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The experiment was conducted to investigate the influence of dietary CuO nanoparticles (CuO NPs) on growth performance, muscle development, antioxidant capacity, and inflammatory responses of juvenile yellow catfish Pelteobagrus fulvidraco. The three diets were formulated with CuO NPs addition at 0 (without CuO NPs addition, low-Cu group, control), 9 (middle-Cu group), and 15 (high-Cu group) mg kg−1 diet, respectively, and final dietary levels for three diets were 2.07 (low-Cu group: served as a control group), 9.19 (middle-Cu group), and 13.96 mg kg−1 (high-Cu group), respectively. Each diet was randomly assigned to three replicate groups of 60 fish. The feeding experiment continued for 12 weeks. Among three dietary groups, yellow catfish juveniles fed a middle Cu diet possessed the highest growth performance, and the lowest feed utilization (P0.05). The Cu content in muscle tissue increased with increasing dietary Cu levels. CuO NPs fortification in the diets influenced mRNA of genes involved in endocytosis and metal ion transpor (P

Abstract The development of soft electronics and soft fiber devices has significantly advanced flexible and wearable technology. However, they still face the risk of damage when exposed to sharp objects in real‐life applications. Taking inspiration from nature, self‐healable materials that can restore their physical properties after external damage offer a solution to this problem. Nevertheless, large‐scale production of self‐healable fibers is currently constrained. To address this limitation, this study leverages the thermal drawing technique to create elastic and stretchable self‐healable thermoplastic polyurethane (STPU) fibers, enabling cost‐effective mass production of such functional fibers. Furthermore, despite substantial research into the mechanisms of self‐healable materials, quantifying their healing speed and time poses a persistent challenge. Thus, transmission spectra are employed as a monitoring tool to observe the real‐time self‐healing process, facilitating an in‐depth investigation into the healing kinetics and efficiency. The versatility of the fabricated self‐healable fiber extends to its ability to be doped with a wide range of functional materials, including dye molecules and magnetic microparticles, which enables modular assembly to develop distributed strain sensors and soft actuators. These achievements highlight the potential applications of self‐healable fibers that seamlessly integrate with daily lives and open up new possibilities in various industries.

Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with Cheetah Optimization Algorithm (BID-DMSFNN-COA), for classifying product color brand images as “Stylish” and “Natural”. By leveraging deep learning techniques and optimization algorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as “Stylish” and “Natural”. Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both “Natural” and “Stylish” classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for “Stylish” and 65 %–70 % for “Natural” product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.

Abstract Background Spiraea L. is a genus comprising approximately 90 species that are distributed throughout the northern temperate regions. China is recognized as the center of species diversity for this genus, hosting more than 70 species, including 47 endemic species. While Spiraea is well-known for its ornamental value, its taxonomic and phylogenetic studies have been insufficient. Results In this study, we conducted sequencing and assembly of the plastid genomes (plastomes) of 34 Asiatic Spiraea accessions (representing 27 Asiatic Spiraea species) from China and neighboring regions. The Spiraea plastid genome exhibits typical quadripartite structures and encodes 113–114 genes, including 78–79 protein-coding genes (PCGs), 30 tRNA genes, and 4 rRNA genes. Linear regression analysis revealed a significant correlation between genome size and the length of the SC region. By the sliding windows method, we identified several hypervariable hotspots within the Spiraea plastome, all of which were localized in the SC regions. Our phylogenomic analysis successfully established a robust phylogenetic framework for Spiraea, but it did not support the current defined section boundaries. Additionally, we discovered that the genus underwent diversification after the Early Oligocene (~ 30 Ma), followed by a rapid speciation process during the Pliocene and Pleistocene periods. Conclusions The plastomes of Spiraea provided us invaluable insights into its phylogenetic relationships and evolutionary history. In conjunction with plastome data, further investigations utilizing other genomes, such as the nuclear genome, are urgently needed to enhance our understanding of the evolutionary history of this genus.

Abstract Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.

BackgroundThe persistently high prevalence of insomnia seriously affects the quality of life of all populations. Studies showed that state-trait anxiety， resilience and neuroticism are related to the occurrence of insomnia， while the research on the relationship among the four factors in college students is still insufficient.ObjectiveTo discuss the impact of neuroticism on insomnia among college students， and to examine the mediating role of state-trait anxiety and resilience in the relationship between neuroticism and insomnia， thus providing references for the intervention of insomnia in college students.MethodsFrom September to December 2020， simple random sampling techniques were utilized to select 1 416 students in a university in Sichuan province， and all subjects were assessed using State-Trait Anxiety Inventory （STAI）， Connor-Davidson Resilience Scale （CD-RISC）， Chinese Big Five Personality Inventory-15 （CBF-PI-15） and Insomnia Severity Index （ISI）. Then Pearson correlation analysis was conducted to examine the correlation among the above four scales， and the mediating role of STAI and CD-RISC in the relationship between CBF-PI-15 neuroticism dimension and ISI was verified by Process macro mediation analysis.ResultsInsomnia was reported in 241/1 416 （17.02%） college students. The prevalence rate of insomnia in male students was higher than that in female students， with statistical difference （χ2=16.417， P

