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Abstract Background The clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model. Methods Patient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model. Results There were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p

Abstract Background The calmodulin (CaM) and calmodulin-like (CML) proteins play regulatory roles in plant growth and development, responses to biotic and abiotic stresses, and other biological processes. As a popular fruit and ornamental crop, it is important to explore the regulatory mechanism of flower and fruit development of passion fruit. Results In this study, 32 PeCaM/PeCML genes were identified from passion fruit genome and were divided into 9 groups based on phylogenetic analysis. The structural analysis, including conserved motifs, gene structure and homologous modeling, illustrates that the PeCaM/PeCML in the same subgroup have relative conserved structural features. Collinearity analysis suggested that the expansion of the CaM/CML gene family likely took place mainly by segmental duplication, and the whole genome replication events were closely related with the rapid expansion of the gene group. PeCaM/PeCMLs were potentially required for different floral tissues development. Significantly, PeCML26 had extremely high expression levels during ovule and fruit development compared with other PeCML genes, suggesting that PeCML26 had potential functions involved in the development of passion fruit flowers and fruits. The co-presence of various cis-elements associated with growth and development, hormone responsiveness, and stress responsiveness in the promoter regions of these PeCaM/PeCMLs might contribute to their diverse regulatory roles. Furthermore, PeCaM/PeCMLs were also induced by various abiotic stresses. This work provides a comprehensive understanding of the CaM/CML gene family and valuable clues for future studies on the function and evolution of CaM/CML genes in passion fruit. Conclusion A total of 32 PeCaM/PeCML genes were divided into 9 groups. The PeCaM/PeCML genes showed differential expression patterns in floral tissues at different development stages. It is worth noting that PeCML26, which is highly homologous to AtCaM2, not only interacts with multiple BBR-BPC TFs, but also has high expression levels during ovule and fruit development, suggesting that PeCML26 had potential functions involved in the development of passion fruit flowers and fruits. This research lays the foundation for future investigations and validation of the potential function of PeCaM/PeCML genes in the growth and development of passion fruit.

Abstract Background Evidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored. Methods We screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives. Results Five genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines. Conclusion The CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

The cardiotoxicity caused by Dox chemotherapy represents a significant limitation to its clinical application and is a major cause of late death in patients undergoing chemotherapy. Currently, there are no effective treatments available. Our analysis of 295 clinical samples from 132 chemotherapy patients and 163 individuals undergoing physical examination revealed a strong positive correlation between intestinal barrier injury and the development of cardiotoxicity in chemotherapy patients. We developed a novel orally available and intestinal targeting protein nanodrug by assembling membrane protein Amuc_1100 (obtained from intestinal bacteria Akkermansia muciniphila), fluorinated polyetherimide, and hyaluronic acid. The protein nanodrug demonstrated favorable stability against hydrolysis compared with free Amuc_1100. The in vivo results demonstrated that the protein nanodrug can alleviate Dox-induced cardiac toxicity by improving gut microbiota, increasing the proportion of short-chain fatty acid-producing bacteria from the Lachnospiraceae family, and further enhancing the levels of butyrate and pentanoic acids, ultimately regulating the homeostasis repair of lymphocytes in the spleen and heart. Therefore, we believe that the integrity of the intestinal barrier plays an important role in the development of chemotherapy-induced cardiotoxicity. Protective interventions targeting the intestinal barrier may hold promise as a general clinical treatment regimen for reducing Dox-induced cardiotoxicity.

Abstract Idiopathic normal pressure hydrocephalus (iNPH) affects mainly aged populations. The gradual shortening of telomere length (TL) is one of the hallmarks of aging. Whereas the genetic contribution of TL to the iNPH is incompletely understood. We aimed to investigate the causal relationship between TL and iNPH through the Mendelian randomization (MR) analysis. We respectively obtained 186 qualified single nucleotide polymorphisms (SNPs) of TL and 20 eligible SNPs of iNPH for MR analysis. The result of MR analysis showed that genetically predicted longer TL was significantly associated with a reduced odd of iNPH (odds ratio [OR] = 0.634 95% Confidence interval [CI] 0.447–0.899, p = 0.011). The causal association remained consistent in multivariable MR (OR = 0.530 95% CI 0.327–0.860, p = 0.010). However, there was no evidence that the iNPH was causally associated with the TL (OR = 1.000 95% CI 0.996–1.004, p = 0.955). Our study reveals a potential genetic contribution of TL to the etiology of iNPH, that is a genetically predicted increased TL might be associated with a reduced risk of iNPH.

