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Increasing evidence have associated pneumonia with early exposure to ambient air pollution. However, the role of indoor environmental factors exposure in early life on childhood pneumonia remains unclear.To examine the association between indoor environmental factors exposure during different timing windows and childhood pneumonia, and to identify the key indoor factor(s) in different critical window(s).A retrospective cohort study of 8689 pre-schoolers was performed in Changsha, China during 2019-2020. Our questionnaire survey was designed to collect information on pre-schooler's outcome and residential environmental exposure containing indoor pollution and allergens during 1 year before pregnancy, pregnancy, first year, and past year. The associations were further estimated stratified by personal exposure level of outdoor NOPre-schooler's pneumonia was significantly related with exposure of new furniture, redecoration, mold/damp stains, and mold or damp clothing or bedding exposure during the four periods, with the strongest associations observed during 1 year before pregnancy based on multi-window model, with ORs (95% CI) of 1.27 (1.12-1.44), 1.26 (1.09-1.46), 1.34 (1.14-1.57), and 1.28 (1.05-1.56) respectively. Environmental tobacco smoke (ETS) including both parental and grandparental smoking were significantly related with increased risk of pre-schooler's pneumonia, and ETS played a more important role in early life, with ORs (95% CI) of 1.17 (1.01-1.36) and 1.19 (1.02-1.39) in pregnancy and first year. Indoor plants particularly nonflowering plants significantly elevated pneumonia risk but only in past year, with ORs (95% CI) of 1.17 (1.05-1.30) and 1.14 (1.03-1.26). Higher pneumonia risk was observed for renovation exposure in pre-birth compared to post-birth, while mold/dampness exerted an accumulative effect with the highest risk for exposure during both pre- and post-birth. Living near traffic road and exposure to high level of traffic-related air pollution and high temperature significantly increased pneumonia risk. Sensitivity analysis found that some sub-groups were more susceptible to pneumonia risk of home environment exposure.Early life exposure to indoor environmental factors plays an important role in pneumonia development, supporting the hypothesis of "Preconceptional and Fetal Origin of Childhood Pneumonia" and "Developmental Origins of Health and Pneumonia".

Sepsis associated encephalopathy (SAE) is a severe disease secondary to sepsis, which is associated with increased mortality and causes long-term cognitive deficits in survivors. Recently, an increasing body of evidence has shown that gut microbiota is closely related to the central nervous system, and could influence brain function via microbiota-gut-brain axis. Therefore, in the occurrence and development of SAE, cholinergic anti-inflammatory pathway is one of the mechanisms by which gut microbiota could improve cognitive function. Efferocytosis, a process of eliminating apoptotic cells in the body, has anti-inflammatory effects and provides organ protection in sepsis. On the other hand, it could be enhanced by some metabolites of gut microbiota, making it another potential mechanism for gut microbiota regulating SAE. This review summarizes the mutual regulation of gut microbiota, efferocytosis and SAE, to explore potential mechanisms and therapeutic targets of SAE.

