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Abstract Balancing kinetics, a crucial priority in catalysis, is frequently achieved by sacrificing activity of elementary steps to suppress side reactions and enhance catalyst stability. Dry reforming of methane (DRM), a process operated at high temperature, usually involves fast C-H activation but sluggish carbon removal, resulting in coke deposition and catalyst deactivation. Studies focused solely on catalyst innovation are insufficient in addressing coke formation efficiently. Herein, we develop coke-free catalysts that balance kinetics of elementary steps for overall thermodynamics optimization. Beginning from a highly active cobalt aluminum oxide (CoAl2O4) catalyst that is susceptible to severe coke formation, we substitute aluminum (Al) with gallium (Ga), reporting a CoAl0.5Ga1.5O4-R catalyst that performs DRM stably over 1000 hours without observable coke deposition. We find that Ga enhances DRM stability by suppressing C-H activation to balance carbon removal. A series of coke-free DRM catalysts are developed herein by partially substituting Al from CoAl2O4 with other metals.

Abstract Accurate offshore surface wind forecasting is crucial for navigation safety and disaster prevention. However, significant biases exist in forecasting sea surface winds due to the uncertainties in estimating sea surface roughness. In this study, we propose a deep learning‐based scheme (DL2023) for estimating sea surface roughness and integrate it into a regionally coupled ocean‐atmosphere‐wave model. Single‐point experiments demonstrate that DL2023 achieves a remarkable 50% reduction in the Root Mean Square Error (RMSE) compared to the four traditional schemes. During five typhoon cases in August 2020, compared to the four traditional schemes, the RMSEs of forecasted surface winds using DL2023 are reduced by 6.02%–14.75%, 11.17%–18.30%, and 11.91%–19.46% at lead times of 24, 48, and 72 hr, respectively. Thus, the DL2023 scheme, trained using data from the Atlantic Ocean, successfully improves the forecast of surface winds over the Northwest Pacific Ocean.

A new algorithm is proposed to estimate the cooling amplitude over North China induced by each Ural blocking event. Taking full account of potential transient temperature rises during the cooling process and the lag time of cooling relative to the blocking, this algorithm provides more detailed analysis which should not be possible by using former methods. The amplitude of the Ural blocking-related cooling events is found to have a broad distribution. Further, although most Ural blocking events lead to severe cold surges over North China, the number of Ural blocking events that do not induce significant cooling over North China cannot be ignored. The possible reasons for the wide range in cooling amplitude are explored in terms of the lifetimes and geographical centers of the blocking highs, the circulation patterns preceding the onset of the cooling events, and the snowfall associated with cooling events. Larger amplitude cooling events occur in Ural blocking highs that have longer lifetimes and northwestward displacements of their geographical centers. The northward displacement of a Ural blocking center favors the advection of extremely cold air from the Arctic regions, which accumulates in West Siberia and subsequently gives rise to the most severe cold surges over North China. The lack of activities of cold surges before the blocking-related cooling events not only amplifies the cooling amplitude directly, but also increases the occurrence probabilities of snowfalls through its modulation on the local specific humidity. The increased albedo and subsequent snow-melt induced cooling further amplify the cooling amplitude.

Abstract Wintertime precipitation, especially snowstorms, significantly impacts people's lives. However, the current forecast skill of wintertime precipitation is still low. Based on data augmentation (DA) and deep learning, we propose a DABU‐Net which improves the Global Forecast System wintertime precipitation forecast over southeastern China. We build three independent models for the forecast lead times of 24, 48, and 72 hr, respectively. After using DABU‐Net, the mean Root Mean Squared Errors (RMSEs) of the wintertime precipitation at the three lead times are reduced by 19.08%, 25.00%, and 22.37%, respectively. The threat scores (TS) are all significantly increased at the thresholds of 1, 5, 10, 15, and 20 mm day−1 for the three lead times. During heavy precipitation days, the RMSEs are decreased by 14% and TS are increased by 7% at the lead times within 48 hr. Therefore, combining DA and deep learning has great prospects in precipitation forecasting.

In response to the demand for high-precision point cloud mapping of subway trains in long tunnel degradation scenarios in major urban cities, we propose a map construction method based on LiDAR and inertial measurement sensors. This method comprises a tightly coupled frontend odometry system based on error Kalman filters and backend optimization using factor graphs. In the frontend odometry, inertial calculation results serve as predictions for the filter, and residuals between LiDAR points and local map plane point clouds are used for filter updates. The global pose graph is constructed based on inter-frame odometry and other constraint factors, followed by a smoothing optimization for map building. Multiple experiments in subway tunnel scenarios demonstrate that the proposed method achieves robust trajectory estimation in long tunnel scenes, where classical multi-sensor fusion methods fail due to sensor degradation. The proposed method achieves a trajectory consistency of 0.1 m in tunnel scenes, meeting the accuracy requirements for train arrival, parking, and interval operations. Additionally, in an industrial park scenario, the method is compared with ground truth provided by inertial navigation, showing an accumulated error of less than 0.2%, indicating high precision.