Background: The prediction of response to immunotherapy mostly depends on the programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) status, and the 22C3 pharmDx assay has been approved in esophageal squamous cell carcinoma (ESCC). However, the widespread use of the 22C3 pharmDx assay is limited due to its availability. Thus, alternative PD-L1 assays are needed. We aimed to investigate the analytical and clinical diagnostic performances of four PD-L1 assays and to compare their concordances with the 22C3 pharmDx assay. Methods: The PD-L1 22C3 pharmDx assay was performed on the Dako Autostainer Link 48 platform, three testing assays (PD-L1 E1L3N XP antibody [Ab], PD-L1 BP6099 Ab and PD-L1 CST E1L3N Ab) on the Leica BOND-MAX/III platform, and one testing assay (PD-L1 MXR006 Ab) on the Roche VENTANA Benchmark Ultra platform. A total of 218 ESCC cases from four centers were included in this retrospective study. Professionals from each center stained and read the IHC slides independently and determined the combined positive score (CPS) and the tumor proportion score (TPS). Results: Regarding analytical performance, the four testing assays demonstrated good correlations with the 22C3 pharmDx assay when evaluated by the TPS or CPS (ρ > 0.8 for all four assays). Regarding diagnostic performance (CPS ≥ 10 was used as the cutoff), the four testing assays showed moderate concordances with the 22C3 pharmDx assay (kappa > 0.7 for all four assays). The overall percent agreements between each testing assay and the 22C3 pharmDx assay was at least 87.2 %. Conclusion: This study provides insight into the potential interchangeability of the four PD-L1 assays with the 22C3 pharmDx assay.

Abstract Due to the BCS theory, hydrogen, the lightest element, would be the prospect of room-temperature superconductor after metallization, but because of the difficulty of the hydrogen metallization, the theory about hydrogen pre-compression was proposed that the hydrogen-rich compounds could be a great option for the high T c superconductors. The superior properties of TmH6, YbH6 and LuH6 indicated the magnificent potential of heavy rare earth elements for low-pressure stability. Here, we designed XTmH12 (X = Y, Yb, Lu, and La) to obtain higher T c while maintaining low pressure stability. Most prominently, YbTmH12 can stabilize at a pressure of 60 GPa. Compared with binary TmH6 hydride, its T c was increased to 48 K. The results provide an effective method for the rational design of moderate pressure stabilized hydride superconductors.

Early warning of geo-hazards based on meteorological factors has played an important supportive role in disaster prevention and mitigation in China since its inception in 2003. This paper summarizes the the 20-year development process, technical methods, and disaster reduction effects of the early warning works. (1) The development process of early warning work process is divided into three stages: initiation and promotion (2003-2009), deepening cooperation (2010-2017), and reform and enhancement (2018-2022). (2) With 24-hour early warning work as the main content, a progressive warning model and a relatively complete warning service system have been gradually formed. (3) Three sets of early warning model technology and method systems have been gradually developed, including critical precipitation threshold model, the threshold model based on geo-hazards risk, and the dynamic early warning models, with the publication of industry standards for warning. (4) The spatial and temporal accuracy of warning products continues to improve, with the national and 26 provincial warning spatial accuracies exceeding 5 km × 5 km. The focus is on 24-hour warnings, with development towards 72-hour and medium- to long-term forecasts. Over 8 provincial-level and some municipal and county-level authorities have implemented 3-hour short-term warnings, gradually forming a work system to support service short impending warning response, medium-term prevention and long term deployment. (5) Where there is warning, there is response. The Ministry of Natural Resources has taken the national early warning as one of the bases for initiating defense responses, and 18 provinces have clarified the working mechanisms of the early warning response linkage. (6) The awareness of multi-party disaster prevention has been continuously enhanced. With strengthened inspections,evacuations, and successful risk aversion after receiving early warning information, the effectiveness of disaster prevention and mitigation is evident. The experience of early warning works in the past 20-year can provide reference for the next step in promoting the early warning of geo-hazards based on meteorological factors, supporting the enhancement of China’s capability and level of geo-hazards prevention and control work.

