7 research outputs found

    Finishing the euchromatic sequence of the human genome

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    The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

    Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

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    IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

    A morphological trait-based approach to environmental assessment models using diatoms

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    Diatom assemblages are excellent indicators for environmental monitoring. However, enumerating diatoms using fine-level taxonomy takes considerable effort, which must be undertaken by specialist taxonomists. One alternative is to enumerate assemblages using morphological traits. In this study, we compared the accuracy of models using 20 morphological traits with those using species assemblages to infer lake water pH, salinity, depth, and total phosphorus concentrations in four data sets, each comprising over 200 lakes. Assemblages aggregated by trait combinations were used to predict environmental variables via weighted averaging regressions, and richness of trait combinations was regressed against the environmental variables. Trait-based weighted averaging regressions showed slightly lower accuracy than species-level analyses and higher accuracy than analyses at the family and sometimes genus level. Richness of trait combinations showed relationships with pH, salinity, and lake depth that were marginally stronger than relationships using species richness. Although species-level analyses are the best approach when time and budgets allow, we suggest that trait combinations could provide an alternative method for water quality assessment programs, where funds do not allow the use of specialist taxonomists or where diatoms are being used as part of a multi-indicator analysis

    Optimizing taxonomic resolution and sampling effort to design cost-effective ecological models for environmental assessment

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    Predictive models relating ecological assemblages to environmental conditions are widely used in environmental impact assessment and biomonitoring. Such models are often parameterized using comprehensive ecological sampling and taxonomic identification efforts. Limited resources mean that expensive sampling and analytical procedures should be planned to maximize information gain and minimize unnecessary expense. However, there has been little consideration of cost-effectiveness in parameterizing predictive models using ecological assemblages and no explicit consideration of cost-effectiveness in balancing investment in the crucial aspects of sample size and taxonomic resolution. Using lacustrine diatom (Bacillariophyceae) assemblages from four large-scale (c. 77 000-1·3 million km2) data sets containing between 207 and 493 lakes, we address the following questions: (1) how does taxonomic resolution affect information content; (2) how does sample size affect information content for different taxonomic resolutions; and (3) what are the most cost-effective strategies for constructing environmental assessment models using diatom assemblages across a range of budgets? We use weighted averaging regression models for pH, phosphorus, salinity and lake depth and realistic data collection costs to examine the relationship between cost and model information content (R2 and root mean squared error of prediction). For diatom-based models, finer taxonomic resolutions almost always provide more cost-effective information content than collecting more samples, with (morpho)species being the most appropriate taxonomic resolution for nearly all budget scenarios. Information content exhibits an asymptotic relationship with sample size and budget, with greatest information gain during initial sample size increases, and little gain beyond c. 100 samples. Smaller sample sizes can also achieve surprising predictive power in some cases, suggesting low-cost regional models may be achievable. However, caution is necessary in such an approach, because spatial dependencies in predictions may be missed and analogues with predicted assemblages may be poor. Synthesis and applications. We demonstrate the utility of explicitly considering cost estimates to determine optimal sampling effort and taxonomic resolution for ecological assemblage models. For large, regional biomonitoring programmes, cost-effective sampling could save millions of dollars. Our framework for determining optimal trade-offs in ecological assemblage models is easily adaptable to other taxa and analytical techniques used in biomonitoring and environmental assessment

    Age of first infection across a range of parasite taxa in a wild mammalian population

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    Newborn mammals have an immature immune system that cannot sufficiently protect them against infectious diseases. However, variation in the effectiveness of maternal immunity against different parasites may couple with temporal trends in parasite exposure to influence disparities in the timing of infection risk. Determining the relationship between age and infection risk is critical in identifying the portion of a host population that contributes to parasite dynamics, as well as the parasites that regulate host recruitment. However, there are no data directly identifying timing of first infection among parasites in wildlife. Here, we took advantage of a longitudinal dataset, tracking infection status by viruses, bacteria, protists and gastro-intestinal worms in a herd of African buffalo (Syncerus caffer) to ask: how does age of first infection differ among parasite taxa? We found distinct differences in the age of first infection among parasites that aligned with the mode of transmission and parasite taxonomy. Specifically, we found that tick-borne and environmentally transmitted protists were acquired earlier than directly transmitted bacteria and viruses. These results emphasize the importance of understanding infection risk in juveniles, especially in host species where juveniles are purported to sustain parasite persistence and/or where mortality rates of juveniles influence population dynamics

    Effect of Antiplatelet Therapy on Survival and Organ Support–Free Days in Critically Ill Patients With COVID-19

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