Pyrethroid resistance status and co-occurrence of V1016G, F1534C and S989P mutations in the Aedes aegypti population from two dengue outbreak counties along the China-Myanmar border.

BACKGROUND: Over the past two decades, dengue fever (DF) has emerged as a significant arboviral disease in Yunnan province, China, particularly in the China-Myanmar border area. Aedes aegypti, an invasive mosquito species, plays a crucial role in transmitting the dengue virus to the local population. Insecticide-based vector control has been the primary tool employed to combat DF, but the current susceptibility status of Ae. aegypti to commonly used insecticides is unknown. Assessment of Ae. aegypti resistance to pyrethroid insecticides and an understanding of the underlying mechanisms of this resistance in the China-Myanmar border region is of significant strategic importance for effectively controlling the DF epidemic in the area. METHODS: Aedes aegypti larvae collected from Ruili and Gengma counties in Yunnan Province were reared to adults in the laboratory and tested for susceptibility to three pyrethroid insecticides (3.20% permethrin, 0.08% lambda-cyhalothrin and 0.20% deltamethrin) by the standard WHO susceptibility bioassay. Genotyping of mutations in the knockdown gene (kdr), namely S989P, V1016G and F1534C, that are responsible for resistance to pyrethroid insecticides was performed using allele-specific PCR methods. A possible association between the observed resistant phenotype and mutations in the voltage-gated sodium channel gene (VGSC) was also studied. RESULTS: Aedes aegypti mosquitoes collected from the two counties and reared in the laboratory were resistant to all of the pyrethroids tested, with the exception of Ae. aegypti from Gengma County, which showed sensitivity to 0.20% deltamethrin. The mortality rate of Ae. aegypti from Ruili county exposed to 3.20% permethrin did not differ significantly from that of Ae. aegypti from Gengma County (χ2 = 0.311, P = 0.577). By contrast, the mortality rate of Ae. aegypti from Ruili County exposed to 0.08% lambda-cyhalothrin and 0.20% deltamethrin, respectively, was significantly different from that of Ae. aegypti from Gengma. There was no significant difference in the observed KDT50 of Ae. aegypti from the two counties to various insecticides. Four mutation types and 12 genotypes were detected at three kdr mutation sites. Based on results from all tested Ae. aegypti, the V1016G mutation was the most prevalent kdr mutation (100% prevalence), followed by the S989P mutation (81.6%) and the F1534C mutation (78.9%). The constituent ratio of VGSC gene mutation types was significantly different in Ae. aegypti mosquitoes from Ruili and those Gengma. The triple mutant S989P + V1016G + F1534C was observed in 274 Ae. aegypti mosquitoes (60.8%), with the most common genotype being SP + GG + FC (31.4%). The prevalence of the F1534C mutation was significantly higher in resistant Ae. aegypti from Ruili (odds ratio [OR] 7.43; 95% confidence interval [CI] 1.71-32.29; P = 0.01) and Gengma (OR 9.29; 95% CI 3.38-25.50; P = 0.00) counties than in susceptible Ae. aegypti when exposed to 3.20% permethrin and 0.08% lambda-cyhalothrin, respectively. No significant association was observed in the triple mutation genotypes with the Ae. aegypti population exposed to 3.20% permethrin and 0.20% deltamethrin resistance (P > 0.05), except for Ae. aegypti from Gengma County when exposed to 0.08% lambda-cyhalothrin (OR 2.86; 95% CI 1.20-6.81; P = 0.02). CONCLUSIONS: Aedes aegypti from Ruili and Gengma counties have developed resistance to various pyrethroid insecticides. The occurrence of multiple mutant sites in VGSC strongly correlated with the high levels of resistance to pyrethroids in the Ae. aegypti populations, highlighting the need for alternative strategies to manage the spread of resistance. A region-specific control strategy for dengue vectors needs to be implemented in the future based on the status of insecticide resistance and kdr mutations.

