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Background: The Guangzhou Nutrition and Health Study (GNHS) aims to assess the determinants of metabolic disease in nutritional aspects, as well as other environmental and genetic factors, and explore possible biomarkers and mechanisms with multi-omics integration. Methods: The population-based sample of adults in Guangzhou, China (baseline: 40–83 years old; n = 5,118) was followed up about every 3 years. All are tracked via on-site follow-up and health information systems. We assessed detailed information on lifestyle factors, physical activities, dietary assessments, psychological health, cognitive function, body measurements, and muscle function. Instrument tests included dual-energy X-ray absorptiometry scanning, carotid artery and liver ultrasonography evaluations, vascular endothelial function evaluation, upper-abdomen and brain magnetic resonance imaging, and 14-day real-time continuous glucose monitoring tests. We also measured multi-omics, including host genome-wide genotyping, serum metabolome and proteome, gut microbiome (16S rRNA sequencing, metagenome, and internal transcribed spacer 2 sequencing), and fecal metabolome and proteome. Results: The baseline surveys were conducted from 2008 to 2015. Now, we have completed 3 waves. The 3rd and 4th follow-ups have started but have yet to end. A total of 5,118 participants aged 40–83 took part in the study. The median age at baseline was approximately 59.0 years and the proportion of female participants was about 69.4%. Among all the participants, 3,628 (71%) completed at least one on-site follow-up, with a median duration of 9.48 years. Conclusion: The cohort will provide data that will be influential in establishing the role of nutrition in metabolic diseases with multi-omics.

Introduction: Although colon (COAD) and rectal adenocarcinoma (READ) combined to refer to colorectal cancer (CRC), substantial clinical evidence urged that CRC should be treated as two different cancers due to compared with READ, COAD showed higher morbidity and worse 5-year survival. Objectives: This study has tried to screen for the crucial gene that caused the worse prognosis and investigate its mechanism for mediating tumor growth and metastases in COAD. Meanwhile, the potential anti-COAD compound implicated in this mechanism was identified and testified from 1,855 food-borne chemical kits. This study aims to bring a new perspective to the development of new anti-COAD drugs and personalized medicine for patients with COAD. Methods and results: The survival-related hub genes in COAD and READ were screened out from The Cancer Genome Atlas (TCGA) database and the results showed that HIGD1A, lower expressed in COAD than in READ, was associated with poor prognosis in COAD patients, but not in READ. Over-expressed HIGD1A suppressed CRC cell proliferation, invasion, and migration in vitro and in vivo. Meanwhile, the different expressed microRNA profiles between COAD and READ showed that miR-501-3p was highly expressed in COAD and inhibited HIGD1A expression by targeting 3′UTR of HIGD1A. MiR-501-3p mimics promoted cell proliferation and metastasis in CRC cells. In addition, Procyanidin C1 (PCC1), a kind of natural polyphenol has been verified as a potential miR-501-3p inhibitor. In vitro and in vivo, PCC1 promoted HIGD1A expression by suppressing miR-501-3p and resulted in inhibited tumor growth and metastasis. Conclusion: The present study verified that miR-501-3p/HIGD1A axis mediated tumor growth and metastasis in COAD. PCC1, a flavonoid that riched in food exerts anti-COAD effects by inhibiting miR-501-3p and results in the latter losing the ability to suppress HIGD1A expression. Subsequently, unfettered HIGD1A inhibited tumor growth and metastasis in COAD.

Due to increasing operational security demands, digital and intelligent condition monitoring of nuclear power plants is becoming more significant. However, establishing an accurate and effective anomaly detection model is still challenging. This is mainly because of data characteristics of nuclear power data, including the lack of clear class labels combined with frequent interference from outliers and anomalies. In this paper, we introduce a Transformer-based unsupervised model for anomaly detection of nuclear power data, a modified loss function based on the maximum correntropy criterion (MCC) is applied in the model training to improve the robustness. Experimental results on simulation datasets demonstrate that the proposed Trans-MCC model achieves equivalent or superior detection performance to the baseline models, and the use of the MCC loss function is proven can obviously alleviate the negative effect of outliers and anomalies in the training procedure, the F1 score is improved by up to 0.31 compared to Trans-MSE on a specific dataset. Further studies on genuine nuclear power data have verified the model's capability to detect anomalies at an earlier stage, which is significant to condition monitoring.

