-proximal RNA-TGB were determined for 43 samples of potato mop-top virus (PMTV) originating from potato tubers and field soil from Sweden, Denmark and the USA. The results showed limited diversity and no strict geographical grouping, suggesting only a few original introductions of PMTV from the Andes. Two distinguishable types of RNA-CP and RNA-TGB were found in the samples, but no specific combination of them correlated with spraing symptoms in tubers. Lack of positive selection in the coding sequences indicates that there is no specific molecular adaptation of PMTV to new vectors or hosts

Genome-wide association Scan of dental caries in the permanent dentition

Background: Over 90% of adults aged 20 years or older with permanent teeth have suffered from dental caries leading to pain, infection, or even tooth loss. Although caries prevalence has decreased over the past decade, there are still about 23% of dentate adults who have untreated carious lesions in the US. Dental caries is a complex disorder affected by both individual susceptibility and environmental factors. Approximately 35-55% of caries phenotypic variation in the permanent dentition is attributable to genes, though few specific caries genes have been identified. Therefore, we conducted the first genome-wide association study (GWAS) to identify genes affecting susceptibility to caries in adults. Methods: Five independent cohorts were included in this study, totaling more than 7000 participants. For each participant, dental caries was assessed and genetic markers (single nucleotide polymorphisms, SNPs) were genotyped or imputed across the entire genome. Due to the heterogeneity among the five cohorts regarding age, genotyping platform, quality of dental caries assessment, and study design, we first conducted genome-wide association (GWA) analyses on each of the five independent cohorts separately. We then performed three meta-analyses to combine results for: (i) the comparatively younger, Appalachian cohorts (N = 1483) with well-assessed caries phenotype, (ii) the comparatively older, non-Appalachian cohorts (N = 5960) with inferior caries phenotypes, and (iii) all five cohorts (N = 7443). Top ranking genetic loci within and across meta-analyses were scrutinized for biologically plausible roles on caries. Results: Different sets of genes were nominated across the three meta-analyses, especially between the younger and older age cohorts. In general, we identified several suggestive loci (P-value ≤ 10E-05) within or near genes with plausible biological roles for dental caries, including RPS6KA2 and PTK2B, involved in p38-depenedent MAPK signaling, and RHOU and FZD1, involved in the Wnt signaling cascade. Both of these pathways have been implicated in dental caries. ADMTS3 and ISL1 are involved in tooth development, and TLR2 is involved in immune response to oral pathogens. Conclusions: As the first GWAS for dental caries in adults, this study nominated several novel caries genes for future study, which may lead to better understanding of cariogenesis, and ultimately, to improved disease predictions, prevention, and/or treatment

Molecular and Physiological Properties Associated with Zebra Complex Disease in Potatoes and Its Relation with Candidatus Liberibacter Contents in Psyllid Vectors

Zebra complex (ZC) disease on potatoes is associated with Candidatus Liberibacter solanacearum (CLs), an α-proteobacterium that resides in the plant phloem and is transmitted by the potato psyllid Bactericera cockerelli (Šulc). The name ZC originates from the brown striping in fried chips of infected tubers, but the whole plants also exhibit a variety of morphological features and symptoms for which the physiological or molecular basis are not understood. We determined that compared to healthy plants, stems of ZC-plants accumulate starch and more than three-fold total protein, including gene expression regulatory factors (e.g. cyclophilin) and tuber storage proteins (e.g., patatins), indicating that ZC-affected stems are reprogrammed to exhibit tuber-like physiological properties. Furthermore, the total phenolic content in ZC potato stems was elevated two-fold, and amounts of polyphenol oxidase enzyme were also high, both serving to explain the ZC-hallmark rapid brown discoloration of air-exposed damaged tissue. Newly developed quantitative and/or conventional PCR demonstrated that the percentage of psyllids in laboratory colonies containing detectable levels of CLs and its titer could fluctuate over time with effects on colony prolificacy, but presumed reproduction-associated primary endosymbiont levels remained stable. Potato plants exposed in the laboratory to psyllid populations with relatively low-CLs content survived while exposure of plants to high-CLs psyllids rapidly culminated in a lethal collapse. In conclusion, we identified plant physiological biomarkers associated with the presence of ZC and/or CLs in the vegetative potato plant tissue and determined that the titer of CLs in the psyllid population directly affects the rate of disease development in plants

A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores(PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.Results: The integrated polygenic prediction improved the average performance (pseudo-R2)for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity","type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension",and "sleep apnea" reaching phenome-wide significance.Conclusions: Our study has expanded the methodological utility of PRS in patient stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICDCM, International Classification of Diseases, Clinical Modification; LD, linkage disequilibrium;MA, multi-ancestry; MAF, minor allele frequency; NIH, National Institutes of Health; PCA,principal component analysis; PheWAS, phenome-wide association study; PCOS, polycysticovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk sc

Lessons learned and recommendations for data coordination in collaborative research: The CSER consortium experience

Integrating data across heterogeneous research environments is a key challenge in multi-site, collaborative research projects. While it is important to allow for natural variation in data collection protocols across research sites, it is also important to achieve interoperability between datasets in order to reap the full benefits of collaborative work. However, there are few standards to guide the data coordination process from project conception to completion. In this paper, we describe the experiences of the Clinical Sequence Evidence-Generating Research (CSER) consortium Data Coordinating Center (DCC), which coordinated harmonized survey and genomic sequencing data from seven clinical research sites from 2020 to 2022. Using input from multiple consortium working groups and from CSER leadership, we first identify 14 lessons learned from CSER in the categories of communication, harmonization, informatics, compliance, and analytics. We then distill these lessons learned into 11 recommendations for future research consortia in the areas of planning, communication, informatics, and analytics. We recommend that planning and budgeting for data coordination activities occur as early as possible during consortium conceptualization and development to minimize downstream complications. We also find that clear, reciprocal, and continuous communication between consortium stakeholders and the DCC is equally important to maintaining a secure and centralized informatics ecosystem for pooling data. Finally, we discuss the importance of actively interrogating current approaches to data governance, particularly for research studies that straddle the research-clinical divide

Fine mapping of a linkage peak with integration of lipid traits identifies novel coronary artery disease genes on chromosome 5

Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas)