Climate change impacts on the extreme power shortage events of wind-solar supply systems worldwide during 1980-2022.

Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980-2000 and 2001-2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future.

MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.

Widespread 2013-2020 decreases and reduction challenges of organic aerosol in China.

High concentrations of organic aerosol (OA) occur in Asian countries, leading to great health burdens. Clean air actions have resulted in significant emission reductions of air pollutants in China. However, long-term nation-wide trends in OA and their causes remain unknown. Here, we present both observational and model evidence demonstrating widespread decreases with a greater reduction in primary OA than in secondary OA (SOA) in China during the period of 2013 to 2020. Most of the decline is attributed to reduced residential fuel burning while the interannual variability in SOA may have been driven by meteorological variations. We find contrasting effects of reducing NOx and SO2 on SOA production which may have led to slight overall increases in SOA. Our findings highlight the importance of clean energy replacements in multiple sectors on achieving air-quality targets because of high OA precursor emissions and fluctuating chemical and meteorological conditions.

Associations between satellite-derived estimates of PM2.5 species concentrations for organic carbon, elemental carbon, nitrate, and sulfate with birth weight and preterm birth in California during 2005-2014.

BACKGROUND: Characterizing the spatial distribution of PM2.5 species concentrations is challenging due to the geographic sparsity of the stationary monitoring network. Recent advances have enabled valid estimation of PM2.5 species concentrations using satellite remote sensing data for use in epidemiologic studies. OBJECTIVE: In this study, we used satellite-based estimates of ambient PM2.5 species concentrations to estimate associations with birth weight and preterm birth in California. METHODS: Daily 24 h averaged ground-level PM2.5 species concentrations of organic carbon, elemental carbon, nitrate, and sulfate were estimated during 2005-2014 in California at 1 km resolution. Birth records were linked to ambient pollutant exposures based on maternal residential zip code. Linear regression and Cox regression were conducted to estimate the effect of 1 µg/m3 increases in PM2.5 species concentrations on birth weight and preterm birth. RESULTS: Analyses included 4.7 million live singleton births having a median 28 days with exposure measurements per pregnancy. In single pollutant models, the observed changes in mean birth weight (per 1 µg/m3 increase in speciated PM2.5 concentrations) were: organic carbon -3.12 g (CI: -4.71, -1.52), elemental carbon -14.20 g (CI: -18.76, -9.63), nitrate -5.51 g (CI: -6.79, -4.23), and sulfate 9.26 g (CI: 7.03, 11.49). Results from multipollutant models were less precise due to high correlation between pollutants. Associations with preterm birth were null, save for a negative association between sulfate and preterm birth (Hazard Ratio per 1 µg/m3 increase: 0.973 CI: 0.958, 0.987).

Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013-2020.

In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 µg m-3 yr-1 in China during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 µg m-3) and anthropogenic emissions (6.7 µg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 µg m-3) were much lower than that in 2013-2017 (5.2 µg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOx) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOx in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOx to improve O3 air quality.

Synergetic roadmap of carbon neutrality and clean air for China.

It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.

Vitamin K2 supplementation improves impaired glycemic homeostasis and insulin sensitivity for type 2 diabetes through gut microbiome and fecal metabolites.

BACKGROUND: There is insufficient evidence for the ability of vitamin K2 to improve type 2 diabetes mellitus symptoms by regulating gut microbial composition. Herein, we aimed to demonstrate the key role of the gut microbiota in the improvement of impaired glycemic homeostasis and insulin sensitivity by vitamin K2 intervention. METHODS: We first performed a 6-month RCT on 60 T2DM participants with or without MK-7 (a natural form of vitamin K2) intervention. In addition, we conducted a transplantation of the MK-7-regulated microbiota in diet-induced obesity mice for 4 weeks. 16S rRNA sequencing, fecal metabolomics, and transcriptomics in both study phases were used to clarify the potential mechanism. RESULTS: After MK-7 intervention, we observed notable 13.4%, 28.3%, and 7.4% reductions in fasting serum glucose (P = 0.048), insulin (P = 0.005), and HbA1c levels (P = 0.019) in type 2 diabetes participants and significant glucose tolerance improvement in diet-induced obesity mice (P = 0.005). Moreover, increased concentrations of secondary bile acids (lithocholic and taurodeoxycholic acid) and short-chain fatty acids (acetic acid, butyric acid, and valeric acid) were found in human and mouse feces accompanied by an increased abundance of the genera that are responsible for the biosynthesis of these metabolites. Finally, we found that 4 weeks of fecal microbiota transplantation significantly improved glucose tolerance in diet-induced obesity mice by activating colon bile acid receptors, improving host immune-inflammatory responses, and increasing circulating GLP-1 concentrations. CONCLUSIONS: Our gut-derived findings provide evidence for a regulatory role of vitamin K2 on glycemic homeostasis, which may further facilitate the clinical implementation of vitamin K2 intervention for diabetes management. TRIAL REGISTRATION: The study was registered at https://www.chictr.org.cn (ChiCTR1800019663).

