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It can be concluded that the school laboratory is prone to more gamma radiation than the class rooms and the administrative block. Therefore, the laboratory instructors and staff, who spend longer time in the laboratory, are more liable to the health risk that could result from years of exposure to gamma radiation in the laboratory.Stubble burning (SB) has been a major source of seasonal aerosol loading and pollution over northern India. The aftereffects of groundwater preservation act i.e., post 2010 era (2011-2020) has seen delay in crop harvesting thereby shifting the peak SB to May (Wheat SB) and to November (Paddy SB) by 8-10 and 10-12 days compared to pre-2010. Groundwater storage depletion rate of 29.2 mm yr-1 was observed over the region. Post 2010 era shows an increase of 1.4% in wheat SB and 21% in Paddy SB fires over Punjab and Haryana with 70% of PM2.5 air mass clusters (high probability > 0.8) advecting to the downwind regions leading to 23-26% increase in PM2.5 and 4-6% in aerosol loading over National Capital Region (NCR). Although the objective of water conservation policy was supposed to preserve the groundwater by delaying the paddy transplantation and sowing, on the contrary the implementation of this policy has seen groundwater storage after 2013 depleting at a rate of 29.2 mmyr-1 over these regions. Post policy implementation has led to shift and shrinking of harvest window with increased occurrences in SB fires which also increase associated particulate matter pollution over North India.Nonalcoholic fatty liver disease (NAFLD) is an emerging cause of chronic liver diseases and a major health problem worldwide. Dietary patterns may play a critical role in controlling and preventing this disease, but the available evidence is scarce. The current study aims to ascertain the association of adherence to the Dietary Approach to Stop Hypertension (DASH) diet and Mediterranean diet (MeD) with nonalcoholic fatty liver disease (NAFLD) among Iranian adults of the Amol Cohort Study (AmolCS). In a cross-sectional analysis among 3220 adults (55.3% men), age ≥ 18 years (46.96 ± 14.67), we measured usual dietary intake with a validated food frequency questionnaire (FFQ) and then calculated dietary pattern scores for DASH and MeD. Sociodemographic and lifestyle factors were collected by a structured questionnaire. The presence and degree of NAFLD were also determined by abdominal sonography. Multiple regression models were used to estimate NAFLD odds across tertiles of DASH and Mediterranean dietary scores. on between DASH and MeD with NAFLD in Iranian adults, especially women and subjects with or without abdominal obesity. Further prospective investigations are needed to confirm the integrity of our findings.Three probabilistic methodologies are developed for predicting the long-term creep rupture life of 9-12 wt%Cr ferritic-martensitic steels using their chemical and processing parameters. The framework developed in this research strives to simultaneously make efficient inference along with associated risk, i.e., the uncertainty of estimation. The study highlights the limitations of applying probabilistic machine learning to model creep life and provides suggestions as to how this might be alleviated to make an efficient and accurate model with the evaluation of epistemic uncertainty of each prediction. Based on extensive experimentation, Gaussian Process Regression yielded more accurate inference ([Formula see text] for the holdout test set) in addition to meaningful uncertainty estimate (i.e., coverage ranges from 94 to 98% for the test set) as compared to quantile regression and natural gradient boosting algorithm. Furthermore, the possibility of an active learning framework to iteratively explore the material space intelligently was demonstrated by simulating the experimental data collection process. This framework can be subsequently deployed to improve model performance or to explore new alloy domains with minimal experimental effort.Herein, four novel and bio-based hydrogel samples using sodium alginate (SA) and chitosan (CH) grafted with acrylamide (AAm) and glycidyl methacrylate (GMA) and their reinforced nanocomposites with graphene oxide (GO) were synthesized and coded as SA-g-(AAm-co-GMA), CH-g-(AAm-co-GMA), GO/SA-g-(AAm-co-GMA), and GO/CH-g-(AAm-co-GMA), respectively. The morphology, net charge, and water absorption capacity of samples were entirely changed by switching the biopolymer from SA to CH and adding a nano-filler. The proficiencies of hydrogels were compared in the immobilization of a model metagenomic-derived xylanase (PersiXyn9). The best performance was observed for GO/SA-g-poly(AAm-co-GMA) sample indicating better stabilizing electrostatic attractions between PersiXyn9 and reinforced SA-based hydrogel. Compared to the free enzyme, the immobilized PersiXyn9 on reinforced SA-based hydrogel showed a 110.1% increase in the released reducing sugar and almost double relative activity after 180 min storage. While immobilized enzyme on SA-based hydrogel displayed 58.7% activity after twelve reuse cycles, the enzyme on CH-based carrier just retained 8.5% activity after similar runs.Primary graft dysfunction (PGD) is a major determinant