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Genome in a Bottle (GIAB) benchmarks have been widely used to validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here we use accurate long and linked reads to expand the prior benchmark to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). We increase coverage of the GRCh38 assembly from 85 % to 92 %, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and assembly errors) that should not have been in the previous version. Our new benchmark reliably identifies both false positives and false negatives across multiple short-, linked-, and long-read based variant calling methods. As an example of its utility, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark, mostly in difficult-to-map regions. To enable robust small variant benchmarking, we still exclude 3.6% of GRCh37 and 5.0% of GRCh38 in (1) highly repetitive regions such as large, highly similar segmental duplications and the centromere not accessible to our data and (2) regions where our sample is highly divergent from the reference due to large indels, structural variation, copy number variation, and/or errors in the reference (e.g., some KIR genes that have duplications in HG002). We have demonstrated the utility of this benchmark to assess performance in more challenging regions, which enables benchmarking in more difficult genes and continued technology and bioinformatics development. The benchmarks are available at: ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG002_NA24385_son/NISTv4.1/ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_v4.2_SmallVariantDraftBenchmark_07092020/

ObjectivesWe investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.DesignInterim analysis of a prospective cohort study.Setting, participants and interventionsParticipants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.ResultsA total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.ConclusionWearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial.Trial registration numberISRCTN51255782; Pre-results.

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tenofovir alafenamide (TAF) during pregnancy were more likely to experience excessive weight gain, diabetes, and hypertensive disorders.METHODS: This is a retrospective cohort study of pregnant persons with HIV who delivered a live infant during the study period (January 1, 2009, to December 31, 2020. ART was classified as including TAF or no TAF. Median weight gain was compared using Wilcoxon rank sum tests (WRST). A Friedman test was used to compare median weights measured at antenatal visits. We compared the proportion of persons with gestational diabetes (GDM) and hypertensive disorders (HDP) by ART groups using chi-square and Fischer's exact tests. RESULTS:We observed 189 persons who were prescribed ART during pregnancy: 30 with TAF and 159 without TAF. Median weight gain was similar between groups: TAF, 7.8 kg (95% CI, 3.4-13.6 kg); no TAF, 6.8 kg (95% CI, 2-10.8 kg), WRST P5.41. Repeated measures of median weight at antenatal visits were similar between groups (Friedman test P5.72). GDM was an infrequent outcome (TAF, 0; no TAF, 5 [3%]; P51.0). HDP were common but similar between groups (TAF, 11/28 [39%]; no TAF, 58/156 [24%]; P5.89].CONCLUSION: Use of TAF during pregnancy did not result in significant weight gain or increased risk of GDM or HDP. Pregnant persons should not be counseled to avoid TAF during pregnancy to avoid these outcomes.