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Accurate forecast precipitation is crucial for hydropower generation, drought and flood warning, and hydrological forecasting. However, raw forecast precipitation often suffers from systematic errors due to inaccurate initial conditions in numerical weather prediction (NWP) models. In this study, we develop a deep-learning-based post-processing method to correct forecast precipitation. Our method leverages convolutional neural networks (CNN) to analyze spatial features and long short-term memory networks (LSTM) to capture temporal dynamics, effectively modeling the local spatiotemporal characteristics (e.g., mean sea level pressure and elevation) of precipitation. Crucially, we also consider the impact of large-scale weather patterns (e.g., high-latitude blockings, the Meiyu trough) on precipitation by extracting relevant features through a CNN model and integrating this information with the local spatiotemporal data to improve forecast accuracy. Results indicate that the proposed CNN-CNN-LSTM method outperforms the three baselines (i.e., CNN-LSTM, CNN, LSTM) for all seasons and lead times (15 days) in the Huaihe River basin of China. Specifically, for the summer precipitation with a one-day lead time, the CNN-CNN-LSTM model achieves a 4.7% reduction in root mean square error and a 30.5% reduction in relative bias compared to CNN-LSTM alone. Furthermore, the relative importance of large-scale predictors is constantly increasing with the extension of lead times. By effectively integrating large-scale weather information and local-scale spatiotemporal information, the proposed CNN-CNN-LSTM method offers a novel approach to enhance the correction effect, providing significant valuable for hydrometeorological applications.

Observational studies have demonstrated an association between white blood cells (WBC) subtypes and type 2 diabetes (T2D) risk. However, it is unknown whether this relationship is causal. We used Mendelian randomization (MR) to investigate the causal effect of WBC subtypes on T2D and glycemic traits. The summary data for neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were extracted from a recent genome-wide association study (n = 173,480). The DIAGRAM and MAGIC consortia offered summary data pertaining to T2D and glycemic characteristics, including fasting glucose (FG) (n = 133,010), glycosylated hemoglobin (HbA1c) (n = 46,368), and homeostatic model assessment-estimated insulin resistance (HOMA-IR) (n = 37,037). A series of MR analyses (univariable MR, multivariable MR, and reverse MR) were used to investigate the causal association of different WBC subtypes with T2D and glycemic traits. Using the inverse-variance weighted method, we found one standard deviation increases in genetically determined neutrophil [odd ratio (OR): 1.086, 95% confidence interval (CI): 0.877-1.345], lymphocyte [0.878 (0.766-1.006)], monocyte [1.010 (0.906-1.127)], eosinophil [0.995 (0.867-1.142)], and basophil [0.960 (0.763-1.207)] were not causally associated with T2D risk. These findings were consistent with the results of three pleiotropy robust methods (MR-Egger, weighted median, and mode-based estimator) and multivariable MR analyses. Reverse MR analysis provided no evidence for the reverse causation of T2D on WBC subtypes. The null causal effects of WBC subtypes on FG, HbA1c, and HOMA-IR were also identified. WBCs play no causal role in the development of insulin resistance and T2D. The observed association between these factors may be explained by residual confounding.

Manipulating the surface acidic/basic property and pore structure of support are two effective approaches to increase catalytic performance of Pd-based catalyst in anthraquinone (eAQ) hydrogenation. Herein, to combine two promoting approaches, array-typed NiO/Al supported Pd catalyst were synthesized. By regulating preparation method, three Ni(OH) 2 /Al support precursors showed different morphologies of nest-like, face-to-face packed and dandelion-like structure, respectively. After loading Pd, three Pd/NiO/Al catalysts exhibited different catalytic performance in eAQ hydrogenation, among which the nest-like catalyst possessed the highest H 2 O 2 space time yield of 107.5 g g Pd −1  h −1 with > 99% selectivity to active anthraquinone. Detailed characterizations were performed to investigate the pore structure, basic property and electronic structure caused by different morphologies of catalysts, to explain the structure-performance relationship. Specifically, on the basis of ensuring effective collision of reactant molecules, the outer opening pores (20–100 nm) could decrease diffusion barriers of eAQ/eAQH 2 , which improves active site accessibility for eAQ and benefits desorption of eAQH 2 . In addition, suitable amount of weak basic sites Ni 2+ –OH with high electronic density appropriately improves surface electronic density of Pd NPs, which moderately enhances H 2 activation/dissociation but could not lead to over hydrogenation to give deeply hydrogenated byproducts. Graphical Abstract

