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

Electronic medical records and genomics (eMERGE) network exploration in cataract: Several new potential susceptibility loci

Purpose Cataract is the leading cause of blindness in the world, and in the United States accounts for approximately 60% of Medicare costs related to vision. The purpose of this study was to identify genetic markers for age-related cataract through a genome-wide association study (GWAS). Methods: In the electronic medical records and genomics (eMERGE) network, we ran an electronic phenotyping algorithm on individuals in each of five sites with electronic medical records linked to DNA biobanks. We performed a GWAS using 530,101 SNPs from the Illumina 660W-Quad in a total of 7,397 individuals (5,503 cases and 1,894 controls). We also performed an age-at-diagnosis case-only analysis. Results: We identified several statistically significant associations with age-related cataract (45 SNPs) as well as age at diagnosis (44 SNPs). The 45 SNPs associated with cataract at p<1×10−5 are in several interesting genes, including ALDOB, MAP3K1, and MEF2C. All have potential biologic relationships with cataracts. Conclusions: This is the first genome-wide association study of age-related cataract, and several regions of interest have been identified. The eMERGE network has pioneered the exploration of genomic associations in biobanks linked to electronic health records, and this study is another example of the utility of such resources. Explorations of age-related cataract including validation and replication of the association results identified herein are needed in future studies

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

Emerging New Crop Pests: Ecological Modelling and Analysis of the South American Potato Psyllid Russelliana solanicola (Hemiptera: Psylloidea) and Its Wild Relatives

© 2017 Syfert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Assessing Potato Psyllid Haplotypes in Potato Crops in the Pacific Northwestern United States

The potato psyllid, Bactericera cockerelli (Šulc), is a vector of the bacterium ‘Candidatus Liberibacter solanacearum’ (Lso) that has been linked to the economically devastating zebra chip disease of potato. To date, four haplotypes of the potato psyllid have been identified and include Central, Western, Northwestern, and Southwestern haplotypes. Zebra chip was reported in potato crops in the Pacific Northwestern United States for the first time in 2011, and the Lso-infected psyllids collected from zebra chip-affected fields were identified as the Western haplotype. Additional studies have reported a mix of the Western and Northwestern psyllid haplotypes in the Pacific Northwest. The present study further examined psyllid population dynamics over the duration of the 2012 potato season in the Pacific Northwest by haplotype analysis of 864 potato psyllids collected from potato fields in Washington, Oregon, and Idaho. In the Yakima Valley of Washington and the lower Columbia Basin of Washington and Oregon, the Northwestern haplotype was predominant (78%), and was detected earlier in the season than the Western haplotype. Interestingly, in south-central Idaho, all four psyllid haplotypes were identified, but the predominant haplotype was the Western haplotype (77%). Here, Northwestern psyllids were detected early in the season from June to mid-August, whereas Central psyllidswere detected in late July and thereafter. These results suggest that haplotype composition of psyllid populations in potato fields throughout the 2012 growing season in south-central Idaho differed greatly from those in Washington and Oregon. Additionally, all psyllids were analyzed for the presence of Lso, and no Lso-positive psyllids were found in Washington and Oregon, whereas Lso-positive psyllids were found in south-central Idaho. These Lso-positive psyllids consisted of the Western, Northwestern, and Central haplotypes

Angiopoietin-Like4 Is a Novel Marker of COVID-19 Severity

IMPORTANCE: Vascular dysfunction and capillary leak are common in critically ill COVID-19 patients, but identification of endothelial pathways involved in COVID-19 pathogenesis has been limited. Angiopoietin-like 4 (ANGPTL4) is a protein secreted in response to hypoxic and nutrient-poor conditions that has a variety of biological effects including vascular injury and capillary leak. OBJECTIVES: To assess the role of ANGPTL4 in COVID-19-related outcomes. DESIGN SETTING AND PARTICIPANTS: Two hundred twenty-five COVID-19 ICU patients were enrolled from April 2020 to May 2021 in a prospective, multicenter cohort study from three different medical centers, University of Washington, University of Southern California and New York University. MAIN OUTCOMES AND MEASURES: Plasma ANGPTL4 was measured on days 1, 7, and 14 after ICU admission. We used previously published tissue proteomic data and lung single nucleus RNA (snRNA) sequencing data from specimens collected from COVID-19 patients to determine the tissues and cells that produce ANGPTL4. RESULTS: Higher plasma ANGPTL4 concentrations were significantly associated with worse hospital mortality (adjusted odds ratio per log CONCLUSIONS AND RELEVANCE: ANGPTL4 is expressed in pulmonary epithelial cells and fibroblasts and is associated with clinical prognosis in critically ill COVID-19 patients

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

Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

Background The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication

Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

© 2015 De et al. Background: Despite heritability estimates of 40-70 for obesity, less than 2 of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Methods: Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. Results: We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. Conclusion: This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.National Institutes of Health; National Heart, Lung and Blood Institute; National Heart, Lung and Blood Institute; Netherlands Heart Foundation; NHGR