Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.

Aims: Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Methods and results: Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Conclusion: Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.

BACKGROUND: Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drivers of psoriasis risk. However, it is increasingly recognized that health behavior arises in the context of larger social, cultural, economic and environmental determinants of health. We aimed to identify the top risk factors that significantly impact the incidence of psoriasis at the neighborhood level using populational data from the province of Quebec (Canada) and advanced tree-based machine learning (ML) techniques. METHODS: Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1 year prior to psoriasis onset were obtained from the Canadian Urban Environment Health Consortium (CANUE) and Statistics Canada (StatCan) and were input as predictors into the gradient boosting ML. Model performance was evaluated using the area under the curve (AUC). Parsimonious models and partial dependence plots were determined to assess directionality of the relationship. RESULTS: The incidence of psoriasis varied geographically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious model (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amongst top predictors, ultraviolet (UV) radiation, maximum daily temperature, proportion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggested a higher psoriasis incidence in the middle socioeconomic class neighborhoods. CONCLUSION: This is the first study to highlight highly variable psoriasis incidence rates on a jurisdictional level and suggests that living environment, notably climate, vegetation, urbanization and neighborhood socioeconomic characteristics may have an association with psoriasis incidence.

Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.

We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.

The regulation of light quality on the substance production and photosynthetic activity of Dunaliella bardawil.

To study the influence and regulation of light quality on the microalgal photosynthetic activity and production of biomass and substances, green alga Dunaliella bardawil was cultured in this study under the monochromatic red light (7R0B), blue light (0R7B), and their combinations with different ratios (xRyB, x + y = 7), as well as a control of white light (W). The results demonstrated that the only advantage for control W was its chlorophyll-a (Chl-a) and Chl-b contents. All substance production at 7R0B were much lower than at control W, except of glycerol. Compared to control W, protein production at 1R6B (259.22 mg/L) was 1.10 times greater, carbohydrate production at 0R7B (306.49 mg/L) was 1.34 times higher, lipid production at 3R4B (133.60 mg/L) was 1.36 times higher, and glycerol production at 4R3B (53.58 mg/L) was 1.13 times greater. In comparison to control W, there was the significant improvements of at least 19%, 20%, and 5%, respectively, in the values of potential maximal relative electron transport efficiency (rETRmax), light intensity with saturated rETR (IK), and actual photochemical efficiency of PSII (QYss) in treatments. The correlation analysis revealed that the content of carotenoids was closely related to non-photochemical quenching (NPQ). The test using Chl-a fluorescence transients (JIP-test) proved that red light inhibited electron transport from reduced Quinone A (QA-) to QB and resulted in a sharp increase in RC/CSm, and that the blue-dominated light enhanced electron transport from QA- to QB and from plastoquinone (PQ) to PSI receptor side. The photosynthetic parameters including Ψo, φEO, φRO, δRO, PIABS, PItotal, DFABS, and DFtotal, which were positively correlated with growth and substance production, were improved by blue-dominated light. The variations in the electron transport chain might provide the signals for metabolic regulation. The results of this study will be helpful to promote the production of Dunaliella bardawil under artificial illumination and to clarify the regulating mechanism of light quality on microalgal photosynthesis.

High transparency, water vapor barrier and antibacterial properties of chitosan/carboxymethyl glucan/poly(vinyl alcohol)/nanoparticles encapsulating citral composite film for fruit packaging.

Utilizing antibacterial packaging material is an effective approach to delay fruit rotting and spoilage thereby minimizing financial losses and reducing health harm. However, the barrier and mechanical properties of biodegradable antibacterial packaging materials are barely compatible with transparency. Herein, antimicrobial nanoparticles encapsulating citral (ANPs) were first prepared by emulsification under the stabilization of oxidized dextran (ODE) and ethylene diamine. Then, composite films with high transparency, good water vapor barrier, and mechanical and antibacterial properties for fruits packaging were prepared from chitosan (CS), carboxymethyl-glucan (CMG), poly(vinyl alcohol) (PVA), and ANPs by solvent casting strategy. The synergistic effects of electrostatic interaction and hydrogen bonding could regulate crystalline architecture, generating high transparency of the composite films (90 %). The mechanical properties of the composite film are improved with elongation at break up to 167 % and stress up to 32 MPa. The water vapor barrier property of the film is appropriate to the packed fruit for less weight loss and firmness remaining. Simultaneously, the addition of ANPs endowed the film with excellent antimicrobial and UV-barrier capabilities to reduce fruit mildew, thereby extending the shelf life of fruits. More importantly, the composite polymer solution could be sprayed or dipped directly on fruits as a coating for food storage to improve food shelf life, substantially expanding its ease of use and scope of use.