Immune checkpoint inhibitors (ICIs) have increasingly been the mainstay of treatment for numerous malignancies. However, due to their association with autoimmunity, ICIs have resulted in a variety of side effects that involve multiple organs including the endocrine system. In this review article, we describe our current understanding of the autoimmune endocrinopathies as a result of the use of ICIs. We will review the epidemiology, pathophysiology, clinical presentation, diagnosis, and management of the most commonly encountered endocrinopathies, including thyroiditis, hypophysitis, Type 1 diabetes, adrenalitis, and central diabetes insipidus.

ObjectivePostoperative red blood cell (RBC) transfusion is widely used during the perioperative period but is often associated with a high risk of infection and complications. However, prediction models for RBC transfusion in patients with orthopedic surgery have not yet been developed. We aimed to identify predictors and constructed prediction models for RBC transfusion after orthopedic surgery using interpretable machine learning algorithms.MethodsThis retrospective cohort study reviewed a total of 59,605 patients undergoing orthopedic surgery from June 2013 to January 2019 across 7 tertiary hospitals in China. Patients were randomly split into training (80%) and test subsets (20%). The feature selection method of recursive feature elimination (RFE) was used to identify an optimal feature subset from thirty preoperative variables, and six machine learning algorithms were applied to develop prediction models. The Shapley Additive exPlanations (SHAP) value was employed to evaluate the contribution of each predictor towards the prediction of postoperative RBC transfusion. For simplicity of the clinical utility, a risk score system was further established using the top risk factors identified by machine learning models.ResultsOf the 59,605 patients with orthopedic surgery, 19,921 (33.40%) underwent postoperative RBC transfusion. The CatBoost model exhibited an AUC of 0.831 (95% CI: 0.824–0.836) on the test subset, which significantly outperformed five other prediction models. The risk of RBC transfusion was associated with old age (>60 years) and low RBC count (

Abstract Objectives To assess whether recurrent gestational diabetes mellitus (GDM) and newly diagnosed GDM share similar risk factors. Methods The study recruited a cohort of 10,151 multipara women with singleton pregnancy who delivered between 2016 and 2019 in Beijing, China. The prevalence of recurrent GDM and associated risk factors were analyzed between women with and without prior GDM history. Results Eight hundred and seventy-five (8.6%) multipara women had a diagnosis of GDM during previous pregnancies. The prevalence of GDM and pre-gestational diabetes mellitus were 48.34% (423/875) and 7.89% (69/875) if the women were diagnosed with GDM during previous pregnancies, as compared to 16.00% (1484/9276) and 0.50% (46/9276) if the women were never diagnosed with GDM before. In women without a history of GDM, a variety of factors including older maternal age, higher pre-pregnancy body mass index (PPBMI), prolonged interval between the two pregnancies, higher early pregnancy weight gain, family history of type 2 diabetes mellitus (T2DM), maternal low birth weight, and higher early pregnancy glycemic and lipid indexes were generally associated with an increased risk of GDM at subsequent pregnancy. In women with a history of GDM, higher PPBMI, higher fasting glucose level and maternal birthweight ≥4000 g were independent risk factors for recurrent GDM. Conclusions GDM reoccurred in nearly half of women with a history of GDM. Risk factors for recurrent GDM and newly diagnosed GDM were different. Identifying additional factors for GDM recurrence can help guide clinical management for future pregnancies to prevent GDM recurrence.

A novel closed loop, continuous flow (CF) reactor system for parahydrogen enhanced nuclear magnetic resonance (NMR) of liquids via heterogeneous catalysis is introduced which enables recycling of unreacted liquid substrate reactant. This system consists of an HPLC pump, a liquid substrate reservoir incorporating a gas diffuser, an all-metal packed bed catalytic reactor, and an AF-2400 tube-in-tube gas permeable membrane for removal of normal H2. Two types of supported metal nanoparticle catalysts were tested: mesoporous silica encapsulated Pt3Sn intermetallic nanoparticles and a Rh on anatase TiO2 support. In the CF hydrogenation of propargyl acetate to allyl acetate, the hyperpolarized signals exhibited stability over 20 min of recirculation, with signal enhancements of up to 626 using 99% p-H2 and negligible leaching of the catalyst into the flowing solutions. These results demonstrate the practicality of performing systematic optimization of conditions for continuous flow catalysis and polarization transfer to heteronuclei with important implications for biomedical magnetic resonance imaging.

