Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. We propose a new method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. We compared our approach to two simpler methods based on interval unions or term frequency-inverse document frequency and evaluated the methods in three ways First, by classifying the cell line, antibody, or tissue type of the region set; second, by assessing whether similarity among embeddings can reflect simulated random perturbations of genomic regions; and third, by testing robustness of the proposed representations to different signal thresholds for calling peaks. Our word2vec-based region set embeddings reduce dimensionality from more than a hundred thousand to 100 without significant loss in classification performance. The vector representation could identify cell line, antibody, and tissue type with over 90% accuracy. We also found that the vectors could quantitatively summarize simulated random perturbations to region sets and are more robust to subsampling the data derived from different peak calling thresholds. Our evaluations demonstrate that the vectors retain useful biological information in relatively lower-dimensional spaces. We propose that vector representation of region sets is a promising approach for efficient analysis of genomic region data. https//github.com/databio/regionset-embedding. https//github.com/databio/regionset-embedding. Hypoxia and inflammation are hallmarks of critical illness, related to multiple organ failure. A possible mechanism leading to multiple organ failure is hypoxia- or inflammation-induced down-regulation of the detoxifying glyoxalase system that clears dicarbonyl stress. The dicarbonyl methylglyoxal (MGO) is a highly reactive agent produced by metabolic pathways such as anaerobic glycolysis and gluconeogenesis. MGO leads to protein damage and ultimately multi-organ failure. Whether detoxification of MGO into D-lactate by glyoxalase functions appropriately under conditions of hypoxia and inflammation is largely unknown. We investigated the effect of inflammation and hypoxia on the MGO pathway in humans in vivo. After prehydration with glucose 2.5% solution, ten healthy males were exposed to hypoxia (arterial saturation 80-85%) for 3.5 h using an air-tight respiratory helmet, ten males to experimental endotoxemia (LPS 2 ng/kg i.v.), ten males to LPS+hypoxia and ten males to none of these interventions (control group). Serial blood samples were drawn, and glyoxalase-1 mRNA expression, MGO, methylglyoxal-derived hydroimidazolone-1 (MG-H1), D-lactate and L-lactate levels, were measured serially. Glyoxalase-1 mRNA expression decreased in the LPS (β (95%CI); -0.87 (-1.24; -0.50) and the LPS+hypoxia groups; -0.78 (-1.07; -0.48) (P<0.001). MGO was equal between groups, whereas MG-H1 increased over time in the control group only (P=0.003). D-Lactate was increased in all four groups. L-Lactate was increased in all groups, except in the control group. Systemic inflammation downregulates glyoxalase-1 mRNA expression in humans. This is a possible mechanism leading to cell damage and multi-organ failure in critical illness with potential for intervention. Systemic inflammation downregulates glyoxalase-1 mRNA expression in humans. This is a possible mechanism leading to cell damage and multi-organ failure in critical illness with potential for intervention. New EEG features became available for use in polysomnography and have shown promise in early studies. They include a continuous index of sleep depth (Odds-Ratio-Product; ORP), agreement between right and left sleep depth (R/L coefficient), dynamics of sleep recovery following arousals (ORP-9), general EEG amplification (EEG Power), alpha intrusion and arousal intensity. This study was undertaken to establish ranges and reproducibility of these features in subjects with different demographics and clinical status. We utilized data from the two phases of the Sleep-Heart-Health-Study (SHHS1 and SHHS2). Polysomnograms of 5804 subjects from SHHS1 were scored to determine the above features. Feature values were segregated according to clinical status of Obstructive Sleep Apnea (OSA), insomnia, insomnia plus OSA, no clinical sleep disorder, and demographics (age, gender and race). Results from SHHS visit2 were compared with SHHS1 results. All features varied widely among clinical groups and demographics. Relative to participants with no sleep disorder, wake ORP was higher in participants reporting insomnia symptoms and lower in those with OSA (p<0.0001 for both), reflecting opposite changes in sleep pressure, while NREM ORP was higher in both insomnia and OSA (p<0.0001), reflecting lighter sleep in both groups. There were significant associations with age, gender, and race. EEG Power, and REM ORP were highly reproducible across the two studies (ICC>0.75). The reported results serve as bases for interpreting studies that utilize novel sleep EEG biomarkers and identify characteristic EEG changes that vary with age, gender and may help distinguish insomnia from OSA. The reported results serve as bases for interpreting studies that utilize novel sleep EEG biomarkers and identify characteristic EEG changes that vary with age, gender and may help distinguish insomnia from OSA. The COVID-19 pandemic has strained clinical microbiology laboratories due to testing supply allocations. As a result, laboratories have had to invest in multiple COVID-19 assays performed on different testing instruments. Comparing the results achieved by testing positive samples between in-use assays can provide insights into which platforms may be interchangeable for testing in times of supply chain emergencies. Nasopharyngeal and nasal swab specimens collected in viral transport media that tested positive on the Xpert® Xpress SARS-CoV-2 assay were tested on the ePlex® SARS-CoV-2 and BD SARS-CoV-2 Reagents for BD Max™ assays. https://www.selleckchem.com/products/repsox.html Positive percent agreement was calculated using the Xpert® Xpress SARS-CoV-2 assay as the reference method. We tested 78 positive swabs, resulting in a positive percent agreement (PPA) of 92% [CI 84-97%] for the BD SARS-CoV-2 assay and 58% [CI 47-70%] for the ePlex® assay. Following development of a new workflow for the ePlex®, we detected SARS-CoV-2 in 7 additional samples, resulting in a new PPA of 68% [CI 56-78].