Epidemiology and risk factors for carbapenemase-producing Enterobacteriaceae carriage in the hospital: A population-based nested case-control study.

OBJECTIVES: This study aims to study the epidemiology of carbapenemase-producing Enterobacteriaceae (CPE) in Hong Kong. METHODS: This is a longitudinal population-based study reporting monthly CPE incidence rate and a nested case-control study for identifying risk factors for CPE carriage. The cases were patients with at least one CPE-positive genotypic test, while the controls were randomly selected from the cohort with negative tests. Up to four controls per case were matched by sex, age group, and admission year-month. The independent risk factors were identified from a conditional logistic regression with potential covariates. RESULTS: From 1 January 2008 to 31 December 2019, 8588 patients received CPE genotyping tests, and 2353 had at least one positive result. Class B carbapenemase was the predominant enzyme in the samples (78.6%). The incidence rate increased from 0.04 in 2015 to 1.62 in 2019 per 10,000 person-year. In the nested case-control study, 1709 cases and 6664 controls were matched. Previous use of any beta-lactam antibiotics (odds ratio:1.37 [1.22-1.53], P < 0.001) was found as an independent risk factor for carriage of CPE. CONCLUSION: The carriage of CPE was found with an increasing trend in Hong Kong. Previous use of any beta-lactam antibiotics is a risk factor for CPE.

Genetically predicted selenium concentrations and thyroid function: A two-sample Mendelian randomization study.

OBJECTIVE: The impact of selenium (Se) on human thyroid function remains unclear, with inconsistent results from recent epidemiological studies. Moreover, the observed associations are prone to bias due to potential confounding and reverse causation. Mendelian randomization (MR) analysis facilitates the large minimization of biases produced by environmental and lifestyle influences, providing unconfounded estimates of causal effects using instrumental variables. We aim to examine the association between Se concentrations and human thyroid function using a two-sample MR analysis. DESIGN AND METHODS: Genetic instruments for Se concentrations, including toenail and blood (TAB) and blood Se concentrations, were identified from a genome-wide association study (GWAS) of blood Se (n = 5477) and toenail Se levels (n = 4162). GWAS summary statistics on thyroid phenotypes were downloaded from the ThyroidOmics consortium, including thyroid-stimulating hormone (TSH) (n = 54,288), free thyroxin (FT4) (n = 49,269), hypo (n = 53,423), and hyperthyroidism (n = 51,823). The MR study was conducted using the inverse-variance weighted (IVW) method, supplemented with the weighted median and the mode-based method. RESULTS: Genetically determined TAB Se was negatively associated with FT4 (ß = -.067; 95% confidence interval [CI] = -0.106, -0.028; p = 0.001) using the IVW analyses, as well in the additional analyses using the weighted median and weighted-mode methods. No evidence in heterogeneity, pleiotropy or outlier single-nucleotide polymorphisms was detected (all p > 0.05). Suggestive casual association between increased genetically determined TAB Se concentrations and decreased hypothyroidism risk was found by the IVW method (odds ratio [OR] = 0.847; 95% CI = 0.728, 0.985; p = 0.031). The causal effect of TAB Se on FT4 was observed in women (ß = -.076; 95% CI = -0.129, -0.024; p = 0.004). However, the influence of genetically determined higher Se concentrations on TSH levels and hyperthyroidism revealed insignificance in the primary and sensitivity analyses. CONCLUSIONS: The present MR study indicated that high Se concentration enable the decreasing of FT4 levels, and the effects of Se concentrations on FT4 remain sex-specific.