BackgroundObservational studies have suggested a potential non-linear association between sleep duration and hyperuricemia. However, the causal nature and sex-specific differences are poorly understood. We aimed to determine the shape of sex-specific causal associations between sleep duration and hyperuricemia in the UK Biobank.MethodsLogistic regression was used to investigate the observational association between self-reported sleep duration and hyperuricemia among 387,980 white British participants (mean age: 56.9 years and 46.0% males). Linear and non-linear Mendelian Randomization (MR) analyses were performed to assess the causal association between continuous sleep duration and hyperuricemia. The causal effects of genetically predicted short (8 h) sleep durations on hyperuricemia were further estimated, respectively.ResultsTraditional observational analysis suggested U- and J-shaped associations between sleep duration and hyperuricemia in females and males, respectively. Linear MR did not support the causal effect of sleep duration on hyperuricemia. Non-linear MR demonstrated an approximately U-shaped causal association between continuous sleep duration and hyperuricemia in overall participants and females, but not in males. Genetically predicted short sleep duration was significantly associated with hyperuricemia in females (OR [95% CI]: 1.21 [1.08–1.36]; P = 0.001), but not in males (1.08 [0.98–1.18]; P = 0.137). By contrast, genetically predicted long sleep duration was not significantly associated with the risk of hyperuricemia in either females or males.ConclusionGenetically predicted short sleep duration is a potential causal risk factor for hyperuricemia for females but has little effect on males. Long sleep duration does not appear to be causally associated with hyperuricemia.

Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localization in implementation, but more difficult to directly assimilate satellite radiance observations, a kind of non-local observations. The vertical locations of radiance observations are undetermined and the transmission of observational information is thereby obstructed. To determine the vertical coordinates of radiance observations, a weighted average hypsometry is proposed. Using this hypsometry, AMSU-A radiance observations are directly assimilated with an ensemble four-dimensional variational (En4DVar) DA system. It consists of a four-dimensional ensemble-variational (4DEnVar) system providing ensemble covariance and a 4DVar system. Observing system simulation experiments show that the hypsometry alleviates the degradations in the late period of medium-range forecast in the Northern Extratropics that occur in the traditional peak-based hypsometry. It obviously improves the analysis qualities and forecast skills of the En4DVar system and its two components, especially in the Southern Extratropics, when incorporating AMSU-A radiance observations. The improvement in the En4DVar-initialized forecast is comparable to that in the 4DVar-initialized forecast in the Southern Extratropics and Tropics. It indicates that a proper hypsometry enables efficient extraction of useful information from AMSU-A radiance observations by 4DEnVar with observation space localization. Therefore, the 4DEnVar provides high-quality ensemble covariances for En4DVar.

In this work, we investigate the influence of electric fields (E-fields) on the room-temperature magnetotransport behavior of an artificial multiferroic heterostructure, a Conetic alloy (Ni77Fe14Cu5Mo4) thin film/Pb(Mg1/3Nb2/3)0.7Ti0.3O3 (011). When the external magnetic field is parallel to the applied current, the switching field increases from 0.8 to 3.3 Oe at 0 and 8 kV/cm, respectively, and the corresponding magnetoresistance (MR) ratio at 20 Oe respectively decreases from 0.14% to 0.03% at 0 and 8 kV/cm. However, when the external magnetic field is perpendicular to the current, the switching field decreases from 10.1 to 1.7 Oe at 0 and 8 kV/cm, and the MR ratio in such a case decreases from −0.001% to −0.10%, respectively. Consequently, under the parallel and perpendicular modes, the tunabilities of the switching field are approximately +313% and −83%, and the MR ratio tunabilities under E-fields are approximately −79% and +9,900%, respectively. Such a large and anisotropic tunability of both the switching field and MR ratio is attributed to the ultrasoft magnetic property of the Conetic alloy thin film and anisotropic in-plane strain-mediated magnetoelectric coupling. However, the anisotropic MR ratio is approximately 0.15% and does not vary with the applied E-fields owing to the intrinsic property of Conetic thin films using transfer and circle transfer curve measurements, rather than the magnetization rotation caused by E-field-induced magnetoelastic anisotropy. This work demonstrates that multiferroic heterostructures with electrically tunable MR show considerable potential in designing energy-efficient electronic and spintronic devices.

Abstract A four‐dimensional ensemble‐variational (4DEnVar) data assimilation (DA) system was developed based on the global forecast system of the Global/Regional Assimilation and Prediction System (GRAPES‐GFS). Instead of using the adjoint technique, this system utilizes a dimension‐reduced projection (DRP) technique to minimize the cost function of the standard four‐dimensional variational (4DVar) DA. It dynamically predicts ensemble background error covariance (BEC) and realizes the explicit flow‐dependence of BEC in the variational configuration. An inflation technique based on a linear combination of analysis increments and balanced random perturbations, is utilized to overcome the problem of underestimation of BEC matrix (B‐matrix) during the assimilation cycle. To mitigate the spurious correlations in the ensemble B‐matrix caused by the insufficient ensemble members, an ensemble‐sample‐based subspace localization method is utilized. In order to evaluate the new system, single‐point observation experiments (SOEs) and observing system simulation experiments (OSSEs) were conducted with sounding and cloud‐derived wind data based on GRAPES‐GFS. The explicit flow‐dependent characteristic of the 4DEnVar system using a localized ensemble covariance was verified in the SOEs. In the OSSEs, the ensemble mean analysis of 4DEnVar outperforms the analysis of 4DVar. The deterministic forecast initialized from the 4DEnVar ensemble mean analysis has better performance in the short‐range forecasts, better (worse) performance in the early (late) period of the medium‐range forecasts in the Northern Extratropics, and opposite performance in the Southern Extratropics, and exhibits slightly worse effects in the Tropics. Moreover, the ensemble mean forecast initialized by the 4DEnVar system has higher forecast skills.

