Search

Peach gum polysaccharides have multiple biological functions, such as anti-oxidation, blood lipids reduction, and immune protection. However, their impact on the gut microbiota was still unclear. Here, high throughput sequencing was used to study the regulatory effects of peach gum polysaccharide on gut microbiota using an in vitro fermentation model of human gut microbiota. The results showed that gut microbiota species increased significantly and α-diversity index (Sobs, Ace, Chao1, Shannon) decreased significantly after polysaccharide fermentation compared with control group (distilled water). At phylum level, the relative abundance of Firmicutes and Actinomyces increased (61.76% vs. 56.64%, 28.24% vs. 23.11%). In contrast, relative abundance of Bacteroides decreased significantly (8.10% vs. 19.21%). At genus level, the relative abundance of Bifidobacteria, Colinella and Parazobacteria increased significantly (21.43% vs. 19.08%, 6.32% vs. 3.73%, 6.37% vs. 0.00%), while the relative abundance of Bacteroides and Prevotella decreased significantly (3.83% vs. 5.52%, 3.49% vs. 12.15%). Precisely, peach gum polysaccharide can effectively regulate the structure of gut microbiota and has potential probiotic effects including anti-obesity, anti-inflammatory, maintenance of gut epithelial barrier and so on.

Abstract Lon protease 1 (LONP1) is an ATP-dependent protease located in the mitochondrial matrix and plays a crucial role in regulating mitochondrial proteostasis, metabolism, and cellular stress responses et al. Aberrant LONP1 expression has been found in the progression of various tumors; however, the role and molecular mechanisms of LONP1 in prostate cancer (PCa) remain poorly understood. Here we show that overexpression of LONP1 is closely related to adverse clinic pathological features and poor prognosis in PCa patients. Mechanistically, the findings reveal that LONP1 is implicated in modulating the metabolic switch from oxidative phosphorylation (OXPHOS) to aerobic glycolysis, thereby promoting tumor proliferation, invasion, and metastasis both in vitro and in vivo. Meanwhile, we prove that LONP1 as a protease directly targets mitochondrial pyruvate carrier 1 (MPC1), a key metabolic protein in the process of glycolysis, and enhances its degradation, which in turn suppresses tricarboxylic acid (TCA) cycle and ultimately promotes the progression of PCa. Furthermore, using PCa in cancer-prone mice homozygous for a prostate-targeted conditional Pten knockout and Lonp1 knockin, we integrate transcriptomic and proteomic analyses of prostate tumors, upon which reveals that Lonp1 overexpression results in a significant downregulation of NADH: ubiquinone oxidoreductase activity, consequently impeding the electron transfer process and mitochondrial ATP synthesis, associated with metastasis of PCa. Collectively, our results highlight that metabolic reprogramming induced by LONP1 in PCa is closely coupled with disease progression, suggesting that targeting the LONP1-mediated cascade in the mitochondrial may provide therapeutic potential for PCa disease.

Abstract In previous investigations it was reported that certain transition metal-rich amorphous alloys consist of distorted bcc nano-clusters. Two kinds of these amorphous alloys are of special interest due to their physical properties: (1) metallic glasses with no boundaries between grains and (2) nanoglasses. Nanoglasses are specified with a high proportion of boundary /Interface between amorphous grains. Similar to the nanoglasses, the nano-sized crystalline clusters such as bcc-Fe clusters consist meanly of two components: (1) nanometer-size bcc-Fe clusters (frequently called nanograins with sizes of about 3 nm or less) and (2) Interfaces between grain. A fundamentally feature of these materials seems to be their magnetic properties that are controlled by a quantum mechanical effect, the indistinguishability effect. Based on the experimentally observed magnetic properties of nano-sized bcc-Fe, it is suggesting that the application of the indistinguishability effect may open the way for the production and understanding of this class of materials.

Abstract Objective The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variability. Subsequently, a prediction model of gastric cancer (GC) serosal invasion was constructed in conjunction with radiomics and deep learning features, and a nomogram was generated to explore the clinical guiding significance. Methods This study enrolled 311 patients from two centers with pathologically confirmed of GC. we employed a deep learning model, U-Mamba, to obtain fully automatic segmentation of the spleen CT images. Subsequently, radiomics features and deep learning features were extracted from the entire spleen CT images, and significant features were identified through dimensionality reduction. The clinical features, radiomic features, and deep learning features were organized and integrated, and five machine learning methods were employed to develop 15 predictive models. Ultimately, the model exhibiting superior performance was presented in the form of a nomogram. Results A total of 18 radiomics features, 30 deep learning features, and 1 clinical features were deemed valuable. The DLRA model demonstrated superior discriminative capacity relative to other models. A nomogram was constructed based on the logistic clinical model to facilitate the usage and verification of the clinical model. Conclusion Radiomics and deep learning features derived from automated spleen segmentation to construct a nomogram demonstrate efficacy in predicting serosal invasion in GC. Concurrently, fully automated segmentation provides a novel and reproducible approach for radiomics research.

This paper investigates the impact of the COVID-19 pandemic on travel modes choice behaviour using a case study from Wuhan, China. A SP-experiment based survey was conducted in Wuhan, based on which an MNL model and a latent class MNL model were established, respectively. The model estimation results show the following conclusions. First, the attributes that are normally believed to significantly affect the residents’ travel mode choice behaviour turned out to be insignificant during the COVID-19 pandemic. Second, attributes such as age, gender, driving license, income trend, use frequency of public transit, currently most-frequent-used mode, household size, monthly household income, distance from metro station to home, number of confirmed/deaths cases, vaccination are significantly affecting the respondents’ travel preferences. Third, the outbreak of the COVID-19 pandemic leads to a decline in the residents’ preferences toward public transit, but the promotion of vaccines can lead residents to return to the public transit system. Fourth, the respondents were divided into three latent classes: high-susceptible, medium-susceptible and low-susceptible classes. These conclusions are believed to provide a reference for the investigation of impact of the COVID-19 pandemic or other similar public health events on the transportation system, and also offer supports for policy-making to effectively deal with such pandemics.

Abstract The proportion of railway high-altitude buried tunnels in complex and dangerous mountainous areas in southwest China is exceptionally high. With the characteristics of suddenness, intermittency, instantaneousness, and destruction, inrushinrushing and collapse is one of the main risks in the tunnel construction process. Therefore, in the design and construction process of tunnels in Hengduan Mountain area, it is very important to identify the mechanism of sudden s inrushing and collapse risks, predict the spatial location and scale of possible inrushing and collapse, and formulate corresponding tunnel design and construction response measures. In order to achieve the above goals, through extensive exploratory attempts and experiments, it has been found that the use of multi-source information during the survey period can analyze the sources and channels of tunnel water inrushing, as well as the mechanism of water inrushing disasters; By utilizing high-definition images of the excavation surface, ultra deep directional drilling information data, and geophysical 3D attribute information data obtained during tunnel excavation, modeling, coordinate conversion, and 3D integration can be carried out. Various information can be compared and verified with each other, and spatial prediction of potential inrushing and collapse risks in front of the excavation surface can be made, and ultimately response measures for potential or existing surge disasters can be determined. This spatial prediction and analysis method is highly effective and has practical and promotional value.