Spatiotemporal assessment of potential drivers of salt marsh dieback in the North Inlet-Winyah Bay estuary, South Carolina (1990-2019).

Previous studies in the Gulf of Mexico and Atlantic states have suggested that a suite of possibly abiotic and biotic attributes is responsible for salt marsh dieback, e.g., drought, soil waterlogging, soil chemistry, top-down consumers control, etc. However, there are no conclusive answers in current literature explaining what led to marsh dieback in past decades, especially from the spatiotemporal perspective. Exploring all Landsat-retrieved marsh dieback events in 1990-2019, this research investigates the spatiotemporal relationships between the dieback series and the associated environmental variables in an intertidal marsh in South Carolina (SC). Based on our previous study, a series of marsh dieback events in the past 30 years were identified and dieback pixels in the estuary were extracted. Among these were the most severe marsh dieback events (1991, 1999, 2000, 2002, 2004, and 2013). Daily Evaporative Demand Drought Index (EDDI), daily precipitation data from Parameter Elevation Regressions on Independent Slopes Model (PRISM), and station-based water quality observations (dissolved oxygen, specific conductivity, salinity, turbidity, pH, and temperature) in the estuary were retrieved. Integrated with the proof-by-exhaustion method, statistical analysis showed marsh dieback were highly related to moisture imbalance in a period of 90 days before the dieback events. Respectively, pH for Clambank and Debidue Creek, salinity and turbidity for Thousand Acre were found to be the key water quality variables influencing marsh dieback besides drought. This study cogitates the environmental influence on coastal marsh dieback from a spatiotemporal perspective using a long-term satellite time series analysis. The findings could provide insights into marsh ecological resilience and facilitate coastal ecosystem management.

Associations between seasonal ambient air pollution and adverse perinatal outcomes: a retrospective cohort study in Wenzhou, China.

Prenatal exposure to ambient air pollution has been associated with adverse perinatal outcomes in previous studies. However, few studies have examined the interaction between air pollution and the season of conception on term low birth weight (TLBW) or macrosomia. Birth registry data of singleton live births in Wenzhou, China, between January 2015 and December 2016 were accessed from the Wenzhou Maternal and Child Health Information Management platform, and data on the ambient air pollutants in Wenzhou were obtained from the Chinese Air Quality Online Monitoring and Analysis Platform. Single-/two-pollutant binary logistic regression models were used to assess the associations between ambient air pollutants (PM2.5, PM10, NO2, SO2, and O3) and TLBW/macrosomia, further exploring whether the season of conception interacts with air pollution to impact birth weight. Finally, 213,959 term newborns were selected, including 2452 (1.1%) infants with TLBW and 13,173 (6.1%) infants with macrosomia. In the single-/two-pollutant models, we observed an increased risk of TLBW associated with maternal exposure to PM2.5, PM10, SO2, and NO2 during the entire pregnancy, especially in the 2nd trimester. Maternal exposure to O3 during the 1st trimester was associated with increased macrosomia risk, and O3 exposure during the 3rd trimester was associated with increased TLBW risk. Pregnant women who conceive in the warm season may experience a more adverse ambient air environment that is related to the risks of TLBW. These findings add to the evidence suggesting that air pollution and the season of conception may have synergistic effects on adverse perinatal outcomes, especially TLBW. Further prospective cohort studies are needed to validate our results.

Risk of cardiovascular disease in breast cancer patients receiving aromatase inhibitors vs. tamoxifen: A systematic review and meta-analysis.