The effects of bound acrylonitrile content, curing system, reinforcing system and plastici-zing system on the corrosion resistance of nitrile rubber (NBR) to common chemical media in the field of petroleum exploitation were studied. The results showed that with the increase of bound acrylonitrile content, the physical and mechanical properties and thermal air aging resistance of NBR vulcanizates increased, the resistant to non-polar media such as kerosene and submarine control fluid was better, the resistance to methanol, distilled water and ethylene glycol aqueous solution decreased gradually, and the resistant to acid, alkali and brine had little diffe-rence. The curing system affected mainly the physical and mechanical properties and heat air aging resistance of NBR vulcanizates, and had little effect on the resistance of NBR vulcanizates to various petroleum exploitation media. For the high hardness NBR vulcanizates, the physical and mechanical properties of the vulcanizates were the best when small particle size carbon black was used as reinforcing system, and the effect of different reinforcing system on the heat air aging resistance of NBR vulcanizates was not obvious. However, the vulcanizates had better resistance to methanol and kerosene when large particle size carbon black was used as a reinforcing filler, and inert reinforcing filler should be selected. The physical and mechanical properties of NBR vulca-nizates added with macromolecule ester plasticizer were better than those with small molecule ester plasticizers and liquid NBR plastici-zer, and the heat air aging properties were similar, but the volume change of resistance to various media was larger, and the resistance to methanol, kerosene and 10% sodium hydroxide solution were poor, while the resistance to other petroleum exploitation media were less affected.

Abstract Background The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD. Methods The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018. We collected 272 metadata points from 1546 individuals. The metadata were input into four interpretable machine learning models to identify important gut microbiota associated with NAFLD. These models were subsequently applied to two validation cohorts [the internal validation cohort (n = 377), and the prospective validation cohort (n = 749)] to assess generalizability. We constructed an individual microbiome risk score (MRS) based on the identified gut microbiota and conducted animal faecal microbiome transplantation experiment using faecal samples from individuals with different levels of MRS to determine the relationship between MRS and NAFLD. Additionally, we conducted targeted metabolomic sequencing of faecal samples to analyse potential metabolites. Results Among the four machine learning models used, the lightGBM algorithm achieved the best performance. A total of 12 taxa-related features of the microbiota were selected by the lightGBM algorithm and further used to calculate the MRS. Increased MRS was positively associated with the presence of NAFLD, with odds ratio (OR) of 1.86 (1.72, 2.02) per 1-unit increase in MRS. An elevated abundance of the faecal microbiota (f__veillonellaceae) was associated with increased NAFLD risk, whereas f__rikenellaceae, f__barnesiellaceae, and s__adolescentis were associated with a decreased presence of NAFLD. Higher levels of specific gut microbiota-derived metabolites of bile acids (taurocholic acid) might be positively associated with both a higher MRS and NAFLD risk. FMT in mice further confirmed a causal association between a higher MRS and the development of NAFLD. Conclusions We confirmed that an alteration in the composition of the core gut microbiota might be biologically relevant to NAFLD development. Our work demonstrated the role of the microbiota in the development of NAFLD.

Abstract Rheumatoid arthritis (RA) is a common inflammatory arthritis in women. The effects of RA on the reproductive system are usually overlooked, as RA is not diagnosed until later in reproductive age. Whether RA itself or its related rheumatoid antibodies have an impact on female reproductive function has long been a thought-provoking issue. In brief, relevant epidemiological evidence has shown that women affected by RA are more likely to have coexisting reproductive disorders, including infertility, endometriosis, and premature ovarian insufficiency (POI), or to subsequently develop them. Furthermore, linkage between RA and pregnancy loss (PL) as well as polycystic ovary syndrome (PCOS) is also well known, albeit controversial in available evidence. RA and reproductive disorders appear to share a similar inflammatory immune response and genetic background. The stress experienced by patients with RA may affect their reproductive choices to some extent. Notably, few studies have explored the impact of rheumatoid antibodies such as rheumatoid factors (RFs) and anti-citrullinated protein antibodies (ACPAs) on reproductive disorders. Although it has been mentioned that the rate of RF and/or ACPA positivity is higher in women with a history of PL and POI, the clinical relevance of this relationship and underlying mechanisms still need to be further clarified.

