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7% [113/196] vs. 38.5% [15/39]) and have lower APACHE3 scores (58.2 ± 22.6 vs. 79.9 ± 16.0). For the total cohort of 235 patients, there was no statistically significant relationship between a patient's initial ascorbic acid or thiamine level and either survival or development of shock. In this analysis of early plasma samples from patients with severe sepsis admitted from the ED to the ICU, we found that mean ascorbic acid and thiamine levels were lower than normal range but that there was no relationship between these levels and outcomes, including 28 day mortality and development of shock.COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723-0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865-0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899-0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.Interspecies hydrogen transfer (IHT) and direct interspecies electron transfer (DIET) are two syntrophy models for methanogenesis. Their relative importance in methanogenic environments is still unclear. Our recent discovery of a novel species Candidatus Geobacter eutrophica with the genetic potential of IHT and DIET may serve as a model species to address this knowledge gap. To experimentally demonstrate its DIET ability, we performed electrochemical enrichment of Ca. G. eutrophica-dominating communities under 0 and 0.4 V vs. Ag/AgCl based on the presumption that DIET and extracellular electron transfer (EET) share similar metabolic pathways. After three batches of enrichment, Geobacter OTU650, which was phylogenetically close to Ca. G. eutrophica, was outcompeted in the control but remained abundant and active under electrochemical stimulation, indicating Ca. G. eutrophica's EET ability. The high-quality draft genome further showed high phylogenomic similarity with Ca. G. eutrophica, and the genes encoding outer membrane cytochromes and enzymes for hydrogen metabolism were actively expressed. A Bayesian network was trained with the genes encoding enzymes for alcohol metabolism, hydrogen metabolism, EET, and methanogenesis from dominant fermentative bacteria, Geobacter, and Methanobacterium. Methane production could not be accurately predicted when the genes for IHT were in silico knocked out, inferring its more important role in methanogenesis. The genomics-enabled machine learning modeling approach can provide predictive insights into the importance of IHT and DIET.Virus-like particles are an emerging class of nano-biotechnology with the Tobacco Mosaic Virus (TMV) having found a wide range of applications in imaging, drug delivery, and vaccine development. TMV is typically produced in planta, and, as an RNA virus, is highly susceptible to natural mutation that may impact its properties. Over the course of 2 years, from 2018 until 2020, our laboratory followed a spontaneous point mutation in the TMV coat protein-first observed as a 30 Da difference in electrospray ionization mass spectrometry (ESI-MS). The mutation would have been difficult to notice by electrophoretic mobility in agarose or SDS-PAGE and does not alter viral morphology as assessed by transmission electron microscopy. The mutation responsible for the 30 Da difference between the wild-type (wTMV) and mutant (mTMV) coat proteins was identified by a bottom-up proteomic approach as a change from glycine to serine at position 155 based on collision-induced dissociation data. Since residue 155 is located on the outer surface of the TMV rod, it is feasible that the mutation alters TMV surface chemistry. However, enzyme-linked immunosorbent assays found no difference in binding between mTMV and wTMV. Functionalization of a nearby residue, tyrosine 139, with diazonium salt, also appears unaffected. Overall, this study highlights the necessity of standard workflows to quality-control viral stocks. We suggest that ESI-MS is a straightforward and low-cost way to identify emerging mutants in coat proteins.Maternal obesity in pregnancy predicts offspring psychopathology risk in childhood but it remains unclear whether maternal obesity or underweight associate with adult offspring mental disorders. We examined longitudinally whether maternal body mass index (BMI) in pregnancy predicted mental disorders in her offspring and whether the associations differed by offspring birth year among 68,571 mother-child dyads of Aberdeen Maternity and Neonatal Databank, Scotland. The offspring were born 1950-1999. Maternal BMI was measured at a mean 15.7 gestational weeks and classified into underweight, normal weight, overweight, moderate obesity and severe obesity. Mental disorders were identified from nationwide registers carrying diagnoses of all hospitalizations and deaths in Scotland in 1996-2017. We found that maternal BMI in pregnancy was associated with offspring mental disorders in a time-dependent manner In offspring born 1950-1974, maternal underweight predicted an increased hazard of mental disorders [Hazard Ratio (HR) = 1.74; 95% Confidence Interval (CI) = 1.01-3.00)]. In offspring born 1975-1999, maternal severe obesity predicted increased hazards of any mental (HR 1.60; 95% CI 1.08-2.38) substance