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Waste tires are known as “black pollution”, which is difficult to degrade. The safe handling and recycling of waste tires have always been the focus of and difficulty for the global rubber industry. Pyrolysis can not only solve the problem of environmental pollution but also completely treat the waste tires and recover valuable pyrolysis products. This paper summarizes research progress on the pyrolysis of waste tires, including the pyrolysis mechanism; the important factors affecting the pyrolysis of waste tires (pyrolysis temperature and catalysts); and the composition, properties, and applications of the three kinds of pyrolysis products. The composition and yield of pyrolysis products can be regulated by pyrolysis temperature and catalysts, and pyrolysis products can be well used in many industrial occasions after different forms of post-treatment.

Organic acids account for around 3% of the dry matter in tea leaves, and their composition and contents vary in different types of tea. They participate in the metabolism of tea plants, regulate nutrient absorption and growth, and contribute to the aroma and taste quality of tea. Compared with other secondary metabolites in tea, the researches on organic acids are still limited. This article reviewed the research progresses of organic acids in tea, including analysis methods, the root secretion and physiological function, the composition of organic acids in tea leaves and related influencing factors, the contribution of organic acids to sensory quality, and the health benefits, such as antioxidation, promotion of digestion and absorption, acceleration of gastrointestinal transit, and regulation of intestinal flora. It is hoped to provide references for related research on organic acids from tea.

In this paper, a novel robust distributed consensus control scheme based on event-triggered adaptive sliding mode control is proposed for multiagent systems with unknown disturbances in a leader-follower framework. First, an adaptive multivariate disturbance observer is utilized to compensate for the disturbance of each agent. Next, a distributed consensus control protocol is constructed via integral sliding mode control, in which a novel adaptive law is designed for the switching gain to overcome the unknown perturbations. An event-triggered strategy is designed to update the control input. Furthermore, the feasibility of the proposed scheme is rigorously analyzed by Lyapunov theory, and a lower bound expression for the inter-event time is derived to guarantee that Zeno behavior can be excluded. The proposed nonlinear consensus algorithm is remarkable in that it does not require any information about the bounds of the disturbances. Finally, compared with existing methods, the proposed algorithm is validated through detailed numerical simulations. In addition, the proposed algorithm is applied to a group of UAVs in this paper, and the results show that it has more application value.

The surface defect detection of industrial products has become a crucial link in industrial manufacturing. It has a series of chain effects on the control of product quality, the safety of the subsequent use of products, the reputation of products, and production efficiency. However, in actual production, it is often difficult to collect defect image samples. Without a sufficient number of defect image samples, training defect detection models is difficult to achieve. In this paper, a defect image generation method DG-GAN is proposed for defect detection. Based on the idea of the progressive generative adversarial, D2 adversarial loss function, cyclic consistency loss function, a data augmentation module, and a self-attention mechanism are introduced to improve the training stability and generative ability of the network. The DG-GAN method can generate high-quality and high-diversity surface defect images. The surface defect image generated by the model can be used to train the defect detection model and improve the convergence stability and detection accuracy of the defect detection model. Validation was performed on two data sets. Compared to the previous methods, the FID score of the generated defect images was significantly reduced (mean reductions of 16.17 and 20.06, respectively). The YOLOX detection accuracy was significantly improved with the increase in generated defect images (the highest increases were 6.1% and 20.4%, respectively). Experimental results showed that the DG-GAN model is effective in surface defect detection tasks.

Dysfunction of microglia is known to play an important role in Alzheimer’s disease (AD). Here, we investigated the role of RIPK1 in microglia mediating the pathogenesis of AD. RIPK1 is highly expressed by microglial cells in human AD brains. Using the amyloid precursor protein (APP)/presenilin 1 (PS1) transgenic mouse model, we found that inhibition of RIPK1, using both pharmacological and genetic means, reduced amyloid burden, the levels of inflammatory cytokines, and memory deficits. Furthermore, inhibition of RIPK1 promoted microglial degradation of Aβ in vitro. We characterized the transcriptional profiles of adultmicroglia from APP/PS1mice and identified a role for RIPK1 in regulating the microglial expression of CH25H and Cst7, a marker for disease-associated microglia (DAM), which encodes an endosomal/lysosomal cathepsin inhibitor named Cystatin F. We present evidence that RIPK1-mediated induction of Cst7 leads to an impairment in the lysosomal pathway. These data suggest that RIPK1 may mediate a critical checkpoint in the transition to the DAM state. Together, our study highlights a non-cell death mechanism by which the activation of RIPK1 mediates the induction of a DAM phenotype, including an inflammatory response and a reduction in phagocytic activity, and connects RIPK1-mediated transcription in microglia to the etiology of AD. Our results support that RIPK1 is an important therapeutic target for the treatment of AD.