Summary: Background: Mapping gut microecological features to serum metabolites (SMs) will help identify functional links between gut microbiome and cardiometabolic health. Methods: This study encompassed 836-1021 adults over 9.7 year in a cohort, assessing metabolic syndrome (MS), carotid atherosclerotic plaque (CAP), and other metadata triennially. We analyzed mid-term microbial metagenomics, targeted fecal and serum metabolomics, host genetics, and serum proteomics. Findings: Gut microbiota and metabolites (GMM) accounted for 15.1% overall variance in 168 SMs, with individual GMM factors explaining 5.65%–10.1%, host genetics 3.23%, and sociodemographic factors 5.95%. Specifically, GMM elucidated 5.5%–49.6% variance in the top 32 GMM-explained SMs. Each 20% increase in the 32 metabolite score (derived from the 32 SMs) correlated with 73% (95% confidence interval [CI]: 53%–95%) and 19% (95% CI: 11%–27%) increases in MS and CAP incidences, respectively. Among the 32 GMM-explained SMs, sebacic acid, indoleacetic acid, and eicosapentaenoic acid were linked to MS or CAP incidence. Serum proteomics revealed certain proteins, particularly the apolipoprotein family, mediated the relationship between GMM-SMs and cardiometabolic risks. Interpretation: This study reveals the significant influence of GMM on SM profiles and illustrates the intricate connections between GMM-explained SMs, serum proteins, and the incidence of MS and CAP, providing insights into the roles of gut dysbiosis in cardiometabolic health via regulating blood metabolites. Funding: This study was jointly supported by the National Natural Science Foundation of China, Key Research and Development Program of Guangzhou, 5010 Program for Clinical Research of Sun Yat-sen University, and the ‘Pioneer' and ‘Leading goose' R&D Program of Zhejiang.

Introduction: The conventional 3-D point cloud-based deformation analysis methods, such as the shortest distance (SD), cloud-to-cloud (C2C), and multiscale model-to-model cloud comparison (M3C2), essentially regard the closest distance between two periods of point cloud data as the deformation, rather than the true position of the same point in 3-D space before and after deformation.Methods: This paper proposes a method based on the ICP algorithm to calculate the differences between the chunked multi-period point clouds to recognizes the 3-D deformations.Results and discussion: The results show that the obtained results are very close to the GNSS data but with a much larger spatial monitoring range. The accuracy is higher than that of the SD method. Moreover, we analyze the statistical relationship between the point cloud block size and the deformation vector error and determine the optimal block size. The aim of this article is to optimize the deformation analysis method and improve its accuracy to provide techniques and ideas for the wider surface deformation monitoring research field. For instance, combining this method with data from contact methods constructs a 3D overall deformation model of the mountain, enabling real-time monitoring and early warning of debris flows.

Introduction: MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods.Methods: Therefore, we propose a new sparse autoencoder and MLP method (SPALP) to predict the association between miRNAs and diseases. In this study, we adopt advanced deep learning technologies, including sparse autoencoder and multi-layer perceptron (MLP), to improve the accuracy of predicting miRNA-disease associations. Firstly, the SPALP model uses a sparse autoencoder to perform feature learning and extract the initial features of miRNAs and diseases separately, obtaining the latent features of miRNAs and diseases. Then, the latent features combine miRNAs functional similarity data with diseases semantic similarity data to construct comprehensive miRNAs-diseases datasets. Subsequently, the MLP model can predict the unknown association among miRNAs and diseases.Result: To verify the performance of our model, we set up several comparative experiments. The experimental results show that, compared with traditional methods and other deep learning prediction methods, our method has significantly improved the accuracy of predicting miRNAs-disease associations, with 94.61% accuracy and 0.9859 AUC value. Finally, we conducted case study of SPALP model. We predicted the top 30 miRNAs that might be related to Lupus Erythematosus, Ecute Myeloid Leukemia, Cardiovascular, Stroke, Diabetes Mellitus five elderly diseases and validated that 27, 29, 29, 30, and 30 of the top 30 are indeed associated.Discussion: The SPALP approach introduced in this study is adept at forecasting the links between miRNAs and diseases, addressing the complexities of analyzing extensive bioinformatics datasets and enriching the comprehension contribution to disease progression of miRNAs.

