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Objective During the commissioning and operation process of activation circuit for FAO (fully automatic operation) trains, there are situations where the train cannot be activated, thereby affecting the operational order of the trains. Hence, it is necessary to analyze and experimentally verify the activation methods and activation circuits for trains in various scenarios, and select appropriate improvement solutions to address the unactivated train issues. Method Problems of using the driver′s cabin key signal as control condition for train activation are discussed, and the impact of single-point fault in control circuits on train activation function is analyzed and solutions for different scenarios are proposed. Result & Conclusion To address the issue of using the driver′s cabin key signal as the control condition for train activation, a mechanical interlock is established between the driver′s cabin key and the driver controller. According to the position of driver controller handle, FAO and manual operation modes are designed to control the interlocking relationship for train activation, thus resolving conflicts in train activation. To address the unactivated train problems due to single-point fault in control circuits, the activation control circuit adopts a dual-redundant design to ensure that even one circuit fails, the other circuit can still function normally, thereby enhancing the safety and reliability of the train control circuit. Through functional testing, the effectiveness of the activation methods and activation circuits for both FAO and manual operation modes is verified.

Background Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. Methods and findings We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R.sup.2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5x10.sup.-6 and 5x10.sup.-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R.sup.2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. Conclusions In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5x10.sup.-6 or 5x10.sup.-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups., Author(s): Yong Liu 1, Xiang-He Meng 2, Chong Wu 3, Kuan-Jui Su 4, Anqi Liu 4, Qing Tian 4, Lan-Juan Zhao 4, Chuan Qiu 4, Zhe Luo 4, Martha I [...]

Background: Deficits in prepulse inhibition may be a common feature in first- episode schizophrenia, bipolar disorder (BD) and major depressive disorder (MDD). We sought to explore the levels and viability of prepulse inhibition to differentiate first- episode schizophrenia, BD and MDD in patient populations. Methods: We tested patients with first- episode schizophrenia, BD or MDD and healthy controls using prepulse inhibition paradigms, namely perceived spatial co-location (PSC-PPI) and perceived spatial separation (PSS-PPI). Results: We included 53 patients with first-episode schizophrenia, 30 with BD and 25 with MDD, as well as 82 healthy controls. The PSS-PPI indicated that the levels of prepulse inhibition were smallest to largest, respectively, in the first-episode schizophrenia, BD, MDD and control groups. Relative to the healthy controls, the prepulse inhibition deficits in the first-episode schizophrenia group were significant (p < 0.001), but the prepulse inhibitions were similar between patients with BD and healthy controls, and between patients with MDD and healthy controls. The receiver operating characteristic curve analysis showed that PSS-PPI (area under the curve [AUC] 0.73, p < 0.001) and latency (AUC 0.72, p < 0.001) were significant for differentiating patients with first-episode schizophrenia or BD from healthy controls. Limitations: The demographics of the 4 groups were not ideally matched. We did not perform cognitive assessments. The possible confounding effect of medications on prepulse inhibition could not be eliminated. Conclusion: The level of prepulse inhibition among patients with first-episode schizophrenia was the lowest, with levels among patients with BD, patients with MDD and healthy controls increasingly higher. The PSS-PPI paradigm was more effective than PSC-PPI to recognize deficits in prepulse inhibition. These results provide a basis for further research on biological indicators that can assist differential diagnoses in psychosis., Introduction Patients with schizophrenia and patients with bipolar disorder (BD) overlap in clinical manifestations, physiology and genetics, (1,2) and studies on cognitive impairments, neural circuit disruptions and electrophysiologic deficits indicate [...]

Metabolic abnormalities are an important feature of tumours. The glutamine-arginine-proline axis is an important node of cancer metabolism and plays a major role in amino acid metabolism. This axis also acts as a scaffold for the synthesis of other nonessential amino acids and essential metabolites. In this paper, we briefly review (1) the glutamine addiction exhibited by tumour cells with accelerated glutamine transport and metabolism; (2) the methods regulating extracellular glutamine entry, intracellular glutamine synthesis and the fate of intracellular glutamine; (3) the glutamine, proline and arginine metabolic pathways and their interaction; and (4) the research progress in tumour therapy targeting the glutamine-arginine-proline metabolic system, with a focus on summarising the therapeutic research progress of strategies targeting of one of the key enzymes of this metabolic system, P5CS (ALDH18A1). This review provides a new basis for treatments targeting the metabolic characteristics of tumours.

Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the contents of 25 metal elements in base liquor of different ages for Maotai-flavor Baijiu from three companies. A discriminant model was constructed using chemometrics to identify key differential metal elements. The results showed that the contents of metal elements in base liquor were positively correlated with age for each brand. Principal component analysis (PCA), hierarchical cluster analysis (HCA) and partial least squares discriminant analysis (PLS-DA) could effectively distinguish among six age groups; among them, PLS-DA had the best degree of discrimination, and the results of the PLS-DA model were consistent with those of the PCA model. Five key differential metal elements, namely Al, Ca, K, Na and Fe, were determined in the base liquor stored for 0–10 years by PLS-DA and one-way analysis of variance (ANOVA). The differential metal elements were found to play a positive role in the storage of base liquor through gas chromatography triple quadrupole mass spectrometry (GC-TQ-MS) detection. The discriminant model could discriminate base liquor of different ages from other companies. In summary, ICP-MS combined with chemometric analysis can effectively classify and identify base liquor of different ages for Maotai-flavor Baijiu, and determine the changes of metal element contents in base liquor for Maotai-flavor Baijiu during storage more accurately and efficiently.

As an important mechanism in multi-agent interaction, communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents. However, the existing communication schemes can bring much timing redundancy and irrelevant messages, which seriously affects their practical application. To solve this problem, this paper proposes a targeted multi-agent communication algorithm based on state control (SCTC). The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard- and self-attention mechanisms, realizing targeted communication message processing. In addition, by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message, the correctness of the final action choice of the agent is ensured. Our evaluation using a challenging set of StarCraft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents, thus ensuring better cooperation between agents.