Urban flooding resilience evaluation with coupled rainfall and flooding models: a small area in Kunming City, China as an example.

Climate change and increasing urbanization have contributed greatly to urban flooding, making it a global problem. The resilient city approach provides new ideas for urban flood prevention research, and currently, enhancing urban flood resilience is an effective means for alleviating urban flooding pressure. This study proposes a method to quantify the resilience value of urban flooding based on the `4R' theory of resilience, by coupling the urban rainfall and flooding model to simulate urban flooding, and the simulation results are used for calculating index weights and assessing the spatial distribution of urban flood resilience in the study area. The results indicate that (1) the high level of flood resilience in the study area is positively correlated with the points prone to waterlogging; the more an area is prone to waterlogging, the lower the flood resilience value. (2) The flood resilience index in most areas shows a significant local spatial clustering effect, the number of areas with nonsignificant local spatial clustering accounting for 46% of the total. The urban flood resilience assessment system constructed in this study provides a reference for assessing the urban flood resilience of other cities, thus facilitating the decision-making process of urban planning and disaster mitigation.

The Development of a Short Chinese Version of the State-Trait Anxiety Inventory.

Background: Few studies on anxiety in China have used the full version of the Spielberger State-Trait Anxiety Inventory (STAI) due to its length, despite its numerous advantages. The goal of this study was to develop a short Chinese version of the STAI and examine its reliability and validity among the general Chinese population and psychiatric patients diagnosed with anxiety. Method: A total of 1,142 participants were invited to test the short Chinese version of the STAI; item analysis, validity testing and internal consistency reliability analysis were performed. Subsequently, 40 participants were enrolled to assess retest reliability 1 week later. Finally, 330 participants, including 33 psychiatric patients with anxiety, were used to assess the criterion and empirical validity. The Self-Rating Anxiety Scale (SAS) and Satisfaction with Life Scale (SWLS) were used as criteria, and receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination of the short version of the STAI between the groups with and without anxiety disorders. Result: The short Chinese version of the STAI contains six items for each subscale (STAI-S-6 and STAI-T-6). The Pearson correlation coefficients between the two shortened Chinese versions of the STAI and the full-length STAI were 0.970 and 0.962, the Cronbach's α coefficients were 0.871 and 0.842, and the retest reliability values were 0.728 and 0.813 (p < 0.001). Confirmatory factor analysis showed that the 2-factor model achieved an adequate model fit: for the STAI-S-6, CFI = 0.986, TLI = 0.974, and RMSEA = 0.075, and for the STAI-T-6, CFI = 0.994, TLI = 0.988, and RMSEA = 0.052. The short Chinese version of the STAI had a significant positive correlation with the SAS score (r = 0.289, 0.313; p < 0.001) and a negative correlation with the SWLS score (r = -0.476, 0.554; p < 0.001). A significant difference in the level of anxiety was found between participants with and without anxiety disorders. The diagnostic accuracy of the STAI-S-6 and STAI-T-6 for major anxiety disorder was acceptable, with areas under the ROC curves of 0.798 and 0.745, respectively. Conclusion: The short Chinese version of the STAI demonstrates sound psychometric properties and is applicable in evaluating the level of anxiety in Chinese populations.

Merging framework for estimating daily surface air temperature by integrating observations from multiple polar-orbiting satellites.