Abstract Background Gestational diabetes mellitus (GDM) and excessive body weight are two key risk factors for adverse perinatal outcomes. However, it is not clear whether restricted gestational weight gain (GWG) is favorable to reduce the risk for adverse pregnancy and neonatal outcomes in women with GDM. Therefore, this study aimed to assess the association of GWG after an oral glucose tolerance test with maternal and neonatal outcomes. Methods This prospective cohort study assessed the association of GWG after an oral glucose tolerance test (OGTT) with pregnancy and neonatal outcomes in 3126 women with GDM, adjusted for age, pre-pregnancy body mass index, height, gravidity, parity, adverse history of pregnancy, GWG before OGTT, blood glucose level at OGTT and late pregnancy. The outcomes included the prevalence of pregnancy-induced hypertension (PIH) and preeclampsia, large for gestational age (LGA), small for gestational age, macrosomia, low birth weight, preterm birth, and birth by cesarean section. GDM was diagnosed according to the criteria established by the International Association of Diabetes and Pregnancy Study Groups. Results GWG after OGTT was positively associated with risk for overall adverse pregnancy outcomes (adjusted odds ratio [aOR] = 1.72, 95% confidence interval [CI] = 1.50–1.97), LGA (aOR = 1.29, 95%CI = 1.13–1.47), macrosomia (aOR = 1.24, 95%CI = 1.06–1.46) and birth by cesarean section (aOR = 1.91, 95%CI = 1.67–2.19) in women with GDM. Further analyses revealed that a combination of excessive GWG before OGTT and after OGTT increased the risk of PIH and preeclampsia, LGA, macrosomia, and birth by cesarean section compared with adequate GWG throughout pregnancy. In contrast, GWG below the Institute of Medicine guideline after OGTT did not increase the risk of adverse perinatal outcomes despite GWG before OGTT. Conclusion Excessive GWG after OGTT was associated with an elevated risk of adverse pregnancy outcomes, while insufficient GWG after OGTT did not increase the risk of LBW. Restricting GWG after diagnosis of GDM in women with excessive GWG in the first half of pregnancy may be beneficial to prevent PIH and preeclampsia, LGA, macrosomia, and birth by cesarean section.

Objective: To establish suggested gestational weight gain (GWG) using several distinct methods in a Chinese population. Methods: This study analyzed data from the medical records of singleton pregnancy women during 2011–2017 in Beijing, China. Suggested GWG was calculated using four distinct methods. In method 1, suggested GWG was identified by the interquartile method. Subsequently, risk models for small for gestational age (SGA) and large for gestational age (LGA) with respect to GWG were constructed. GWG was treated as a continuous variable in method 2, and as a categorized variable in methods 3 and 4. Results: An average GWG of 15.78 kg with a prevalence of LGA at 19.34% and SGA at 2.12% was observed among the 34,470 participants. Methods 1 and 2 did not yield clinically applicable results. The suggested GWGs were 11–17/11–16 kg, 9–19/9–15 kg, 4–12/4–10 kg, and 0–12/0–6 kg by method 3/method 4 for underweight, normal-weight, overweight, and obese women, respectively. The GWG range suggested by method 3 resulted in a larger proportion of participants (62.03%) within range, while the suggested GWG range by method 4 was associated with a lower risk of LGA compared to that conferred by the Institute of Medicine (IOM) criteria. Conclusion: This study suggests a modest GWG goal compared to IOM recommendations based on a large Chinese cohort.

As a prototype of a strongly correlated electron system, bulk vanadium dioxide (VO2) exhibits a large and reversible metal–insulator transition (MIT) near 340 K, concomitantly accompanied by a monoclinic–rutile structural phase transformation (SPT). In this study, we systematically investigated the SPT across the MIT in a (010)-VO2/(0001)-Al2O3 epitaxial thin film by simultaneously determining three lattice constants (a, b, and c) and the mismatch angle (Δβ) using high-resolution X-ray diffraction. The lattice constants a, b, and c were approximately 5.723, 4.521, and 5.393 Å, respectively, at room temperature, and the mismatch angle was approximately 122.02°. As the temperature increased, the lattice constants and mismatch angle did not change significantly until the temperature reached the MIT point. Then, a, b, and c suddenly increased to approximately 5.689 Å, 4.538 Å, and 5.411 Å, respectively, and retained this value up to nearly 90°C. However, the mismatch angle first slightly increased and then sharply decreased to 122.00°. Additionally, the lattice constants and mismatch angle were almost reproducible with decreasing temperature, except for hysteresis in the MIT region. These results verify that VO2 undergoes an MIT, simultaneously accompanied by SPT, in thicker films with small strain and weak substrate constraints, analogous to bulk VO2. This was further confirmed by in-situ varying-temperature Raman characterization. These findings provide insights into the SPT and reveal an angular parameter for judging the SPT in VO2 systems.

