Moreover, the contribution of PMS5 to arsenic (3+, 5+) bioprocessing under oligotrophic conditions was confirmed in fixed-bed reactors fed with the TDE of the gold factory (R1) and synthetic water containing As5+ (R2). According to biofilm assays such as biofilm staining, cell count, detachment assay and SEM, the arsenic significantly reduced the biofilm density of the examined reactors compared to that of the control (R3). Arsenate reduction and arsenite oxidation under bioreactor conditions were respectively obtained as 75.5-94.7% and 8%. Furthermore, negligible arsenic volatilization (1.2 ppb) was detected.The fall of 2020 brought several new variants of SARS-CoV-2 circulating across the globe, and the steadily increasing COVID-19 cases are responsible for the emergence of these variants. read more All the SARS-CoV-2 variants reported to date have multiple mutations in the spike (S) protein, specifically in the receptor-binding domain (RBD). Here, we employed an integrated computational approach involving structure and sequence based predictions to study the effect of naturally occurring variations in the S-RBD on its stability and ACE2 binding affinity. The hotspot stabilizing residue mutations N501I, N501Y, Q493L, Q493H and K417R, strengthen the RBD-ACE2 complex by modulating the interaction statistics at the interface. Thus, we report here some critical mutations that could increase the binding affinity of the SARS-CoV-2 RBD with ACE2, increasing the viral infectivity and pathogenicity. Understanding the effect of these mutations will help in developing potential vaccines and therapeutics.Snoring is the most direct symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) and implies a lot of information about OSAHS symptoms. This paper aimed to identify OSAHS patients by analyzing acoustic features derived from overnight snoring sounds. Mel-frequency cepstral coefficients, 800 Hz power ratio, spectral entropy and other 10 acoustic features were extracted from snores, and Top-6 features were selected from the extracted 10 acoustic features by a feature selection algorithm based on random forest, then 5 kinds of machine learning models were applied to validate the effectiveness of Top-6 features on identifying OSAHS patients. The results showed that when the classification performance and computing efficiency were taken into account, the combination of logistic regression model and Top-6 features performed best and could successfully distinguish OSAHS patients from simple snorers. The proposed method provides a higher accuracy for evaluating OSAHS with lower computational complexity. The method has great potential prospect for the development of a portable sleep snore monitoring device.

Sleep disturbances are features of Parkinson’s disease (PD), that can already occur before PD diagnosis. The most investigated prodromal PD sleep disorder is REM sleep behavior disorder (RBD). The relation between other polysomnographic (PSG) alterations and the prediagnostic stages of PD, however, is less clear.

We performed a retrospective case-control study to characterize polysomnographic alterations in PD and prediagnostic PD. We included 63 PD subjects (33 subjects that underwent a video-PSG before PD diagnosis [13 with and 20 without RBD] and 30 subjects that underwent a PSG after PD diagnosis) and 30 controls. PSGs were analyzed for sleep stages, different RSWA variables, body position, arousals, periodic limb movements, and REM density.

Higher subscores of all RSWA variables were observed in subjects with PD and prediagnostic PD (with and without RBD). Total RSWA, tonic RSWA and chin RSWA severity were significant predictors for all PD and prediagnostic PD groups. Our study also shows a higher percentage of nocturnal supine body position in all PD and prediagnostic PD groups. Supine body position percentage is the highest in the PD group and has a positive correlation with time since diagnosis.

These findings suggest that increased total, tonic and chin RSWA as well as nocturnal supine body position are already present in prediagnostic PD, independently of RBD status. Prospective longitudinal studies are necessary to confirm the additional value of these PSG abnormalities as prodromal PD biomarkers.

These findings suggest that increased total, tonic and chin RSWA as well as nocturnal supine body position are already present in prediagnostic PD, independently of RBD status. Prospective longitudinal studies are necessary to confirm the additional value of these PSG abnormalities as prodromal PD biomarkers.This study aimed to investigate the effects of pre-bedtime blue-light exposure on ratio of deep sleep and sleep quality. In this study, 11 healthy young men were exposed to three conditions for 1 h before bedtime 1) incandescent light, 2) blue-light, or 3) blue light-blocking glasses on. The following morning, subjective sleep quality was measured using the Oguri-Shirakawa-Azumi Sleep Inventory. Sleep time, ratio of sleep, ratio of deep sleep, and body movements during sleep were measured using a mat sleep-scan (sleep scan, SL- 504; TANITA Corp., Japan) and an ambulatory portable sleep study system (LS-140; Fukuda Denshi Co. Ltd., Japan). Ratio of deep sleep was significantly decreased in the blue-light exposure group compared to the groups with incandescent light and blue light-blocking glasses (p less then 0.01), There were no differences noted in sleep time or body movements among the three groups. These results suggest that blue-light exposure to affects sleep quality by reducing the ratio of deep sleep.

Social jetlag has been reported to predict obesity-related indices, independent of sleep duration, with associations in female adolescents but not males. However, such sex-specific relationships have not been investigated in pre-adolescents.

To examine (i) the relationships between sleep characteristics, including social jetlag, and obesity-related outcomes during childhood, and (ii) whether these relationships are moderated by sex.

This cross-sectional study included 381 children aged 9-11 years (49.6% female). Average sleep duration, social jetlag, and physical activity were assessed via wrist-worn accelerometry. Sleep disturbances were quantified from the Children’s Sleep Habits Questionnaire. Obesity-related outcomes included age-specific body mass index Z-scores (zBMI) and waist-to-height ratio. Additionally % fat, total fat mass, and fat mass index were assessed via bioelectrical impedance analysis. Linear mixed models that nested children within schools were used to identify relationships among sleep characteristics and obesity-related outcomes.