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[Introduction] The green and low-carbon transformation and industrial restructuring under the premise of energy security is a serious challenge faced by Zhejiang Province to achieve its "carbon peaking and carbon neutrality" goals. Given the high proportion of coal-fired power generation in Zhejiang, carbon capture, utilization and storage (CCUS) technology, as an important choice to realize the low-carbon utilization of fossil energy, is vital to the "double carbon" technology system in the energy sector and industrial circle of the province. [Method] This paper presents a research review of CCUS technology from multiple aspects, including CO2 capture, transportation, utilization and storage, and introduces the development of CCUS demonstration projects in Zhejiang. This study summarizes the features of the major technologies throughout the full CCUS chain, and briefly analyzes the existing challenges amid the ongoing technological advancements. Besides, the prospect of CCUS technology in Zhejiang is explored along with corresponding suggestions. [Result] Zhejiang boasts a solid technical foundation in the field of CCUS, especially in CO2 capture, and has numerous full-chain demonstration projects employing various process routes, including pre-combustion and post-combustion capture (CO2 capture), biological, mineral, and chemical utilization (CO2 utilization), as well as geological storage (CO2 storage). However, technology readiness level differs largely across the process chain, in addition to other challenges, such as small scale, generally high initial capital and operating costs, and dispersed spatial distribution, which hinder the development of industrial clusters. [Conclusion] It is important to promote CCUS integration projects to adapt to local conditions, based on a thorough understanding of Zhejiang's potential and source/sink conditions related to CCUS in key areas. Additionally, continuous investment in technological research and development is necessary, along with guarantees in terms of incentive mechanisms, policies, regulations, and business models.

Abstract The BTB-TAZ protein is a subfamily of the BTB protein family and plays a crucial regulatory role in plant resistance to pathogen infection. However, the function of the maize BTB-TAZ protein ZmBT2a in maize resistance to pathogens has not been reported. Here, we investigate the role of ZmBT2a in maize resistance to pathogen infection by examining its expression characteristics. The results showed that the expression level of ZmBT2a changed significantly in response to biotic and abiotic stresses in maize. Moreover, ZmBT2a-OE exhibited enhanced resistance to Botrytis cinerea and Pseudomonas syringae pv. tomato DC3000, while the maize ZmBT2a mutants Zmbt2a-1 and Zmbt2a-2 showed increased sensitivity to Fusarium graminearum and Cochliobolus carbonum infections. Notably, the expression of the key synthetic genes ZmLOXs and pathogenesis-related genes ZmPRs in the JA synthesis pathway was significantly down-regulated during F. graminearum infection. Additionally, yeast two-hybrid assays confirmed the direct interaction between ZmBT2a and E3 ubiquitinated proteins ZmCUL3a and ZmCUL3b. These findings suggest that the ZmBT2a gene is crucial in maize resistance to pathogen infection. It is hypothesized that ZmBT2a binds to ZmCUL3 and, through ubiquitination, affects the transcriptional regulation of ZmLOXs and ZmPRs by downstream transcription factors, thus participating in the disease resistance process of maize.

Abstract This study was intended to investigate the macular vascular and photoreceptor changes for diabetic macular edema (DME) at the early stage. A total of 255 eyes of 134 diabetes mellitus patients were enrolled and underwent an ophthalmological and systemic evaluation in this cross-sectional study. Early DME was characterized by central subfoveal thickness (CST) value between 250 and 325 μm, intact ellipsoid zone, and an external limiting membrane. While non-DME was characterized by CST