Metal-free catalysts can offer uniquely different activity and selectivity from transition metal-based counterparts. Here, the authors report metal-free nitrogen-assembly carbon with closely-placed nitrogen as active sites, achieving catalytic cleavage of strong bonds including H-H, C-O and C-H.

Numerous previous studies have pointed out that the South Asia monsoon (SAM) contributes most moisture to the southern Tibetan Plateau, whilst the moisture over the Northern Tibetan Plateau (NTP) is supplied by the westerlies, but the moisture sources for extreme precipitation events remain unclear. In this study, the tracking of external moisture sources was performed on ten extreme precipitation events over each of six target subregions of the NTP during the summer of 2010–2018. We found that the SAM provided most of the external moisture for extreme precipitation events in the NTP, except for the largest contribution from East Asia to extreme precipitation in the easternmost subregion. The moisture carried by westerly winds is the second foreign source over the western NTP. In addition, more than 40% of the NTP extreme precipitation events occurred under the synergy of weak westerlies and enhanced SAM, and these events have a longer duration than others. Thus, SAM plays a key role in moisture transport for the extreme precipitation events over the NTP, even though its contribution to the climatological moisture is not significant.

Summary: Electric-field (E-field) control of magnetic switching provides an energy-efficient means to toggle the magnetic states in spintronic devices. The angular tunneling magnetoresistance (TMR) of an magnetic tunnel junction (MTJ)/PMN-PT magnetoelectronic hybrid indicates that the angle-dependent switching fields of the free layer can decrease significantly subject to the application of an E-field. In particular, the switching field along the major axis is reduced by 59% from 28.0 to 11.5 Oe as the E-field increases from 0 to 6 kV/cm, while the TMR ratio remains intact. The switching boundary angle decreases (increases) for the parallel (antiparallel) to antiparallel (parallel) state switch, resulting in a shrunk switching window size. The non-volatile and reversible 180° magnetization switching is demonstrated by using E-fields with a smaller magnetic field bias as low as 11.5 Oe. The angular magnetic switching originates from competition among the E-field-induced magnetoelastic anisotropy, magnetic shape anisotropy, and Zeeman energy, which is confirmed by micromagnetic simulations.

Nanoconfinement effects on changing molecular transport and reaction kinetics in heterogeneous catalysis have been widely recognized. Here, the authors design a core-shell nanocatalyst with aligned linear nanopores to uncover nanoconfinement effects on catalytic activity and adsorption strength by single molecule imaging.

Abstract Background Polycystic ovary syndrome (PCOS) has been consistently associated with subsequent gestational diabetes mellitus (GDM). Women with PCOS showed a high prevalence of obesity, which raises the question regarding the role of obesity or PCOS pe ser in development of GDM. In this study we conducted a 2-phase study to compare the risk of GDM and its associated early pregnancy metabolic factors in women with and without PCOS, stratified by pre-pregnancy body mass index (BMI). Methods A 2-phase design was used in this study. The initial phase of the study included 566 age- and pre-pregnancy BMI-matched singleton pregnant women (242 with and 324 without PCOS). Risk of GDM and associated early-pregnancy risk factors were explored between women with and without PCOS, after stratification by pre-pregnancy BMI. Stratified analysis was conducted in normal weight (pre-pregnancy BMI

Objective: Timing of gestational weight gain (GWG) may influence perinatal outcomes differently. This study aimed to find associations of latent GWG patterns with risk of large for gestational age (LGA) in women with overweight or obesity. Methods: A total of 4,438 women with overweight or obesity were included in the analyses. Latent trajectories of GWG associated with LGA were identified by trajectory analysis. GWG, risk of LGA and early pregnancy factors were compared between these identified groups. Results: This study identified four distinct GWG trajectory groups associated with LGA, each group including, respectively, 78.6, 19.0, 1.4, and 0.9% of the participants. Group 1 presented a typical curve with lower GWG in early pregnancy and relatively higher GWG in mid- and late pregnancy. Women in group 2 showed sustained high GWG and high LGA prevalence than women in group 1 (48.24 vs. 21.56%, p < 0.0001). A catch-up in GWG after low weight gain did not result in significantly higher risk of LGA in group 3 compared to group 1. On the other hand, a rapid GWG in the first two-thirds of pregnancy followed by a strict weight control was associated with elevated risk of LGA in group 4 than group 1 (62.50 vs. 21.56%, p < 0.0001). Conclusions: Women affected by overweight or obesity combined with high GWG in early mid-pregnancy were at elevated risk of LGA. Early clinical recognition of a poor GWG trajectory will enable early intervention in high-risk groups.