Reconstructing spatially continuous surface air temperature (SAT) is of great significance to climate and environmental studies. Substantial efforts have been made to estimate daily SAT based on land surface temperature (LST) derived from polar-orbiting satellites. However, previous studies are nearly all limited to estimating daily SAT based on MODIS LST from NASA's Terra or Aqua by applying different statistical learning methods. Various satellites from earth observation missions, particularly the missions for meteorological satellites, are capable of acquiring thermal infrared observations, but its implications for SAT estimation are significantly ignored. In this study, for the first time, we proposed a merging framework for estimating daily mean SAT by integrating LST datasets from multiple polar-orbiting satellites, including Metop-B from EUMETSAT's Polar System (EPS), SNPP and JPSS-1 from NOAA's Joint Polar Satellites System (JPSS), and Terra and Aqua from NASA's EOS. This study is also the first to explore the estimating of daily SAT based on LST derived from the meteorological satellites in EPS and JPSS. The framework integrates 10 estimation models based on different LST from the five satellites and generates daily merged SAT by averaging the daily SAT estimates from the models. Here we show that the framework significantly improves the spatial coverage of daily SAT estimates for cloud-free areas by an overall increase of 39% with respect to the mean coverage of the LST datasets from the five satellites. Daily coverage of the merged SAT from the framework is nearly all above 75% with an average of 91%. Compared to the SAT estimated from MODIS LST, overall increases in the coverage of daily SAT are 37%-51%. Estimation models in the framework all achieved comparable and satisfactory predicative performances with an average RMSE of 1.7-1.9 K for sample-based cross-validation, and 1.9-2.2 K for site-based cross-validation.

Correction: Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context window.

[This corrects the article DOI: 10.1371/journal.pone.0236347.].

Access to public transport and childhood obesity: A systematic review.

The lack of access to public transport is generally considered to be a risk factor for childhood obesity by discouraging active transport and thus physical activity. To explore the association between access to public transport and childhood obesity, we have conducted a systematic literature search in the Cochrane Library, PubMed, and Web of Science for studies published before January 1, 2019. A total of 25 cross-sectional and two longitudinal studies conducted in 10 countries were identified. Inconsistent findings were identified arising from a great variety of sample characteristics, definitions of exposure (ie, access to public transport), and outcome variables (eg, obesity), and analysis methods. While over half of the studies showed null associations between access to public transport and childhood obesity, we have observed more positive than negative associations among the rest of the studies. These observations suggest that an increased level of access to public transport may have a health-promoting effect and hence prevent the development of childhood obesity. However, this conclusion needs to be further corroborated in future research on the basis of large-sample health surveys, in situ observations, and comparative analyses among different study areas.

Alterations in reproductive parameters and gene expression in Balb/c mice testes after exposure to silver nanoparticles.

Silver nanoparticles (AgNPs) have become one of the most common nanomaterials in various commercial products; however, its potential toxicity to the male reproductive system and the possible mechanisms remains unknown. Our study aimed to investigate the toxicity of silver nanoparticle (AgNPs) in the testis and to elucidate its possible mechanisms. We exposed 6-week-old Balb/c male mice to AgNP daily [0 (control), 30 or 125 mg/kg BW] for 90 days. The histological structure, sperm production and levels of reproductive hormones were assessed; we also observed apoptotic cell nuclei and the ultrastructural characteristics of the testis. Microarray analyses were used to identify differentially expressed genes, and dysregulated apoptosis-related genes and protein were also analysed. Our results indicated that 125 mg/kg AgNP changed testis morphology and decreased sperm production. AgNP treatment also increased apoptosis of germ cells and induced the presence of swollen or dissolved mitochondria in the testis. Microarray analysis showed the expression of 383 genes was altered by AgNP treatment, with apoptosis-related genes showing the greatest changes. Furthermore, we verified dysregulated apoptosis-related genes and proteins (caspase3 and Myc). These results demonstrated that AgNP induced changes of testis morphology, sperm production and apoptosis-related genes, suggested this process maybe associated with apoptosis.

Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context window.

Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole-spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. We believe this model is a necessary supplement for traditional textual-language semantic analyses of words obtained by word-embedding technologies. This study has the potential to improve the quality of recommendation systems by considering relevant spatial context semantics, and benefits linguistic semantic research by emphasising the spatial cognition among words.

Geosystems' Pathways to the Future of Sustainability.