Air pollution health burden embodied in China's supply chains.

Product trade plays an increasing role in relocating production and the associated air pollution impact among sectors and regions. While a comprehensive depiction of atmospheric pollution redistribution through trade chains is missing, which may hinder targeted clean air cooperation among sectors and regions. Here, we combined five state-of-the-art models from physics, economy, and epidemiology to track the anthropogenic fine particle matters (PM2.5) related premature mortality along the supply chains within China in 2017. Our results highlight the key sectors that affect PM2.5-related mortality from both production and consumption perspectives. The consumption-based effects from food, light industry, equipment, construction, and services sectors, caused 2-22 times higher deaths than those from a production perspective and totally contributed 63% of the national total. From a cross-boundary perspective, 25.7% of China's PM2.5-related deaths were caused by interprovincial trade, with the largest transfer occurring from the central and northern regions to well-developed east coast provinces. Capital investment dominated the cross-boundary effect (56% of the total) by involving substantial equipment and construction products, which greatly rely on product exports from regions with specific resources. This supply chain-based analysis provides a comprehensive quantification and may inform more effective joint-control efforts among associated regions and sectors from a health risk perspective.

Association of Circulating Branched-Chain Amino Acids with Cardiovascular Diseases: A Mendelian Randomization Study.

BACKGROUND: There have been reports linking branched-chain amino acids (BCAAs) to the hazard of various cardiovascular diseases (CVDs); however, the causal role of this relationship is still unclear. We conducted a study using bi-directional two-sample Mendelian randomization (MR) with the aim of investigating the possible causal correlation between BCAAs and 13 types of cardiovascular diseases. METHODS: The study analyzed data of the largest genome-wide association studies (GWAS) published for the total BCAAs, encompassing isoleucine, leucine, and valine, which were obtained from the UK Biobank, as well as data for 13 cardiovascular endpoints from the MRC-IEU, the FinnGen consortium, and the EBI database. The approach of the primary dissection used became the inverse-variance-weighted (IVW) approach, with additional analyses using the MR-PRESSO global test as well as MR-Egger regression with a view to determining horizontal pleiotropy. Heterogeneity was evaluated by means of Cochran's Q test. The study also conducted logistic regression dissection for the sake of investigating the correlation between cardiovascular events and serum BCAAs in the UK biobank cohort study. RESULTS: In this study, it was found that individuals with a genetic predisposition to more elevated levels for circulating total BCAAs had a higher hazard of peripheral arterial disease (OR 1.400, 95% CI 1.063, 1.844; p = 0.017) in addition to stroke (OR 1.266, 95% CI 1.012, 1.585; p = 0.039); circulating valine casually increased the risk of intracerebral hemorrhage (OR 1.760, 95% CI 1.116, 2.776; p = 0.015), along with stroke (OR 1.269, 95% CI 1.079, 1.492; p = 0.004); genetically predicted isoleucine showed a positive association with peripheral arterial disease (OR 1.466, 95% CI 1.044, 2.058; p = 0.027), along with cardioembolic stroke (OR 1.547, 95% CI 1.126, 2.124; p = 0.007); furthermore, leucine causally associated with stroke (OR 1.310, 95% CI 1.031, 1.663, p = 0.027). In the UK Biobank cohort study, we detected that total BCAAs (OR: 1.285; 95% CI: 1.009, 1.636), valine (OR: 1.287; 95% CI: 1.009, 1.642), and isoleucine (OR: 1.352; 95% CI: 1.064, 1.718) were independently linked to stroke, but not leucine (OR: 1.146; 95% CI: 0.901, 1.458). No such association was found for BCAAs with peripheral arterial disease and intracerebral hemorrhage in the cohort study. CONCLUSIONS: In summary, circulating total BCAAs and valine may be causally associated with stroke. The association of BCAAs with other CVD events needs further study.