WHAT IS KNOWN AND OBJECTIVE: Breast cancer is one of the leading causes of morbidity and mortality in women worldwide. In order to reduce the risks of its recurrence, endocrine therapies, such as tamoxifen and aromatase inhibitors are commonly administered. Despite having a similar efficacy in preventing breast cancer recurrence, these drugs differ in terms of instigating cardiovascular morbidities. Recent randomized controlled trials and cohort studies provide inconclusive evidence of the cardiovascular risks associated with the administration of these endocrine therapies. This present review and meta-analysis evaluates the comparative cardiovascular adverse event outcomes in breast cancer patients receiving tamoxifen and aromatase inhibitors. To evaluate the comparative cardiovascular adverse outcomes, such as venous thromboembolism, heart failure, angina, myocardial infarction and stroke in patients with breast cancer receiving tamoxifen and aromatase inhibitors. METHODS: A systematic search of the academic literature was performed according to the PRISMA guidelines across five databases, including Web of Science, EMBASE, CENTRAL, Scopus, and MEDLINE. A random-effect meta-analysis was conducted to compare the cardiovascular adverse events (i.e. venous thromboembolism, heart failure, angina, myocardial infarction, stroke) in breast cancer patients treated with tamoxifen and aromatase inhibitors. RESULTS AND DISCUSSION: From 993 studies, 20 eligible studies were identified, with 174,142 female breast cancer patients (mean age: 67.4 ± 3.8 years). A meta-analysis revealed insignificantly (p > 0.05) higher risks of venous thromboembolism (Odds ratio, 95% CI: 1.70, 0.91-3.18) in patients treated with tamoxifen as compared to aromatase inhibitors. We also observed insignificantly higher risks of stroke (0.93, 0.45-1.91), angina (0.77, 0.12-4.59), myocardial infarction (0.74, 0.30-1.79), and heart failure (0.81, 0.22-2.91) in patients receiving aromatase inhibitors as compared to tamoxifen. WHAT IS NEW AND CONCLUSIONS: The study provides evidence regarding the comparative cardiovascular adverse outcomes between breast cancer patients consuming tamoxifen and aromatase inhibitors. The study reports an insignificant increase in the events of stroke, angina, myocardial infarction, and heart failure in breast cancer patients treated with aromatase inhibitors as compared to tamoxifen. The study also reports that tamoxifen treatment is associated with an insignificant increase in the events of venous thromboembolism as compared to treatment with aromatase inhibitors.

What matters: non-pharmaceutical interventions for COVID-19 in Europe.

OBJECTIVES: The purpose of this study is to describe the situation of COVID-19 in European countries and to identify important factors related to prevention and control. METHODS: We obtained data from World Health Statistics 2020 and the Institute for Health Metrics and Evaluation (IHME). We calculated the Rt values of 51 countries in Europe under different prevention and control measures. We used lasso regression to screen factors associated with morbidity and mortality. For the selected variables, we used quantile regression to analyse the relevant influencing factors in countries with different levels of morbidity or mortality. RESULTS: The government has a great influence on the change in Rt value through prevention and control measures. The most important factors for personal and group prevention and control are the mobility index, testing, the closure of educational facilities, restrictions on large-scale gatherings, and commercial restrictions. The number of ICU beds and doctors in medical resources are also key factors. Basic sanitation facilities, such as the proportion of safe drinking water, also have an impact on the COVID-19 epidemic. CONCLUSIONS: We described the current status of COVID-19 in European countries. Our findings demonstrated key factors in individual and group prevention measures.

Bell's palsy following vaccination with mRNA (BNT162b2) and inactivated (CoronaVac) SARS-CoV-2 vaccines: a case series and nested case-control study.