The world's future development depends on effective human-computer linkages. From local to global, the virtual illustrations of a geographical place have to emphasize in an integrative approach peoples' key position in the Geosystem. Human values and social networks are now empowered by the unlimited creativity of smartphone applications. Our Geosystem grounded theory envisions that the sustainable management of natural resources is a lifelong learning environment where the poor communities have access to the new technological advances. This paper will attempt to show the effectiveness of Geomedia techniques in the Geosystems identification, evaluation, and valorization processes for the benefit of local inhabitants. This present research methodology uses smartphone apps, Google Earth environmental datasets, Global Positioning Systems, and WebGIS for a geographical investigation and objective assessment of regions throughout the world. The results demonstrate that self-sustainable Geosystems will always be capable to regulate, control and assess progress towards their dynamic equilibrium state, continuously adapting to environmental and societal changes.

Assessing Spatial Accessibility to Hierarchical Urban Parks by Multi-Types of Travel Distance in Shenzhen, China.

Urban green spaces play a critical role in public health and human wellbeing for urban residents. Due to the uneven spatial distribution of urban green spaces in most of cities, the issue of the disparity between supply and demand has aroused public concern. In a case of Shenzhen, a modified Gaussian-based two-step floating catchment area (2SFCA) method is adopted to evaluate the disparity between park provision and the demanders in terms of accessibility at hierarchical levels under four types of distance (e.g., Euclidean distance, walking distance, bicycling distance, and driving distance), which is well aligned with hierarchical systems in urban green spaces in urban planning practice. By contrast and correlation analysis, among the four types of distance, the statistical correlations are relatively high between Euclidean distance and the other three. Nonetheless, the pattern of spatial accessibility under different type of travel distance is apparently variant. Accessibility calculated by Euclidean distance is overestimated relative to that of the other three, while the pattern of walking distance and bicycling distance is similar to each other. The choice of type of distance is worthy of caution when evaluating spatial accessibility by 2SFCA method. Results show that the accessibility to parks at all hierarchical levels is high particularly, particularly at the natural level. However, the disparity between the supply and demand is significant. The percentage of communities that have high population density but low park accessibility is over 40% (equivalent to approximately 55% of the population). The finding may provide implications on access to urban greens paces for urban planners and authorities to develop effective planning strategies.

Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.

The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.

Spatiotemporal Changes in Fine Particulate Matter Pollution and the Associated Mortality Burden in China between 2015 and 2016.

In recent years, research on the spatiotemporal distribution and health effects of fine particulate matter (PM2.5) has been conducted in China. However, the limitations of different research scopes and methods have led to low comparability between regions regarding the mortality burden of PM2.5. A kriging model was used to simulate the distribution of PM2.5 in 2015 and 2016. Relative risk (RR) at a specified PM2.5 exposure concentration was estimated with an integrated exposure-response (IER) model for different causes of mortality: lung cancer (LC), ischaemic heart disease (IHD), cerebrovascular disease (stroke) and chronic obstructive pulmonary disease (COPD). The population attributable fraction (PAF) was adopted to estimate deaths attributed to PM2.5. 72.02% of cities experienced decreases in PM2.5 from 2015 to 2016. Due to the overall decrease in the PM2.5 concentration, the total number of deaths decreased by approximately 10,658 per million in 336 cities, including a decrease of 1400, 1836, 6312 and 1110 caused by LC, IHD, stroke and COPD, respectively. Our results suggest that the overall PM2.5 concentration and PM2.5-related deaths exhibited decreasing trends in China, although air quality in local areas has deteriorated. To improve air pollution control strategies, regional PM2.5 concentrations and trends should be fully considered.

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization.

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device's built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

Comparative effectiveness of 9 ovulation-induction therapies in patients with clomiphene citrate-resistant polycystic ovary syndrome: a network meta-analysis.