Abstract Background SARS-CoV-2, the pathogen of COVID-19, is a worldwide threat to human health and causes a long-term burden on the cardiovascular system. Individuals with pre-existing cardiovascular diseases are at higher risk for SARS-CoV-2 infection and tend to have a worse prognosis. However, the relevance and pathogenic mechanisms between COVID-19 and cardiovascular diseases are not yet completely comprehended. Methods Common differentially expressed genes (DEGs) were obtained in datasets of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) infected with SARS-CoV-2 and myocardial tissues from heart failure patients. Further GO and KEGG pathway analysis, protein–protein interaction (PPI) network construction, hub genes identification, immune microenvironment analysis, and drug candidate predication were performed. Then, an isoproterenol-stimulated myocardial hypertrophy cell model and a transverse aortic constriction-induced mouse heart failure model were employed to validate the expression of hub genes. Results A total of 315 up-regulated and 78 down-regulated common DEGs were identified. Functional enrichment analysis revealed mitochondrial metabolic disorders and extensive immune inflammation as the most prominent shared features of COVID-19 and cardiovascular diseases. Then, hub DEGs, as well as hub immune-related and mitochondria-related DEGs, were screened. Additionally, nine potential therapeutic agents for COVID-19-related cardiovascular diseases were proposed. Furthermore, the expression patterns of most of the hub genes related to cardiovascular diseases in the validation dataset along with cellular and mouse myocardial damage models, were consistent with the findings of bioinformatics analysis. Conclusions The study unveiled the molecular networks and signaling pathways connecting COVID-19 and cardiovascular diseases, which may provide novel targets for intervention of COVID-19-related cardiovascular diseases.

Pseudomonas koreensis is a notable colonizer that acts as a plant growth-promoting rhizobacteria and has extensive significance in soil health improvement, sustainable agricultural practices, and maintenance of rhizospheric microbial diversity. Here, we report high-quality genome information of P. koreensis FP1691, isolated from the cowpea rhizosphere. The genome was assembled into a single circular chromosome of 6.30 Mb with a G + C content of 60.23%. FP1691 was predicted to contain 5,548 genes, including 642 genes related to general function prediction only, 535 genes associated with amino acid transport and metabolism, and 119 genes linked to secondary metabolite biosynthesis, transport, and catabolism. Furthermore, there are 12 secondary metabolite gene clusters in the FP1691 genome, including a potential biosynthetic gene cluster responsible for lokisin synthesis that exhibited more than 95% identity with the known clusters. Therefore, we inferred that the synthesis of antimicrobial peptides is an important mechanism of FP1691 as an invaluable resource for biocontrol agents. [Figure: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Abstract To achieve carbon neutrality, integrating intermittent renewable energy sources, such as solar and wind energy, necessitates the use of large-scale energy storage. Among various emerging energy storage technologies, redox flow batteries are particularly promising due to their good safety, scalability, and long cycle life. In order to meet the ever-growing market demand, it is essential to enhance the power density of battery stacks to lower the capital cost. One of the key components that impact the battery performance is the flow field, which is to distribute electrolytes onto electrodes. The design principle of flow fields is to maximize the distribution uniformity of electrolytes at a minimum pumping work. This review provides an overview of the progress and perspectives in flow field design and optimization, with an emphasis on the scale-up process. The methods used to evaluate the performance of flow fields, including both experimental and numerical techniques, are summarized, and the benefits of combining diverse methods are highlighted. The review then investigates the pattern design and structure optimization of serpentine- and interdigitated-based flow fields before discussing challenges and strategies for scaling up these flow fields. Finally, the remaining challenges and the prospects for designing highly efficient flow fields for battery stacks are outlined.

Objective To investigate the effect of applying antimicrobial drugs and the timing of intervention on pregnancy outcome in patients with risk factors for threatened abortion combined with intrauterine infection. Methods A retrospective survey of 1527 patients with risk factors for threatened abortion combined with intrauterine infection from January 2018 to December 2022 in Zhengzhou Women & Infants Hospital was conducted, divided into the group with antimicrobial drugs (n=471) and the group without antimicrobial drugs (n=1 056) according to whether antimicrobial drugs were applied, and their pregnancy outcomes were observed separately. The impact of the presence of 1, 2,≥3 intrauterine infection high-risk factors on pregnancy outcomes in both groups was also observed. Results There was no statistically significant difference in the age, parity, body mass index (BMI), gestational age prolongation, postpartum infection rate of pregnant women, fetal gestational age, and birth weight between the two groups (P＞0.05). The neonatal survival rate in the antimicrobial agent group was lower than that in the non-antimicrobial agent group (61.36% vs 82.39%, χ2=78.735, P＜0.01), and the rate of complications in surviving newborns was higher in the antimicrobial agent group compared to the non-antimicrobial agent group (26.64% vs 10.57%, χ2=44.973, P＜0.01). Stratified analysis showed no statistically significant differences in neonatal survival rates and complication rates when there were 1, 2, ≥3 intrauterine infection high-risk factors in both groups (P＞0.05). Conclusion There is an overall effect of using or not using antimicrobial drugs on neonatal outcome, but there is no difference in the stratification of different numbers of risk factors.