BACKGROUND: Bell's palsy is a rare adverse event reported in clinical trials of COVID-19 vaccines. However, to our knowledge no population-based study has assessed the association between the inactivated SARS-CoV-2 vaccines and Bell's palsy. The aim of this study was to evaluate the risk of Bell's palsy after BNT162b2 and CoronaVac vaccination. METHODS: In this case series and nested case-control study done in Hong Kong, we assessed the risk of Bell's palsy within 42 days following vaccination with BNT162b2 (Fosun-BioNTech [equivalent to Pfizer-BioNTech]) or CoronaVac (from Sinovac Biotech, Hong Kong) using data from voluntary surveillance reporting with the Hospital Authority, the COVID-19 Vaccine Adverse Event Online Reporting system for all health-care professionals, and the Hospital Authority's territory-wide electronic health records from the Clinical Data Analysis and Reporting System. We described reported cases of Bell's palsy among vaccine recipients (aged 18-110 years for CoronaVac and aged 16-110 years for BNT162b2). We compared the estimated age-standardised incidence of clinically confirmed cases among individuals who had received the CoronaVac or BNT162b2 vaccination (up to 42 days before presentation) with the background incidence in the population. A nested case-control study was also done using conditional logistic regression to estimate the odds ratio (OR) for risk of Bell's palsy and vaccination. Cases and controls were matched (1:4) by age, sex, admission setting, and admission date. FINDINGS: Between February 23 and May 4, 2021, 451 939 individuals received the first dose of CoronaVac and 537 205 individuals received the first dose of BNT162b2. 28 clinically confirmed cases of Bell's palsy were reported following CoronaVac and 16 cases were reported following BNT162b2. The age-standardised incidence of clinically confirmed Bell's palsy was 66·9 cases per 100 000 person-years (95% CI 37·2 to 96·6) following CoronaVac vaccination and 42·8 per 100 000 person-years (19·4 to 66·1) for BNT162b2 vaccination. The age-standardised difference for the incidence compared with the background population was 41·5 (95% CI 11·7 to 71·4) for CoronaVac and 17·0 (-6·6 to 40·6) for BNT162b2, equivalent to an additional 4·8 cases per 100 000 people vaccinated for CoronaVac and 2·0 cases per 100 000 people vaccinated for BNT162b2. In the nested case-control analysis, 298 cases were matched to 1181 controls, and the adjusted ORs were 2·385 (95% CI 1·415 to 4·022) for CoronaVac and 1·755 (0·886 to 3·477) for BNT162b2. INTERPRETATION: Our findings suggest an overall increased risk of Bell's palsy after CoronaVac vaccination. However, the beneficial and protective effects of the inactivated COVID-19 vaccine far outweigh the risk of this generally self-limiting adverse event. Additional studies are needed in other regions to confirm our findings. FUNDING: The Food and Health Bureau of the Government of the Hong Kong Special Administrative Region, China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Spatiotemporal dynamics of ecosystem water use efficiency over the Chinese Loess Plateau base on long-time satellite data.

Ecosystem water use efficiency (eWUE), defined as the ratio between carbon gains and water loss from the system, has been recognized as an important characteristic of carbon and water balances. The long-lasting "Grain for Green" Program (GFGP) initiated in 1999 in China's Loess Plateau (CLP) is a large-scale ecological program in the world, which aims to improve the CLP's ecosystem resilience by enhancing vegetation cover and productivity. Understanding how the GFGP can affect eWUE is imperative to ensuring sustainable water resources and to promoting sustainable management strategies. In this study, we evaluated the spatiotemporal variability of growing-season eWUE and examined its response to both climate change and vegetation coverage from 1982 to 2017. Our results indicate that growing-season eWUE, gross primary productivity (GPP), and evapotranspiration (ET) in CLP area increased significantly from 1982 to 2017. Specifically, eWUE, GPP, and ET increased more rapidly after China established the program. The most significant growth area of eWUE was found in main areas conducting GFGP project, including the Loess hilly and gully area (LHGA). Spatially, eWUE, GPP, and ET in the growing season increased from northwest to southeast, and higher eWUE was found in areas with high vegetation cover. The spatial and temporal variability of eWUE was related to vegetation cover (expressed as leaf area index, LAI) and climatic variability. Significant positive correlations were observed between growing-season LAI, temperature, and eWUE, because the LAI and temperature have a greater effect on photosynthesis than ET. Our results suggested that the GFGP was the main driving force that causes the spatial-temporal variability of eWUE in CLP.

Placental mtDNA copy number and methylation in association with macrosomia in healthy pregnancy.