Background: This study aimed to explore the expression level and transcriptional regulation mechanism of Extra Spindle Pole Bodies Like 1 (ESPL1) in bladder cancer (BC). Methods: A multicentre database of samples (n = 1391) was assayed for ESPL1 mRNA expression in BC and validated at the protein level by immunohistochemical (IHC) staining of in-house samples (n = 202). Single-cell sequencing (scRNA-seq) analysis and enrichment analysis explored ESPL1 distribution and their accompanying molecular mechanisms. ATAC-seq, ChIP-seq and Hi-C data from multiple platforms were used to investigate ESPL1 upstream transcription factors (TFs) and potential epigenetic regulatory mechanisms. Immune-related analysis, drug sensitivity and molecular docking of ESPL1 were also calculated. Furthermore, upstream microRNAs and the binding sites of ESPL1 were predicted. The expression level and early screening efficacy of miR-299-5p in blood (n = 6625) and tissues (n = 537) were examined. Results: ESPL1 was significantly overexpressed at the mRNA level (p

BackgroundLong COVID, also known as Post-COVID-19 syndrome, is characterized by multisystemic symptoms that persists for weeks to years beyond acute infection. It disproportionately affects women and those with pre-existing anxiety/depression, conditions more prevalent in females. The vagus nerve, with its extensive innervation and regulation of critical bodily functions, has become a focal point for therapeutic interventions. Transcutaneous vagus nerve stimulation (t-VNS) has emerged as a promising non-invasive treatment for COVID-19 conditions.MethodsThis pilot study assessed the efficacy of t-VNS in 24 female Long COVID patients (45.8 ± 11.7 years old; 20.2 ± 7.1 months since infection), who underwent a 10-day t-VNS intervention at home (30 min/session, twice a day). Cognition was considered the primary outcome, with anxiety, depression, sleep, fatigue, and smell as secondary outcomes. Outcomes were measured at baseline, post-intervention, and 1-month follow-up.ResultsSignificant improvements were observed in various cognitive functions, anxiety, depression, and sleep at post-intervention, with benefits remaining or progressing at 1-month follow-up. Improvements in fatigue were delayed, reaching statistical significance at 1-month follow-up compared to baseline. No significant changes were noted in olfactory performance.ConclusionThis pilot study provides preliminary evidence supporting the potential of t-VNS as a therapeutic intervention for female Long COVID patients. The encouraging results justify further rigorous investigation through larger, randomized controlled trials to confirm the efficacy of t-VNS, assess its generalizability to male cohorts, and explore biological markers to inform personalized treatment approaches. Our findings support the allocation of resources to conduct such trials and advance the understanding of t-VNS as a potential treatment for Long COVID.

Abstract Living in the global-changing era, intelligent and eco-friendly electronic components that can sense the environment and recycle or reprogram when needed are essential for sustainable development. Compared with solid-state electronics, composite hydrogels with multi-functionalities are promising candidates. By bridging the self-assembly of azobenzene-containing supramolecular complexes and MXene nanosheets, we fabricate a MXene-based composite gel, namely MXenegel, with reversible photo-modulated phase behavior. The MXenegel can undergo reversible liquefication and solidification under UV and visible light irradiations, respectively, while maintaining its conductive nature unchanged, which can be integrated into traditional solid-state circuits. The strategy presented in this work provides an example of light-responsive conducting material via supramolecular bridging and demonstrates an exciting platform for functional soft electronics.

Abstract Background The early life stage is critical for the gut microbiota establishment and development. We aimed to investigate the lifelong impact of famine exposure during early life on the adult gut microbial ecosystem and examine the association of famine-induced disturbance in gut microbiota with type 2 diabetes. Methods We profiled the gut microbial composition among 11,513 adults (18–97 years) from three independent cohorts and examined the association of famine exposure during early life with alterations of adult gut microbial diversity and composition. We performed co-abundance network analyses to identify keystone taxa in the three cohorts and constructed an index with the shared keystone taxa across the three cohorts. Among each cohort, we used linear regression to examine the association of famine exposure during early life with the keystone taxa index and assessed the correlation between the keystone taxa index and type 2 diabetes using logistic regression adjusted for potential confounders. We combined the effect estimates from the three cohorts using random-effects meta-analysis. Results Compared with the no-exposed control group (born during 1962–1964), participants who were exposed to the famine during the first 1000 days of life (born in 1959) had consistently lower gut microbial alpha diversity and alterations in the gut microbial community during adulthood across the three cohorts. Compared with the no-exposed control group, participants who were exposed to famine during the first 1000 days of life were associated with consistently lower levels of keystone taxa index in the three cohorts (pooled beta − 0.29, 95% CI − 0.43, − 0.15). Per 1-standard deviation increment in the keystone taxa index was associated with a 13% lower risk of type 2 diabetes (pooled odds ratio 0.87, 95% CI 0.80, 0.93), with consistent results across three individual cohorts. Conclusions These findings reveal a potential role of the gut microbiota in the developmental origins of health and disease (DOHaD) hypothesis, deepening our understanding about the etiology of type 2 diabetes.