The comparative efficacies of ovulation-induction treatments in patients with clomiphene citrate-resistant (CCR) polycystic ovary syndrome (PCOS) are not well known. Therefore, we conducted a network meta-analysis to rank the reproductive efficacies of these treatments. We ultimately included 26 randomized clinical trials with 2722 participants and 9 types of therapies: clomiphene citrate (CC), metformin, letrozole, follicle stimulating hormone (FSH), human menopausal gonadotropin (hMG), unilateral laparoscopic ovarian drilling (ULOD), bilateral laparoscopic ovarian drilling (BLOD), the combination of metformin with letrozole (metformin+letrozole), and the combination of metformin with CC (metformin+CC). The network meta-analysis demonstrates that hMG therapy result in higher pregnancy rates than BLOD, ULOD and CC therapies. Pregnancy, live birth and ovulation rates are significantly higher in metformin+letrozole and FSH groups than CC group. The abortion rate in the metformin+letrozole group is significantly lower than that in the metformin+CC group. Ranking probabilities show that, apart from gonadotropin (FSH and hMG), metformin+letrozole is also potentially more effective in improving reproductive outcomes than other therapies. In conclusion, owing to the low quality of evidence and the wide confidence intervals, no recommendation could be made for the treatment of ovulation-induction in patients with CCR PCOS.

Causal inference between bioavailability of heavy metals and environmental factors in a large-scale region.

The causation between bioavailability of heavy metals and environmental factors are generally obtained from field experiments at local scales at present, and lack sufficient evidence from large scales. However, inferring causation between bioavailability of heavy metals and environmental factors across large-scale regions is challenging. Because the conventional correlation-based approaches used for causation assessments across large-scale regions, at the expense of actual causation, can result in spurious insights. In this study, a general approach framework, Intervention calculus when the directed acyclic graph (DAG) is absent (IDA) combined with the backdoor criterion (BC), was introduced to identify causation between the bioavailability of heavy metals and the potential environmental factors across large-scale regions. We take the Pearl River Delta (PRD) in China as a case study. The causal structures and effects were identified based on the concentrations of heavy metals (Zn, As, Cu, Hg, Pb, Cr, Ni and Cd) in soil (0-20 cm depth) and vegetable (lettuce) and 40 environmental factors (soil properties, extractable heavy metals and weathering indices) in 94 samples across the PRD. Results show that the bioavailability of heavy metals (Cd, Zn, Cr, Ni and As) was causally influenced by soil properties and soil weathering factors, whereas no causal factor impacted the bioavailability of Cu, Hg and Pb. No latent factor was found between the bioavailability of heavy metals and environmental factors. The causation between the bioavailability of heavy metals and environmental factors at field experiments is consistent with that on a large scale. The IDA combined with the BC provides a powerful tool to identify causation between the bioavailability of heavy metals and environmental factors across large-scale regions. Causal inference in a large system with the dynamic changes has great implications for system-based risk management.

Silver nanoparticle induced toxicity to human sperm by increasing ROS(reactive oxygen species) production and DNA damage.

Silver nanoparticles (AgNPs) have been used in medical products and industrial coatings, due to their antimicrobial properties. Excessive use of AgNPs can have adverse effects on the human body, however, their toxicity characteristics to human sperm and the potential mechanisms are not entirely clear. In this study, we exposed human sperm to different doses of AgNPs (0, 50µgml-1, 100µgml-1, 200µgml-1 or 400µgml-1) for various times (15min, 30min, or 60min), followed by analyses of the sperm viability, motility and the ratio of abnormal to normal sperm.Then, transmission electron microscopy(TEM) was used to explore the sperm ultrastructural characteristics. Reactive oxygen species production and DNA fragmentation were tested using standard kits and the sperm chromatin dispersion method, respectively. The results showed a dose- and time-dependent decline in sperm viability and motility and an increased ratio of abnormal to normal sperm after 30min and 60min of exposure to AgNPs at 200µgml-1 and 400µgml-1. The most common abnormalities were sperm heads with disrupted chromatin or absent acrosomes, bent tails, and curved mid-pieces. The ultrastructural characteristics of AgNP-treated sperm included disrupted, swollen, granular and vacuolar defects of the chromatin. In addition, ROS(reactive oxygen species)production and DNA fragmentation were markedly increased after 60min of exposure to AgNPs at 200µgml-1 and 400µgml-1. Our results indicated that AgNPs caused detrimental changes in human sperm characteristics, and the excessive use of AgNPs should be carried out with caution.