Early warning of geo-hazards based on meteorological factors in China began in 2003, and early warning informations are issued to the public every year during the flood season to alert the public to guard against geo-hazards such as landslides, debris flows, and mudslides caused by rainfall. To better understand the current situation and future trends of research for early warning of geo-hazards based on meteorological factors, the visualization tool CiteSpace was utilized to review the domestic and foreign literature on geo-hazards meteorological risk warning published from 2003 to 2023 and construct the scientific knowledge map. The CNKI database and SCI-Expanded databases were used as data sources. The result show that: (1) The study of critical rainfall thresholds based on the relationship between historical rainfall and disaster is in the leading position in research on early warning of geo-hazards based on meteorological factors, and the study of rainfall-related thresholds will continue to be a research hotspot in the future. (2) Keyword cluster analysis shows that a large number of studies have conducted research on early warning and prediction models and critical thresholds of geo-hazards under different formation conditions, meteorological conditions, different types, and genetic models, indicating that refinement is an important direction for the development of early warning of geo-hazards based on meteorological factors. (3) In the international research, Chinese scholars account for 39% of the total number of publications, showing an absolute advantage in research on geo-hazards meteorological risk early warning. The research results objectively demonstrate the development context, domestic and foreign research hotspots, and trends of research on geo-hazards meteorological risk warning in China, aiming to contribute to the progress of geo-hazards research and early warning forcast business in China.

Improving the geo-hazard meteorological risk warning capabilities is an important component of the comprehensive prevention system for geological disasters. Since 2003, Sichuan Province has been carrying out geo-hazard meteorological risk warning work. Building on the previous foundation, research has been focused on the analysis and establishment of warning models, system design and implementation, and graded warning operation modes since 2019. A geo-hazard warning model library, an integrated geological disaster meteorological risk warning interconnection system at the provincial, municipal, and county levels, and a graded warning mechanism have been created. We have achieved closed-loop management of interconnection and graded early warning based on integrated platform such as rainfall data transmission, early warning model analysis and calculation, and early warning information release in province, cities, and counties. This has solved the three problems of lack of technology, capacity, and funding in cities and counties, as well as the interference caused by multiple early warning information releases on grassroots levels. In 2023, 86% of the successful risk avoidance cases in Sichuan Province were affected by meteorological risk warning, effectively reducing casualties and property losses caused by geo-hazard.

Abstract Background Concurrent follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL)was reported in some studies, while the diagnosis of TdT (terminal deoxynucleotydil transferase) positive high grade B cell lymphoma (HGBL) with MYC and BCL2 rearrangements (“double hit”) transformed from FL/DLBCL has been rarely reported. Herein, we described the clinical features and mutation profiles of a case diagnosed with TdT positive “double hit” HGBL following the treatment of FL/DLBCL. Case presentation This is a 43-year-old Chinese man who was diagnosed with low grade FL (account for 80%) combined with DLBCL (20%) at a stage of IVB. The patient presented with BCL2/IGH translocation without MYC rearrangement, as well as the expressions of CD20, CD19, CD10 and BCL2 at the initial diagnosis of FL/DLBCL. MYC rearrangement and TdT expression occurred after the treatment. The targeted sequencing revealed mutations in KMT2D, FOXO1, CREBBP, ATM, STAT6, BCL7A, DDX3X, MUC4, FGFR3, ARID5B, DDX11 and PRKCSH genes were the co-mutations shared by the FL/DLBCL and TdT positive “double hit” HGBL, while CCND3, BIRC6, ROBO1 and CHEK2 mutations specifically occurred after the treatment. The overall survival time was 37.8 and 17.8 months after the initial diagnosis of FL/DLBCL and TdT positive “double hit” HGBL, respectively. Conclusion This study reports a rare case of TdT positive “double hit” HGBL following the treatment of concurrent FL/DLBCL and highlights the mutation characteristics. Collectively, this study will help enrich the knowledge of TdT positive “double hit” HGBL transformed from FL/DLBCL.

