Numerical prediction model for long- and short-term concentration of indoor volatile organic compounds from building materials.

Emission models of volatile organic compounds (VOCs) from individual indoor building materials have been developed and validated. However, multiple indoor building materials release VOCs simultaneously, and neither single building material nor multiple building material emission models can predict the entire release cycle of VOCs accurately. This study established a long- and short-term numerical prediction model for indoor VOC concentration. The model includes an attenuation coefficient θ. To describe the decay rate of the total VOC content, which is mainly influenced by time, and by designing experiments and testing in environmental warehouses under different seasonal conditions, the value of θ was first obtained. Then, after successfully plotting the emission curve of indoor pollutant concentration over time through numerical solution and using θ, the VOC content was corrected for various seasonal conditions. On the basis of this model, an exposure dose integration algorithm was proposed to evaluate the environmental health risks, as an application of this model. In comparison with previous research results and experimental data, this model has better predictive performance.

MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.

MOTIVATION: Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. RESULTS: MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. AVAILABILITY AND IMPLEMENTATION: https://github.com/1160300611/MCDHGN.

PSPGO: Cross-Species Heterogeneous Network Propagation for Protein Function Prediction.

How to use computational methods to effectively predict the function of proteins remains a challenge. Most prediction methods based on single species or single data source have some limitations: the former need to train different models for different species, the latter only to infer protein function from a single perspective, such as the method only using Protein-Protein Interaction (PPI) network just considers the protein environment but ignore the intrinsic characteristics of protein sequences. We found that in some network-based multi-species methods the networks of each species are isolated, which means there is no communication between networks of different species. To solve these problems, we propose a cross-species heterogeneous network propagation method based on graph attention mechanism, PSPGO, which can propagate feature and label information on sequence similarity (SS) network and PPI network for predicting gene ontology terms. Our model is evaluated on a large multi-species dataset split based on time and is compared with several state-of-the-art methods. The results show that our method has good performance. We also explore the predictive performance of PSPGO for a single species. The results illustrate that PSPGO also performs well in prediction for single species.

Prediction of the disease causal genes based on heterogeneous network and multi-feature combination method.

At present, the prediction of disease causal genes is mainly based on heterogeneous. Research shows that heterogeneous network contains more information and have better prediction results. In this paper, we constructed a heterogeneous network including four node types of disease, gene, phenotype and gene ontology. On this basis, we use a machine learning algorithm to predict disease-causing genes. The algorithm is divided into three steps: preprocess and training sample extraction, features extraction and combination, model training and prediction. In the process of feature extraction and combination, by using network representation method, the representation vectors of nodes are generated as the embedding features of the nodes. We also extracted the structural features of each node in the network and then the embedding features and structure features are combined. The results of training and prediction show that the prediction algorithm based on all features combined together achieves the best prediction performance. Moreover, the combination of each network representation method's embedding features and structural features has also achieved performance improvement. In the process of training samples extraction, we propose three improvement directions according to the network structure and data set distribution. Firstly, a positive sample algorithm based on network connectivity is proposed, we try to keep the connectivity of the whole heterogeneous graph in the sampling process to avoid the negative impact of embedding features' extraction. Moreover, the influence of sample sampling ratio on experimental results was tested in the range of 0-1 with step size of 0.1. The influence of different proportion of positive and negative samples on the results was also tested. These improvements are intended to enhance the balance and robustness of the method. When the positive sample ratio is 0.1 and the proportion of negative and positive samples is 3, the model achieves the optimal result, and its AUC value and accuracy are 0.9887% and 94.55%, respectively, which are significantly higher than other models.

Reducing indoor relative humidity can improve the circulation and cardiorespiratory health of older people in a cold environment: A field trial conducted in Chongqing, China.

The changing climate is one of the most important factors affecting public health. Older people are particularly threatened due to their less efficient immune systems. To evaluate the potential benefits of short-term indoor dehumidification on their circulation and cardiopulmonary health, we conducted a random, cross-over experiment with 36 healthy residents of an aged-care center in Chongqing, China in 2020. Vapor compression dehumidifiers were used over two 48-h periods. At the end of each 48 h, we immediately measured sixteen circulatory system biomarkers of inflammation, coagulation, and oxidative stress; lung function; blood pressure; and heart rate. Indoor temperature and relative humidity were monitored throughout the study period. Linear, mixed-effect models were used to associate health endpoints with indoor relative humidity. This intervention study showed that when the indoor relative humidity decreased from 75% to 45%: (1) the coagulation indicators, sCD40l, and PAI-1, decreased significantly, by 58.82% and 23.50%, respectively; (2) the inflammatory indicators, CRP, IL-6, and TNF-α decreased significantly, by 4.09%, 25.78%, and 10.60%, respectively; (3) PEF, FEV1 and FVC were increased significantly by 20.08%, 14.54%, and 15.75% respectively. To the best of our knowledge, this is the first study to examine the impact of short-term dehumidification on clinical and biochemical measures of cardiorespiratory health in humid areas, and our study suggests that RH in the dehumidified rooms (46.9 ± 8.7%) may be healthier than that in humid rooms (75.2 ± 7.9%). Humidity may be involved in the development of atherosclerosis by activating oxidative stress and mediating the secretion of inflammatory indicators. At the same time, platelet activation induced by oxidative stress stimulates thrombosis to increase cardiovascular risk in older people. Conclusion: This intervention study shows that in a Chinese city like Chongqing with serious indoor environmental humidity, indoor short-term dehumidification has obvious cardiopulmonary benefits for the healthy elderly.