INTRODUCTION: Fetal growth and development depend on metabolic energy from placental mitochondria. However, the impact of placental mitochondria on the occurrence of macrosomia remains unclear. We aimed to explore the association between macrosomia without gestational diabetes mellitus (non-GDM) and changes in placental mitochondrial DNA (mtDNA) copy number and methylation. METHODS: Fifty-four newborns with macrosomia and 54 normal birthweight controls were enrolled in this study. Placental mtDNA copy number and mRNA expression of nuclear genes related to mitochondrial replication or ATP synthesis-related genes were measured by real-time quantitative polymerase chain reaction (qPCR). Methylation levels of the non-coding regulatory region D-loop and ATP synthesis-related genes were detected by targeted bisulfite sequencing. RESULTS: Newborns with macrosomia had lower placental mtDNA copy number and higher methylation rates of the CpG15 site in the D-loop region (D-CpG15) and CpG6 site in the cytochrome C oxidase III (COX3) gene (COX3-CpG6) than normal birth weight newborns. After adjusting for potential covariates (gestational age, prepregnancy BMI, and infant sex), decreased placental mtDNA copy number (adjusted odds ratio [aOR] = 2.09, 95% confidence interval [CI] 1.03-4.25), elevated methylation rate of D-CpG15 (aOR = 2.06, 95% CI 1.03-4.09) and COX3-CpG6 (aOR = 2.13, 95% CI 1.08-4.20) remained significantly associated with a higher risk of macrosomia. DISCUSSION: Reduced mtDNA copy number and increased methylation levels of specific loci at mtDNA would increase the risk of macrosomia. However, the detailed molecular mechanism needs further identification.

Spatial heterogeneity of changes in cropland ecosystem water use efficiency and responses to drought in China.

Understanding cropland ecosystem water use efficiency (eWUE) responses to drought is important for sustainable water resource management and food security. Today in China, the spatiotemporal patterns of eWUE and responses to drought across different cropland classes remain poorly quantified. In this study, we characterized the spatial temporal variability in cropland eWUE and response to drought in China from 1982 to 2017 using the satellite-retrieved evapotranspiration (ET), gross primary production (GPP), and self-calibrating Palmer Drought Severity Index (scPDSI), in conjunction with the Global Food Security-support Analysis Data product for Crop Dominance (GFSAD1KCD) data. Results indicated that (1) mean annual cropland eWUE had a spatial range from 0 to 9.94 g C kg-1 H2O, with higher values (2.06 g C kg-1 H2O) in class 4 (rainfed: wheat, rice, and soybeans dominant), whereas the lowest eWUE (1.58 g C kg-1 H2O) occurred in class 2 (irrigated mixed crop 1: wheat, rice, barley, and soybeans). (2) Annual eWUE, GPP, and ET values for croplands in China increased significantly between 1982 and 2017. Class 1 (irrigated wheat and rice) had the highest trend of 0.011 g C kg-1 H2O yr-1, and class 6 (rainfed: corn and soybeans) had the lowest of 0.0007 g C kg-1 H2O yr-1. Apart from class 4, annual GPP and ET were enhanced in most cropland classes from 1982 to 2017 (p<0.01). (3) Rainfed croplands generally had higher eWUE, GPP, and ET values than irrigated croplands. Except for rainfed cropland eWUE, all other cropland variables increased significantly (p<0.001) from 1982 to 2017. (4) Correlation analysis found that the 19.66% (15.62%) of cropland had significant negative (positive) correlations between eWUE and current-year scPDSI. The legacy effects of drought on cropland eWUE indicated that previous and current-year drought impacts on cropland eWUE were in the same direction. Our results provide insights into variability in cropland eWUE and its response to drought in China, where there is a growing demand for agricultural water resource management.

Health economics modeling of antiretroviral interventions amongst HIV serodiscordant couples.