Abstract Marked by large interannual variability, East Asian summer monsoon (EASM) rainfall has profound socio‐economic impacts through its dominant influence on floods and droughts. Improving predictions of the interannual variations of EASM rainfall has important implications for over 20% of the world's population. While coupled modeling systems have demonstrated some prediction skill related to the El Niño Southern Oscillation with remote influence on EASM rainfall, the impact of soil moisture has heretofore not been systematically investigated. Using a weakly coupled data assimilation (WCDA) system to constrain the soil moisture and soil temperature in a coupled climate model with a global land data assimilation product, this study demonstrates significant improvements in simulating the interannual variations of EASM rainfall, capturing the notable shift to a “wetter‐South‐drier‐North” rainfall pattern in China in the early 1990s. Hindcast simulations initialized with the well‐balanced states from a coupled simulation with WCDA also show significant multi‐year rainfall prediction skill over East China and Tibetan Plateau. Improvements in predicting the EASM rainfall are attributed to the strong land‐atmosphere coupling in large areas over China, which allows improved predictions of soil moisture to influence precipitation through soil moisture‐precipitation feedback, and the effects of land anomalies on the EASM circulation. This study highlights the significant contribution of land to the interannual predictability of EASM rainfall, with a great potential to advance skillful interannual predictions of benefit to the large populations influenced by the annual whiplash of the summer monsoon rain.

Abstract A team effort to develop a Community Integrated Earth System Model (CIESM) was initiated in China in 2012. The model was based on NCAR Community Earth System Model (Version 1.2.1) with several novel developments and modifications aimed to overcome some persistent systematic biases, such as the double Intertropical Convergence Zone problem and underestimated marine boundary layer clouds. Aerosols' direct and indirect effects are prescribed using the MACv2‐SP approach and data sets. The spin‐up of a 500‐year preindustrial simulation and three historical simulations are described and evaluated. Prominent improvements include alleviated double Intertropical Convergence Zone problem, increased marine boundary layer clouds, and better El Niño Southern Oscillation amplitude and periods. One deficiency of the model is the significantly underestimated Arctic and Antarctic sea ice in warm seasons. The historical warming is about 0.55 °C greater than observations toward 2014. CIESM has an equilibrium climate sensitivity of 5.67 K, mainly resulted from increased positive shortwave cloud feedback. Our efforts on porting and redesigning CIESM for the heterogeneous Sunway TaihuLight supercomputer are also introduced, including some ongoing developments toward a future version of the model.

ABSTRACT Women with gestational diabetes mellitus (GDM) have different gut microbiota in late pregnancy compared to women without GDM. It remains unclear whether alterations of gut microbiota can be identified prior to the diagnosis of GDM. This study characterized dynamic changes of gut microbiota from the first trimester (T1) to the second trimester (T2) and evaluated their relationship with later development of GDM. Compared with the control group (n = 103), the GDM group (n = 31) exhibited distinct dynamics of gut microbiota, evidenced by taxonomic, functional, and structural shifts from T1 to T2. Linear discriminant analysis (LDA) revealed that there were 10 taxa in T1 and 7 in T2 that differed in relative abundance between the GDM and control groups, including a consistent decrease in the levels of Coprococcus and Streptococcus in the GDM group. While the normoglycemic women exhibited substantial variations of gut microbiota from T1 to T2, their GDM-developing counterparts exhibited clearly reduced inter-time point shifts, as corroborated by the results of Wilcoxon signed-rank test and balance tree analysis. Moreover, cooccurrence network analysis revealed that the interbacterial interactions in the GDM group were minimal compared with those in the control group. In conclusion, lower numbers of dynamic changes in gut microbiota in the first half of pregnancy are associated with the development of GDM. IMPORTANCE GDM is one of the most common metabolic disorders during pregnancy and is associated with adverse short-term and long-term maternal and fetal outcomes. The aim of this study was to examine the connection between dynamic variations in gut microbiota and development of GDM. Whereas shifts in gut microbiota composition and function have been previously reported to be associated with GDM, very little is known regarding the early microbial changes that occur before the diagnosis of GDM. This study demonstrated that the dynamics in gut microbiota during the first half of pregnancy differed significantly between GDM and normoglycemic women. Our findings suggested that gut microbiota may potentially serve as an early biomarker for GDM.

Abstract A new weakly coupled data assimilation (CDA) system based on the dimension‐reduced projection four‐dimensional variational data assimilation (DRP‐4DVar) with a simplified off‐line localization technique and a fully coupled model, i.e., the Grid‐point Version 2 of Flexible Global Ocean‐Atmosphere‐Land System Model (FGOALS‐g2), was developed for the initialization of decadal predictions. A 1‐month assimilation window was adopted for the CDA system, in which monthly mean temperature and salinity analyses were assimilated along the trajectory of the coupled model during the initialization for the period of 1945–2006. The system is efficient because the 62‐year initialization only takes about 2.375 times of the time cost of the uninitialized run for the same period. Compared with the uninitialized simulation and the initialization without localization, ocean temperature and salinity, sea surface elevation, surface air temperature, and precipitation are in better agreement with the verification data. Furthermore, climate variabilities in the Pacific and Atlantic regions such as El Niño‐Southern Oscillation, Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) are more realistically captured. Starting from the initial conditions (ICs) generated by the initialization, 10‐member ensemble decadal prediction experiments were conducted each year from 1961 to 1996. The results demonstrate that higher decadal prediction skills of surface air temperature anomalies averaged over the globe, ocean, land, and the North Pacific subpolar gyre are achieved than those obtained from persistence, the uninitialized simulation, and the prediction initialized from the ICs without localization. Besides, PDO and AMO indices exhibit significant correlation skills in most lead times.