Abstract Methane activation by photocatalysis is one of the promising sustainable technologies for chemical synthesis. However, the current efficiency and stability of the process are moderate. Herein, a PdCu nanoalloy (~2.3 nm) was decorated on TiO2, which works for the efficient, stable, and selective photocatalytic oxidative coupling of methane at room temperature. A high methane conversion rate of 2480 μmol g−1 h−1 to C2 with an apparent quantum efficiency of ~8.4% has been achieved. More importantly, the photocatalyst exhibits the turnover frequency and turnover number of 116 h−1 and 12,642 with respect to PdCu, representing a record among all the photocatalytic processes (λ > 300 nm) operated at room temperature, together with a long stability of over 112 hours. The nanoalloy works as a hole acceptor, in which Pd softens and weakens C-H bond in methane and Cu decreases the adsorption energy of C2 products, leading to the high efficiency and long-time stability.

Abstract A series of high‐performance linear and cross‐linked polyimide (PI) aerogels with different molecular structures have been successfully synthesised using the freeze‐drying process. In this study, the comprehensive regulation of microstructure, thermodynamic, thermal insulation and dielectric properties of PI aerogels are achieved by controlling the rigid/flexible structure and composition of polymerised monomers. The increase in rigidity of PI molecular structure could promote the formation of denser pores, which is beneficial to improve the thermodynamic and thermal insulation properties of aerogels. Notably, the cross‐linked PI aerogel prepared by introducing cross‐linking agent (tris(4‐aminophenyl) amine, [TPA]) into linear PI exhibits high thermal stability (Td5% > 560°C), excellent ultralow permittivity (εr = 1.31, f = 106 Hz) and good thermal insulation property (k = 0.056 W/m · K). This innovative strategy promotes the wider application of the cross‐linked polyimide aerogel in the field of integrated circuits and aerospace exploration.

Abstract The significant progresses of polymer‐based nanocomposites with improved dielectric performances are urgently calling for an effect way to realise commercial production. Up to now, the biaxial stretching technology is still a powerful method to produce the high‐performance dielectric films applied in the film capacitors due to its full‐blown applications. In this work, a classical composite system of BaTiO3/polypropylene was applied to reveal the connection between the microstructure changes and dielectric properties of the corresponding nanocomposite films in the biaxial stretching process. The permittivity of BT‐30 wt% nanocomposite reached 2.8 at 103 Hz after stretching, and its breakdown strength reached 340 MV/m. In addition, the breakdown strength of BT‐10 wt% nanocomposite could even be promoted to 452 MV/m, which was 1.3 times higher than that before stretching. The microstructure test demonstrated that the rearrangement of nanofillers, high crystallinity and the oriented polypropylene crystals were advantageous to the improvement of breakdown strength for the stretched nanocomposite films. Therefore, the application of biaxial stretching technology into the preparation of nanocomposite dielectric film is an enormous potential way for the energy storage film capacitors.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model’s detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.

