In this study, eight subjects were exposed in a simulated office to 31 combinations of indoor environmental conditions, assigned by orthogonal design and uniform design. Conditions comprised variations of Predicted Mean Vote (PMV), illuminance, sound pressure and CO2 concentration (independent of a consistent ventilation rate) as indicators of thermal, lighting, acoustic and indoor air quality. Participant satisfaction with each of the four factors and with overall environmental conditions were measured with a questionnaire. Multiple interactions were detected with a partial correlation analysis and regression analysis.Results showed an adjusted effect of illuminance on perceived acoustic environment, a significant effect of the thermal environment on indoor air quality satisfaction, and a slight effect of sound pressure on indoor air quality satisfaction. Linear and geometric mean regression models were investigated for predicting overall satisfaction from the factor satisfaction scores. For the linear model, it was determined that multicollinearity among factor satisfaction levels may result in non-significant and biased estimated coefficients. The geometric mean regression model provides better prediction accuracy than the linear regression model with fewer coefficients, and accounts for the finding that the lowest satisfaction level with any environmental factor appears to drive overall satisfaction.

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Little information is available regarding the effect of melatonin on the quality and fertilization capability of sex-sorted bull sperm, and even less about the associated mechanism. Sex-sorted sperm from three individual bulls were washed twice in wash medium and incubated in a fertilization medium for 1.5 h, and each was supplemented with melatonin (0, 10−3 M, 10−5 M, 10−7 M, and 10−9 M). The reactive oxygen species (ROS) and endogenous antioxidant activity (glutathione peroxidase (GPx); superoxide dismutase (SOD); catalase (CAT)), apoptosis (phosphatidylserine [PS] externalization; mitochondrial membrane potential (Δψm)), acrosomal integrity events (malondialdehyde (MDA) level; acrosomal integrity), capacitation (calcium ion [Ca2+]i level; cyclic adenosine monophosphate (cAMP); capacitation level), and fertilization ability of the sperm were assessed. Melatonin receptor 1 (MT1) and 2 (MT2) expression were examined to investigate the involvement of melatonin receptors on sex-sorted bull sperm capacitation. Our results show that treatment with 10−5 M melatonin significantly decreased the ROS level and increased the GPx, SOD, and CAT activities of sex-sorted bull sperm, which inhibited PS externalization and MDA levels, and improved Δψm, acrosomal integrity, and fertilization ability. Further experiments showed that melatonin regulates sperm capacitation via MT1. These findings contribute to improving the fertilization capacity of sex-sorted bull sperm and exploring the associated mechanism.

Venting range hoods can control indoor air pollutants emitted during residential cooktop and oven cooking. To quantify their potential benefits, it is important to know how frequently and under what conditions range hoods are operated during cooking. We analyzed data from 54 single family houses and 17 low-income apartments in California in which cooking activities, range hood use, and fine particulate matter (PM2.5) were monitored for one week per home. Range hoods were used for 36% of cooking events in houses and 28% in apartments. The frequency of hood use increased with cooking frequency across homes. In both houses and apartments, the likelihood of hood use during a cooking event increased with the duration of cooktop burner use, but not with the duration of oven use. Actual hood use rates were higher in the homes of participants who self-reported more frequent use in a pre-study survey, but actual use was far lower than self-reported frequency. Residents in single family houses used range hoods more often when cooking caused a discernible increase in PM2.5. In apartments, residents used their range hood more often only when high concentrations of PM2.5 were generated during cooking.

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The prevention of infectious diseases is a global health priority area. The early detection of possible epidemics is the first and important defense line against infectious diseases. However, conventional surveillance systems, e.g., the Centers for Disease Control and Prevention (CDC), rely on clinical data. The CDC publishes the surveillance results weeks after epidemic outbreaks. To improve the early detection of epidemic outbreaks, we designed a syndromic surveillance system to predict the epidemic trends based on disease-related Google search volume. Specifically, we first represented the epidemic trend with multiple alert levels to reduce the noise level. Then, we predicted the epidemic alert levels using a continuous density HMM, which incorporated the intrinsic characteristic of the disease transmission for alert level estimation. Respective models are built to monitor both national and regional epidemic alert levels of the U.S. The proposed system can provide real-time surveillance results, which are weeks before the CDC's reports. This paper focusses on monitoring the infectious disease in the U.S., however, we believe similar approach may be used to monitor epidemics for the developing countries as well.

Background The use of accurate prediction tools and early intervention are important for addressing severe coronavirus disease 2019 (COVID-19). However, the prediction models for severe COVID-19 available to date are subject to various biases. This study aimed to construct a nomogram to provide accurate, personalized predictions of the risk of severe COVID-19. Methods This study was based on a large, multicenter retrospective derivation cohort and a validation cohort. The derivation cohort consisted of 496 patients from Jiangsu Province, China, between January 10, 2020, and March 15, 2020, and the validation cohort contained 105 patients from Huangshi, Hunan Province, China, between January 21, 2020, and February 29, 2020. A nomogram was developed with the selected predictors of severe COVID-19, which were identified by univariate and multivariate logistic regression analyses. We evaluated the discrimination of the nomogram with the area under the receiver operating characteristic curve (AUC) and the calibration of the nomogram with calibration plots and Hosmer-Lemeshow tests. Results Three predictors, namely, age, lymphocyte count, and pulmonary opacity score, were selected to develop the nomogram. The nomogram exhibited good discrimination (AUC 0.93, 95% confidence interval [CI] 0.90–0.96 in the derivation cohort; AUC 0.85, 95% CI 0.76–0.93 in the validation cohort) and satisfactory agreement. Conclusions The nomogram was a reliable tool for assessing the probability of severe COVID-19 and may facilitate clinicians stratifying patients and providing early and optimal therapies.

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