Coating the catalyst with a nanoporous layer has been demonstrated to be an effective approach to improve catalyst stability. Herein, we systematically investigate two types of core-shell mesoporous silica nanoparticles with a platinum nanocatalyst using a variety of characterization methods. One of the mesoporous particles has a unique amine ring structure in the middle of a shell (Ring-mSiO2/Pt-5.0/SiO2), and the other one has no ring structure (mSiO2/Pt-5.0/SiO2). Brunauer–Emmett–Teller/Barrett–Joyner–Halenda (BET/BJH) presented a similar surface area for both particles, and the pore size was 2.4 nm. Ultra-Small-Angle X-ray Scattering (USAXS)/ Small-Angle X-ray Scattering (SAXS) showed the size of mSiO2/Pt-5.0/SiO2 and Ring-mSiO2/Pt-5.0/SiO2 were 420 nm and 272 nm, respectively. It also showed that the ring structure was 30 nm above the silica core. Using high-resolution Transmission Electron Microscopy (TEM), it was found that the platinum nanoparticles are loaded evenly on the surface of the silica. In situ SAXS heating experiments and Thermogravimetric Analysis (TGA) indicated that the mSiO2/Pt-5.0/SiO2 were more stable during the high temperature, while the Ring-mSiO2/Pt-5.0/SiO2 had more change in the particle.

Despite their potential application as drug-delivery carriers, covalent organic frameworks (COF) have been only evaluated in vitro. Here the authors show by real time tracking in vivo the cell uptake of anticancer-drug loaded and water dispersible COFs.

While precipitation over the Tibetan Plateau is a vital resource for glacier mass balance, river runoff and local ecology, the controlling mechanisms are poorly understood. Here, the authors combine observations and simulations and show that convective storms over India sweep moisture up and over the plateau.

Localization techniques are commonly used in ensemble-based data assimilation (e.g., the Ensemble Kalman Filter (EnKF) method) because of insufficient ensemble samples. They can effectively ameliorate the spurious long-range correlations between the background and observations. However, localization is very expensive when the problem to be solved is of high dimension (say 106 or higher) for assimilating observations simultaneously. To reduce the cost of localization for high-dimension problems, an approach is proposed in this paper, which approximately expands the correlation function of the localization matrix using a limited number of principal eigenvectors so that the Schür product between the localization matrix and a high-dimension covariance matrix is reduced to the sum of a series of Schür products between two simple vectors. These eigenvectors are actually the sine functions with different periods and phases. Numerical experiments show that when the number of principal eigenvectors used reaches 20, the approximate expansion of the correlation function is very close to the exact one in the one-dimensional (1D) and two-dimensional (2D) cases. The new approach is then applied to localization in the EnKF method, and its performance is evaluated in assimilation-cycle experiments with the Lorenz-96 model and single assimilation experiments using a barotropic shallow water model. The results suggest that the approach is feasible in providing comparable assimilation analysis with far less cost.

An area-weighted k-means clustering method based on pattern correlations is proposed and used to explore the relationship between the Siberian High (SH) and Arctic Oscillation (AO) during the winter months (December-January-February) of 1948-2014. Five regimes are identified. Four of these five regimes (comprising 171 of 201 months) show a negative correlation between the SH and AO indices, while the last regime (30 months) shows a positive correlation. The location of the SH shifts southward into China under two of the four negative-correlation regimes (117 months), with pressure variations over the center of activity for the SH opposite to pressure variations over the climatological center of the SH (which is used to define the SH index). Adjusting the SH index to account for these spatial shifts suggests positive rather than negative correlations between major variations in the SH and AO under these regimes. Under one of the two remaining negative-correlation regimes, pressure anomalies are weak over the Arctic Ocean. In total, only one regime comprising 21 of 201 months strictly obeys the negative correlation between the SH and AO reported by previous studies. The climate regime characterized by an intensified SH is associated with a greater frequency of cold surges over northern and southeastern China, and the weakening of the East Asian winter monsoon during the 1980s was accompanied by a sharp reduction in the occurrence of this regime.