River bottom tearing scour (RBTS) has a strong effect on the scouring and moulding of channel in the Yellow River. Due to the special forming conditions, complex influencing factors, and limited observed data, it is difficult to predict whether RBTS will occur accurately. By collecting and disposing of the hydrodynamic, sediment, and initial boundary data of 246 flood events related to RBTS in three typical reaches of the Yellow River basin, the correlation between different characteristic influencing factors and the occurrence and absence of RBTS were analysed, and prediction models based on machine learning algorithms were constructed. The results showed that under the existing data conditions, the maximum sediment concentration Sm, average sediment concentration Sp, flood growth rate ν, and shape coefficient δ were the four key indices to more easily distinguish whether RBTS will occur. The support vector machine algorithm model had the best performance results and exhibited higher accuracy and precision in predicting its occurrence compared with other models under given water and sediment conditions. The method proposed in this study provides a new method for accurately predicting RBTS in the Yellow River. HIGHLIGHTS The maximum sediment concentration, average sediment concentration, flood growth rate, and shape coefficient are the four key indices for distinguishing whether RBTS will occur.; Prediction models of RBTS based on machine learning algorithms were built.; The SVM model showed the best predictive performance in the case study of the middle reach of the Yellow River.;

The rainfall-induced shallow landslides are primarily debris landslides, which features simultaneity with significant hazard, and the hydrological response mechanism of water table and soil moisture content to precipitation of this type of landslide is sophisticated, which makes it difficult to predict the slope stability accurately. To further study the influence of the rainfall-triggered internal hydrological responses on slope-stability, on-site precipitation infiltration monitoring, correlation analysis and mechanical analysis were carried out on the Houshanli landslide in Qingchuan County, Sichuan Province. The relationship between precipitation and water table was proposed based on climate and hydrological monitoring data obtained within three year interval. The response of rainfall infiltration, soil volumetric water content and water table were analyzed. The results indicate that: (1) groundwater exhibits periodic fluctuations throughout the year, characterized by three phases of slow decline, rapid decline, and rapid ascent; a linear negative correlation between precipitation and water table was found, and no significant correlation was observed with the water table increment; (2) through the infinite slope model and the relationship between precipitation and water table, a prediction model for shallow landslide stability was constructed. The precipitation threshold (81.8 mm/d) and water table threshold (0.73 m) were determined which has good agreement with the actual situations. This provides an early warning method for rainfall-induced shallow landslides by monitoring these two factors.

Unfavorable environmental cues severely affect crop productivity resulting in significant economic losses to farmers. In plants, multiple regulatory genes, such as the WRKY transcription factor (TF) family, modulate the expression of defense genes. However, the role of the pineapple WRKY genes is poorly understood. Here, we studied the pineapple WRKY gene, AcWRKY28, by generating AcWRKY28 over-expressing transgenic pineapple plants. Overexpression of AcWRKY28 enhanced the salt stress resistance in transgenic pineapple lines. Comparative transcriptome analysis of transgenic and wild-type pineapple plants showed that “plant-pathogen interaction” pathway genes, including 9 calcium-dependent protein kinases (CPKs), were up-regulated in AcWRKY28 over-expressing plants. Furthermore, chromatin immunoprecipitation and yeast one-hybrid assays revealed AcCPK12, AcCPK3, AcCPK8, AcCPK1, and AcCPK15 as direct targets of AcWRKY28. Consistently, the study of AcCPK12 over-expressing Arabidopsis lines showed that AcCPK12 enhances salt, drought, and disease resistance. This study shows that AcWRKY28 plays a crucial role in promoting salt stress resistance by activating the expression of AcCPK genes.

Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms—Random Forest, Multilayer Perceptron, and Convolutional Neural Network—are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930–0.957 and an AUC value of 0.955. The CNN-based warning model demonstrated an accuracy of 0.945–0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930–0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on 5 August 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multi-classification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this study contribute to valuable attempts in landslide disaster warning model research, with anticipated further improvements through the gradual accumulation of samples and practical application verification.

