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In sepsis, brain dysfunction is known as Sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Our systematic review and meta-analysis aimed to explore the diagnostic and prognostic value of serum S100 calcium-binding protein B (S100B) in SAE patients. We conducted a systematic search of the databases PubMed, Web of Science, Embase, Cochrane databases, CNKI, VIP, and WFSD from their inception dates until August 20, 2022. A Meta-analysis of the included studies was also performed using Review Manager version 5.4 and Stata16.0. This meta-analysis included 28 studies with 1401 serum samples from SAE patients and 1591 serum samples from no-encephalopathy septic (NE) patients. The Meta-Analysis showed that individuals with SAE had higher serum S100B level than NE controls (MD, 0.49 [95% CI (0.37)-(0.60), Z =8.29, < 0.00001]), and the baseline level of serum S100B in septic patients with burn was significantly higher than average (1.96 [95% CI (0.92)-(2.99), Z =3.71, P < 0.0002]) In addition, septic patients with favorable outcomes had lower serum S100B levels than those with unfavorable outcomes (MD, -0.35 [95% CI (-0.50)-(-0.20), Z =4.60, < 0.00001]). Our Meta-Analysis indicates that higher serum S100B level in septic patients are moderately associated with SAE and unfavorable outcomes (The outcomes here mainly refer to the mortality). The serum S100B level may be a useful diagnostic and prognostic biomarker of SAE.

Circular RNAs (circRNAs) play important roles in gastric cancer progression but the regulatory role of circRNAs in controlling macrophage function remains elusive. Exosomes serve as cargo for circRNAs and play a crucial role as mediators in facilitating communication between cancer cells and the tumor microenvironment. In this study, we found that circATP8A1, a previously unreported circular RNA, is highly expressed in both gastric cancer tissues and exosomes derived from plasma. Increased circATP8A1 was associated with advanced TNM stage and worse prognosis in patients with gastric cancer. We showed that  the circATP8A1 knockdown significantly inhibited gastric cancer proliferation and invasion in vitro and in vivo. Functionally, exosome circATP8A1 induced the M2 polarization of macrophages through the STAT6 pathway instead of the STAT3 pathway. Mechanistically, circATP8A1 was shown to activate the STAT6 pathway through competitive binding to miR-1-3p, as confirmed by Fluorescence In Situ Hybridization (FISH), RNA immunoprecipitation, RNA pulldown, and Luciferase reporter assays. The reversal of circATP8A1-induced STAT6 pathway activation and macrophage polarization was observed upon blocking miR-1-3p. Macrophages treated with exosomes from gastric cancer cells overexpressing circATP8A1 were able to promote gastric cancer migration, while knockdown of circATP8A1 reversed these effects in vivo. In summary, exosome-derived circATP8A1 from gastric cancer cells induce macrophages M2 polarization via the circATP8A1/miR-1-3p/STAT6 axis, and tumor progression. Our results highlight circATP8A1 as a potential prognostic biomarker and therapeutic target in gastric cancer.

This study investigates interactions among morphology, word order, and syntactic directionality across 55 languages from 11 families. We quantify morphological richness (moving-average mean size of paradigm), word order flexibility (entropy), and syntactic directionality (dependency direction), linking linguistic structure to information-theoretic principles. Analyses show that morphological richness is only weakly related to word order entropy and does not provide a robust predictor after statistical correction. Rich morphology facilitates the predictability of syntactic functions. Languages with richer morphology consistently favor head-final structures, whereas minimally inflected languages lean toward head-initial patterns, indicating that syntactic directionality is more closely associated with morphological complexity than with surface word order. Overall, the findings indicate that languages maintain a balance between redundancy and flexibility in optimizing information transmission, providing quantitative evidence for efficiency-driven trade-offs in human language.