Although standard statistical methods and climate models can simulate and predict sea-ice changes well, it is still very hard to distinguish some direct and robust factors associated with sea-ice changes from its internal variability and other noises. Here, with long-term observations (38 years from 1980 to 2017), we apply the causal effect networks algorithm to explore the direct precursors of September Arctic sea-ice extent by adjusting the maximal lead time from one to eight months. For lead time of more than three months, June downward longwave radiation flux in the Canadian Arctic Archipelago is the only one precursor. However, for lead time of 1⁻3 months, August sea-ice concentration in Western Arctic represents the strongest positive correlation with September sea-ice extent, while August sea-ice concentration factors in other regions have weaker influences on the marginal seas. Other precursors include August wind anomalies in the lower latitudes accompanied with an Arctic high pressure anomaly, which induces the sea-ice loss along the Eurasian coast. These robust precursors can be used to improve the seasonal predictions of Arctic sea ice and evaluate the climate models.

Coastal mangroves are increasingly recognized as valuable natural resources and important sites of water and carbon exchange. In this study, we examine atmospheric water cycling in the boundary layer above a coastal mangrove forest in southern China. We collected site observations of isotopic ratios in water vapor and precipitation along with core meteorological variables during July 2017. Our evaluation of these data highlights the influences of large-scale atmospheric transport and rain–vapor exchange in the boundary layer water budget. Rain–vapor exchange takes different forms for different types of rainfall events. The evolution of isotopic ratios in water vapor suggests that substantial rain recycling occurs during the passage of large-scale organized convective systems, but that this process is much weaker during rainfall associated with less organized events of local origin. We further examine the influences of large-scale transport during the observation period using a Lagrangian trajectory-based moisture source analysis. More than half (63%) of the boundary layer moisture during the study period traced back to the South China Sea, consistent with prevailing southerly to southwesterly flow. Other important moisture sources included mainland Southeast Asia and the Indian Ocean, local land areas (e.g., Hainan Island and the Leizhou Peninsula), and the Pacific Ocean. Together, these five regions contributed more than 90% of the water vapor. The most pronounced changes in isotopic content due to large-scale transport during the study period were related to the passage of Tropical Storm Talas. The outer rain bands of this tropical cyclone passed over the measurement site on 15–17 July, causing a sharp reduction in the heavy isotopic content of boundary layer water vapor and a substantial increase in deuterium excess. These changes are consistent with extensive isotopic distillation and rain–vapor exchange in downdrafts associated with the intense convective systems produced by this storm.

Most of the coupled models contain a strong warm bias in sea surface temperature (SST) over the Atlantic Cold Tongue (ACT) region (10° S–3° N, 20° W–10° E) during June–August (JJA) and September–November (SON). In this study, the origins of the ACT SST bias and their relative contributions to the bias are explored by conducting a set of sensitivity experiments, which are based on an ocean-ice model, and by ignoring the nonlinear effects of each origin. The origins for the warm bias over the ACT in the coupled climate model during JJA are estimated as follows: westerly wind bias along the equator (5° S–5° N) during March–May (MAM; contributes approximately 32.6% of the warm bias), northerly bias over the southern tropical Atlantic (25° S–3° N, 40° W–20° E) during MAM and JJA (21.4%), bias in the surface specific humidity and surface air temperature (11.9%), and downward shortwave radiation bias (6.5%). The origins of the ACT bias during SON are as follows: northerly bias over the southern tropical Atlantic during SON (31.2%), bias in the surface specific humidity and surface air temperature (27.9%), downward shortwave radiation bias (17.4%), and zonal wind bias (13.4%). Note that these contribution ratios of these origins may be model-dependent. In addition, the local and non-local effects of the zonal wind bias are explored explicitly, while those of all the other biases are examined implicitly. Therefore, a better-performing atmospheric component is crucial when simulating zonal winds during MAM along the equator (5° S–5° N) and meridional winds during MAM, JJA, and SON over the southern tropical Atlantic, which will alleviate the warm bias over the ACT region in the coupled climate model.

Nine precipitation regimes over South China are obtained by applying the Self-Organizing Map (SOM) technique to the sub-daily precipitation during the pre-monsoon season (April to June) of 1979–2015. These nine regimes are distinct from each other in terms of precipitation amount and spatial pattern. The relationships between precipitation and different atmospheric dynamic and thermodynamic factors (large-scale divergence, water vapor flux, low-level jet, precipitable water, convective available potential energy (CAPE), and K index) are explored under the nine regimes. The upper-level divergence performs best in indicating the geographic positions of precipitation centers, which are also modulated by the orientations of low-level jets. The estimation of water vapor transport reveals that there are two major moisture sources for the precipitation during the pre-monsoon season, i.e., the Bay of Bengal (for all the nine regimes) and the South China Sea and West Pacific Ocean (for five regimes). Furthermore, the occurrence probability of more precipitation increases with the water vapor transported from the South China Sea and West Pacific Ocean. Compared to CAPE, K index performs better in indicating the precipitation centers and has a tighter relationship with area-average precipitation. The precipitable water exhibits complicated relationships with spatial patterns and amounts of precipitation, indicating that it may be not a good indicator for precipitation during pre-monsoon season over South China. Estimation of the persistence and transformation probabilities for precipitation regimes reveals that the persistence probabilities basically decrease with the precipitation amounts, and the transformations between different precipitation regimes are inclined to be associated with the southward shifts of precipitation centers.