Objective: To study the moisturizing and laxative effects of ginseng peptides (GPs) on the constipation model of ICR mice, and provide theoretical basis for the further development and utilization of GPs and clinical adjuvant treatment of constipation. Methods: Sixty adult male ICR mice were randomly divided into 6 groups, they were normal group (NG), constipation group (CG), whey protein group (WPG), low-dose GPs group (LG), medium-dose GPs group (MG), high-dose GPs group (HG). Except NG, mouse constipation model was established using loperamide hydrochloride (LH). After 7 consecutive days of intervention with GPs, the small intestine peristalsis and defecation time, as well as pathological changes in the small intestine villus tissue of each group of mice were observed and compared. The improvement effect of GPs on mouse constipation was evaluated by detecting the content of brain gut peptide indicators in the serum of each group of mice, including substance P (SP) and vasoactive intestinal peptide (VIP). Results: Compared with the model group, the high, medium, and low dose groups of GPs significantly increased the small intestine ink advance rate in mice (P

Abstract The current electronic voting system ensures the confidentiality of the ballot but does not explicitly address the anonymity of the voter’s identity. The voting process is entirely managed by smart contracts, which diminishes the efficiency of contract execution and poses challenges for achieving large-scale voting. There is a lack of comprehensive explanation regarding the voting process and the verifiability of ballot information. This article introduces a multi-party secure verifiable electronic voting scheme based on blockchain technology. The solution uses IPFS distributed file system to alleviate the problem of limited block storage. The task allocation and vote counting processes are managed separately through management and computation contracts to improve efficiency. Ring signature technology is used to ensure the anonymity of voting identities. The vote counting process uses a key-sharing system to ensure the privacy of the votes, and the calculation results are encrypted and uploaded to the blockchain, making them verifiable. Finally, a security proof and performance analysis of the scheme were conducted.

In order to solve the problem of missing detection and false detection caused by the inaccuracy of motion feature extraction in the existing video key frame extraction algorithms, a reinforcement learning and feature fusion for key frame extraction algorithm and video stream classification is proposed. The fusion features are obtained by combining the original statistical features via S-GCN and ResNet50 network. Some fusion features are more effective than the original statistical features. Therefore, in order to extract useful information for classification, the original features and fusion features are combined to produce composite features. At the same time, the number of features increases and there are redundant and irrelevant features. Embedded feature selection method and random forest classifier are used to select the best feature subset. Finally, the attention mechanism is used to calculate the importance of video frames, and reinforcement learning is used to extract and optimize key frames. The experimental results show that the new algorithm can solve the problem of error detection in the key frame extraction of motion video, and performs well in the detection of video frames containing key actions. The algorithm has high accuracy and strong stability.

Abstract Objective Isolated REM sleep behavior disorder (iRBD) is considered as the strongest predictor of Parkinson's disease (PD). Reliable and accurate biomarkers for iRBD detection and the prediction of phenoconversion are in urgent need. This study aimed to investigate whether α‐Synuclein (α‐Syn) species in plasma neuron‐derived extracellular vesicles (NDEVs) could differentiate between iRBD patients and healthy controls (HCs). Methods Nanoscale flow cytometry was used to detect α‐Syn‐containing NDEVs in plasma. Results A total of 54 iRBD patients and 53 HCs were recruited. The concentrations of total α‐Syn, α‐Syn aggregates, and phosphorylated α‐Syn at Ser129 (pS129)‐containing NDEVs in plasma of iRBD individuals were significantly higher than those in HCs (p

Abstract In recent years, the rapid proliferation of digital services and resources on Industrial Internet has imposed higher demands on universality and privacy of identity management. Particularly with the advent of the digital economy, prudent users are urged to maintain control over their digital identity credentials. However, traditional identity management methods have failed to meet this requirement and thus have been prone to raise users' concerns about potential financial loss. Specifically, conventional identity management systems(IDMS) have been plagued by imperceptible privacy disclosure, which derives from the flaws in single points of failure, excessive disclosure, correlation analysis, traceability, and revocation. The emerging Self-Sovereign Identity (SSI) architecture aims to tackle these issues and is propelling the evolution of privacy-enhanced distributed identity management. To this end, we proposed a privacy-enhanced distributed identity management scheme with sequential aggregate issuance, threshold traceability and revocability in the setting of multiple credential issuers and regulators. We adopted the Decentralized identifiers(DIDs) and verifiable credentials(VCs) based on the SSI architecture to ensure the hierarchical identity authentication. The security and performance analysis shows that our proposal achieves the desired design goals and is feasible for distributed Industrial Internet.

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation (DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory (Bi-LSTM) based quasi-static magnetic field (QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.

Abstract Background Enzalutamide (Enz) resistance is a poor prognostic factor for patients with castration-resistant prostate cancer (CRPC), which often involves aberrant expression of the androgen receptor (AR). Myosin VI (MYO6), one member of the myosin family, plays an important role in regulating cell survival and is highly expressed in prostate cancer (PCa). However, whether MYO6 is involved in Enz resistance in CRPC and its mechanism remain unclear. Methods Multiple open-access databases were utilized to examine the relationship between MYO6 expression and PCa progression, and to screen differentially expressed genes (DEGs) and potential signaling pathways associated with the MYO6-regulated Enz resistance. Both in vitro and in vivo tumorigenesis assays were employed to examine the impact of MYO6 on the growth and Enz resistance of PCa cells. Human PCa tissues and related clinical biochemical data were utilized to identify the role of MYO6 in promoting PCa progression and Enz resistance. The molecular mechanisms underlying the regulation of gene expression, PCa progression, and Enz resistance in CRPC by MYO6 were investigated. Results MYO6 expression increases in patients with PCa and is positively correlated with AR expression in PCa cell lines and tissues. Overexpression of AR increases MYO6 expression to promote PCa cell proliferation, migration and invasion, and to inhibit PCa cell apoptosis; whereas knockdown of MYO6 expression reverses these outcomes and enhances Enz function in suppressing the proliferation of the Enz- sensitive and resistant PCa cells both in vitro and in vivo. Mechanistically, AR binds directly to the promoter region (residues − 503 to − 283 base pairs) of MYO6 gene and promotes its transcription. Furthermore, MYO6 activates focal adhesion kinase (FAK) phosphorylation at tyrosine-397 through integrin beta 8 (ITGB8) modulation to promote PCa progression and Enz resistance. Notably, inhibition of FAK activity by Y15, an inhibitor of FAK, can resensitize CRPC cells to Enz treatment in cell lines and mouse xenograft models. Conclusions MYO6 has pro-tumor and Enz-resistant effects in CRPC, suggesting that targeting MYO6 may be beneficial for ENZ-resistant CRPC therapy through the AR/MYO6/FAK signaling pathway.