Reducing particulates in indoor air can improve the circulation and cardiorespiratory health of old people: A randomized, double-blind crossover trial of air filtration.

Exposure to indoor air particulate pollution increases respiratory and cardiovascular morbidity and mortality, especially in the elderly. To assess a short-term, indoor air filtration's potential benefit on circulatory and cardiopulmonary health among healthy older people, a randomized, double-blind crossover trial was conducted with 24 healthy residents of an aged-care center in Chongqing, China in 2020. Each room received a high-efficiency particulate air filter air purifier and a placebo air purifier for two days. Fifteen circulatory system biomarkers of inflammation, coagulation, and oxidative stress; lung function; blood pressure (BP); heart rate (HR) and fractional exhaled nitric oxide (FeNO) were measured end of each two days. Indoor air particulate pollution was monitored throughout the study period. Linear mixed-effect models were used to associate health outcome variables with indoor particles. This intervention study demonstrated that air filtration was associated with significantly decreased concentrations of inflammatory and coagulation biomarkers, but not of biomarkers of oxidative stress and lung function. Just 48 h of air filtration can improve the cardiopulmonary health of the elderly. Air purifiers may be a public health measure that can be taken to improve circulatory and cardiopulmonary health among older people.

Evaluating the effect of building construction periods on household dampness/mold and childhood diseases corresponding to different energy efficiency design requirements.

Despite concerns about building dampness and children' health, few studies have examined the effects of building energy efficiency standards. This study explored the connections between self-reported household dampness and children' adverse health outcomes across buildings corresponding to construction periods (pre-2001, 2001-2010, post-2010). Significant differences of dampness-related indicators were found between buildings; the prevalence was remarkable in pre-2001 buildings. The prevalence of lifetime-ever doctor-diagnosed diseases for children was significantly associated with building dampness (adjust odd ratios > 1), but was not affected by construction periods. The hygrothermal performance for a typical residence was simulated, varying in U-values of envelopes and air change rates. The simulated performance improvement increased indoor temperatures in 2001-2010 and post-2010 buildings. The frequency with higher indoor relative humidity was higher in pre-2001 buildings, leading to the highest values for maximum mold index (Mmax ) on wall surface, especially in winter. Compared to buildings in 2001-2010, increased insulation and lower air change rate led to a relatively higher relative humidity in post-2010 buildings, adversely increasing the Mmax values. The findings addressed the positive and negative role of building standard development, which help suggesting appropriate environmental and design solutions to trade-off energy savings and dampness/mold risk in residences.

Associations of household dampness with asthma, allergies, and airway diseases among preschoolers in two cross-sectional studies in Chongqing, China: Repeated surveys in 2010 and 2019.

Many studies have investigated the associations between household damp indicators, and allergies and respiratory diseases in childhood. However, the findings are rather inconsistent. In 2010, we conducted a cross-sectional study of preschoolers aged three-six years in three urban districts of Chongqing, China. In 2019, we repeated this cross-sectional study with preschoolers of the same ages and districts. Here, we selected data for 2935 and 2717 preschoolers who did not change residences since birth in the 2010 and 2019 studies, respectively. We investigated associations of household damp indicators with asthma, allergic rhinitis, pneumonia, eczema, wheeze, and rhinitis in childhood in the two studies. The proportions of residences with household damp indicators and the prevalence of the studied diseases (except for allergic rhinitis) were significantly lower in 2019 than in 2010. In the two-level (district-child) logistic regression analyses, household damp exposures that showed by different indicators were significantly associated with the increased odds of lifetime-ever asthma (range of adjusted odds ratio (AOR): 1.69-3.50 in 2019; 1.13-1.90 in 2010), allergic rhinitis (1.14-2.39; 0.67-1.61), pneumonia (1.09-1.64; 1.21-1.59), eczema (0.96-1.83; 0.99-1.56), wheeze (1.64-2.79; 1.18-1.91), rhinitis (1.43-2.71; 1.08-1.58), and current (in the past 12 months before the survey) eczema (0.46-2.08; 0.99-1.48), wheeze (0.97-2.86; 1.26-2.07) and rhinitis (1.34-2.25; 1.09-1.56) in most cases. The increased odds ratios (ORs) of most diseases had exposure-response relationships with the cumulative number (n) of household damp indicators in the current and early residences. Our results indicated household damp exposure could be a risk factor for childhood allergic and respiratory diseases, although the magnitudes of these effects could be different in different studies.

Effects of process parameters on cutting temperature in dry machining of ball screw.

Temperature in the cutting zone during dry machining has a significant effect on the tool life and surface integrity of the workpiece. This paper describes a comprehensive research on the cutting temperature in dry machining of ball screw under whirling milling by using infrared imaging. The effects of tool parameter and geometric parameter of workpiece together with the cutting parameters on the maximum and average temperatures in the cutting zone were analyzed in full detail. The influencing degree of these parameters on the maximum and average temperatures was affected by the value ranges of the parameters. In addition, the regression model and back propagation (BP) neural network model were proposed for predicting the maximum and average temperatures in the cutting zone. The verification of the predictive models showed that compared to the regression model, BP neural network model could predict the cutting temperature with high precision. The R2 of BP neural network model for predicting the maximum and average cutting temperatures in the cutting zone was higher than 99.8%, and the mean relative error and root mean square error were less than 4% and 19%, respectively.