Abstract Background and Objective High‐grade glioma (HGG) is known to be characterized by a high degree of malignancy and a worse prognosis. The classical treatment is safe resection supplemented by radiotherapy and chemotherapy. Tumor treating fields (TTFields), an emerging physiotherapeutic modality that targets malignant solid tumors using medium‐frequency, low‐intensity, alternating electric fields to interfere with cell division, have been used for the treatment of new diagnosis of glioblastoma, however, their administration in HGG requires further clinical evidence. The efficacy and safety of TTFields in Chinese patients with HGG were retrospectively evaluated by us in a single center. Methods We enrolled and analyzed 52 patients with newly diagnosed HGG undergoing surgery and standard chemoradiotherapy regimens from December 2019 to June 2022, and followed them until June 2023. Based on whether they used TTFields, they were divided into a TTFields group and a non‐TTFields group. Progression‐free survival (PFS) and overall survival (OS) were compared between the two groups. Results There were 26 cases in the TTFields group and 26 cases in the non‐TTFields group. In the TTFields group, the median PFS was 14.2 months (95% CI: 9.50–18.90), the median OS was 19.7 months (95% CI: 14.95–24.25) , the median interval from surgery to the start of treatment with TTFields was 2.47 months (95% CI: 1.47–4.13), and the median duration of treatment with TTFields was 10.6 months (95% CI: 9.57–11.63). 15 (57.69%) patients experienced an adverse event and no serious adverse event was reported. In the non‐TTFields group, the median PFS was 9.57 months (95% CI: 6.23–12.91) and the median OS was 16.07 months (95% CI: 12.90–19.24). There was a statistically significant difference in PFS (p = 0.005) and OS (p = 0.007) between the two groups. Conclusions In this retrospective analysis, TTFields were observed to improve newly diagnosed HGG patients' median PFS and OS. Compliance was much higher than reported in clinical trials and safety remained good.

Summary: The 2022 mpox outbreak led the World Health Organization (WHO) to declare it a public health emergency of international concern (PHEIC). There is a need to develop more effective and safer mpox virus (MPXV)-specific vaccines in response to the mpox epidemic. The mRNA vaccine is a promising platform to protect against MPXV infection. In this study, we construct two bivalent MPXV mRNA vaccines, designated LBA (B6R-A29L) and LAM (A35R-M1R), and a quadrivalent mRNA vaccine, LBAAM (B6R-A35R-A29L-M1R). The immunogenicity and protective efficacy of these vaccines alone or in combination were evaluated in a lethal mouse model. All mRNA vaccine candidates could elicit potential antigen-specific humoral and cellular immune responses and provide protection against vaccinia virus (VACV) infection. The protective effect of the combination of two bivalent mRNA vaccines and the quadrivalent vaccine was superior to that of the individual bivalent mRNA vaccine. Our study provides valuable insights for the development of more efficient and safer mRNA vaccines against mpox.

Landslide disasters occur frequently in Fujian Province, and early warning of landslide disasters on a regional scale is an important means of effective disaster prevention and mitigation. Due to the complex mechanism of landslide disasters, the traditional regional landslide early warning methods have such problems as insufficient accuracy. Deep learning mainly refers to the technology of feature extraction, abstraction, representation and learning by constructing the neural network model, which is a kind of machine learning. As a classical deep learning algorithm, convolutional neural network has more powerful classification and representation ability than traditional machine learning. Taking Fujian Province as the research area, this paper introduces the convolution neural network into the field of landslide disaster early warning and constructs a regional landslide early warning model of Fujian Province. The process is as follows: (1) The SMOTE optimization algorithm is used to optimize the sample database of landslide disasters in Fujian Province from 2010 to 2018, enlarging the number of positive samples and expanding the proportion of positive and negative samples from 1∶3.4 to 1∶2, and the total number of samples reaches 18040. (2) Construct a convolution neural network model structure, which includes an input layer, two convolution layers, two maximum pooling layers, a full connection layer and an output layer. (3) Use the convolution neural network to train the optimized samples (80% of the samples from 2010 to 2018 as the training set), and use the Bayesian optimization algorithm to optimize the model parameters to obtain the regional landslide early warning model of Fujian Province. (4) The model is tested with 20% of the samples from 2010 to 2018 as the test set, and the confusion matrix and ROC curve are used to test the model. The results show that the accuracy of the model ranges from 0.96 to 0.97, the AUC value is 0.977, indicating that the model accuracy and generalization ability are good. (5) The actual situation of the landslide disaster in the flood season of 2019 is taken as a positive sample, negative samples are collected through the method of time-space sampling, and the 2019 regional landslide sample verification set (603 samples) is constructed. The model is further verified by using the confusion matrix and ROC curve. The results show that the accuracy of the model ranges from 0.75 to 0.85, and the AUC value is 0.852. Although only the actual landslide samples in the flood season of 2019 is used for verification, good results is also achieved. In this paper, the convolution neural network algorithm is applied to the regional landslide early warning, which provides a new way to establish the regional landslide early warning model. The preliminary verification shows that the model is effective and will be further applied and verified in Fujian Province in the future.