Background: Sepsis-associated encephalopathy (SAE) is one of the most serious complications of sepsis, along with brain function and cognitive behavioral changes. The process of efferocytosis with microglia was restrained in SAE rats’ hippocampus. We have previously shown that milk fat globule EGF factor 8 protein (MFG-E8) evoked efferocytosis in SAE rats’ brain, and alleviated the inflammnation in hippocampus. The present study investigated the the contribution of MFG-E8 to the brain functional connectivity (FC) of hippocampus and lateral entorhinal cortex in SAE rats by resting-state functional MRI scan.Methods: Sixty adult male Sprague Dawley rats were divided randomly into Sham operation group, cecal ligation and puncture (CLP) group, CLP+MFGE8 group, and CLP+MFGE8+Cilengitide group. After cecal ligation and puncture (CLP) surgery, MFGE8 (3.3ug) was injected intracerebroventricularly (i.c.v.) and cilengitide (10 mg/kg) was injected intraperitoneal. The Morris water maze (MWM) and open filed test (OFT) were performed continuously for five days. Rats were detected by T2-MRI and rs-fMRI scan 8 days post-surgery, then brain tissues were taken for western blotting and pathological observation by HE stained. One-way ANOVA with Tukey’s post-test of all pair of columns was employed statistical analyses.Results: MFGE8 improved performance of neurobehavioral tests in MWM test and OFT. Expression of IL-1β and the proteins of MFGE8, Rac1, and LC3 were up regulated in CLP+MFGE8 group. On the contrary, expression of IL-6, Rac1, and LC3 were down regulated in CLP+MFGE8+cilengitide group. MFGE8 significantly decreased the hyperintense region in hippocampus detected by T2-MRI, and increased ALFF value in SAE rats’ bilateral sensory cortex, motor cortex and visual cortex by rs-fMRI. In addition, MFGE8 increased FC of CA1 and LEnt in same side of cerebral hemisphere, and the FC of CA1 and LEnt was consistent with the behavior result in Morris water maze test.Conclusions: MFGE8 activated efferocytosis with microglia and improved brain function and functional connectivity between CA1 and LEnt which were reflected in behavior tests and fMRI.

Mounting evidence have linked ambient air pollution and temperature with childhood pneumonia, but it is unclear whether there is an interaction between air pollution and temperature on childhood pneumonia. We aim to assess the combined effect of ambient air pollution and temperature exposure during preconception and pregnancy on pneumonia by a case-control study of 1510 children aged 0-14 years in Changsha, China. We obtained the data of childhood pneumonia from XiangYa Hospital electrical records. We estimated personal exposure to outdoor air pollution (PM

The prevalence of childhood allergies has increased during past decades leading to serious hospitalization and heavy burden worldwide, yet the key factors responsible for the onset of early symptoms and development of diagnosed diseases are unclear.To explore the role of early life exposure to ambient air pollution and indoor environmental factors on early allergic symptoms and doctor diagnosed allergic diseases.A retrospective cohort study of 2598 preschool children was conducted at 36 kindergartens in Changsha, China from September of 2011 to February of 2012. A questionnaire was developed to survey each child's early onset of allergic symptoms (wheeze and rhinitis-like symptoms) and doctor diagnosis of allergic diseases (asthma and rhinitis) as well as home environments. Each mother's and child's exposures to ambient air pollutants (PMChildhood early allergic symptoms (33.9%) including wheeze (14.7%) and rhinitis-like symptoms (25.4%) before 2 years old were not associated with outdoor air pollution exposure but was significantly associated with maternal exposure of window condensation at home in pregnancy with ORs (95% CI) of 1.33 (1.11-1.59), 1.30 (1.01-1.67) and 1.27 (1.04-1.55) respectively, and was associated with new furniture during first year after birth with OR (95% CI) of 1.43 (1.02-2.02) for early wheeze. Childhood diagnosed allergic diseases (28.4%) containing asthma (6.7%) and allergic rhinitis (AR) (7.2%) were significantly associated with both outdoor air pollutants (mainly for SOOur study indicates that early life exposure to indoor environmental factors plays a key role in early onset of allergic symptoms in children, and further exposure to ambient air pollution and indoor environmental factors contribute to the later development of asthma and allergic rhinitis.

The novel coronavirus disease 2019 (COVID-19) has spread rapidly around the world since December 8, 2019. However, the key factors affecting the duration of recovery from COVID-19 remain unclear.To investigate the associations of long recovery duration of COVID-19 patients with ambient air pollution, temperature, and diurnal temperature range (DTR) exposure.A total of 427 confirmed cases in Changsha during the first wave of the epidemic in January 2020 were selected. We used inverse distance weighting (IDW) method to estimate personal exposure to seven ambient air pollutants (PMWe found that long recovery duration among COVID-19 patients was positively associated with short-term exposure to CO during past week with OR (95% CI) = 1.42 (1.01-2.00) and PMOur findings suggest that both TRAP exposure and temperature indicators play important roles in prolonged recovery among COVID-19 patients, especially for the sensitive populations, which provide potential strategies for effective reduction and early prevention of long recovery duration of COVID-19.