BackgroundUrinary dysfunction is an early manifestation of autonomic dysfunction in Multiple System Atrophy (MSA) and often precedes orthostatic hypotension. This study investigated the diagnostic efficacy of post-void residual (PVR) urine volume in differentiating possible MSA from early-stage Parkinson’s disease (PD) and sought to identify a feasible combination of autonomic nervous system indicators for clinical use. The distribution of α-Synuclein (α-Syn) forms in erythrocyte was preliminary explored.MethodsThis study included 70 patients with MSA-P, 73 with MSA-C, and 71 with PD. All participants underwent assessments including bladder residual urine ultrasound, the supine-to-standing test (STS), external anal sphincter electromyography (EAS-EMG), brain MRI, Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). Receiver operating characteristic (ROC) curves determined the diagnostic value of PVR urine volume and other autonomic indicators for possible MSA. Immunofluorescence staining of α-Syn forms in red blood cells (RBCs) was also performed.ResultsPVR urine volume, ΔSBP and ΔDBP (at 1 and 3 min), and EAS-EMG parameters were significantly increased in MSA-P and MSA-C patients compared to PD (p

IntroductionRehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.MethodsEight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.ResultsAmong the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.ConclusionOur results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.

Dietary l-carnosine supplementation has been shown to enhance animal performance and improve meat quality. However, the mechanisms underlying the effects of l-carnosine on the physiological functions of animals have not been fully elucidated. We investigated the effects of dietary l-carnosine supplementation on growth performance, intestinal microbiota diversity, and the serum metabolome in fattening lambs to reveal the molecular mechanism underlying the effect of l-carnosine on the growth performance of sheep. Sixty 3-month-old male crossbred lambs (Dorper ♂ × Small Tail Han ♀) with an average body weight of 30 ± 5 kg were randomly divided into two groups: a control group (group C) fed a basal diet, and an experimental group (group L) fed a basal diet supplemented with 400 mg/kg of l-carnosine. At the end of the 60-day experiment, all sheep were weighed, and fecal and blood samples were collected from 12 random sheep. The fecal microbiota was analyzed using 16S rRNA sequencing, and serum metabolites were analyzed using liquid chromatography–tandem mass spectrometry. Spearman correlation analysis was employed to assess the associations between intestinal microbiota and serum metabolite biomarkers. The results showed that weight gain and daily weight gain were significantly increased in group L compared to group C (p 0.05). At the genus level, the abundances of Syntrophococcus (p 1, p

Introduction: Morphological abnormalities of erythrocytes/red blood cells (RBCs), e.g., increased acanthocytes, in Parkinson’s disease (PD) have been reported previously, although the underlying mechanisms remain to be characterized. In this study, the potential roles of α-synuclein (α-syn), a protein critically involved in PD and highly abundant in RBCs, were studied in PD patients as well as in a PD mouse model. Methods: Transgenic [PAC-Tg (SNCAA53T), A53T] mice overexpressing A53T mutant α-syn and SNCA knockout mice were employed to characterize the effect of α-syn on RBC morphology. In addition to A53T and SNCA knockout mice, the morphology of RBCs of PD patients was also examined using scanning electron microscopy. The potential roles of α-syn were further investigated in cultured RBCs and mice. Results: Morphological abnormalities of RBCs and increased accumulation of aggregated α-syn on the RBC membrane were observed in PD patients. A similar phenomenon was also observed in A53T mice. Furthermore, while mice lacking α-syn expression showed a lower proportion of acanthocytes, treating RBCs derived from SNCA knockout mice with aggregated α-syn resulted in a higher percentage of acanthocytes. In a follow-up proteomic investigation, several major classes of proteins were identified as α-syn-associated proteins on the RBC membrane, seven of which were calcium-binding proteins. Applying aggregated α-syn to the RBC membrane directly induced extracellular calcium influx along with morphological changes; both observations were adequately reversed by blocking calcium influx. Conclusions: This study demonstrated that α-syn plays a critical role in PD-associated morphological abnormalities of RBCs, at least partially via a process mediated by extracellular calcium influx.

Abstract The effect of circumference and displacement of the third fracture fragment on fracture healing after intramedullary nailing of femoral shaft fractures with a third fracture fragment was investigated. A retrospective cohort study was conducted to analyze the data of 142 patients who suffered femoral shaft fractures with a third fracture fragment and were admitted to the First People’s Hospital of Lianyungang from February 2016 to December 2021. According to the circumference of the third fracture fragments, these were divided into three types of type 1: 71 cases; type 2: 52 cases; and type 3: 19 cases. On the basis of the diaphyseal diameter, the degree of displacement of the third fracture fragment was classified into three degrees of degree I: 95 cases; degree II: 31 cases; and degree III: 16 cases. Postoperative follow-up was performed to compare the fracture healing rate, healing time, and the modified Radiographic Union Scale for Tibia (mRUST) at 9th month after surgery in each group. All 142 patients were followed up after operation, with an average of (14.7 ± 4.1) months, and the overall healing rate was 73.4%. When the third fracture fragments were displaced in degree II and III, the mRUST score at 9th month in the type 1 group was higher than that in the type 2 and 3 groups (P = 0.017). Logistic regression analysis showed that greater displacement of third fracture fragments and greater circumference were associated with lower fracture healing rates (P

Abstract The current research focuses on the effects of nutritional supplementation and exercise on dialysis patients, but whether physical activity (PA) can reduce the risk of adverse outcomes for patients with different nutritional status is not clear. The maintenance hemodialysis (MHD) patients were recruited from April 2021 to April 2022. The information of PA was obtained from the international physical activity questionnaire (IPAQ). The outcomes were cardiovascular death, myocardial infarction, stroke, heart failure, atrial fibrillation, tumor and all-cause death. We used COX proportional risk model to estimate the association between PA and the outcomes of MHD patients. Patients are classified into two groups based on geriatric nutritional risk index (GNRI) and classified by age, and we used COX proportional risk model to estimate the association of PA and outcomes in subgroups. The isotemporal substitution model (ISM) was used to estimate the effects of replacing light physical activity (LPA) with moderate physical activity (MPA) or vigorous physical activity (VPA) on risk of cardiovascular events, tumors, and all-cause death in different subgroups. The effects of PA on ankle-brachial index (ABI) and body fat content were analyzed in different IPAQ groups. A total of 241 maintenance hemodialysis patients were included, 105 peoples developed cardiovascular death, myocardial infarction, stroke, heart failure, atrial fibrillation, tumor and all-cause death (43.6%). The median follow-up time was 12 months. MPA reduced the risk of outcome in MHD patients or high GNRI patients (40% vs 39%).In MHD patients who was under 65 years with high GNRI, MPA reduced cardiovascular death, myocardial infarction, stroke, heart failure, atrial fibrillation, tumor and all-cause death by 55%.PA reduced the risk of cardiovascular event by 65%, but did not reduce the risk of tumor or all-cause death. Replacing LPA with VPA did not improve clinical outcomes. It actually increases the risk of heart failure 0.4%. MPA reduced the risk of cardiovascular death, myocardial infarction, stroke, heart failure, atrial fibrillation, tumor, all-cause death in MHD patients under 65 years, while VPA had no health benefit. Trial registration: ChiCTR210050998.

