Development of a hierarchical model for predicting microbiological contamination of private groundwater supplies in a geologically heterogeneous region

Private groundwater sources in the Republic of Ireland provide drinking water to an estimated 750,000 people or 16% of the national population. Consumers of untreated groundwater are at increased risk of infection from pathogenic microorganisms. However, given the volume of private wells in operation, remediation or even quantification of public risk is both costly and time consuming. In this study, a hierarchical logistic regression model was developed to ‘predict’ contamination with E. coli based on the results of groundwater quality analyses of private wells (n = 132) during the period of September 2011 to November 2012. Assessment of potential microbial contamination risk factors were categorised into three groups: Intrinsic (environmental factors), Specific (local features) and Infrastructural (groundwater source characteristics) which included a total of 15 variables. Overall, 51.4% of wells tested positive for E. coli during the study period with univariate analysis indicating that 11 of the 15 assessed risk factors, including local bedrock type, local subsoil type, septic tank reliance, 5 day antecedent precipitation and temperature, along with well type and depth, were all significantly associated with E. coli presence (p \u3c 0.05). Hierarchical logistic regression was used to develop a private well susceptibility model with the final model containing 8 of the 11 associated variables. The model was shown to be highly efficient; correctly classifying the presence of E. coli in 94.2% of cases, and the absence of E. coli in 84.7% of cases. Model validation was performed using an external data set (n = 32) and it was shown that the model has promising accuracy with 90% of positive E. coli cases correctly predicted. The developed model represents a risk assessment and management tool that may be used to develop effective water-quality management strategies to minimize public health risks both in Ireland and abroad

Cropland Carbon Uptake Delayed and Reduced by 2019 Midwest Floods

While large‐scale floods directly impact human lives and infrastructures, they also profoundly impact agricultural productivity. New satellite observations of vegetation activity and atmospheric CO₂ offer the opportunity to quantify the effects of such extreme events on cropland carbon sequestration. Widespread flooding during spring and early summer 2019 induced conditions that delayed crop planting across the U.S. Midwest. As a result, satellite observations of solar‐induced chlorophyll fluorescence from TROPOspheric Monitoring Instrument and Orbiting Carbon Observatory reveal a 16‐day shift in the seasonal cycle of photosynthesis relative to 2018, along with a 15% lower peak value. We estimate a reduction of 0.21 PgC in cropland gross primary productivity in June and July, partially compensated in August and September (+0.14 PgC). The extension of the 2019 growing season into late September is likely to have benefited from increased water availability and late‐season temperature. Ultimately, this change is predicted to reduce the crop productivity in the Midwest Corn/Soy belt by ~15% compared to 2018. Using an atmospheric transport model, we show that a decline of ~0.1 PgC in the net carbon uptake during June and July is consistent with observed CO₂ enhancements of up to 10 ppm in the midday boundary layer from Atmospheric Carbon and Transport‐America aircraft and over 3 ppm in column‐averaged dry‐air mole fractions from Orbiting Carbon Observatory. This study quantifies the impact of floods on cropland productivity and demonstrates the potential of combining solar‐induced chlorophyll fluorescence with atmospheric CO₂ observations to monitor regional carbon flux anomalies

Model Lessons to Use With The Student Atlas of Oregon

Model lessons for teachers to use with The Student Atlas of Oregon.https://pdxscholar.library.pdx.edu/geographyed_instructional/1001/thumbnail.jp

Systematic discovery of unannotated genes in 11 yeast species using a database of orthologous genomic segments

<p>Abstract</p> <p>Background</p> <p>In standard BLAST searches, no information other than the sequences of the query and the database entries is considered. However, in situations where two genes from different species have only borderline similarity in a BLAST search, the discovery that the genes are located within a region of conserved gene order (synteny) can provide additional evidence that they are orthologs. Thus, for interpreting borderline search results, it would be useful to know whether the syntenic context of a database hit is similar to that of the query. This principle has often been used in investigations of particular genes or genomic regions, but to our knowledge it has never been implemented systematically.</p> <p>Results</p> <p>We made use of the synteny information contained in the Yeast Gene Order Browser database for 11 yeast species to carry out a systematic search for protein-coding genes that were overlooked in the original annotations of one or more yeast genomes but which are syntenic with their orthologs. Such genes tend to have been overlooked because they are short, highly divergent, or contain introns. The key features of our software - called SearchDOGS - are that the database entries are classified into sets of genomic segments that are already known to be orthologous, and that very weak BLAST hits are retained for further analysis if their genomic location is similar to that of the query. Using SearchDOGS we identified 595 additional protein-coding genes among the 11 yeast species, including two new genes in <it>Saccharomyces cerevisiae</it>. We found additional genes for the mating pheromone a-factor in six species including <it>Kluyveromyces lactis</it>.</p> <p>Conclusions</p> <p>SearchDOGS has proven highly successful for identifying overlooked genes in the yeast genomes. We anticipate that our approach can be adapted for study of further groups of species, such as bacterial genomes. More generally, the concept of doing sequence similarity searches against databases to which external information has been added may prove useful in other settings.</p