Background Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) share overlapping features but arise from distinct molecular mechanisms. Competitive endogenous RNA (ceRNA) networks, where long non-coding RNAs (lncRNAs) and mRNAs compete for shared microRNAs (miRNAs), represent a key regulatory layer. This study sought to identify disorder-specific and convergent ceRNA regulatory signatures across these conditions. Method We constructed an integrative analysis of whole-transcriptome and small RNA sequencing data from peripheral blood samples of monozygotic twin pairs discordant for disease. Differentially expressed mRNAs, lncRNAs, and miRNAs were identified within each discordant pair and integrated with co-expression modules from external transcriptomic datasets constructed by Multiscale Embedded Gene Co-expression Network Analysis (MEGENA). This enabled the construction of disorder-specific ceRNA networks and the identification of core regulatory components. Results We identified ceRNA networks for each disorder, revealing 19 miRNAs shared across all three disorders, while lncRNAs and mRNAs were primarily disorder-specific. Among the shared miRNAs, hsa-miR-29a-3p was downregulated in both SCZ and MDD, regulating distinct ceRNA axes involving COL6A6 . Functional enrichment analysis of hub ceRNA networks revealed the convergent involvement of extracellular matrix (ECM)-receptor interaction pathways. Notably, COL6A6 (SCZ and MDD) and ITGB8 (BD) were key components of these pathways. Validation using independent brain and blood transcriptomic datasets demonstrated strong predictive potential for ITGB8 in the blood and prefrontal cortex for BD and SCZ, and moderate predictive potential for COL6A6 in the blood and anterior cingulate gyrus for SCZ. Conclusions This study identifies non-coding RNA–mediated regulatory networks implicated in the molecular etiology of psychiatric disorders. Our findings provide a foundation for precision diagnostics and targeted therapeutic strategies in psychiatry.

We assessed whether omega-3 supplementation could improve glucose and lipid metabolism, insulin resistance, and inflammatory factors in individuals with gestational diabetes mellitus (GDM). In this meta-study, we used a random-effects or fixed-effects meta-analysis model to analyze the mean differences (MD) and corresponding 95 % confidence intervals (CI) before and after omega-3 and placebo supplementation, thus evaluating the effects of omega-3 on glucose and lipid metabolism, insulin resistance, and inflammatory factors. Six randomized controlled trials (331 participants) were included in the meta-analysis. The levels of fasting plasma glucose (FPG) (WMD = −0.25 mmol/L; 95 % CI: −0.38, −0.12), fasting insulin (WMD = −17.13 pmol/L; 95 % CI: −27.95, −6.30), and homeostasis model of assessment-insulin resistance (WMD = −0.51; 95 % CI: −0.89, −0.12) were lower in the omega-3 group compared to their levels in the placebo group. The results of the analysis of lipid metabolism showed that triglycerides (WMD = −0.18 mmol/L; 95 % CI: −0.29, −0.08) and very low-density lipoprotein cholesterol (WMD = −0.1 mmol/L; 95 % CI: −0.16, −0.03) decreased in the omega-3 group, while high-density lipoproteins (WMD = 0.06 mmol/L; 95 % CI: 0.02, 0.10) increased. Compared to the placebo group, inflammatory factor serum C-reactive protein (SMD = −0.68 mmol/L; 95 % CI: −0.96, −0.39) decreased in the omega-3 group. Omega-3 supplementation can decrease the levels of FPG and inflammatory factors, enhance blood lipid metabolism, and reduce insulin resistance in patients with GDM. •The paper provides a safe and feasible method using non-medication intervention to improve metabolism in people with GDM.•Omega-3 supplementation could improve glucose and lipid metabolism in individuals with GDM.•Omega-3 could also improve insulin resistance and inflammation factor CRP in individuals with GDM.

Honey Badger algorithm (HBA) is an intelligent adaptive meta-heuristic optimization algorithm with few parameters, fast convergence and good convergence accuracy for single-objective problems. However, many real-world optimization problems involve multiple conflicting objectives that need to be optimized simultaneously. A new multi-objective Honey Badger algorithm is proposed to solve the combined economic and environmental power scheduling problem. The proposed MOHBA combines the HBA with the Pareto dominance principle to produce a non-dominated solution. It uses an external elite storage mechanism with congested distance ordering to maintain the diversity of the distribution during the evolution of the Pareto optimal solutions. Furthermore, a fuzzy decision strategy is used to select the best compromise solution from the obtained Pareto bound. Then, to validate the performance of the proposed MOHBA, 20 different benchmark test functions are used to test it against other multi-objective optimization techniques. Moreover, the method is implemented on the multi-objective CEEPD problem for the IEEE 30-bus 6 generator and IEEE 118-bus 14 generator systems. Various objective function s in a multi-objective optimization space is confirmed by comparative studies with minimization schemes and fuzzy decision strategies are utilized to achieve the best scheduling solution for energy and emissions savings. The predominance of the algorithm and its potentiality to handle CEEPD problem several other algorithms.

