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In this paper, the calculation methods of the real eigenvalues and LR fuzzy eigenvectors of clear real symmetry matrices are deeply considered. The original fuzzy feature problem is extended by using the arithmetic algorithm of LR fuzzy numbers into a simple feature problem with a high-order clear real symmetry matrix. We discuss two cases: (a) λ is a non-negative unknown eigenvalue; (b) λ is a negative unknown eigenvalue. We established two computational models and proposed an algorithm for finding the fuzzy eigenvectors of the true symmetry matrix. Some numerical examples are used to illustrate our proposed method.

Atherosclerosis (AS) is one of the major catalysts of ischemic cerebrovascular disease, and the death and disease burden from AS and its cerebrovascular complications are increasing. Z-ligustilide (Z-LIG) is a key active ingredient in Angelica sinensis (Oliv.) Diels and Ligusticum chuanxiong Hort. In this paper, we first introduced LIG’s physicochemical properties and pharmacokinetics. Then, we reviewed Z-LIG’s intervention and therapeutic mechanisms on AS and its cerebrovascular complications. The mechanisms of Z-LIG intervention in AS include improving lipid metabolism, antioxidant and anti-inflammatory effects, protecting vascular endothelium, and inhibiting vascular endothelial fibrosis, pathological thickening, and plaque calcification. In ischemic cerebrovascular diseases complicated by AS, Z-LIG exerts practical neuroprotective effects in ischemic stroke (IS), transient ischemic attack (TIA), and vascular dementia (VaD) through anti-neuroinflammatory, anti-oxidation, anti-neuronal apoptosis, protection of the blood-brain barrier, promotion of mitochondrial division and angiogenesis, improvement of cholinergic activity, inhibition of astrocyte proliferation, and endoplasmic reticulum stress. This paper aims to provide a basis for subsequent studies of Z-LIG in the prevention and treatment of AS and its cerebrovascular complications and, thus, to promote the development of interventional drugs for AS.

Multiple dynamic impact signals are widely used in a variety of engineering scenarios and are difficult to identify accurately and quickly due to the signal adhesion phenomenon caused by nonlinear interference. To address this problem, an intelligent algorithm combining wavelet transforms with lightweight neural networks is proposed. First, the features of multiple impact signals are analyzed by establishing a transfer model for multiple impacts in multibody dynamical systems, and interference is suppressed using wavelet transformation. Second, a lightweight neural network, i.e., fast-activated minimal gated unit (FMGU), is elaborated for multiple impact signals, which can reduce computational complexity and improve real-time performance. Third, the experimental results show that the proposed method maintains excellent feature recognition results compared to gate recurrent unit (GRU) and long short-term memory (LSTM) networks under all test datasets with varying impact speeds, while its metrics for computational complexity are 50% lower than those of the GRU and LSTM. Therefore, the proposed method is of great practical value for weak hardware application platforms that require the accurate identification of multiple dynamic impact signals in real time.

Metal nanoparticles (NPs) are widely used in heterogeneous catalysis. Their performance in catalytic reactions is closely related to the shape, surface structure, and composition. In the past two decades, a number of in-situ experiments have observed that under real reaction conditions, the structures of metal NPs are not static, but undergo remarkable dynamic evolution due to the presence of gas molecules. Therefore, it is imminent to develop reliable theoretical models to provide accurate predictions and comprehensive understandings of the structure reconstructions and dynamic behaviors of catalysts in response to the different reactive environments. In this review, we summarize a series of progress and achievements made by first-principle-based theoretical models in analyzing the shape evolution of metal NPs and surface segregation of alloys under different gas conditions in recent years. We also discuss the understanding of the catalytic performance of NPs by considering the reaction-condition-dependent structures. In addition, the real-time dynamic simulation methods of catalysts under reaction conditions are introduced. The perspective of simulating the kinetic process of in situ structural change is provided at last.