The use of scatterplots is an important method for multivariate data visualization. The point distribution on the scatterplot, along with variable values represented by each point, can help analyze underlying patterns in data. However, determining the multivariate data variation on a scatterplot generated using projection methods, such as multidimensional scaling, is difficult. Furthermore, the point distribution becomes unclear when the data scale is large and clutter problems occur. These conditions can significantly decrease the usability of scatterplots on multivariate data analysis. In this study, we present a cluster-based visual abstraction method to enhance the visualization of multivariate scatterplots. Our method leverages an adapted multilabel clustering method to provide abstractions of high quality for scatterplots. An image-based method is used to deal with large scale data problem. Furthermore, a suite of glyphs is designed to visualize the data at different levels of detail and support data exploration. The view coordination between the glyph-based visualization and the table lens can effectively enhance the multivariate data analysis. Through numerical evaluations for data abstraction quality, case studies and a user study, we demonstrate the effectiveness and usability of the proposed techniques for multivariate data analysis on scatterplots.

Video annotation is an effective way to facilitate content-based analysis for videos. Automatic machine learning methods are commonly used to accomplish this task. Among these, active learning is one of the most effective methods, especially when the training data cost a great deal to obtain. One of the most challenging problems in active learning is the sample selection. Various sampling strategies can be used, such as uncertainty, density, and diversity, but it is difficult to strike a balance among them. In this paper, we provide a visualization-based batch mode sampling method to handle such a problem. An iso-contour-based scatterplot is used to provide intuitive clues for the representativeness and informativeness of samples and assist users in sample selection. A semisupervised metric learning method is incorporated to help generate an effective scatterplot reflecting the high-level semantic similarity for visual sample selection. Moreover, both quantitative and qualitative evaluations are provided to show that the visualization-based method can effectively enhance sample selection in active learning.

Numerical weather predictions have been widely used for weather forecasting. Many large meteorological centers are producing highly accurate ensemble forecasts routinely to provide effective weather forecast services. However, biases frequently exist in forecast products because of various reasons, such as the imperfection of the weather forecast models. Failure to identify and neutralize the biases would result in unreliable forecast products that might mislead analysts; consequently, unreliable weather predictions are produced. The analog method has been commonly used to overcome the biases. Nevertheless, this method has some serious limitations including the difficulties in finding effective similar past forecasts, the large search space for proper parameters and the lack of support for interactive, real-time analysis. In this study, we develop a visual analytics system based on a novel voting framework to circumvent the problems. The framework adopts the idea of majority voting to combine judiciously the different variants of analog methods towards effective retrieval of the proper analogs for calibration. The system seamlessly integrates the analog methods into an interactive visualization pipeline with a set of coordinated views that characterizes the different methods. Instant visual hints are provided in the views to guide users in finding and refining analogs. We have worked closely with the domain experts in the meteorological research to develop the system. The effectiveness of the system is demonstrated using two case studies. An informal evaluation with the experts proves the usability and usefulness of the system.

In multidimensional data analysis, one important task is to investigate the inner relations and patterns. Numerous visual representations have been proposed, such as parallel coordinates and scatter plots. However, parallel coordinates emphasize the overall patterns and data distributions while ignoring the correlation of data values over more than three dimensions. And when rendering more poly lines, the visual clutter may occur. Scatter plots are powerful in revealing the data distributions but the overall patterns are restrained. We propose Pattern Track to reveal the implied relations and patterns while also maintaining flexible operations on data values. The layout of Pattern Track is composed of a mixture of automatic computations and interactive adjustments by mapping all dimension axes to concentric circles and integrating three levels of concentric group -- data values, patterns and gradient circles. Multi-interactive operations are supported to help users dig out the implied multivariate correlations. Experimental results demonstrate the effectiveness.