Abstract Background Neurocognitive and psychiatric disorders have been proved that they can comorbid more often with idiopathic normal pressure hydrocephalus (iNPH) than general population. However, the potential causal association between these disorders and iNPH has not been assessed. Thus, our study aims to investigate the causal relationship between them based on a bidirectional Mendelian randomization (MR) analysis. Methods Random effects of the inverse variance weighted (IVW) method were conducted to obtain the causal association among the neurocognitive disorders, psychiatric disorders, and iNPH. Genome‐wide association studies (GWAS) of 12 neurocognitive and psychiatric disorders were downloaded via the OpenGWAS database, GWAS Catalog, and Psychiatric Genomics Consortium, whereas GWAS data of iNPH were obtained from the FinnGen consortium round 9 release, with 767 cases and 375,610 controls of European ancestry. We also conducted the sensitivity analysis in these significant causal inferences using weighted median model, Cochrane's Q test, MR‐Egger regression, MR Pleiotropy Residual Sum and Outlier detect and the leave‐one‐out analysis. Results For most of the neurocognitive and psychiatric disorders, no causal association was established between them and iNPH. We have found that iNPH (odds ratio [OR] = 1.030, 95% confidence interval [CI]: 1.011–1.048, p = .001) is associated with increased risk for schizophrenia, which failed in validation of sensitivity analysis. Notably, genetically predicted Parkinson's disease (PD) is associated with increased risk of iNPH (OR = 1.256, 95% CI: 1.045–1.511, p = .015). Conclusion Our study has revealed the potential causal effect in which PD associated with an increased risk of iNPH. Further study is warranted to investigate the association between PD and iNPH and the potential underlying mechanism.

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures.

Pineapple is a globally significant tropical fruit, but its cultivation faces numerous challenges due to abiotic and biotic stresses, affecting its quality and quantity. WRKY transcription factors are known regulators of stress responses, however, their specific functions in pineapple are not fully understood. This study investigates the role of AcWRKY31 by overexpressing it in pineapple and Arabidopsis. Transgenic pineapple lines were obtained using Agrobacterium-mediated transformation methods and abiotic and biotic stress treatments. Transgenic AcWRKY31-OE pineapple plants showed an increased sensitivity to salt and drought stress and an increased resistance to biotic stress from pineapple mealybugs compared to that of WT plants. Similar experiments in AcWRKY31-OE, AtWRKY53-OE, and the Arabidopsis Atwrky53 mutant were performed and consistently confirmed these findings. A comparative transcriptomic analysis revealed 5357 upregulated genes in AcWRKY31-OE pineapple, with 30 genes related to disease and pathogen response. Notably, 18 of these genes contained a W-box sequence in their promoter region. A KEGG analysis of RNA-Seq data showed that upregulated DEG genes are mostly involved in translation, protein kinases, peptidases and inhibitors, membrane trafficking, folding, sorting, and degradation, while the downregulated genes are involved in metabolism, protein families, signaling, and cellular processes. RT-qPCR assays of selected genes confirmed the transcriptomic results. In summary, the AcWRKY31 gene is promising for the improvement of stress responses in pineapple, and it could be a valuable tool for plant breeders to develop stress-tolerant crops in the future.