Antiretroviral treatment (ART) and pre-exposure prophylaxis (PrEP) for HIV-serodiscordant couples, effectively reduce mortality, transmission events and influence quality of life at the expense of increased costs. We aimed to evaluate health economics of antiretroviral-based strategies for HIV-serodiscordant couples in the China context. A deterministic model of HIV evolution and transmission within a cohort of serodiscordant couples was parameterized using the real-world database of Zhoukou city and published literature. We evaluated the mid-ART (a historical strategy, initiating ART with CD4 < 500 cells/mm3), early-ART (the current strategy, offering ART regardless of CD4 cell counts) and a hypothetical strategy (early-ART combined short-term daily PrEP) versus the late-ART (the baseline strategy, initiating ART with CD4 < 350 cells/mm3) offered by 2008 national guidelines. We estimated the incremental cost-effectiveness ratios (ICER) and incremental cost-utility ratios (ICUR) from a societal perspective, derived by clinical benefits and HIV-caused life quality respectively, and portrayed their changes over a 0-30 year's timeframe. The model projections indicated that the antiretroviral-based interventions were more likely to obtain clinical benefits but difficult to improve quality of life, and cumulative ICER and ICUR were generally decreasing without achieving cost-saving. Scale-up access to ART for the HIV-positive among serodiscordant couples was easily fallen within the range of paying for incremental life-years and quality adjusted life years by the societal willingness. The hypothetical strategy had the potential to prevent most seroconversion events within marriages but required enormous upfront costs, thus it took a long time to reach established thresholds. The current strategy of early-ART is the most cost-effective. Clarifying the obstacles of high cost of PrEP and improving life quality for HIV-serodiscordant couples have emerged as an urgent requisition.

Optimizing the dynamic and thermodynamic properties of hybridization in DNA-mediated nanoparticle self-assembly.

DNA-directed nanoparticle (DNA-NP) systems provide various applications in sensing, medical diagnosis, data storage, plasmonics and photovoltaics. Bonding probability and melting properties are helpful to evaluate the selectivity, thermostability and thermosensitivity of these applications. We investigated the influence of temperature, nanoparticle size, DNA chain length and surface grafting density of DNA on one nanoparticle on the DNA dynamic hybridization percentage and melting properties of DNA-NP assembly systems by molecular dynamics simulation. The high degree of consistency of free energy estimations for DNA hybridization via our theoretical deduction and the nearest-neighbor rule generally used in experiments validates reasonably our DNA model. The melting temperature and thermosensitivity parameter are determined by the sigmoidal melting curves based on hybridization percentage versus temperature. The results indicated that the hybridization percentage presents a downward trend with increasing temperature and nanoparticle size. Applications based on DNA-NP systems with bigger nanoparticle size, such as DNA probes, have better selectivity, thermostability and thermosensitivity. There exist optimal DNA chain length and surface grafting density where the hybridization percentage reaches the maximal value. The melting temperature reaches a maximum at the point of optimal grafting density, while the thermosensitivity parameter presents an upward trend with the increase of grafting density. Several physical quantities consisting of the radial density function, root mean square end-to-end distance, contact distance parameter and effective volume fraction are used to analyse DNA chain conformations and DNA-NP packing in the assembly process. Our findings provide the theoretical basis for the improvement and optimization of applications based on DNA-NP systems.

The impact of childhood pneumococcal conjugate vaccine immunisation on all-cause pneumonia admissions in Hong Kong: A 14-year population-based interrupted time series analysis.

BACKGROUND: Nine years after the introduction of pneumococcal conjugate vaccine (PCV) in the United States, Hong Kong (HK) introduced the vaccine to its universal childhood immunisation programme in 2009. We aimed to assess the impact of childhood PCV immunisation on all-cause pneumonia (ACP) admissions among the overall population of HK. METHODS: In this population-based interrupted time series analysis, we used territory-wide population-representative electronic health records in HK to evaluate the vaccine impact. We identified hospitalised patients with a diagnosis of pneumonia from any cause between 2004 and 2017. We applied segmented Poisson regression to assess the gradual change in the monthly incidence of ACP admissions between pre- and post-vaccination periods. Negative outcome control, subgroup and sensitivity analyses were used to test the robustness of the main analysis. FINDINGS: Over the 14-year study period, a total of 587,607 ACP episodes were identified among 357,950 patients. The monthly age-standardised incidence of ACP fluctuated between 33.42 and 87.44 per 100,000-persons. There was a marginal decreasing trend in pneumonia admissions after PCV introduction among overall population (incidence rate ratio [IRR]: 0·9965, 95% confidence interval [CI]: 0·9932-0·9998), and older adults (≥65 years, IRR: 0·9928, 95% CI: 0·9904-0·9953) but not in younger age groups. INTERPRETATION: There was a marginally declining trend of overall ACP admissions in HK up to eight years after childhood PCV introduction. The significance disappeared when fitting sensitivity analyses. The results indicate the complexities of using non-specific endpoints for measuring vaccine effect and the necessity of enhancing serotype surveillance systems for replacement monitoring. FUNDING: Health and Medical Research Fund, Food and Health Bureau of the Government of Hong Kong (Reference number: 18171272).