use (HR 1.91; 95% CI 1.03-3.57) and schizophrenia spectrum (HR 2.80; 95% CI 1.40-5.63) disorders. Our findings of time-specific associations between maternal prenatal BMI and adult offspring mental disorders may carry important public health implications by underlining possible lifelong effects of maternal BMI on offspring psychopathology.We investigated the use of an Artificial Neural Network (ANN) to predict the Local Bond Stress (LBS) between Ultra-High-Performance Concrete (UHPC) and steel bars, in order to evaluate the accuracy of our LBS equation, proposed by Multiple Linear Regression (MLR). The experimental and numerical LBS results of specimens, based on RILEM standards and using pullout tests, were assessed by the ANN algorithm using the TensorFlow platform. For each specimen, steel bar diameters ([Formula see text] of 12, 14, 16, 18, and 20, concrete compressive strength ([Formula see text]), bond lengths ([Formula see text]), and concrete covers ([Formula see text]) of [Formula see text], [Formula see text], [Formula see text] and [Formula see text] were used as input parameters for our ANN. To obtain an accurate LBS equation, we first modified the existing formula, then used MLR to establish a new LBS equation. Finally, we applied ANN to verify our new proposed equation. see more The numerical pullout test values from ABAQUS and experimental results from our laboratory were compared with the proposed LBS equation and ANN algorithm results. The results confirmed that our LBS equation is logically accurate and that there is a strong agreement between the experimental, numerical, theoretical, and the predicted LBS values. Moreover, the ANN algorithm proved the precision of our proposed LBS equation.Direct tests of gene function have historically been performed in a limited number of model organisms. The CRISPR/Cas system is species-agnostic, offering the ability to manipulate genes in a range of models, enabling insights into evolution, development, and physiology. Astatotilapia burtoni, a cichlid fish from the rivers and shoreline around Lake Tanganyika, has been extensively studied in the laboratory to understand evolution and the neural control of behavior. Here we develop protocols for the creation of CRISPR-edited cichlids and create a broadly useful mutant line. By manipulating the Tyrosinase gene, which is necessary for eumelanin pigment production, we describe a fast and reliable approach to quantify and optimize gene editing efficiency. Tyrosinase mutants also remove a major obstruction to imaging, enabling visualization of subdermal structures and fluorophores in situ. These protocols will facilitate broad application of CRISPR/Cas9 to studies of cichlids as well as other non-traditional model aquatic species.Alcohol consumption is a major risk factor for premature mortality. Although alcohol control policies are known to impact all-cause mortality rates, the effect that policies have on specific age groups is an important area of research. This study investigates the effect of alcohol control policies implemented in 2009 and 2017 in Lithuania on all-cause mortality rates. All-cause mortality rates (deaths per 100,000 people) were obtained for 2001-2018 by 10-year age groups (20-29, 30-39, 40-49 years, etc.). All-cause mortality rates, independent of macro-level secular trends (e.g., economic trends) were examined. Following a joinpoint analysis to control for secular trends, an interrupted time series analysis showed that alcohol control policies had a significant effect on all-cause mortality rates (p = .018), with the most significant impact occurring among young adults (20-29 and 30-39 years of age). For these age groups, their mortality rate decreased during the 12 months following policy implementation (following the policy in 2009 for those 20-29 years of age, p = .0026, and following the policy in 2017 for those 30-39 years of age, p = .011). The results indicate that alcohol control policy can impact all-cause mortality rates, above and beyond secular trends, and that the impact is significant among young adults.The lockdown measures that were taken to combat the COVID-19 pandemic minimized anthropogenic activities and created natural laboratory conditions for studying air quality. Both observations and WRF-Chem simulations show a 20-50% reduction (compared to pre-lockdown and same period of previous year) in the concentrations of most aerosols and trace gases over Northwest India, the Indo Gangetic Plain (IGP), and the Northeast Indian regions. It is shown that this was mainly due to a 70-80% increase in the height of the boundary layer and the low emissions during lockdown. However, a 60-70% increase in the pollutants levels was observed over Central and South India including the Arabian sea and Bay of Bengal during this period, which is attributed to natural processes. Elevated (dust) aerosol layers are transported from the Middle East and Africa via long-range transport, and a decrease in the wind speed (20-40%) caused these aerosols to stagnate, enhancing the aerosol levels over Central and Southern India. A 40-60% increase in relative humidity further amplified aerosol concentrations.