Terpinen-4-ol (T-4-O) is an important component of tea tree oil and has anti-inflammatory effects. Currently, there are very few studies on the mechanisms by which T-4-O improves lipopolysaccharide (LPS)-induced macrophage inflammation. In this study, LPS-stimulated mouse RAW264.7 macrophages were used as a model to analyze the effects of T-4-O on macrophage inflammatory factors and related metabolic pathways in an inflammatory environment. The results showed that T-4-O significantly decreased the expression levels of inflammatory cytokines induced by LPS. Cellular metabolism results showed that T-4-O significantly decreased the ratio of the extracellular acidification rate and oxygen consumption rate. Non-targeted metabolomics results showed that T-4-O mainly affected glutamine and glutamate metabolism and glycine, serine, and threonine metabolic pathways. qPCR results showed that T-4-O increased the transcript levels of GLS and GDH and promoted glutamine catabolism. Western blotting results showed that T-4-O inhibited the mTOR and IκB, thereby decreasing NF-κB activity. The overall results showed that T-4-O inhibited mTOR phosphorylation to promote glutamine metabolism and increased cell oxidative phosphorylation levels, thereby inhibiting the expression of LPS-induced inflammatory cytokines.

Brain metastasis is the leading cause of death in most cancer patients; thus, anti‐brain metastasis is a crucial step in cancer treatment. The unique microenvironment and pathophysiological characteristics of brain metastases hamper the development of effective treatment methods, while the advances in nanomedicine demonstrate its immense potential for addressing this challenge. In recent years, due to breakthroughs in nanotechnology, functional and complex nanomedicine is prepared and it has been extensively explored to improve the treatment of brain metastasis. Compared to traditional treatment methods, nanomedicine can combine multiple traditional treatment methods into one single medicinal format and target unique characteristics of brain metastases to enhance the treatment efficacy. This review summarizes the main obstacles in the field of brain metastasis treatment and clinical/preclinical studies and surveys the use of a variety of nanomedicine candidates as antibrain metastasis strategies for different types of brain metastases.

ObjectiveAnalyzing the epidemiological characteristics of influenza cases among children aged 0–17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures.MethodsThe influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence.ResultsThe relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0–3 presented protective effects and the 0–3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7–12 years group.ConclusionTemperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0–3 years, but significantly increases the risk for those aged 7–12 years.

Considering the mass reservoirs construction, it is important to study the cascade reservoir construction sequence, which directly affects the water and sediment resource allocation, ecological environment restoration, and socioeconomic development of the basin. Thus, we constructed an evaluation index system by comprehensively considering the basin water and sediment discharge index, basin ecological and environmental maintenance index, and basin social and economic development index. The evaluation model was established to assess the impact of the cascade reservoir construction sequence on basin multifunctional synergy development index. The model was applied to evaluate the cascade reservoir construction sequence in the Yangtze and Yellow River Basin of China. The results revealed that: (1) The basin water and sediment discharge index, basin social and economic development index, and basin multifunctional synergy development index benefits gradually increased, the basin ecological and environmental maintenance index benefits progressively decreased in the Yangtze and Yellow River Basin. (2) The basin water and sediment discharge index, and basin ecological and environmental maintenance index of the planned cascade reservoir construction sequence ranked top in the Yellow River Basin; the basin water and sediment discharge index, and basin social and economic development index of the planned cascade reservoir construction sequence ranked top in the Yangtze River Basin. (3) The cascade reservoir construction sequence focused more on basin social and economic development index benefits when the level of national economic development was low, and more on basin water and sediment discharge index benefits when it was high. (4) Reservoir capacity and installed capacity were the main factors influencing the cascade reservoir construction sequence in the Yangtze and Yellow River Basin, respectively. These findings provide useful information regarding the cascade reservoir construction sequence.

Magnesium–lithium alloys are arguably the lightest metal structural materials but have low strength. In order to increase strength, friction stir processing (FSP) is applied to a hot-rolled Mg-10Li-3Al-3Zn (LA103Z) sheet to study the effects on the microstructure and mechanical properties. In this study, the strengthening mechanisms of various FSP regions of an Mg-Li alloy were clarified by a combination of numerical simulation and experimental method. Based on ANSYS APDL, a finite element model with a moving heat source is established. Rotational speeds of 800, 1000, and 1200 rpm and traverse speeds of 100, 110, and 120 mm/min were used in this research. The simulation results confirm that the influence of the rotation speed on the alloy temperature field is greater than that of the travel speed. The temperature of the processing area increases with an increase in rotation speed and decreases with an increase in travel speed. Then, hot-rolled LA103Z alloy plates are processed by FSP. The correspondence between the numerical simulation and experiment was verified by infrared thermography. The results indicate that FSP decreases the grain size significantly for the dynamic recrystallization and dramatic mechanical crushing of the stirring pin. The α-Mg and AlLi are solid soluted in the β-Li matrix. The tensile strength of the processing zone is 260.67 MPa (1000 rpm, 110 mm/min) versus the 170.47 MPa of the base metal. The SZ has the highest microhardness of 77.8 HV (800 rpm, 120 mm/min) and decreases gradually to the BM. The severe deformation, recrystallization, and solid solution of the α-Mg are important factors contributing to the improved mechanical properties.