Abstract Bronchial artery‐pulmonary artery fistula (BPF) is a rare vascular malformation of the bronchial arteries, associated with congenital heart disease, pulmonary hypertension, or pulmonary infections. It typically presents in adulthood and has a low incidence. Another rare vascular anomaly, bronchial Dieulafoy disease (BDD), is characterized by an abnormally developed artery within the submucosa of the bronchial tree. This condition is thought to arise from a combination of congenital dysplasia and chronic airway inflammation or injury. Both BPF and BDD are uncommon causes of hemoptysis, and their simultaneous occurrence is exceptionally rare. This report discusses a case of BPF complicated by BDD in a patient who presented with recurrent hemoptysis. The diagnosis was established through bronchial artery angiography, bronchoscopy, and surgical pathology. By examining the clinical features, imaging findings, and diagnostic criteria of these conditions, this case report aims to enhance clinicians' understanding of cryptogenic hemoptysis and provide valuable insights for clinical practice.

BackgroundStereoelectroencephalography (SEEG), as a minimally invasive method that can stably collect intracranial electroencephalographic information over long periods, has increasingly been applied in the diagnosis and treatment of intractable epilepsy in recent years. Over the past 20 years, with the advancement of materials science and computer science, the application scenarios of SEEG have greatly expanded. Bibliometrics, as a method of scientifically analyzing published literature, can summarize the evolutionary process in the SEEG field and offer insights into its future development prospects.MethodsThis article selected all the literature records retrieved on November 4, 2024, from the Web of Science Core Collection (WoSCC). The search terms were as follows: “Stereo-electroencephalography” or “Stereo electroencephalography” or “Stereo-EEG” or “Stereo EEG” or “SEEG.” The document types included were research articles and reviews. For analysis, VOSviewer, CiteSpace, and the R package “bibliometrix” were employed to analyze various aspects of the SEEG field, including authors, institutions, countries and regions, and research hotspots.ResultsWe reviewed a total of 1,383 non-duplicate literature records from 2002 to 2023, including 1,241 research articles, 116 review articles and 26 letters. Observing the annual publication trends, there has been an overall increase since 2002. The most influential journal in this field is Epilepsia. Other journals with considerable impact include Clinical Neurophysiology, Epileptic Disorders, Epilepsy Research, NeuroImage, and Epilepsy & Behavior. The top 5 most influential scholars are Bartolomei F, Tassi L, Nobili L, Russo GL, and Mc Gonigal A. As for the analysis of countries and regions, France occupies a leading position in this field with its early start, while China and the United States have also emerged as focal points since 2020. Research on SEEG has expanded beyond its initial use for localizing epileptic foci and thermo-coagulation treatments and have been employed as a medium to facilitate real-time prediction of epileptic seizures and enabling the exploration of brain network connectivity.ConclusionAs a minimally invasive tool for collecting intracranial electroencephalographic signals, SEEG continues to offer vast potential for development and application. Advances in electrode materials and robotic-assisted stereotactic techniques, have enabled SEEG to simultaneously sample multiple brain regions, acquire electrical signals from deep brain structures. These advantages significantly enhance the precision of epileptic focus localization in diagnosis and treatment, addressing the limitations of subdural electrodes. Through bibliometric analysis, this paper traces the developmental trajectory of SEEG and identifying key technological milestones, thereby providing a reference for scholarly research directions.

Age has a significant impact on goat meat flavor. However, reporting the effects of age on free amino acid (FAAs), lipid profiles and aromas of goat meat is limited. Here, the FAAs, lipid profiles and aromas in the Chongming white goat with 12 months and 24 months were investigated in this study. A total 1164 lipids were identified using lipidomics, including 31 subclasses. Multivariate statistical analysis showed that 201 lipids had significant changes, FFA, TG and DG increased with goat age. Furthermore, the pathway analysis indicated that glycerophospholipid metabolism and glycerolipid metabolism were the key pathways that relate to lipid profile changes during goat growth. Thirty-five volatile compounds were identified, among them, 14 aromas with odor activity value greater than 1 were considered as potential characteristic aroma compounds of Chongming white goat meat. These findings reveal the FAAs, lipids, and aromas profiles in Chongming white goat meat are affected by age, which provides a basis for improving goat meat flavor.

Summary: Background: Essential tremor (ET) significantly impacts patients' daily lives and quality of life, presenting a considerable challenge in clinical practice. In recent years, novel therapeutic regimens have been investigated in randomized controlled trials (RCTs). This study aims to investigate and evaluate the relative efficacy and safety of various therapeutic interventions for ET. Methods: We did a systematic review and Bayesian Model-based Network Meta-analysis (NMA) of RCTs. Following PRISMA-NMA guidelines, a comprehensive database search was conducted up to April 1, 2024 to identify RCTs focused on ET treatments. The Bayesian Markov Chain Monte Carlo (MCMC) method was utilized for the analysis, evaluating the relative efficacy and safety of treatments using standardized mean difference (SMD) and log odds ratios (log ORs), respectively. Additionally, the Surface Under the Cumulative Ranking Curve (SUCRA) was applied to assess the relative efficacy of the treatment modalities. PROSPERO registration: CRD42023415752. Findings: This study included 33 RCTs involving 1251 patients, covering 19 oral medication treatments and six non-oral medication treatments. NMA showed that deep brain stimulation (DBS) (SMD = −4.93; 95% CI: [−7.73, −2.13]), CX-8998 (SMD = −2.69; 95% CI: [−5.26, −0.14]), atenolol (SMD = −2.36; 95% CI: [−4.70, −0.10]), and propranolol (SMD = −1.59; 95% CI: [−2.25, −0.67]) showed relative efficacy compared to placebo, with DBS demonstrating relative efficacy compared to 15 other treatment methods. However, GRADE assessment indicated that the evidence level for these conclusions was “low” or “very low.” According to SUCRA rankings, DBS (0.97) ranked first in relative efficacy, followed by CX-8998 (0.80), thalamotomy (0.79), atenolol (0.76), metoprolol (0.66), propranolol (0.64), magnetic resonance guided focus ultrasound (MR-FUS) (0.624), ICI-118551 (0.620), nimodipine (0.61) and phenobarbitone (0.59). In terms of safety, as a network graph could not be constructed, DBS and thalamotomy were excluded from the NMA, while other effective treatments showed no significant differences in safety compared to placebo. Interpretation: Our study results indicate that CX-8998, propranolol, and atenolol demonstrate relative efficacy and safety in treating ET. DBS is effective for medication-resistant ET and ranks first in relative efficacy, though our NMA lacks safety data for DBS. Given the low overall grade of evidence, these results should be applied cautiously in clinical practice. Further large-scale, head-to-head RCTs are needed. Funding: This work was supported by grants from the National Nature Science Foundation of China (Grant No. 82271459).