Background: Causal evidence of circulating lipids especially the remnant cholesterol with cardiovascular and cerebrovascular disease (CVD) is lacking. This research aimed to explore the causal roles of extensive lipid traits especially the remnant lipids in CVD.Methods: Two-sample Mendelian randomization (TSMR) analysis was performed based on large-scale meta-analysis datasets in European ancestry. The causal effect of 15 circulating lipid profiles including 6 conventional lipids and 9 remnant lipids on coronary heart disease (CHD) and ischemic stroke (IS), as well as the subtypes, was assessed.Results: Apolipoprotein B (Apo B), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) were still important risk factors for CHD and myocardial infarction (MI) but not for IS. Apo B is the strongest which increased the CHD and MI risk by 44% and 41%, respectively. The odds ratios (ORs) of total TG on CHD and MI were 1.25 (95% confidence interval [CI], 1.13–1.38) and 1.24 (95% CI, 1.11–1.38), respectively. A one standard deviation difference increased TG in medium very-low-density lipoproteins (M.VLDL.TG), TG in small VLDL (S.VLDL.TG), TG in very small VLDL (XS.VLDL.TG), TG in intermediate-density lipoproteins (IDL.TG), TG in very large HDL (XL.HDL.TG), and TG in small HDL (S.HDL.TG) particles also robustly increased the risk of CHD and MI by 9–28% and 9–27%, respectively. TG in very/extremely large VLDL (XXL.VLDL.TG and XL.VLDL.TG) were insignificant or even negatively associated with CHD (in multivariable TSMR), and negatively associated with IS as well.Conclusion: The remnant lipids presented heterogeneity and two-sided effects for the risk of CHD and IS that may partially rely on the particle size. The findings suggested that the remnant lipids were required to be intervened according to specific components. This research confirms the importance of remnant lipids and provides causal evidence for potential targets for intervention.

Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated listening tests, and automated oral proficiency assessments. The application of LLMs for AES in the context of non-native Japanese, however, remains limited. This study explores the potential of LLM-based AES by comparing the efficiency of different models, i.e. two conventional machine training technology-based methods (Jess and JWriter), two LLMs (GPT and BERT), and one Japanese local LLM (Open-Calm large model). To conduct the evaluation, a dataset consisting of 1400 story-writing scripts authored by learners with 12 different first languages was used. Statistical analysis revealed that GPT-4 outperforms Jess and JWriter, BERT, and the Japanese language-specific trained Open-Calm large model in terms of annotation accuracy and predicting learning levels. Furthermore, by comparing 18 different models that utilize various prompts, the study emphasized the significance of prompts in achieving accurate and reliable evaluations using LLMs.

The Grassland Ecological Compensation Policy (abbreviated as GECP), which aims to realize the ecological protection by reducing the stock-carrying capacity of pastures and promote the transformation of pasture animal husbandry by improving the herders’ breeding methods, has been a major project in China’s grassland pastoral areas and grassland ecological construction. This study, thus, sought to measure the breeding efficiency of herders before and after the implementation of GECP. Moreover, the study also thought to analyze the effect and the effecting path of the implementation of GECP on the efficiency of herders’ livestock breeding. GECP enables herders to obtain financial subsidies while minimizing the utilization of grassland, which brings challenges and opportunities to herders’ traditional livestock production. This study used the two-stage data obtained from a randomly selected sample of 449 herders in the Inner Mongolia grassland area of China in 2018. Data envelopment analysis (DEA) and parallel mediating effect (PME) models were used to analyze the data. The results show that the general effect of GECP on the breeding efficiency of herders in the Inner Mongolia is positive (P < 0.01), and the change of breeding methods (direct effect) is the main influence path. Specifically, the grassland circulation behavior (P < 0.01) and the scale of breeding (P < 0.01) are part of the mediating effect. While the mediating effect of the breeding structure is not significant (P > 0.1). This study also shows that the non-agricultural and animal husbandry income of herders has a negative impact on the breeding efficiency (P < 0.01), and herders’ age and breeding scale have a positive effect on the breeding efficiency (P<0.01). This study has not only answered the question whether the GECP can improve the efficiency of husbandry, but also focused on the analysis of the impacting mechanism of policies on efficiency. It is of great significance to further improve GECP and the related supporting policies and promote the transformation of China’s grassland animal husbandry.