ObjectiveThe Omicron BA.5.2 variant of SARS-CoV-2 has undergone several evolutionary adaptations, leading to multiple subvariants. Rapid and accurate characterization of these subvariants is essential for effective treatment, particularly in critically ill patients. This study leverages Next-Generation Sequencing (NGS) to elucidate the clinical and immunological features across different Omicron BA.5.2 subvariants.MethodsWe enrolled 28 patients infected with the Omicron variant, hospitalized in Zhangjiajie People’s Hospital, Hunan, China, between January 20, 2023, and March 31, 2023. Throat swabs were collected upon admission for NGS-based identification of Omicron subvariants. Clinical data, including qSOFA scores and key laboratory tests, were collated. A detailed analysis of lymphocyte subsets was conducted to ascertain the extent of immune cell damage and disease severity.ResultsPatients were infected with various Omicron subvariants, including BA.5.2.48, BA.5.2.49, BA.5.2.6, BF.7.14, DY.1, DY.2, DY.3, and DY.4. Despite having 43 identical mutation sites, each subvariant exhibited unique marker mutations. Critically ill patients demonstrated significant depletion in total lymphocyte count, T cells, CD4, CD8, B cells, and NK cells (P < 0.05). However, there were no significant differences in clinical and immunological markers across the subvariants.ConclusionThis study reveals that critically ill patients infected with different Omicron BA.5.2 subvariants experience similar levels of cellular immune dysfunction and inflammatory response. Four mutations - ORF1a:K3353R, ORF1a:L3667F, ORF1b:S997P, S:T883I showed correlation with immunological responses although this conclusion suffers from the small sample size. Our findings underscore the utility of NGS in the comprehensive assessment of infectious diseases, contributing to more effective clinical decision-making.

Abstract Information about regional‐level carbon (C) stocks in agroforestry systems (AFS), as well as the annual loss of agroforests and associated C stocks, is scarce, limiting our capacity for increasing C sequestration through establishing, retaining, and enhancing these systems. This study quantified regional‐level C stocks and the associated incremental economic value in the forest land‐use component of three common AFS (hedgerows, shelterbelts, and silvopastures), estimated the annual loss of hedgerow and silvopasture forests and the associated C, and assessed the potential to enhance C storage through the expansion of shelterbelts in central Alberta, Canada, using publicly available satellite imagery, previously collected field data and the Google Earth Engine platform. Results showed that forests in the three AFS stored 699.9 million tons (Mt) C across 9.5 million hectares (Mha) of land in central Alberta and were valued at $102.7 billion based on the 2021 Canadian C tax rate of $40 t−1 CO2‐equivalent. Silvopasture forests in the studied region had the highest C stocks, which were 14.2 and 67.2 times that found in hedgerow and shelterbelt forests, respectively. Between 2001 and 2020, forests in hedgerows and silvopastures declined at rates of 468.1 and 1957.1 ha year−1, respectively, leading to an 8.4 Mt decline in total C storage over the 20 years. However, there is potential to establish new shelterbelts at many road/field margins, which could increase C stocks by 2.3 times the current C stocks in shelterbelt forests. These results highlight the importance of retaining existing and establishing new AFS for increasing C sequestration, emphasizing the impact of agroforest loss on reducing C storage within agroecosystems. The development of policies that assist or reward landowners for providing the ecosystem service of C storage by retaining, establishing, and enhancing agroforests as part of existing agroecosystem management should be encouraged for mitigating climate change.

Abstract Background Enteromorpha prolifera (E. prolifera) polysaccharide has become a promising feed additive with a variety of physiological activities, such as anti-oxidant, anti-cancer, anti-diabetic, immunomodulatory, hypolipidemic, and cation chelating ability. However, whether Enteromorpha polysaccharide-trace element complex supplementation regulates amino acid and fatty acid metabolism in chicken is largely unknown. This study was conducted to investigate the effects of E. prolifera polysaccharide (EP)-Zn supplementation on growth performance, amino acid, and fatty acid metabolism in chicken. Methods A total of 184 one-day-old Ross-308 broiler chickens were randomly divided into two treatment groups with 8 replicates, 12 chickens per replicate, and fed either the basal diet (control group) or basal diet plus E. prolifera polysaccharide-Zinc (400 mg EP-Zn/kg diet). Results Dietary EP-Zn supplementation significantly increased (P

The present study was conducted to evaluate the effects of 3 meals administered daily with varying dietary crude protein (CP) contents on hepatic lipid metabolism with a pig model. Pigs were divided into 3 groups according to the following feeding patterns: feeding a basal CP diet 3 times daily (3C); feeding a high CP diet for breakfast, the basal CP diet for lunch, and a low CP diet for dinner (HCL); and feeding the low CP diet for breakfast, the basal CP diet for lunch, and the high protein diet for dinner (LCH). Three groups took equivalent diet per meal ensuring that every pig was fed with similar dietary formulae daily. Results showed that HCL feeding pattern reduced the relative kidney weight (P