A high-fidelity RNA-targeting Cas13 restores paternal Ube3a expression and improves motor functions in Angelman syndrome mice.

Angelman syndrome (AS) is a rare neurodevelopmental disorder caused by loss of function mutations in maternally expressed UBE3A. No gene-specific treatment is available for patients so far. Although intact and transcriptionally active, paternally inherited UBE3A is silenced by elongation of antisense long noncoding RNA UBE3A-ATS in neurons. Here, we demonstrated that RNA targeting of paternal Ube3a-ATS with a high-fidelity CRISPR-Cas13 (hfCas13x.1) system could restore Ube3a expression to similar levels as that of maternal Ube3a in the cultured mouse neurons. Furthermore, injection into lateral ventricles with neuron-specific hSyn1 promoter-driven hfCas13x.1 packaged in adeno-associated virus (AAV-PHP.eb) could restore paternal Ube3a expression in cortex and hippocampus of neonatal AS mice for up to 4 months after treatment. Behavioral tests showed that expression of paternal Ube3a significantly alleviated AS-related symptoms, including obesity and motor function. Our results suggested that hfCas13x.1-mediated suppression of the Ube3a-ATS lncRNA potentially serves as a promising targeted intervention for AS.

Analysis of microRNA-199a-3p expression in CD4+ T cells of systemic lupus erythematosus.

OBJECTIVES: Accumulating evidence have suggested microRNAs (miRNAs) play important roles in the pathogenesis of systemic lupus erythematosus (SLE). Here we aimed to explore aberrant expression of miRNAs in CD4+ T cells from SLE patients and their potential function in SLE pathogenesis. METHODS: First, next-generation sequencing was performed on CD4+ T cells from four SLE patients and three healthy controls (HCs). Candidate miRNAs were then validated in CD4+ T cells from 97 patients with SLE, 16 patients with rheumatoid arthritis, and 12 HCs using qRT-PCR. Then the relationship between the candidate miRNA and clinical characteristics was analyzed. Bioinformatics analysis and validation of the target genes of the candidate miRNA were performed. RESULTS: A total of 66 upregulated miRNAs and 70 downregulated miRNAs were found between SLE and normal CD4+ T cells samples. miR-199a-3p was identified significant upregulation in the CD4+ T cells of lupus patients. High expression of miR-199a-3p was correlated with several clinical characteristics including low C3 level, positive anti-dsDNA antibody, high ESR level, active lupus nephritis, and active disease activity. When distinguishing active LN from non-LN or active lupus from stable lupus, the AUCs of miR-199a-3p were 0.68 and 0.70, respectively. And the expression of miR-199a-3p, involved in JAK-STAT signaling pathway, was negatively correlated with the STAM expression in CD4+ T cells of SLE. CONCLUSION: Our study suggested a novel and promising role of miR-199a-3p in CD4+ T cells for SLE. Further studies are needed to precisely determine the function of miR-199a-3p in this disease. Key Points • Aberrant expression of miRNAs in CD4+ T cells and their potential function in SLE pathogenesis remained unclear. • miR-199a-3p in CD4+ T cells plays a novel role in the pathogenesis of SLE and serves as a potential target for SLE.

The PM2.5 concentration reduction improves survival rate of lung cancer in Beijing.

INTRODUCTION: Long-term exposure to ambient fine particulate matter (PM2.5) has been linked to increases in the incidence of lung cancer. However, more evidence is needed to conclude its effects on lung cancer survival. OBJECTIVES: The study aimed to explore the relationship between long-term PM2.5 exposure and lung cancer survival and evaluated the benefits of clean air actions in Beijing. METHODS: A whole-population cohort study was conducted on lung cancer patients diagnosed between 2001 and 2017. An atmospheric chemical transport model was used to estimate exposure under a counterfactual scenario without the policy and then quantified the effect of the policy. Cox regression models were used with the seasonality-adjusted PM2.5 as the main effect. RESULTS: A 10 µg/m3 increase in PM2.5 was estimated to be with a 6.5 % (95 % CI: 4.8 %, 8.2 %) increase in the mortality rates. The association was heterogeneous and modified by individual-level characteristics. The clean air actions were estimated to have prevented 3548 (95 % CI: 3280, 3825) premature deaths and to have prolonged survival time by 4.29 months (95 % CI: 0.01, 25.11). CONCLUSION: Our findings suggest that PM2.5 exposure lowers the survival rate for lung cancer. The clean air actions implemented in Beijing can protect lung cancer patients by increasing their survival time. SYNOPSIS: Long-term exposure to PM2.5 can lower lung patients' survival rates whereas the clean air actions in Beijing have prolonged these patients' survival time by reducing PM2.5 level.