Elusive Origins of the Extra Genes in Aspergillus oryzae

The genome sequence of Aspergillus oryzae revealed unexpectedly that this species has approximately 20% more genes than its congeneric species A. nidulans and A. fumigatus. Where did these extra genes come from? Here, we evaluate several possible causes of the elevated gene number. Many gene families are expanded in A. oryzae relative to A. nidulans and A. fumigatus, but we find no evidence of ancient whole-genome duplication or other segmental duplications, either in A. oryzae or in the common ancestor of the genus Aspergillus. We show that the presence of divergent pairs of paralogs is a feature peculiar to A. oryzae and is not shared with A. nidulans or A. fumigatus. In phylogenetic trees that include paralog pairs from A. oryzae, we frequently find that one of the genes in a pair from A. oryzae has the expected orthologous relationship with A. nidulans, A. fumigatus and other species in the subphylum Eurotiomycetes, whereas the other A. oryzae gene falls outside this clade but still within the Ascomycota. We identified 456 such gene pairs in A. oryzae. Further phylogenetic analysis did not however indicate a single consistent evolutionary origin for the divergent members of these pairs. Approximately one-third of them showed phylogenies that are suggestive of horizontal gene transfer (HGT) from Sordariomycete species, and these genes are closer together in the A. oryzae genome than expected by chance, but no unique Sordariomycete donor species was identifiable. The postulated HGTs from Sordariomycetes still leave the majority of extra A. oryzae genes unaccounted for. One possible explanation for our observations is that A. oryzae might have been the recipient of many separate HGT events from diverse donors

High-Frequency Dynamics of Ocean pH: A Multi-Ecosystem Comparison

The effect of Ocean Acidification (OA) on marine biota is quasi-predictable at best. While perturbation studies, in the form of incubations under elevated pCO2, reveal sensitivities and responses of individual species, one missing link in the OA story results from a chronic lack of pH data specific to a given species' natural habitat. Here, we present a compilation of continuous, high-resolution time series of upper ocean pH, collected using autonomous sensors, over a variety of ecosystems ranging from polar to tropical, open-ocean to coastal, kelp forest to coral reef. These observations reveal a continuum of month-long pH variability with standard deviations from 0.004 to 0.277 and ranges spanning 0.024 to 1.430 pH units. The nature of the observed variability was also highly site-dependent, with characteristic diel, semi-diurnal, and stochastic patterns of varying amplitudes. These biome-specific pH signatures disclose current levels of exposure to both high and low dissolved CO2, often demonstrating that resident organisms are already experiencing pH regimes that are not predicted until 2100. Our data provide a first step toward crystallizing the biophysical link between environmental history of pH exposure and physiological resilience of marine organisms to fluctuations in seawater CO2. Knowledge of this spatial and temporal variation in seawater chemistry allows us to improve the design of OA experiments: we can test organisms with a priori expectations of their tolerance guardrails, based on their natural range of exposure. Such hypothesis-testing will provide a deeper understanding of the effects of OA. Both intuitively simple to understand and powerfully informative, these and similar comparative time series can help guide management efforts to identify areas of marine habitat that can serve as refugia to acidification as well as areas that are particularly vulnerable to future ocean change

Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information

Funding Information: This work was supported by the National Institute of Mental Health / U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium ), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience . Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. Funding Information: MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was supported by the National Institute of Mental Health/ U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience. Statistical analyses were carried out on the LISA/Genetic Cluster Computer (https://userinfo.surfsara.nl/systems/lisa) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. This material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting true views of the U.S. Department of the Army or the Department of Defense. We thank the investigators who comprise the PGC-PTSD working group and especially the more than 206,000 research participants worldwide who shared their life experiences and biological samples with PGC-PTSD investigators. We thank Mark Zervas for his critical input. Full acknowledgments are in Supplement 1. MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc. RallyPoint Networks, Inc. Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled ?Genotype-guided dosing of opioid agonists,? filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological PsychiatryBackground: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.publishersversionpublishe

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care