The logistics industry is a derivative industry of manufacturing services extraposition. A variety of strategies to develop the manufacturing industry are important programs of action for China's manufacturing strategic power, and it is of great significance to promote the high-quality development of the logistics industry. This paper takes strong manufacturing provinces with the development of the logistics industry as the research object and applies network DEA measuring the production efficiency and service efficiency of the logistics industry from 2004 to 2017. This paper adopts the \"Made in China 2025\" strategy as a natural experiment and uses double difference to study the impact of manufacturing policies on the high-quality development of the logistics industry. The empirical results show that compared with the Reference group, the impact of the \"Made in China 2025\" strategy led to a significant increase in the production efficiency and service efficiency of the experimental group. The group-based test based on innovation type shows that independent innovation has a significant positive effect on the high-quality development of the logistics industry, which shows that from the perspective of technological innovation, independent innovation is the main path of the \"Made in China 2025\" strategy to promote the high-quality development of the logistics industry. This paper not only identifies the causal relationship between the \"Made in China 2025\" strategy and the high-quality development of the logistics industry but also helps clarify the mechanism of how manufacturing policies improve the high-quality development of the logistics industry, which has important implications for further promoting the combined development between manufacturing and logistics.

The Internet of Things has broad application in military field, commerce, environmental monitoring, and many other fields. However, the open nature of the information media and the poor deployment environment have brought great risks to the security of wireless sensor networks, seriously restricting the application of wireless sensor network. Internet of Things composed of wireless sensor network faces security threats mainly from Dos attack, replay attack, integrity attack, false routing information attack, and flooding attack. In this paper, we proposed a new intrusion detection system based on K-nearest neighbor (K-nearest neighbor, referred to as KNN below) classification algorithm in wireless sensor network. This system can separateabnormal nodes from normal nodes by observing their abnormal behaviors, and we analyse parameter selection and error rate of the intrusion detection system. The paper elaborates on the design and implementation of the detection system. This system has achieved efficient, rapid intrusion detection by improving the wireless ad hoc on-demand distance vector routing protocol (Ad hoc On-Demand Distance the Vector Routing, AODV). Finally, the test results show that: the system has high detection accuracy and speed, in accordance with the requirement of wireless sensor network intrusion detection.

Glass Fiber Reinforced Polymer (GFRP) bars and Unsaturated Polyester resin Concrete (UPC) offer superior corrosion resistance, making them viable alternatives to steel bars and traditional concrete in water-related projects. When both materials function as load-bearing components in water environment engineering, the performance of their bonding properties is of critical importance. This study examines the bond properties of GFRP bars-UPC under various aging conditions by establishing water environments at temperatures of 25 °C, 40 °C, and 60 °C. Central pull-out specimens of GFRP bars-UPC were subjected to these environments to evaluate their bond strength and bond-slip curves at various aging stages. The study reveals that the bond strength of GFRP bars-UPC diminishes as temperature and aging duration increase. Additionally, the relative slip values and residual bond stresses of aged specimens are lower compared to unaged specimens. The MBPE and continuous curve models accurately represent the bond-slip behavior of aging GFRP bars-UPC.

Inertial attitude estimation is a crucial component of many modern systems and applications. Attitude estimation from commercial-grade inertial sensors has been the subject of an abundance of research in recent years due to the proliferation of Inertial Measurement Units (IMUs) in mobile devices, such as the smartphone. Traditional methodologies involve probabilistic, iterative-state estimation; however, these approaches do not generalise well over changing motion dynamics and environmental conditions, as they require context-specific parameter tuning. In this work, we explore novel methods for attitude estimation from low-cost inertial sensors using a self-attention-based neural network, the Attformer. This paper proposes to part ways from the traditional cycle of continuous integration algorithms, and formulate it as an optimisation problem. This approach separates itself by leveraging attention operations to learn the complex patterns and dynamics associated with inertial data, allowing for the linear complexity in the dimension of the feature vector to account for these patterns. Additionally, we look at combining traditional state-of-the-art approaches with our self-attention method. These models were evaluated on entirely unseen sequences, over a range of different activities, users and devices, and compared with a recent alternate deep learning approach, the unscented Kalman filter and the iOS CoreMotion API. The inbuilt iOS had a mean angular distance from the true attitude of 117.31∘, the GRU 21.90∘, the UKF 16.38∘, the Attformer 16.28∘ and, finally, the UKF–Attformer had mean angular distance of 10.86∘. We show that this plug-and-play solution outperforms previous approaches and generalises well across different users, devices and activities.