IntroductionDanwei communities are a testament to China’s socialist urban development, characterized by the self-sufficiency, strong social networks, and institutional management. In the historical context of urban development in China, many old communities have evolved from former housing areas of Danwei. After more than 40 years of use, the buildings, and environments in the old Danwei communities remain in disrepair, dirty, disorderly and poor condition. Many functions have failed that negatively affect the quality of life and health of residents. After Covid-19, improving the mental health of residents has become a major goal of public policies in various countries.MethodsTo explore the residents’ mental health in the micro-renewal, this study carried out a survey regarding residents’ evaluation on the micro-renewal and their mental health in two renewed Danwei communities in Hengyang, China. More than 800 respondents joined the interview, among them, 634 samples are effective for analysis. Entropy-TOPSIS is applied to analyze the weights of various indicators of micro-renewal. And multinominal logit model is used to examine the relationship between the personal factors, satisfaction on various items of micro-renewal and mental health.Results and DiscussionThe findings indicates that the mental health level of residents living in Danwei community is affected by micro-renewal. The socio-demographic characteristics and behavior factors can influence on the possibility of being in different levels of mental health. The satisfaction on the components of micro-renewal and improvements after micro-renewal is also determiner of residents’ mental health level. In addition, the heterogeneity is address in mental health.

Nearshore water-level prediction has a substantial impact on the daily lives of coastal residents, fishing operations, and disaster prevention and mitigation. Compared to the limitations and high costs of traditional empirical forecasts and numerical models for nearshore water-level prediction, data-driven artificial intelligence methods can more efficiently predict water levels. Attention mechanisms have recently shown great potential in natural language processing and video prediction. Convolutional long short-term memory(ConvLSTM) combines the advantages of convolutional neural networks (CNN) and long short-term Memory (LSTM), enabling more effective data feature extraction. Therefore, this study proposes a ConvLSTM nearshore water level prediction model that incorporates an attention mechanism. The ConvLSTM model extracts multiscale information from historical water levels, and the attention mechanism enhances the importance of key features, thereby improving the prediction accuracy and timeliness. The model structure was determined through experiments and relevant previous studies using five years of water level data from the Zhuhai Tide Station and the surrounding wind speed and rainfall data for training and evaluation. The results indicate that this model outperforms the four other baseline models of PCCs, RMSE, and MAE, effectively predicting future water levels at nearshore stations up to 48 h in advance. Compared to the ConvLSTM model, the model with the attention mechanism showed an average improvement of approximately 10% on the test set, with a greater error reduction in short-term forecasts than that in long-term forecasts. During Typhoon Higos, the model reduced the MAE of the best-performing baseline model by approximately 3.2 and 2.4 cm for the 6- and 24-hour forecasts, respectively, decreasing forecast errors by approximately 18% and 11%, effectively enhancing the ability of the model to forecast storm surges. This method provides a new approach for forecasting nearshore tides and storm surges.

The use of antibiotics in animal production raises great public safety concerns; therefore, there is an urgent need for the development of substitutes for antibiotics. In recent decades, plant-derived feed additives have been widely investigated as antibiotic alternatives for use in animal health and production because they exert multiple biological functions and are less likely to induce resistance development. This review summarizes the research history and classification of phytogenic feed additives and their main functions, potential modes of action, influencing factors, and potential negative effects. Further, we highlight the challenges in developing sustainable, safe, and affordable plant-derived antibiotic alternatives for use in livestock production.

Abstract Background The latitudinal diversity gradient (LDG), characterized by an increase in species richness from the poles to the equator, is one of the most pervasive biological patterns. However, inverse LDGs, in which species richness peaks in extratropical regions, are also found in some lineages and their causes remain unclear. Here, we test the roles of evolutionary time, diversification rates, and niche conservatism in explaining the inverse LDG of Potentilla (ca. 500 species). We compiled the global distributions of ~ 90% of Potentilla species, and reconstructed a robust phylogenetic framework based on whole-plastome sequences. Next, we analyzed the divergence time, ancestral area, diversification rate, and ancestral niche to investigate the macroevolutionary history of Potentilla. Results The genus originated in the Qinghai-Tibet Plateau during the late Eocene and gradually spread to other regions of the Northern Hemisphere posterior to the late Miocene. Rapid cooling after the late Pliocene promoted the radiating diversification of Potentilla. The polyploidization, as well as some cold-adaptive morphological innovations, enhanced the adaptation of Potentilla species to the cold environment. Ancestral niche reconstruction suggests that Potentilla likely originated in a relatively cool environment. The species richness peaks at approximately 45 °N, a region characterized by high diversification rates, and the environmental conditions are similar to the ancestral climate niche. Evolutionary time was not significantly correlated with species richness in the latitudinal gradient. Conclusions Our results suggest that the elevated diversification rates in middle latitude regions and the conservatism in thermal niches jointly determined the inverse LDG in Potentilla. This study highlights the importance of integrating evolutionary and ecological approaches to explain the diversity pattern of biological groups on a global scale.

Abstract Background Bladder cancer (BCa) stands out as a prevalent and highly lethal malignancy worldwide. Chemoresistance significantly contributes to cancer recurrence and progression. Traditional Tumor Node Metastasis (TNM) stage and molecular subtypes often fail to promptly identify treatment preferences based on sensitivity. Methods In this study, we developed a prognostic signature for BCa with uni-Cox + LASSO + multi-Cox survival analysis in multiple independent cohorts. Six machine learning algorithms were adopted to screen out the hub gene, RAC3. IHC staining was used to validate the expression of RAC3 in BCa tumor tissue. RT-qPCR and Western blot were performed to detect and quantify the mRNA and protein levels of RAC3. CCK8, colony formation, wound healing, and flow cytometry analysis of apoptosis were employed to determine cell proliferation, migration, and apoptosis. Molecular docking was used to find small target drugs, PIK-75. 3D cell viability assay was applied to evaluate the ATP viability of bladder cancer organoids before and after PIK-75 treated. Results The established clinical prognostic model, GIRS, comprises 13 genes associated with gemcitabine resistance and immunology. This model has demonstrated robust predictive capabilities for survival outcomes across various independent public cohorts. Additionally, the GIRS signature shows significant correlations with responses to both immunotherapy and chemotherapy. Leveraging machine learning algorithms, the hub gene, RAC3, was identified, and potential upstream transcription factors were screened through database analysis. IHC results showed that RAC3 was higher expressed in GEM-resistant BCa patients. Employing molecular docking, the small molecule drug PIK-75, as binding to RAC3, was identified. Experiments on cell lines, organoids and animals validated the biological effects of PIK-75 in bladder cancer. Conclusions The GIRS signature offers a valuable complement to the conventional anatomic TNM staging system and molecular subtype stratification in bladder cancer. The hub gene, RAC3, plays a crucial role in BCa and is significantly associated with resistance to gemcitabine. The small molecular drug, PIK-75 having the potential as a therapeutic agent in the context of gemcitabine-resistant and immune-related pathways.