Woody-biomass projections and drivers of change in sub-Saharan Africa.

Africa's ecosystems have an important role in global carbon dynamics, yet consensus is lacking regarding the amount of carbon stored in woody vegetation and the potential impacts to carbon storage in response to changes in climate, land use, and other Anthropocene risks. Here, we explore the socio-environmental conditions that shaped the contemporary distribution of woody vegetation across sub-Saharan Africa and evaluate ecosystem response to multiple scenarios of climate change, anthropogenic pressures, and fire disturbance. Our projections suggest climate change will have a small but negative effect on above ground woody biomass at the continental scale, and the compounding effects of population growth, increasing human pressures, and socio-climatic driven changes in fire behavior further exacerbate climate-driven trends. Relatively modest continental-scale trends obscure much larger regional perturbations, with climatic and anthropogenic factors leading to increased carbon storage potential in East Africa, offset by large deficits in West, Central, and Southern Africa.

Study becomes insight: Ecological learning from machine learning.

The ecological and environmental science communities have embraced machine learning (ML) for empirical modelling and prediction. However, going beyond prediction to draw insights into underlying functional relationships between response variables and environmental 'drivers' is less straightforward. Deriving ecological insights from fitted ML models requires techniques to extract the 'learning' hidden in the ML models.We revisit the theoretical background and effectiveness of four approaches for deriving insights from ML: ranking independent variable importance (Gini importance, GI; permutation importance, PI; split importance, SI; and conditional permutation importance, CPI), and two approaches for inference of bivariate functional relationships (partial dependence plots, PDP; and accumulated local effect plots, ALE). We also explore the use of a surrogate model for visualization and interpretation of complex multi-variate relationships between response variables and environmental drivers. We examine the challenges and opportunities for extracting ecological insights with these interpretation approaches. Specifically, we aim to improve interpretation of ML models by investigating how effectiveness relates to (a) interpretation algorithm, (b) sample size and (c) the presence of spurious explanatory variables.We base the analysis on simulations with known underlying functional relationships between response and predictor variables, with added white noise and the presence of correlated but non-influential variables. The results indicate that deriving ecological insight is strongly affected by interpretation algorithm and spurious variables, and moderately impacted by sample size. Removing spurious variables improves interpretation of ML models. Meanwhile, increasing sample size has limited value in the presence of spurious variables, but increasing sample size does improves performance once spurious variables are omitted. Among the four ranking methods, SI is slightly more effective than the other methods in the presence of spurious variables, while GI and SI yield higher accuracy when spurious variables are removed. PDP is more effective in retrieving underlying functional relationships than ALE, but its reliability declines sharply in the presence of spurious variables. Visualization and interpretation of the interactive effects of predictors and the response variable can be enhanced using surrogate models, including three-dimensional visualizations and use of loess planes to represent independent variable effects and interactions.Machine learning analysts should be aware that including correlated independent variables in ML models with no clear causal relationship to response variables can interfere with ecological inference. When ecological inference is important, ML models should be constructed with independent variables that have clear causal effects on response variables. While interpreting ML models for ecological inference remains challenging, we show that careful choice of interpretation methods, exclusion of spurious variables and adequate sample size can provide more and better opportunities to 'learn from machine learning'.