Body mass index effects sperm quality: a retrospective study in Northern China.

Excess weight and obesity have become a serious problem in adult men of reproductive age throughout the world. The purpose of this retrospective study was to assess the relationships between body mass index and sperm quality in subfertile couples in a Chinese Han population. Sperm analyses were performed and demographic data collected from 2384 male partners in subfertile couples who visited a reproductive medical center for treatment and preconception counseling. The subjects were classified into four groups according to their body mass index: underweight, normal, overweight, and obese. Of these subjects, 918 (38.3%) had a body mass index of >25.0 kg m-0 2 . No significant differences were found between the four groups with respect to age, occupation, level of education, smoking status, alcohol use, duration of sexual abstinence, or the collection time of year for sperm. The results clearly indicated lower sperm quality (total sperm count, sperm concentration, motile sperm, relative amounts of type A motility, and progressive motility sperm [A + B]) in overweight and obese participants than in those with normal body mass index. Normal sperm morphology and sperm volume showed no clear difference between the four groups. This study indicates that body mass index has a negative effect on sperm quality in men of subfertile couples in a Northern Chinese population. Further study should be performed to investigate the relationship between body mass index and sperm quality in a larger population.

Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China.

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord [Formula: see text] method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method's ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

Spatial Patterns of Ischemic Heart Disease in Shenzhen, China: A Bayesian Multi-Disease Modelling Approach to Inform Health Planning Policies.

Incorporating the information of hypertension, this paper applies Bayesian multi-disease analysis to model the spatial patterns of Ischemic Heart Disease (IHD) risks. Patterns of harmful alcohol intake (HAI) and overweight/obesity are also modelled as they are common risk factors contributing to both IHD and hypertension. The hospitalization data of IHD and hypertension in 2012 were analyzed with three Bayesian multi-disease models at the sub-district level of Shenzhen. Results revealed that the IHD high-risk cluster shifted slightly north-eastward compared with the IHD Standardized Hospitalization Ratio (SHR). Spatial variations of overweight/obesity and HAI were found to contribute most to the IHD patterns. Identified patterns of IHD risk would benefit IHD integrated prevention. Spatial patterns of overweight/obesity and HAI could supplement the current disease surveillance system by providing information about small-area level risk factors, and thus benefit integrated prevention of related chronic diseases. Middle southern Shenzhen, where high risk of IHD, overweight/obesity, and HAI are present, should be prioritized for interventions, including alcohol control, innovative healthy diet toolkit distribution, insurance system revision, and community-based chronic disease intervention. Related health resource planning is also suggested to focus on these areas first.

Inhibition of Lysine-Specific Demethylase-1 (LSD1/KDM1A) Promotes the Adipogenic Differentiation of hESCs Through H3K4 Methylation.

Given their totipotency, human embryonic stem cells (hESCs) can differentiate into all types of cells, including adipocytes, and provide an excellent research model for studying diseases associated with the metabolism of adipocytes, such as obesity and diabetes mellitus. Epigenetic regulation, including DNA methylation and histone modification, plays an essential role in the development and differentiation of hESCs. Lysine-specific demethylase 1 (LSD1), a well-characterized histone-modifying enzyme, demethylates dimethylated histone H3 lysine 4 (H3K4) through a flavin adenine dinucleotide (FAD)-dependent oxidative reaction. LSD1 affects the growth and differentiation of human and mouse ES cells, and the deletion of this gene in mice leads to embryonic lethality. Here, we investigated the functional role of LSD1 during the adipogenic differentiation of hESCs involving the demethylation of H3K4. We also found that treating hESCs with the LSD1 inhibitor CBB1007 promotes the adipogenic differentiation of hESCs.