Due to the severe corrosion environment, corrosion problems caused by sulfur deposition are one important reason for the failure of composite pipes in the long-term service process when Incoloy825/X65 bimetallic composite pipes are used in high-sulfur oil and gas transportation. In this paper, an Incoloy825/X65 bimetallic composite pipe was subjected to an immersion corrosion test in suspended sulfur solution to observe the corrosion morphology and characterize the corrosion products using a SEM, EDS, and XRD. The adsorption behavior of the Incoloy825 alloy in terms of sulfur elements was investigated. The results show that the heat-affected zone (HAZ) of the welding joint is the preferred region for pitting corrosion. The film of corrosion product on the Incoloy825 was mainly composed of NiS, FeS, and Cr2S3, and its thickness was 7–13 μm. With prolongation of the immersion time, the pitting resistance of the surface product film of nickel-based alloys is weakened and then enhanced, and the corrosion product film can act as a barrier to anion transfer and inhibit the occurrence of pitting.

African swine fever virus (ASFV) is the causative agent of African swine fever (ASF), a severe hemorrhagic disease with a mortality rate reaching 100%. Despite extensive research on ASFV mechanisms, no safe and effective vaccines or antiviral treatments have been developed. Live attenuated vaccines generated via gene deletion are considered to be highly promising. We developed a novel recombinant ASFV strain by deleting MGF360-10L and MGF505-7R, significantly reducing virulence in pigs. In the inoculation experiment, pigs were infected with 104 50% hemadsorption doses (HAD50) of the mutant strain. All the animals survived the observation period without showing ASF-related clinical signs. Importantly, no significant viral infections were detected in the cohabitating pigs. In the virus challenge experiment, all pigs succumbed after being challenged with the parent strain. RNA-seq analysis showed that the recombinant virus induced slightly higher expression of natural immune factors than the parent ASFV; however, this level was insufficient to provide immune protection. In conclusion, our study demonstrates that deleting MGF360-10L and MGF505-7R from ASFV CN/GS/2018 significantly reduces virulence but fails to provide protection against the parent strain.

The onset time of the South China Sea summer monsoon (SCSSM) is widely recognized as being closely related to the distribution of summer rainfall belts in East Asia. This study examines the sub-seasonal prediction bias of SCSSM onset and investigates the associated oceanic and atmospheric processes using the hindcasts from the ECMWF sub-seasonal-to-seasonal (S2S) forecasting system. The system can accurately predict the onset time two pentads in advance. As the lead time increases, the predicted monsoon onset time tends to be later than the observations. When the forecast lead time exceeds 15 days, the inaccuracies in the northward propagation of tropical convection result in the absence of initial atmospheric constraints. This lack of initial constraints, along with their influence on the boundary forcing over the Bay of Bengal, may lead to a late onset bias of the SCSSM in this forecasting system.

Reducing CO2 emissions represents a global challenge, and the mobility sectors account for a considerable portion of total emissions, with marked disparities across diverse economies. Viewed from a macroscopic perspective, countries and regions around the world can be categorized in various ways. However, relying on a single or a few indicators often proves inadequate to meet the classification requirements for carbon reduction and sustainable development. In this study, employing machine learning and guided by 10 economic indicators, we classified 188 countries/regions into 6 identifiable clusters. Subsequently, by applying ratio analysis and random forest methodologies, we conducted a matrix-based analysis that elucidates the distinct emission characteristics of each mobility sector. Feature importance analysis revealed that for highly developed economies, the total population's contribution was significant, especially in domestic and international aviation, accounting for 50% and 25% of emissions, respectively. In contrast, for lower-middle-income countries and regions, while the total population still played a pivotal role, its influence was most pronounced in railway transportation, reaching 67%. For rapidly developing economies, domestic aviation emissions reached a peak influence of 61%. These insights emphasize the necessity for strategies tailored to the unique attributes of economic entities.

ABSTRACT The widespread sulfonamide resistance genes sul1, sul2, and sul3 in food and gut bacteria have attracted considerable attention. In this study, we assessed the in vivo fitness of sul gene-dependent sulfonamide-resistant Escherichia coli, using a murine model. High fitness costs were incurred for sul1 and sul3 gene-dependent E. coli strains in vivo. A fitness advantage was found in three of the eight mice after intragastric administration of sul2 gene-dependent E. coli strains. We isolated three compensatory mutant strains (CMSs) independently from three mice that outcompeted the parent strain P2 in vivo. Whole-genome sequencing revealed seven identical single nucleotide polymorphism (SNP) mutations in the three CMSs compared with strain P2, an additional SNP mutation in strain S2-2, and two additional SNP mutations in strain S2-3. Furthermore, tandem mass tag-based quantitative proteomic analysis revealed abundant differentially expressed proteins (DEPs) in the CMSs compared with P2. Of these, seven key fitness-related DEPs distributed in two-component systems, galactose and tryptophan metabolism pathways, were verified using parallel reaction monitoring analysis. The DEPs in the CMSs influenced bacterial motility, environmental stress tolerance, colonization ability, carbohydrate utilization, cell morphology maintenance, and chemotaxis to restore fitness costs and adapt to the mammalian gut environment.IMPORTANCESulfonamides are traditional synthetic antimicrobial agents used in clinical and veterinary medical settings. Their long-term excessive overuse has resulted in widespread microbial resistance, limiting their application for medical interventions. Resistance to sulfonamides is primarily conferred by the alternative genes sul1, sul2, and sul3 encoding dihydropteroate synthase in bacteria. Studying the potential fitness cost of these sul genes is crucial for understanding the evolution and transmission of sulfonamide-resistant bacteria. In vitro studies have been conducted on the fitness cost of sul genes in bacteria. In this study, we provide critical insights into bacterial adaptation and transmission using an in vivo approach.

Though the use of per-and polyfluoroalkyl substances (PFAS) is strictly restricted worldwide, PFAS have been increasingly detected in aqueous environment with high human exposure risk. Effective PFAS remediation requires the simultaneous concentration and decomposition of these compounds from dilute solutions, presenting a significant challenge. The present work evaluated the suitability of layered double hydroxide (LDH) materials, promising adsorbents for anionic pollutants, for the removal of long-chain and short-chain PFAS in micro-polluted water. Additionally, it explored their potential for PFAS degradation after modification. The results suggested that LDH adsorbents have limited ability to extract short-chain PFAS such as perfluorobutane sulfonate (PFBS) from water matrices, especially in dilute solutions. Although organic modification of LDHs could enhance their uptake efficacy on PFAS, it cannot improve the decomposition of PFAS. Innovatively, a composite featuring zero-valent iron (ZVI) particles coupled with LDH was developed, in which the nanoscale ZVI core is coated with LDH to adsorb and decompose perfluorooctanoic acid (PFOA) synergistically. Characterization of the composite showed that LDH coating not only hinders the aggregation of ZVI particles, but also reduces the passivation of ZVI. The PFOA removal efficacy of the composite can be further facilitated in acid environment. By-product analysis revealed that the composite decomposed PFOA mainly through decarboxylation and formation of unstable alcohol.