Abstract Compared with aqueous single‐ion batteries, rechargeable aqueous hybrid ion batteries, especially Li+/Zn2+hybrid ion batteries, are receiving extensive interest owing to their low cost, high operating voltage, and energy density. However, their working voltage and lifespan are limited by the decomposition of water and the growth of Zn dendrites. Herein, detrimental side reactions induced by the water reduction and the Zn dendrite growth are successfully suppressed by a poly(propylene glycol) (PPG)‐based hybrid ion electrolyte [(1 m Zn(TFSI)2 + 10 m LiTFSI) in PPG/H2O]. The addition of PPG in the electrolyte can not only enhance the bonding strength of hydrogen‐bond in water but also tailor the solvation sheath of Zn2+ as revealed by synchrotron X‐rays. The participated solvation of PPG with Zn2+ can weaken Zn−H2O interactions and redistribute Zn2+ flux on the surface of the Zn anode, thus inducing favorably even deposition of Zn. In addition, the decomposition of TFSI− contributes a ZnF2 ‐enriched solid electrolyte interface at the Zn anode to further prevent water decomposition and restrain Zn dendrites. The PPG‐based electrolyte enables 2.1 V LiMnO2//Zn batteries to deliver high specific capacities (121.7 mAh g−1 for a coin cell and 90 mAh g−1 for a pouch cell), and maintain 80% of the capacity over 700 cycles at 0.5 C, suggesting a promising pathway for highly reversible aqueous hybrid ion batteries.

Cancer poses a serious threat to human health and overall well-being. Conventional cancer treatments predominantly encompass surgical procedures and radiotherapy. Nevertheless, the substantial side effects and the emergence of drug resistance in patients significantly diminish their quality of life and overall prognosis. There is an acute need for innovative, efficient therapeutic agents to address these challenges. Plant-based herbal medicines and their derived compounds offer promising potential for cancer research and treatment due to their numerous advantages. Solanum nigrum (S. nigrum), a traditional Chinese medicine, finds extensive use in clinical settings. The steroidal compounds within S. nigrum, particularly steroidal alkaloids, exhibit robust antitumor properties either independently or when combined with other drugs. Many researchers have delved into unraveling the antitumor mechanisms of the active components present in S. nigrum, yielding notable progress. This literature review provides a comprehensive analysis of the research advancements concerning the active constituents of S. nigrum. Furthermore, it outlines the action mechanisms of select monomeric anticancer ingredients. Overall, the insights derived from this review offer a new perspective on the development of clinical anticancer drugs.

Early Responsive to Dehydration (ERD) genes are rapidly induced in response to various biotic and abiotic stresses, such as bacteria, drought, light, temperature and high salt in Arabidopsis thaliana. Sixteen ERD of Arabidopsis thaliana (AtERD) genes have been previously identified. The lengths of the coding region of the genes are 504–2838 bp. They encode 137–745 amino acids. In this study, the AtERD genes structure and promoter are analyzed through bioinformatics, and a overall function is summarized and a systematic signal pathway involving AtERD genes is mapped. AtERD9, AtERD11 and AtERD13 have the GST domain. AtERD10 and AtERD14 have the Dehyd domain. The promoters regions contain 32 light responsive elements, 23 ABA responsive elements, 5 drought responsive elements, 5 meristem expression related elements and 132 core promoter elements. The study provides a theoretical guidance for subsequent studies of AtERD genes.

The mitogen-activated protein kinase (MAPK) cascade pathway is a highly conserved plant cell signaling pathway that plays an important role in plant growth and development and stress response. Currently, MAPK cascade genes have been identified and reported in a variety of plants including Arabidopsis thaliana, Oryza sativa, and Triticum aestivum, but have not been identified in foxtail millet (Setaria italica). In this study, a total of 93 MAPK cascade genes, including 15 SiMAPKs, 10 SiMAPKKs and 68 SiMAPKKKs genes, were identified by genome-wide analysis of foxtail millet, and these genes were distributed on nine chromosomes of foxtail millet. Using phylogenetic analysis, we divided the SiMAPKs and SiMAPKKs into four subgroups, respectively, and the SiMAPKKKs into three subgroups (Raf, ZIK, and MEKK). Whole-genome duplication analysis revealed that there are 14 duplication pairs in the MAPK cascade family in foxtail millet, and they are expanded by segmental replication events. Results from quantitative real-time PCR (qRT-PCR) revealed that the expression levels of most SiMAPKs and SiMAPKKs were changed under both exogenous hormone and abiotic stress treatments, with SiMAPK3 and SiMAPKK4–2 being induced under almost all treatments, while the expression of SiMAPKK5 was repressed. In a nutshell, this study will shed some light on the evolution of MAPK cascade genes and the functional mechanisms underlying MAPK cascade genes in response to hormonal and abiotic stress signaling pathways in foxtail millet (Setaria italica).