Calculating impact forces in asymmetrical lateral structures has been a complex challenge that spans decades in engineering. Traditional models often fall short due to the inherent complexity of asymmetrical members and the need for significant computational resources or a vast pool of training data. This paper develops an approximate formula for accurately calculating the impact force of asymmetrical lateral-impact members under lateral impact. Existing methods for assessing impact forces have been limited in their application due to the inherent complexity of asymmetrical members and the significant computational resources or extensive training data they often require. Our approach employs the residuals margin method, and Laplace transforms to derive an efficient and accurate formula for impact force calculation. The paper rigorously validates this Formula through experimental testing, demonstrating high precision with an error margin of less than 5%. Further validation against diverse impact data from multiple studies on different materials and loadings under static and dynamic conditions confirmed the Formula's consistency. Despite simplifying assumptions, this research contributes a novel and computationally efficient approach for calculating impact forces. The formula offers engineers a practical tool while advancing a fundamental understanding of asymmetric impact dynamics. Rigid experimentation verified its significant accuracy, establishing the formula as a valuable structural impact analysis and design resource. This research presents an analytical formula for calculating impact forces on structures like buildings, bridges, and vehicles experiencing asymmetrical lateral impacts. Such impacts commonly occur due to falling debris, vehicle collisions, seismic pounding, and derailed train strikes. However, existing design formulas often oversimplify impact mechanics or require complex simulations. The proposed method provides engineers with a simple spreadsheet-compatible equation relating impact force directly to tangible mechanical quantities like momentum. This enables rapid impact load assessments essential for performance-based design against accidental hazards. The formula was validated through laboratory impact tests on reinforced concrete slabs, demonstrating precision within 5% of measured forces. Additional validations against published experimental and simulation data on different construction materials confirmed accuracy. The proposed formula equips structural engineers and safety analysts with a practical impact analysis tool by offering a computationally efficient approach with proven reliability. It facilitates assessing design performance for asymmetrical impact load scenarios, helping improve resilience for critical facilities subjected to hazardous lateral impacts. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of collision and impact problems' numerical solutions, focusing on geometric, contact, and material nonlinearities, all essential in solving large deformation problems during a collision. The initial discussion revolves around the stress and strain of large deformation during a collision, followed by explanations of the fundamental finite element solution method for addressing such issues. The hourglass mode's control methods, such as single-point reduced integration and contact-collision algorithms are detailed and implemented within the finite element framework. The paper further investigates the dynamic response and failure modes of Reinforced Concrete (RC) members under asymmetrical impact using a 3D discrete model in ABAQUS that treats steel bars and concrete connections as bond slips. The model's validity was confirmed through comparisons with the node-sharing algorithm and system energy relations. Experimental parameters were varied, including the rigid hammer's mass and initial velocity, concrete strength, and longitudinal and stirrup reinforcement ratios. Findings indicated that increased hammer mass and velocity escalated RC member damage, while increased reinforcement ratios improved impact resistance. Contrarily, increased concrete strength did not significantly reduce lateral displacement when considering strain rate effects. The study also explores material nonlinearity, examining different materials' responses to collision-induced forces and stresses, demonstrated through an elastic rod impact case study. The paper proposes a damage criterion based on the residual axial load-bearing capacity for assessing damage under the asymmetrical impact, showing a correlation between damage degree hammer mass and initial velocity. The results, validated through comparison with theoretical and analytical solutions, verify the ABAQUS program's accuracy and reliability in analyzing impact problems, offering valuable insights into collision and impact problems' nonlinearities and practical strategies for enhancing RC structures' resilience under dynamic stress. [ABSTRACT FROM AUTHOR]