Marine algae polysaccharides have been shown to regulate various biological activities, such as immune modulation, antioxidant, antidiabetic, and hypolipidemic. However, litter is known about the interaction of these polysaccharides with the gut microbiota. This study aimed to evaluate the effects of marine algae Enteromorpha (Ulva) prolifera polysaccharide (EP) supplementation on growth performance, immune response, and caecal microbiota of broiler chickens. A total of 200 1-day-old Ross-308 broiler chickens were randomly divided into two treatment groups with ten replications of ten chickens in each replication. The dietary treatments consisted of the control group (fed basal diet), and EP group (received diet supplemented with 400 mg EP/kg diet). Results showed that chickens fed EP exhibited significantly higher (P < 0.05) body weight and average daily gain than the chicken-fed basal diet. In addition, significantly longer villus height, shorter crypt depth, and higher villus height to crypt depth ratio were observed in the jejunal and ileal tissues of chickens fed EP. EP supplementation upregulated the mRNA expression of NF-κB, TLR4, MyD88, IL-2, IFN-α, and IL-1β in the ileal and jejunal tissues (P < 0.05). Besides, we observed significantly higher (P < 0.05) short-chain volatile fatty acids (SCFAs) levels in the caecal contents of the EP group than in the control group. Furthermore, 16S-rRNA analysis revealed that EP supplementation altered gut microbiota and caused an abundance shift at the phylum and genus level in broiler chicken. Interestingly, we observed an association between microbiota and SCFAs production. Overall, this study demonstrated that supplementation of diet with EP promotes growth performance, improves intestinal immune response and integrity, and modulates the caecal microbiota of broiler chickens. This study highlighted the application of marine algae polysaccharides as an antibiotic alternative for chickens. Furthermore, it provides insight to develop marine algae polysaccharide-based functional food and therapeutic agent.

Coal gangue is an inevitable product in coal mining and processing and is the most important source of pollution in mines. Vegetation restoration of coal gangue piles must consider its special site conditions. Therefore, we conducted unmanned air vehicle (UAV) temperature monitoring, field investigation and experimental analysis on spontaneous combustion coal gangue piles in Lu’an mining area. In the vegetation construction of coal gangue piles, high-temperature stress affects plant survival. The spontaneous combustion coal gangue piles have abnormal temperature, high surface temperature and few vegetation types. The plant community species diversity index (Shannon–Wiener index, Pielou’s index and Species abundance index) is small, the plant community is single and the plant diversity is low. Spontaneous combustion of coal gangue leads to soil acidification, reducing soil water content, soil organic carbon (SOM), available nitrogen (AN), available potassium (AK) and available phosphorus (AP). These factors are single or interactive in plants and have an impact on plant survival and growth. The research results are of great significance to the vegetation restoration of spontaneous combustion coal gangue piles, ecological reconstruction and the improvement of the ecological environment of coal mine areas.

Objective: The Omicron BA.5.2 variant of SARS-CoV-2 has undergone several evolutionary adaptations, leading to multiple subvariants. Rapid and accurate characterization of these subvariants is essential for effective treatment, particularly in critically ill patients. This study leverages Next-Generation Sequencing (NGS) to elucidate the clinical and immunological features across different Omicron BA.5.2 subvariants. Methods: We enrolled 28 patients infected with the Omicron variant, hospitalized in Zhangjiajie People's Hospital, Hunan, China, between January 20, 2023, and March 31, 2023. Throat swabs were collected upon admission for NGS-based identification of Omicron subvariants. Clinical data, including qSOFA scores and key laboratory tests, were collated. A detailed analysis of lymphocyte subsets was conducted to ascertain the extent of immune cell damage and disease severity. Results: Patients were infected with various Omicron subvariants, including BA.5.2.48, BA.5.2.49, BA.5.2.6, BF.7.14, DY.1, DY.2, DY.3, and DY.4. Despite having 43 identical mutation sites, each subvariant exhibited unique marker mutations. Critically ill patients demonstrated significant depletion in total lymphocyte count, T cells, CD4, CD8, B cells, and NK cells (P < 0.05). However, there were no significant differences in clinical and immunological markers across the subvariants. Conclusion: This study reveals that critically ill patients infected with different Omicron BA.5.2 subvariants experience similar levels of cellular immune dysfunction and inflammatory response. Four mutations - ORF1a:K3353R, ORF1a: L3667F, ORF1b:S997P, S:T883I showed correlation with immunological responses although this conclusion suffers from the small sample size. Our findings underscore the utility of NGS in the comprehensive assessment of infectious diseases, contributing to more effective clinical decision-making. [ABSTRACT FROM AUTHOR]