Objective: To investigate the effects of combined treatment of Scutellaria baicalensis (SB) and polygonatum (HH) for lung cancer and explore the underlying mechanism. Purpose: The study was designed to investigate the anti-tumor effect of HH and reveal its mechanisms. Methods: Based on the Traditional Chinese Medicine (TCM) theory and network pharmacological analysis. The inhibitory effect of HH on the proliferation of A549 and LLC lung cancer cells was predicted by CCK8 assay in a subcutaneous tumor model in vivo. Tumor tissues were collected for proteomics, qPCR and immunohistochemistry assays. A549 and LLC cell proliferation was detected by CCK8 assay after treatment with HH combined with ferroptosis inhibitors and ferroptosis inducer Erastin for 48 h respectively. The expression of Transferrin Receptor (TFRC) was detected by qPCR and Western Blot. Changes in free iron content and lipid peroxide in cytoplasm and mitochondria were observed by laser confocal microscopy. Changes in Mitochondrial Membrane Potential (MMP, Δψm) and ROS content were detected by flow cytometry. Results: Search of Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database demonstrated that HH played a role in lung cancer treatment. CCK8 assay showed that lung cancer cell proliferation was inhibited by HH in a dose-dependent manner. The animal experiment results showed that the tumor volume in HH-treated group was significantly smaller than that in Control group in the LLC allograft model (P

The lower play (Permian to Triassic) in the hinterland is the most important strategic succession field for oil and gas exploration in the Junggar Basin. Multiple wells have been drilled into high-quality clastic rock reservoirs below 6 000 m, greatly breaking through the deadline of buried depth of traditional clastic rock effective reservoirs. It has been made clear that the development status and pore evolution process are the key issues that determine whether oil and gas can be accumulated. Taking typical drilling wells in the hinterland as an example, this paper comprehensively analyzed the petrology, physical properties and pore structure characteristics of deep and ultra-deep clastic reservoirs in the hinterland of the Junggar Basin from a qualitative and quantitative perspective, and quantitatively restored the pore evolution process by integrating the microscopic analysis of rock thin sections, porosity and permeability tests, image analysis technology, quantitative characterization of porosity evolution, temperature measurement of inclusions, basin modeling and other methods. The results show that the clastic rock in the lower play of the hinterland is mainly developed in the Permian Upper Wuerhe Formation, Triassic Baikouquan Formation and Triassic Kelamayi Formation, of which the sand in the Triassic Baikouquan Formation is the most developed, followed by the Permian Upper Wuerhe Formation and Triassic Kalamayi Formation. There is little difference in rock types among different layers, which mainly composed of lithic sandstone with a small amount of feldspar lithic sandstone. The composition of rock debris is mainly medium-basic volcanic rock debris, with low content of feldspar and quartz, and the sum of them is generally less than 20%. The Kelamayi Formation is dominated by primary pores with the best reservoir property and the highest porosity of 13.18%. The Upper Wuerhe Formation and Baikouquan Formation are dominated by secondary corrosion pores, and the corrosion materials are mainly medium-basic volcanic debris, laumontite cement and a small amount of feldspar. The reservoir properties of them are not as good as those of the Kelamayi Formation. The pore evolution of the Kelamayi Formation has experienced weak compaction (21.08% pore reduction by compaction), weak cementation (2.88% pore reduction by cementation), and weak corrosion (1.4% pore increase by corrosion). Today's high porosity is mainly due to the large amount of preservation of primary pores under weak compaction and late weak cementation. The Baikouquan Formation and Upper Wuerhe Formation have undergone strong compaction (26.60% and 26.43% pore reduction by compaction, respectively), strong cementation (7.43% and 11% pore reduction by cementation, respectively), and strong corrosion (6.32% and 4.21% pore increase by corrosion, respectively). Secondary corrosion is the main way for them to increase porosity, but it is insufficient to compensate for the porosity reduction effect of strong compaction and cementation, resulting in lower porosity in the two formations.