To solve security issues arising from multiple attackable vulnerabilities and key leakage in BACnet/IP authentication, a security-enhanced BACnet/IP-SA protocol authentication scheme was proposed.By analyzing the authentication message flow model of the protocol and modeling it using colored Petri net theory and CPN Tools, vulnerabilities in the security of BACnet/IP were identified.An improvement scheme was proposed based on the Dolev-Yao attacker model and formal analysis method.The BACnet/IP-SA protocol utilized the device’s pseudo-identity to safeguard the actual identity information.It emploied the PUF response for authentication and verified the authenticity of the counterparty’s identity.The session key was generated through the authentication value of the multi-information set.The protocol’s security was demonstrated by combining BAN logic and non-formal methods.The experimental results indicate that the proposed scheme can effectively resist security threats from multi-class attacks and key leakage, enhancing the security of the protocol authentication while reducing computational overhead.

Pedestrian positioning system (PPS) using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare, emergency rescue, soldier positioning, etc. The performance of traditional PPS is limited by the cumulative error of inertial sensors, complex motion modes of pedestrians, and the low robustness of the multi-sensor collaboration structure. This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging (H-PPS). A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node, and enhanced by a novel ellipsoid constraint model. In addition, a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes, which effectively reduces the cumulative error originating from inertial sensors. Finally, a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator, and multi-level constraints are extracted to further improve the performance of the overall system. Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

Abstract Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This poses a threat to the property security of users and hinders the healthy development of the entire blockchain community. While numerous studies have been conducted on identifying cryptocurrency phishing users, there is a lack of research that integrates class imbalance and transaction time characteristics. This paper introduces a novel graph neural network-based account identification model called CT-GCN+, which utilizes blockchain cryptocurrency phishing data. It incorporates an imbalanced data processing module for graphs to consider cryptocurrency transaction time. The model initially extracts time characteristics from the transaction graph using LSTM and Attention mechanisms. These time characteristics are then fused with underlying features, which are subsequently inputted into a combined SMOTE and GCN model for phishing user classification. Experimental results demonstrate that the CT-GCN+ model achieves a phishing user identification accuracy of 97.22% and a phishing user identification area under the curve of 96.67%. This paper presents a valuable approach to phishing detection research within the blockchain and cryptocurrency ecosystems.

Abstract Industrial data trading can considerably enhance the economic and social value of abundant data resources. However, traditional data trading models are plagued by critical flaws in fairness, security, privacy and regulation. To tackle the above issues, we first proposed a distributed industrial data trading architecture based on blockchain and cloud for multiple data owners. Subsequently, we realized implemented distributed identity management by the distributed verifiable credentials scheme that possesses the desirable properties, i.e., selective disclosure, multi-show unlinkability, threshold traceability, and public verifiability. Finally, we presented a fair trading mechanism without trusted third parties based on smart contracts, and we employed blockchain and multi-signature to ensure data integrity during data storage and trading. The security and performance analysis shows that our proposal is feasible for sensitive data trading for multiple data owners and provides a useful exploration for future industrial data trading and management.

Presently, with the increase in the operating speed of a train, the off-line phenomenon of its pantograph and the contact wire occurs intermittently, which affects the quality of its receiving current. In this paper, a distributed parameter-based Habedank arc model for a fully parallel AT power supply network is developed to examine the electrical characteristics under the typical operating conditions, including the normal travel and entry/exit stations. The incorporation of the train acceleration parameter facilitates further enhancement of the Habedank arc model and the equivalent dynamic resistance, also validating them. The results show that the off-line discharge arc voltage of the train pantograph and the locomotive voltage waveform during the overcurrent phase separation interval show a decreasing and increasing trend respectively when entering and leaving the station. Due to the nonlinear dynamic characteristics of the arc, the overall waveform contains many harmonics. The advantage of this model is that it is a simple, rapid, and highly accurate method for constructing an off-line discharge circuit model of the pantograph and catenary (PAC) under the working conditions. This paper introduces a novel approach to studying the electrical characteristics of the PAC discharge model with distributed parameters in a fully parallel AT power supply network. Also, it considers the effects of the operational conditions of the train, such as the entering and leaving stations, on the discharge characteristics. The research results of this paper can provide a reference for the identification of the electrical characteristics and protection of a PAC equipment during the operation of the train under the working conditions.

The off-line discharge between the pantograph and catenary (PAC) occurs frequently in the actual operation of an electric locomotive, which affects the quality of the received current and the service life of the pantograph. The current experiments on the off-line discharge between the PAC are mainly low-power ones and the models established are static. To study the electrical behaviors during the PAC relative motion under the actual working conditions, in this paper, the voltage and current of the contact wire (CW) during its motion are measured. And the electrical behaviors during the rise, fall and reciprocating motions of the pantograph are investigated by combining a finite element method (FEM) model based on the magnetohydrodynamics with the auto transformer (AT) traction network circuit and moving mesh method. It is found that the harmonic frequencies are mainly in the range of 0-30 MHz. The voltage distortion is more obvious during the fall of the pantograph. And the negative polarity spike pulse has a larger amplitude and occurs more frequently. The novelty of this paper lies in the experimental test with the high current and high power, as well as the systematic analysis of the electrical behaviors and multi-physical field evolution during the motion of the pantograph under the working conditions. Its contribution is to present an effective method to study the dynamic behaviors of the PAC. The research results can provide an important reference for the identification of the electrical characteristics of the PAC and the protection of the PAC equipment.

The flavour of new make spirit (NMS) plays an important role in whisky, which is the final stage before maturation. However, less research has been done on NMS aromas. The aim of this study was to identify aroma compounds that contribute to the sensory properties of NMS and to provide theoretical support for the aroma of NMS prior to maturation. The aromas of three NMS were characterized and evaluated through sensory evaluation experiments. All the samples were produced from different provinces in China. The results of the sensory evaluation showed that sample A (Hunan) had a strong oily aroma, sample B (Sichuan) had a more pronounced sweet aroma, and sample C (Shandong) had a distinctive sulfury flavor and a strong grainy aroma. Headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS) was used to detect the volatile compounds in the three NMS. The study established a vocabulary to describe the aroma attributes of NMS, which can be categorized into floral (rose, violets, ylang-ylang, jasmine); sweet (honey, burnt sweet); grainy (bake, malt); fruity (banana, citrus, apple, pineapple); oily (fat, nutty); grassy; feinty; sulfury categories. This study will provide a theoretical basis for future research on the aroma of NMS and the control of NMS flavor.

In this paper, the microstructure and mechanical properties at the interface of Incoloy825/X65 bimetallic composite pipe achieved by explosive welding were systematically studied. Results show that a periodic wavy bonding interface was formed between the Incoloy825 and X65 due to the jets caused by the high-impact stresses during explosive welding, which was beneficial for improving the bonding strength of the Incoloy825 and X65. Atomic diffusion and localized melting occurred near the interface during explosive welding. Some fine crystal grains were generated in the vicinity of the interface due to the strong plastic deformation and dynamic recrystallization, whereas some elongated grains growing in the perpendicular direction to the interface were found in the wider localized melted zone. Room temperature nanoindentation test showed that due to the existence of fine grains resulting from the recrystallization at the interface, the hardness of the interface was higher than that of the base metals. Voids and defects were prone to appearing in the vortex regions near the interface, where the weak point in the interface was located.