of morbidity and mortality following lung transplantation. Delineating basic mechanisms and molecular signatures of PGD remain a fundamental challenge. This pilot study examines if the pulmonary volatile organic compound (VOC) spectrum relate to PGD and postoperative outcomes. The VOC profiles of 58 bronchoalveolar lavage fluid (BALF) and blind bronchial aspirate samples from 35 transplant patients were extracted using solid-phase-microextraction and analyzed with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. The support vector machine algorithm was used to identify VOCs that could differentiate patients with severe from lower grade PGD. Using 20 statistically significant VOCs from the sample headspace collected immediately after transplantation ( less then  6 h), severe PGD was differentiable from low PGD with an AUROC of 0.90 and an accuracy of 0.83 on test set samples. The model was somewhat effective for later time points with an AUROC of 0.80. Three major chemical classes in the model were dominated by alkylated hydrocarbons, linear hydrocarbons, and aldehydes in severe PGD samples. These VOCs may have important clinical and mechanistic implications, therefore large-scale study and potential translation to breath analysis is recommended.Alterations in the three chemosensory modalities-smell, taste, and chemesthesis-have been implicated in Coronavirus Disease 2019 (COVID-19), yet emerging data suggest a wide geographic and ethnic variation in the prevalence of these symptoms. Studies on chemosensory disorders in COVID-19 have predominantly focused on Caucasian populations whereas Asians remain understudied. We conducted a nationwide, multicentre cross-sectional study using an online questionnaire on a cohort of RT-PCR-confirmed adult COVID-19 patients in Malaysia between 6 June and 30 November 2020. The aim of our study was to investigate their presenting symptoms and assess their chemosensory function using self-ratings of perceived smell, taste, chemesthesis, and nasal blockage. In this cohort of 498 patients, 41.4% reported smell and/or taste loss when diagnosed with COVID-19, which was the commonest symptom. Blocked nose, loss of appetite, and gastrointestinal disturbances were independent predictors of smell and/or taste loss on multivariate analysis. Self-ratings of chemosensory function revealed a reduction in smell, taste, and chemesthesis across the entire cohort of patients that was more profound among those reporting smell and/or taste loss as their presenting symptom. Perceived nasal obstruction accounted for only a small proportion of changes in smell and taste, but not for chemesthesis, supporting viral disruption of sensorineural mechanisms as the dominant aetiology of chemosensory dysfunction. Our study suggests that chemosensory dysfunction in COVID-19 is more widespread than previously reported among Asians and may be related to the infectivity of viral strains.Study Registration NMRR-20-934-54803 and NCT04390165.To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center's PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 ± 17.64 years (range 18-89) were enrolled in the study. VX-561 modulator The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 ± 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute.Microbial community metabolism and functionality play a key role modulating global biogeochemical processes. However, the metabolic activities and contribution of actively growing prokaryotes to ecosystem energy fluxes remain underexplored. Here we describe the temporal and spatial dynamics of active prokaryotes in the different water masses of the Mediterranean Sea using a combination of bromodeoxyuridine labelling and 16S rRNA gene Illumina sequencing. Bulk and actively dividing prokaryotic communities were drastically different and depth stratified. Alteromonadales were rare in bulk communities (contributing 0.1% on average) but dominated the actively dividing community throughout the overall water column (28% on average). Moreover, temporal variability of actively dividing Alteromonadales oligotypes was evinced. SAR86, Actinomarinales and Rhodobacterales contributed on average 3-3.4% each to the bulk and 11, 8.4 and 8.5% to the actively dividing communities in the epipelagic zone, respectively. SAR11 and Nitrosopumilales contributed less to the actively dividing than to the bulk communities during all the study period. Noticeably, the large contribution of these two taxa to the total prokaryotic communities (23% SAR11 and 26% Nitrosopumilales), especially in the meso- and bathypelagic zones, results in important contributions to actively dividing communities (11% SAR11 and 12% Nitrosopumilales). The intense temporal and spatial variability of actively dividing communities revealed in this study strengthen the view of a highly dynamic deep ocean. Our results suggest that some rare or low abundant phylotypes from surface layers down to the deep sea can disproportionally contribute to the activity of the prokaryotic communities, exhibiting a more dynamic response to environmental changes than other abundant phylotypes, emphasizing the role they might have in community metabolism and biogeochemical processes.
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