Ferroptosis, a form of iron-dependent cell death driven by cellular metabolism and iron-dependent lipid peroxidation, has been implicated as a tumor-suppressor function for cancer therapy. Recent advance revealed that the sensitivity to ferroptosis is tightly linked to numerous biological processes, including metabolism of amino acid and the biosynthesis of glutathione. Here, by using a high-throughput CRISPR/Cas9-based genetic screen in HepG2 hepatocellular carcinoma cells to search for metabolic proteins inhibiting ferroptosis, we identified a branched-chain amino acid aminotransferase 2 (BCAT2) as a novel suppressor of ferroptosis. Mechanistically, ferroptosis inducers (erastin, sorafenib, and sulfasalazine) activated AMPK/SREBP1 signaling pathway through iron-dependent ferritinophagy, which in turn inhibited BCAT2 transcription. We further confirmed that BCAT2 as the key enzyme mediating the metabolism of sulfur amino acid, regulated intracellular glutamate level, whose activation by ectopic expression specifically antagonize system Xc – inhibition and protected liver and pancreatic cancer cells from ferroptosis in vitro and in vivo. On the contrary, direct inhibition of BCAT2 by RNA interference, or indirect inhibition by blocking system Xc – activity, triggers ferroptosis. Finally, our results demonstrate the synergistic effect of sorafenib and sulfasalazine in downregulating BCAT2 expression and dictating ferroptotic death, where BCAT2 can also be used to predict the responsiveness of cancer cells to ferroptosis-inducing therapies. Collectively, these findings identify a novel role of BCAT2 in ferroptosis, suggesting a potential therapeutic strategy for overcoming sorafenib resistance.

Objectives We aimed to develop a model for predicting the 4-year risk of knee osteoarthritis (KOA) based on survey data obtained via a random, nationwide sample of Chinese individuals. Methods Data was analyzed from 8193 middle-aged and older adults included in the China Health and Retirement Longitudinal Study (CHARLS). The incident of symptomatic KOA was defined as participants who were free of symptomatic KOA at baseline (CHARLS2011) and diagnosed with symptomatic KOA at the 4-year follow-up (CHARLS2015). The effects of potential predictors on the incident of KOA were estimated using logistic regression models and the final model was internally validated using the bootstrapping technique. Model performance was assessed based on discrimination—area under the receiver operating characteristic curve (AUC)—and calibration. Results A total of 815 incidents of KOA were identified at the 4-year follow-up, resulting in a cumulative incidence of approximately 9.95%. The final multivariable model included age, sex, waist circumference, residential area, difficulty with activities of daily living (ADLs)/instrumental activities of daily living (IADLs), history of hip fracture, depressive symptoms, number of chronic comorbidities, self-rated health status, and level of moderate physical activity (MPA). The risk model showed good discrimination with AUC = 0.719 (95% confidence interval [CI] 0.700–0.737) and optimism-corrected AUC = 0.712 after bootstrap validation. A satisfactory agreement was observed between the observed and predicted probability of incident symptomatic KOA. And a simple clinical score model was developed for quantifying the risk of KOA. Conclusion Our prediction model may aid the early identification of individuals at the greatest risk of developing KOA within 4 years.

Mutations in the optineurin (OPTN) gene have been implicated in both familial and sporadic amyotrophic lateral sclerosis (ALS). However, the role of this protein in the central nervous system (CNS) and how it may contribute to ALS pathology are unclear. Here, we found that optineurin actively suppressed receptor-interacting kinase 1 (RIPK1)–dependent signaling by regulating its turnover. Loss of OPTN led to progressive dysmyelination and axonal degeneration through engagement of necroptotic machinery in the CNS, including RIPK1, RIPK3, and mixed lineage kinase domain–like protein (MLKL). Furthermore, RIPK1- and RIPK3-mediated axonal pathology was commonly observed in SOD1G93A transgenic mice and pathological samples from human ALS patients. Thus, RIPK1 and RIPK3 play a critical role in mediating progressive axonal degeneration. Furthermore, inhibiting RIPK1 kinase may provide an axonal protective strategy for the treatment of ALS and other human degenerative diseases characterized by axonal degeneration.

Radio Frequency Fingerprint Identification (RFFI) is a promising device authentication technique that utilizes inherent hardware flaws in transmitters to achieve device identification, thus effectively maintaining the security of the Internet of Things (IoT). However, time-varying channels degrade accuracy due to factors like device aging and environmental changes. To address this, we propose an RFFI method integrating Zero-Forcing (ZF) equalization and KAN-ResNet. Firstly, the Wi-Fi preamble signals under the IEEE 802.11 standard are Zero-Forcing equalized, so as to effectively reduce the interference of time-varying channels on RFFI. We then design a novel residual network, KAN-ResNet, which adds a KAN module on top of the traditional fully connected layer. The module combines the B-spline basis function and the traditional activation function Sigmoid Linear Unit (SiLU) to realize the nonlinear mapping of the complex function, which enhance the classification ability of the network for RFF features. In addition, to improve the generalization of the model, the grid of B-splines is dynamically updated and L1 regularization is introduced. Experiments show that on datasets collected 20 days apart, our method achieves 99.4% accuracy, reducing the error rate from 6.3% to 0.6%, outperforming existing models.

Background Local adaptation drives complex traits in domestic animals, but the roles of positive and balancing selection in Chinese indigenous pig breeds remain unclear. We performed whole-genome resequencing of 79 Yangxin pigs and applied positive and balance selection, integrated findings with PigQTLdb and FarmGTEx. Results In this study, we detected ~ 98 Mb of the genome under adaptive selection, mainly involving non-coding variants. Positive selection signals were concentrated in regulatory regions affected to muscle traits, whereas balancing selection was enriched at loci associated with reproduction. The strongest overlap with regulatory elements was observed in muscle tissue. Additionally, significant enrichment of the Hippo signaling pathway was detected in testis. DNA-level annotation revealed reproductive QTL to be most overrepresented in balancing-selection regions. Notable candidate genes and pathways include VGLL3 and Hippo signaling, and oxytocin, circadian entrainment, vascular smooth muscle contraction, and adrenergic signaling. Conclusion These results underscore a regulatory basis for muscle phenotype variation, and the role of balancing selection in preserving fertility-related diversity. Our findings provide genomic markers for breeding and conservation strategies aimed at enhancing productivity in the Yangxin pig.