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Background and aimPathological changes in the central nervous system (CNS) begin before the clinical symptoms of Alzheimer’s Disease (AD) manifest, with the hippocampus being one of the first affected structures. Current treatments fail to alter AD progression. Traditional Chinese medicine (TCM) has shown potential in improving AD pathology through multi-target mechanisms. This study investigates pathological changes in AD hippocampal tissue and explores TCM active components that may alleviate these changes.MethodsGSE5281 and GSE173955 datasets were downloaded from GEO and normalized to identify differentially expressed genes (DEGs). Key functional modules and hub genes were analyzed using Cytoscape and R. Active TCM components were identified from literature and the Pharmacopoeia of the People’s Republic of China. Enrichment analyses were performed on target genes overlapping with DEGs.ResultFrom the datasets, 76 upregulated and 363 downregulated genes were identified. Hub genes included SLAMF, CD34, ELN (upregulated) and ATP5F1B, VDAC1, VDAC2, HSPA8, ATP5F1C, PDHA1, UBB, SNCA, YWHAZ, PGK1 (downregulated). Literature review identified 33 active components from 23 herbal medicines. Target gene enrichment and analysis were performed for six components: dihydroartemisinin, berberine, naringenin, calycosin, echinacoside, and icariside II.ConclusionMitochondrial to synaptic vesicle dysfunction pathways were enriched in downregulated genes. Despite downregulation, UBB and SNCA proteins accumulate in AD brains. TCM studies suggest curcumin and echinacoside may improve hippocampal pathology and cognitive impairment in AD. Further investigation into their mechanisms is needed.

The effects of 200 keV and 10 MeV electron-irradiation accelerators on the eradication of lasioderma serricorne and bacteria in tobacco and on the quality of tobacco leaf are investigated. By controlling the parameters of electron-beam irradiation, all lasioderma serricorne and bacteria inside tobacco can be eradicated efficiently and rapidly. The irradiated tobacco exhibits good retention based on a validation experiment, and no colonies are detected within 38 d. The irradiation has no significant effects on the basic physical and chemical indexes or the organic-acid and mineral-element contents of tobacco. Excessive energy levels reduce the volatilization loss of neophytadiene in tobacco from 589.91 to 418.64 μg/g, which indicates that the aroma quality of the tobacco deteriorates to a certain extent. Fluka simulation results show that a 3 MeV electron beam is sufficient to penetrate a full box of stored tobacco at existing tobacco stacking densities.

The electrocatalytic CO2 reduction reaction (CO2RR) can not only alleviate the negative effects caused by excessive CO2, but also produce the carbon-containing fuels to alleviate energy shortages. However, the reactive paths of CO2RR are relatively complicated, and the problems such as low selectivity, low current density and poor stability exist. It is urgent to develop efficient and inexpensive catalysts to promote its development. Ultra-thin materials have the advantages of large specific surface area, fully exposed active sites, accelerated kinetic mass transfer, and adjustable electronic structure. They are expected to break the bottleneck of CO2RR, thus receiving widespread attention. Here, the synthesis and application of ultra-thin materials in the past four years in electrocatalytic CO2RR to produce liquid fuels (formic acid, methanol, acetic acid) were briefly summarized. The advantages of ultra-thin materials over bulk materials and their influence on catalytic activity, selectivity and reaction paths were discussed. Also, some suggestions for future development trends, including the synthesis methodology of ultra-thin materials, their potential as supports, mechanism analysis and machine learning were put forward.

As a global issue, peer victimization is closely associated with adolescent mental health. Although previous research has explored the relationship between peer victimization and mental health in some contexts, the school context, particularly within the Chinese cultural context, has not received sufficient attention. Based on the Healthy Context Paradox, this study aimed to explore the moderating role of school-level victimization in the relationship between individual-level peer victimization and mental health. This study tested two hypotheses by using a multilevel design: higher individual-level and school-level peer victimization are associated with higher depressive symptoms and lower life satisfaction (Hypothesis 1); school-level victimization moderates the association between individual-level peer victimization and mental health (Hypothesis 2). Participants were 39,720 adolescents (50.41% females; M age = 13.68, SD = 2.39) across 292 Chinese schools. They completed a set of questionnaires, including the Center for Epidemiologic Studies Depression Scale, the single-item Life Satisfaction Questionnaire, the Peer Victimization Scale, and demographics. The multilevel model indicated that both individual-level and school-level peer victimization were positively correlated with depressive symptoms and negatively correlated with life satisfaction. In schools with lower levels of victimization, there was a stronger association between individual peer victimization and adolescent mental health. A potential explanation for these results might be the victimization visibility and perceived severity in different contexts. These findings extended the discussion of the Healthy Context Paradox within the Chinese school context and provided valuable insights for developing school support strategies for victimized adolescents. School management might play a significant role in affecting the mental health of victimized adolescents. [ABSTRACT FROM AUTHOR]