Abstract Background The neurological symptoms caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of increasing concern. Convulsions are among the main neurological manifestations reported in children with coronavirus disease-2019 (COVID-19), and cause serious harm to physical and mental health. This study aimed to investigate the risk factors for convulsion in children with COVID-19. Methods This prospective study was conducted at the Children’s Hospital of Soochow University. In total, 102 COVID-19 patients with convulsion, 172 COVID-19 patients without convulsion, and 50 healthy controls were enrolled in the study. The children’s clinical and laboratory data were analyzed to assess the risk factors for convulsion in COVID-19 patients. Results Convulsions occurred in 37.2% of children, mostly those aged 1–3 years, who were hospitalized with the Omicron variant. The neutrophil count, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume-to-platelet ratio (MPR) were significantly higher in the convulsion group than those in the non-convulsion and control groups (P

Abstract Background Patients with type 2 diabetes mellitus (T2DM) are commonly at high risk for developing cognitive dysfunction. Antidiabetic agents might be repurposed for targeting cognitive dysfunction in addition to modulation on glucose homeostasis. This study aimed to evaluate the impact of dipeptidyl peptidase-4 inhibitors (DPP-4i) on cognitive function in T2DM. Methods PubMed, Embase, Cochrane Library and Web of Science were systematically searched from inception to September 30, 2023. Weighted mean differences were calculated using the Mantel-Haenszel (M-H) fixed or random effects model based on the degree of heterogeneity among studies. Heterogeneity was evaluated using a Chi-squared test and quantified with Higgins I2. Sensitivity analysis was performed with the leave-one-out method, and publication bias was evaluated according to Begg’s and Egger’s tests. Results Six clinical trials involving 5,178 participants were included in the pooled analysis. Administration of DPP-4i generally correlated with an increase of Mini-Mental State Examination (MMSE) scores (1.09, 95% CI: 0.22 to 1.96). DPP-4i alleviated cognitive impairment in the copying skill subdomain of MMSE (0.26, 95% CI: 0.12 to 0.40). Treatment with DPP-4i also resulted in an increase of Instrumental Activities of Daily Living (IADL) scores (0.82, 95% CI: 0.30 to 1.34). However, DPP-4i produced no significant effects on Barthel Activities of Daily Living (BADL) scores (0.37, 95% CI: -1.26 to 1.99) or other test scores. Conclusions DPP-4i treatment favourably improved cognitive function in patients with T2DM. Further trials with larger samples should be performed to confirm these estimates and investigate the association of different DPP-4i with cognitive function among diabetic patients. Trial registration in PROSPERO CRD42023430873.

AbstractContext Luteolin can affect multiple biological functions, such as anti-inflammatory, antioxidant and immune enhancement processes. Luteolin can inhibit inflammation of T2-high asthma, but its role in neutrophilic asthma has been insufficently studied.Objective This study determines the effect of luteolin on IL-36γ secretion-mediated MAPK pathway signalling in neutrophilic asthma.Materials and methods The asthma model was established by using ovalbumin/lipopolysaccharide (OVA/LPS). Female 6–8-week-old C57BL/6 mice were divided into control, asthma, luteolin (20 mg/kg) and asthma + luteolin (20 mg/kg) groups. To explore the mechanism of anti-inflammatory effects of luteolin in neutrophilic asthma, Beas-2B cells were treated with luteolin (20 µmol/L), LPS (100 ng/mL), recombinant human IL-36γ protein (rhIL-36γ; 100 ng/mL) or IL-36γ siRNA.Results IL-36γ secretion and MAPK/IL-1β signalling were significantly increased in the asthma mouse model compared with the control (p

Abstract A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions.

Interbasin water-transfer schemes provide an engineering solution for reconciling the conflict between water demand and availability. In the context of climate change, which brings great uncertainties to water resource distribution, interbasin water transfer plays an increasingly important role in the global water–food–energy nexus. However, the transfer of water resources simultaneously changes the hydrological regime and the characteristics of local water bodies, affecting biotic communities accordingly. Compared with high economic and technical inputs water-transfer projects require, the environmental and ecological implications of water-transfer schemes have been inadequately addressed. This work selects the largest water-transfer project in China, the South-to-North Water Diversion (SNWD) Project, to critically review its eco-environmental impacts on donor and recipient basins, as well as on regions along the diversion route. The two operated routes of the SNWD Project represent two typical water diversion approaches: The Middle Route uses an excavated canal, while the East Route connects existent river channels. An overview of the eco-environmental implications of these two routes is valuable for the design and optimization of future water-transfer megaprojects.

Protein from peony seed meal was extracted by alkali dissolution and acid precipitation. The extraction process was optimized by single factor and orthogonal experiments, and the physical and chemical properties and functional properties including isoelectric point, molecular weight, secondary structure, foaming property and foaming stability of the protein were determined. The results showed that the influencing factors on protein yield were: pH>extraction time>extraction temperature>solid-liquid ratio. The optimal extraction conditions were under the optimum conditions of material liquid ratio 1:20 g/mL, extraction temperature 70 ℃, pH9 and extraction time 45 min, the yield of protein from peony seed meal was 79.83%±1.22%. SDS-PAGE showed that there were five kinds of protein with molecular weight, two of which were between 15~25 kDa, two of which were between 35~40 kDa, and one of which was between 55~70 kDa. Fourier infrared spectroscopy showed that the protein in peony seed meal was mainly α-helix and β-folding, containing intermolecular hydrogen bonds and a small amount of carbohydrate. The scanning electron microscope showed that the protein in peony seed meal was mainly composed of β-folding composition. Compared with soybean protein, peanut protein and pea protein, peony seed meal protein had a good oil holding capacity of 4.5 g/g, which was similar to soybean protein in foaming and emulsification. This study can provide a reference for the application of peony seed meal protein in the food industry.

ObjectiveMesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level.MethodsThis study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy.ResultsWe found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome.SignificanceOur research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy.

This study explores the role of transactive memory capability as a mediator in the relationship between social media usage and job performance. Drawing on transactive memory theory, we hypothesized that individuals who use social media more intensively for task-oriented purposes and relationship building are more likely to develop transactive memory capability, which in turn enhances their job performance. A survey was conducted among 816 employees in China from various industries to collect data on their social media usage patterns, transactive memory capability, and job performance. Results from structural equation modeling indicate that social media usage has a positive impact on job performance. Additionally, transactive memory capability mediates the relationship between social media usage and job performance. This study has contribution in literature by demonstrating the beneficial effects of social media usage on the development of transactive memory capability and job performance. It is suggested that social media usage can be used a valuable tool for enhancing performance of employees. Employees should gain an understanding of how social media fosters the development of transactive memory capability to utilize it more effectively. These findings also suggest that the way individuals use social media can influence their ability to access and share knowledge within their social networks, ultimately impacting their job performance.