Reverse osmosis concentrate (ROC) contains relatively high levels of refractory organic pollutants, posing significant challenges due to its difficult treatment and high environmental risks. Therefore, efficient and convenient removal strategies are essential. In this study, a self-developed iron-modified activated carbon fiber (Fe-ACF) was employed as an adsorbent to remove organic pollutants from ROC. Additionally, response surface methodology (RSM) was applied to model the adsorption process, identify and evaluate key influencing parameters, and optimize operational conditions. The adsorption mechanisms and regeneration stability of Fe-ACF were also investigated. Kinetic analysis revealed that the adsorption process is predominantly governed by chemisorption, with intraparticle diffusion identified as the primary rate-limiting step. Isothermal adsorption studies demonstrated that the Langmuir–Freundlich model best describes the adsorption behavior, yielding a theoretical maximum adsorption capacity of 12.21 ± 0.80 mg/g. Thermodynamic analysis confirmed that the adsorption process is spontaneous, endothermic, and driven by an increase in entropy. The RSM optimization identified pH as the dominant factor. The optimal adsorption conditions were a pH of 4.18, a temperature of 34.63 °C, a stirring speed of 547.91 rpm, and an adsorbent dosage of 1.55 g/L. The adsorption mechanism involves hydrogen bonding, π–π interactions, surface complexation, and electrostatic forces. Fe-ACF exhibits competitive regeneration stability and structural integrity. In summary, Fe-ACF demonstrates significant potential as a treatment material for ROC.

Objectives This study is designed to analyze the potential influencing factors of hyperlipidemia, and to explore the relationship between liver function indicators such as gamma-glutamyltransferase (GGT) and hyperlipidemia. Methods Data were derived from 7599 outpatients who visited the Department of Endocrinology of the First Hospital of Jilin University (2017–2019). A multinomial regression model is used to identify related factors of hyperlipidemia and the decision tree method is used to explore the general rules in hyperlipidemia patients and non-hyperlipidemia patients on these factors. Results The average of age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure, aspartate aminotransferase, alanine aminotransferase (ALT), GGT and glycosylated hemoglobin (HbA1c) in the hyperlipidemia group are higher than those in the non-hyperlipidemia group. In multiple regression analysis, SBP, BMI, fasting plasma glucose, 2-h postprandial blood glucose, HbA1c, ALT, GGT are associated with triglyceride. For people with HbA1c less than 6.0%, controlling GGT within 30 IU/L reduces the prevalence of hypertriglyceridemia by 4%, and for people with metabolic syndrome with impaired glucose tolerance controlling GGT within 20 IU/L reduces the prevalence of hypertriglyceridemia by 11%. Conclusions Even when GGT is in the normal range, the prevalence of hypertriglyceridemia increases with its gradual increase. Controlling GGT in people with normoglycemia and impaired glucose tolerance can reduce the risk of hyperlipidemia.

MicroRNAs have crucial roles in development and progression of human cancers, including osteosarcoma. Recent studies have shown that miR-124 was down-regulated in many cancers; however, the role of miR-124 in osteosarcoma development is unknown. In this study, we demonstrate that expression of miR-124 is significantly downregulated in osteosarcoma tissues and cell lines, compared to the adjacent tissues. The expression of miR-124 in the metastases osteosarcoma tissues was lower than that in non- metastases tissues. We identified and confirmed Rac1 as a novel, direct target of miR-124 using prediction algorithms and luciferase reporter gene assays. Overexpression of miR-124 suppressed Rac1 protein expression and attenuated cell proliferation, migration, and invasion and induced apoptosis in MG-63 and U2OS in vitro. Moreover, overexpression of Rac1 in miR-124-transfected osteosarcoma cells effectively rescued the inhibition of cell invasion caused by miR-124. Therefore, our results demonstrate that miR-124 is a tumor suppressor miRNA and suggest that this miRNA could be a potential target for the treatment of osteosarcoma in future.

The South-to-North Water Division Middle Route Project (MRP) is currently the longest inter-basin water diversion project in the world. It benefits a large population and its water quality has attracted much attention. In this study, seasonal investigations on 11 sampling sites along the MRP were conducted from 2018 to 2019; water temperature, pH, turbidity, transparency, COD Mn , dissolved oxygen, total phosphorus, phosphate, total nitrogen, ammonia, nitrate, and chlorophyll a were determined synchronously. Single leakage distance clustering analysis identified the spatio-seasonal heterogeneity of physiochemical parameters. The trophic level index (TLI) and the water quality status were assessed: TLI increased and WQI decreased from south to north; TLI and WQI had seasonal differences ( p < 0.001), the best water quality was observed in autumn, and the lowest TLI was observed in winter. The trophic level was “oligotrophic to mesotrophic”; water quality status was “good.” The multiple linear stepwise regression analysis confirmed that total nitrogen (TN), Chl a , and COD Mn were the driving factors in water quality. These factors were applied to build the simplified WQI model, which was confirmed as a reliable method of water quality assessment for the MRP and a fitting substitute for TLI and WQI. The results provided support for the water quality evaluation of the MRP.