In order to study the effects of different functional strains on alleviating protein oxidation and enhancing flavor in fermented sausage, Lactiplantibacillus plantarum NJAU-01 (LpN), with excellent antioxidant activity, L. plantarum CGMCC 18217 (Lp10), with the ability to improve the flavor of fermented sausage, and Staphylococcus saprophytic CGMCC 3475 (Ss), with high protease activity, were selected for use in a multi-strain starter culture for fermented sausage. The degree of protein degradation and oxidation in fermented sausage during fermentation and the final flavor of the product were evaluated. The results showed that the Lp10 + LpN + Ss starter culture could reduce the degree of protein oxidation in fermented sausage and increase the types and contents of volatile flavor substances, while endowing fermented sausage with better color and improving its texture characteristics, and the fermented product had high sensory scores. This study showed that the combination of Lp10, LpN and Ss could improve the quality and flavor of fermented sausage while delaying the oxidation of protein.

Many Chinese migrant older adults are more prone to mental health problems due to their “migrant” status. During the COVID-19 pandemic, restrictions on their mobility exacerbated these conditions. Mental health is a crucial dimension of healthy aging. Network analysis offers a novel method for exploring interactions between mental health problems at the symptom level. This study employs network analysis to examine the interactions between comorbid depressive and anxiety symptoms across different stages of the COVID-19 pandemic. Surveys were conducted from September 2019 to January 2020 (T1), September 2020 to January 2021 (T2), and September 2021 onwards (T3). Depression and anxiety symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9) and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Expected Influence (EI) and Bridge Expected Influence (Bridge EI) were used to identify central and bridge symptoms in the network. Network stability and accuracy tests were performed. Among the Chinese migrant older adults, the anxiety prevalence was 18.50% at T1, 21.11% at T2, and 9.38% at T3. The prevalence of depression was 26.95% at T1, 55.44% at T2, and 60.24% at T3. The primary central symptoms included ‘Afraid something will happen’ (A2), ‘Irritability’ (A6), ‘Panic’ (A7), ‘Feeling of worthlessness’ (D6), ‘Anhedonia’ (D1), and ‘Feeling of fear’ (A5). The major bridge symptoms included ‘Feeling of fear’ (A5), ‘Panic’ (A7), ‘Irritability’ (A6), ‘Fatigue’ (D4), ‘Anhedonia’ (D1), and ‘Depressed or sad mood’ (D2). Differences in network structure were observed across the periods. The network analysis further revealed the evolving relationships between central and bridge symptoms over time, highlighting the importance of targeted intervention strategies for central and bridge symptoms of comorbid depression and anxiety at different periods.

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Most biomolecular systems exhibit computation abilities, which are often achieved through complex networks such as signal transduction networks. Particularly, molecular competition in these networks can introduce crosstalk and serve as a hidden layer for cellular information processing. Despite the increasing evidence of competition contributing to efficient cellular computation, how this occurs and the extent of computational capacity it confers remain elusive. In this study, we introduced a mathematical model for molecular competition networks (MCNs) and employed a machine learning-based optimization method to explore their computational capacity. Our findings revealed that MCNs, when compared to their noncompetitive counterparts, demonstrate superior performance in both discrete decision-making and analog computation tasks. Furthermore, we highlighted the nonnegligible role of weak interactions and limited amounts of resources and examined how biological constraints influence the computational capacity of MCNs. The study suggested the potential of MCNs as efficient computational structures and provided new insights into cellular information processing.

Abstract Silicon (Si) is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal reserves. However, large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial applications. Here, we propose a multilevel carbon architecture with vertical graphene sheets (VGSs) grown on surfaces of subnanoscopically and homogeneously dispersed Si–C composite nanospheres, which are subsequently embedded into a carbon matrix (C/VGSs@Si–C). Subnanoscopic C in the Si–C nanospheres, VGSs, and carbon matrix form a three‐dimensional conductive and robust network, which significantly improves the conductivity and suppresses the volume expansion of Si, thereby boosting charge transport and improving electrode stability. The VGSs with vast exposed edges considerably increase the contact area with the carbon matrix and supply directional transport channels through the entire material, which boosts charge transport. The carbon matrix encapsulates VGSs@Si–C to decrease the specific surface area and increase tap density, thus yielding high first Coulombic efficiency and electrode compaction density. Consequently, C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode conditions. In particular, the full cells show high energy densities of 603.5 Wh kg−1 and 1685.5 Wh L−1 at 0.1 C and maintain 80.7% of the energy density at 3 C.