Federated learning is a widely applied distributed machine learning method that effectively protects client privacy by sharing and computing model parameters on the server side, thus avoiding the transfer of data to third parties. However, information such as model weights can still be analyzed or attacked, leading to potential privacy breaches. Traditional federated learning methods often disturb models by adding Gaussian or Laplacian noise, but under smaller privacy budgets, the large variance of the noise adversely affects model accuracy. To address this issue, this paper proposes a Symmetric Partition Mechanism (SPM), which probabilistically perturbs the sign of local model weight parameters before model aggregation. This mechanism satisfies strict ϵ -differential privacy, while introducing a variance constraint mechanism that effectively reduces the impact of noise interference on model performance. Compared with traditional methods, SPM generates smaller variance under the same privacy budget, thereby improving model accuracy and being applicable to scenarios with varying numbers of clients. Through theoretical analysis and experimental validation on multiple datasets, this paper demonstrates the effectiveness and privacy-protection capabilities of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Data security and user privacy concerns are receiving increasing attention. Federated learning models based on differential privacy offer a distributed machine learning framework that protects data privacy. However, the noise introduced by the differential privacy mechanism may affect the model's usability, especially when reasonable gradient clipping is absent. Fluctuations in the gradients can lead to issues like gradient explosion, compromising training stability and potentially leaking privacy. Therefore, gradient clipping has become a crucial method for protecting both model performance and data privacy. To balance privacy protection and model performance, we propose the Adaptive Weight-Based Differential Privacy Federated Learning (AWDP-FL) framework, which processes model gradient parameters at the neural network layer level. First, by designing and recording the change trends of two-layer historical gradient sequences, we analyze and predict gradient variations in the current iteration and calculate the corresponding weight values. Then, based on these weights, we perform adaptive gradient clipping for each data point in each training batch, which is followed by gradient momentum updates based on the third moment. Before uploading the parameters, Gaussian noise is added to protect privacy while maintaining model accuracy. Theoretical analysis and experimental results validate the effectiveness of this framework under strong privacy constraints. [ABSTRACT FROM AUTHOR]

Objective is to examine the background of IAS and the variations in drugs that trigger it among patients who have been genetically tested, aiming to deepen our understanding of this condition. HLA Analysis of 68 cases showed that DR4 is predominant, especially in individuals of East Asian descent, notably in DRB1 *0406. Methimazole was the primary drug associated with IAS in these populations, while in Caucasian individuals, the emphasis was on DRB1 *0403, with lipoic acid being the common inducer. The key factor determining disease risk is the combination of chromosomal allele variations, with HLA class II allele DR4 positive patients showing a strong association with DQA1 *0301/DQB1 *0302. [ABSTRACT FROM AUTHOR]

Creating a diverse dipolar microenvironment around the active site is of great significance for the targeted induction of intermediate behaviors to achieve complicated chemical transformations. Herein, an efficient and general strategy is reported to construct hypercross‐linked polymers (HCPs) equipped with tunable dipolar microenvironments by knitting arene monomers together with dipolar functional groups into porous network skeletons. Benefiting from the electron beam irradiation modification technique, the catalytic sites are anchored in an efficient way in the vicinity of the microenvironment, which effectively facilitates the processing of the reactants delivered to the catalytic sites. By varying the composition of the microenvironment scaffold structure, the contact and interaction behavior with the reaction participants can be tuned, thereby affecting the catalytic activity and selectivity. As a result, the framework catalysts produced in this way exhibit excellent catalytic performance in the synthesis of glycinate esters and indole derivatives. This manipulation is reminiscent of enzymatic catalysis, which adjusts the internal polarity environment and controls the output of products by altering the scaffold structure. [ABSTRACT FROM AUTHOR]

Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station. [ABSTRACT FROM AUTHOR]

The Affective Reactivity Index (ARI) is one of the most studied scales for assessing youth irritability, but little is known about its measurement performance in community populations. This study applied item response theory (IRT), network analysis, and classical test theory (CTT) to examine the psychometric properties of the ARI in a sample of n = 395 community-based children (M age = 13.44, SD = 2.51) and n = 403 parents. In this sample, the ARI demonstrated good reliability, as well as convergent and concurrent validity. The one-factor structure was supported by both confirmatory factor analysis (CFA) and network analysis. IRT analysis revealed that the ARI effectively distinguished between various levels of irritability within the community population. Network analysis identified "Loses temper easily,""Gets angry frequently," and "Often loses temper" are central aspects of irritability. The findings support the ARI as a brief, reliable, and valid instrument to assess irritability in community children and adolescents. [ABSTRACT FROM AUTHOR]

The accurate and sensitive determination of tyrosine is of paramount value. In this work, we reported the synthesis and characterization of a new nanohybrid composed of silver nanoparticles and hollow carbon nanospheres (Ag/HMCS) for the amperometric sensing of tyrosine. The Ag/HMCS nanohybrid was used to modify electrodes and to fabricate an amperometric sensor for tyrosine at a working potential of 450 mV in aqueous 0.1 M phosphate buffer at pH 7.0. Under optimal conditions, the sensor determined tyrosine from 0.08 to 100 μM with a detection limit of 0.02 μM. [ABSTRACT FROM AUTHOR]