Develop an efficient and specific AAV-based labeling system for Muller glia in mice.

Reprogramming Müller glia (MG) into functional cells is considered a promising therapeutic strategy to treat ocular diseases and vision loss. However, current AAV-based system for MG-tracing was reported to have high leakage in recent studies. Here, we focused on reducing the leakage of AAV-based labeling systems and found that different AAV serotypes showed a range of efficiency and specificity in labeling MG, leading us to optimize a human GFAP-Cre reporter system packaged in the AAV9 serotype with the woodchuck hepatitis virus post-transcriptional regulatory element (WPRE) removed. The leakage ratio of the AAV9-hGFAP-Cre-ΔWPRE decreased by an approximate 40-fold compared with the AAV9-hGFAP-Cre-WPRE labeling system. In addition, we validated the specificity of the AAV-ΔWPRE system for tracing MG reprogramming under Ptbp1-suppression and observed strict non-MG-conversion, similar to previous studies using genetic lineage tracking mouse models. Thus, the AAV9-hGFAP-Cre-ΔWPRE system showed high efficiency and specificity for MG labeling, providing a promising tool for tracing cell fate in vivo.

Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000.

PM2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely updated information on China's PM2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting-Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM2.5 data. PM2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of -9.0, -7.2, -8.1, -8.4, and -9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM2.5 components. As part of Tracking Air Pollution in China (http://tapdata.org.cn), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.

Co-benefits of CO2 emission reduction from China's clean air actions between 2013-2020.

Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO2 emission mitigation co-benefits by affecting the local energy system. Here, we evaluate the effect of China's stringent clean air actions on its energy use and CO2 emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country's energy system. The co-benefits of China's clean air measures far outweigh the additional CO2 emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO2 from 2013-2020, exceeding the accumulated CO2 emission increase in China (2.03 Gt CO2) during the same period. Our study indicates that China's efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO2 reduction co-benefits deserve further attention in future policy design.

New WHO global air quality guidelines help prevent premature deaths in China.

The World Health Organization has issued new air quality guidelines (AQG). Based on 2020 data, achieving the new AQG for PM2.5 could prevent an additional 285,000 chronic deaths and 13,000 acute deaths, across China, compared with the previous AQG. The new AQG can better protect health but cannot be achieved without coordinated air-pollution-control and climate-mitigation efforts.

Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013-2019.

Evaluating ozone levels at high resolutions and accuracy is crucial for understanding the spatiotemporal characteristics of ozone distribution and assessing ozone exposure levels in epidemiological studies. The national models with high spatiotemporal resolutions to predict ground ozone concentrations are limited in China so far. In this study, we aimed to develop a random forest model by combining ground ozone measurements from fixed stations, ozone simulations from the Community Multiscale Air Quality (CMAQ) modeling system, meteorological parameters, population density, road length, and elevation to predict ground maximum daily 8-h average (MDA8) ozone concentrations at a daily level and 1 km × 1 km spatial resolution. The model cross-validation R2 and root mean squared error (RMSE) were 0.80 and 20.93 µg/m3 at daily level in 2013-2019, respectively. CMAQ ozone simulations and near-surface temperature played vital roles in predicting ozone concentrations among all predictors. The population-weighted median concentrations of predicted MDA8 ozone were 89.34 µg/m3 in mainland China in 2013, and reached 100.96 µg/m3 in 2019. However, the long-term temporal variations among regions were heterogeneous. Central and Eastern China, as well as the Southeast Coastal Area, suffered higher ozone pollution and higher increased rates of ozone concentrations from 2013 to 2019. The seasonal pattern of ozone pollution varied spatially. The peak-season ozone pollution with the highest 6-month ozone concentrations occurred in different months among regions, with more than half domain in April-September. The predictions showed that not only the annual mean concentrations but also the percentages of grid-days with MDA8 ozone concentrations higher than 100/160 µg/m3 have been increasing in the past few years in China; meanwhile, majority areas in mainland China suffered peak-season ozone concentrations higher than the air quality guidelines launched by the World Health Organization in September 2021. The proposed model and ozone predictions with high spatiotemporal resolution and full coverage could provide health studies with flexible choices to evaluate ozone exposure levels at multiple spatiotemporal scales in the future.