Abstract Purpose To investigate the feasibility and clinical utility of a compressed-sensing-accelerated subtractionless whole-body MRA (CS-WBMRA) protocol with only contrast injection for suspected arterial diseases, by comparison to conventional dual-pass subtraction-based whole-body MRA (conventional-WBMRA) and available computed tomography angiography (CTA). Materials and methods This prospective study assessed 86 patients (mean age, 56 years ± 16.4 [standard deviation]; 25 women) with suspected arterial diseases from May 2021 to December 2022, who underwent CS-WBMRA (n = 48, mean age, 55.9 years ± 16.4 [standard deviation]; 25 women) and conventional-WBMRA (n = 38, mean age, 48 years ± 17.4 [standard deviation]; 20 women) on a 3.0 T MRI after random group assignment based on the chronological order of enrolment. Of all enrolled patients administered the CS-WBMRA protocol, 35% (17/48) underwent CTA as required by clinical demands. Two experienced radiologists independently scored the qualitative image quality and venous enhancement contamination. Quantitative image assessment was carried out by determining and comparing the apparent signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of four representative arterial segments. The total examination time and contrast-dose were also recorded. The independent samples t-test or the Wilcoxon rank sum test was used for statistical analysis. Results The overall scores of CS-WBMRA outperformed those of conventional-WMBRA (3.40 ± 0.60 vs 3.22 ± 0.55, P 0.05) between the two protocols. In the quantitative analysis, overall apparent SNRs were significantly higher in the conventional-WBMRA group than in the CS-WBMRA group (214.98 ± 136.05 vs 164.90 ± 118.05; P 0.05). In the CS-WBMRA group, 7 of 1776 (0.4%) vessel segments were contaminated severely by venous enhancement, while in the convention-WBMRA group, 317 of 1406 (23%) were rated as severe contamination. In the CS-WBMRA group, total examination and reconstruction times were only 7 min and 10 min, respectively, vs 20 min and

This study focuses on the prediction of concrete cover separation (CCS) in reinforced concrete beams strengthened by fiber-reinforced polymer (FRP) in flexure. First, machine learning models were constructed based on linear regression, support vector regression, BP neural networks, decision trees, random forests, and XGBoost algorithms. Secondly, the most suitable model for predicting CCS was identified based on the evaluation metrics and compared with the codes and the researcher’s model. Finally, a parametric study based on SHapley Additive exPlanations (SHAP) was carried out, and the following conclusions were obtained: XGBoost is best-suited for the prediction of CCS and codes, and researchers’ model accuracy needs to be improved and suffers from over or conservative estimation. The contributions of the concrete to the shear force and the yield strength of the reinforcement are the most important parameters for the CCS, where the shear force at the onset of CCS is approximately proportional to the contribution of the concrete to the shear force and approximately inversely proportional to the yield strength of the reinforcement.

Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs.

Abstract Finding highly efficient hydrogen evolution reaction (HER) catalysts is pertinent to the ultimate goal of transformation into a net-zero carbon emission society. The design principles for such HER catalysts lie in the well-known structure-property relationship, which guides the synthesis procedure that creates catalyst with target properties such as catalytic activity. Here we report a general strategy to synthesize 10 kinds of single-atom-doped CoSe2-DETA (DETA = diethylenetriamine) nanobelts. By systematically analyzing these products, we demonstrate a volcano-shape correlation between HER activity and Co atomic configuration (ratio of Co-N bonds to Co-Se bonds). Specifically, Pb-CoSe2-DETA catalyst reaches current density of 10 mA cm−2 at 74 mV in acidic electrolyte (0.5 M H2SO4, pH ~0.35). This striking catalytic performance can be attributed to its optimized Co atomic configuration induced by single-atom doping.

[Introduction] Nuclear power operation data is characterized by high dimension and large volume, and the complexity of the internal system of nuclear power plant makes it difficult to build a corresponding mechanism model. Therefore, it is very difficult to manually screen out relevant parameters from nuclear power data, and the introduction of non-relevant parameters will greatly affect the accuracy of the model. By means of improving the model accuracy, the purpose of accurate modeling can be reached. [Method] This paper proposed a correlation analysis method based on multi-scale time window. This method extracted state switch points for target parameters, classifies each sensor according to the characteristics of the data recorded by different sensors, and then designs detection windows for different kinds of sensors that meet their characteristics. The state switch detection was carried out in the corresponding time neighborhood of each sensor, and the correlation matching rate between each sensor and the target sensor was calculated to judge the correlation. [Result] Based on the actual historical operation data of nuclear power plant, the sensor parameters associated with the target sensor are selected successfully by the established correlation matching rate rule. [Conclusion] The experimental results show that the proposed method can screen out the correlation parameters more accurately. Compared with the commonly used Pearson correlation coefficient, the proposed method is more accurate.