Abstract. Background:. In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. Methods:. We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes. Results:. These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709–0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Conclusions:. Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Abstract Glioblastoma multiforme (GBM) is the most therapeutically challenging primary brain tumor owing to the unique physiological structure of the blood–brain barrier. Lately, research on targeted therapy for gliomas has shifted focus toward the tumor microenvironment and local immune responses. Pyroptosis is a newly identified cellular demise characterized by the release of numerous inflammatory factors. While pyroptosis shows promise in impeding the occurrence and progression of GBM, the regulatory mechanisms governing this process in gliomas still require further investigation. The function of the Enhancer of zeste homolog 2 (EZH2) in pyroptosis remains unexplored. In this study, we discovered that 3-Deazaneplanocin A (DZNep), an inhibitor of EZH2, can induce pyroptosis in GBM in vitro experiments. Moreover, our investigation unveiled that the signal transducer and activator of transcription (STAT3) could serve as a downstream regulator influenced by EZH2, impacting pyroptosis in GBM. Following treatment with DZNep and the STAT3 inhibitor (SH-4–54), there was an elevation in the levels of pyroptosis-related factors, namely NOD-like receptor thermal protein domain-associated protein 3 (NLRP3) and Gasdermin D (GSDMD). Moreover, simultaneous inhibition of both EZH2 and STAT3 led to the expression of inflammatory factors such as IL-1β and IL-18. In summary, we have identified that EZH2 regulates pyroptosis in GBM through STAT3, and pyroptosis could potentially be targeted for immunotherapy in GBM.

Brassica oleracea has been developed into many important crops, including cabbage, kale, cauliflower, broccoli and so on. The genome and gene annotation of cabbage (cultivar JZS), a representative morphotype of B. oleracea, has been widely used as a common reference in biological research. Although its genome assembly has been updated twice, the current gene annotation still lacks information on untranslated regions (UTRs) and alternative splicing (AS). Here, we constructed a high-quality gene annotation (JZSv3) using a full-length transcriptome acquired by nanopore sequencing, yielding a total of 59 452 genes and 75 684 transcripts. Additionally, we re-analyzed the previously reported transcriptome data related to the development of different tissues and cold response using JZSv3 as a reference, and found that 3843 out of 11 908 differentially expressed genes (DEGs) underwent AS during the development of different tissues and 309 out of 903 cold-related genes underwent AS in response to cold stress. Meanwhile, we also identified many AS genes, including BolLHCB5 and BolHSP70, that displayed distinct expression patterns within variant transcripts of the same gene, highlighting the importance of JZSv3 as a pivotal reference for AS analysis. Overall, JZSv3 provides a valuable resource for exploring gene function, especially for obtaining a deeper understanding of AS regulation mechanisms.

Abstract Background Mutations in the SLC5A7 gene cause congenital myasthenia, a rare genetic disorder. Mutation points in the SLC5A7 gene differ among individuals and encompass various genetic variations; however, exon deletion variants have yet to be reported in related cases. This study aims to explore the clinical phenotype and genetic traits of a patient with congenital myasthenic syndrome due to SLC5A7 gene variation and those of their family members. Case presentation We describe a case of a Chinese male with congenital myasthenic syndrome presenting fluctuating limb weakness. Genetic testing revealed a heterozygous deletion mutation spanning exons 1–9 in the SLC5A7 gene. QPCR confirmed a deletion in exon 9 of the SLC5A7 gene in the patient’s mother and brother. Clinical symptoms of myasthenia improved following treatment with pyridostigmine. Conclusion Exons 1, 5, and 9 of the SLC5A7 gene encode the choline transporter’s transmembrane region. Mutations in these exons can impact the stability and plasma membrane levels of the choline transporter. Thus, a heterozygous deletion in exons 1–9 of the SLC5A7 gene could be the pathogenic cause for this patient. In patients exhibiting fluctuating weakness, positive RNS, and seronegativity for myasthenia gravis antibodies, a detailed family history should be considered, and enhanced genetic testing is recommended to determine the cause.

The high-altitude, low-oxygen environment of the Qinghai-Tibet Plateau poses significant challenges for the introduction of superior livestock breeds. However, local plateau species have adapted to thrive and reproduce under these harsh conditions. Understanding the molecular mechanisms behind plateau animals’ adaptation to low-oxygen environments is essential for breeding livestock suited to high-altitude regions. Tibetan pigs, which have undergone long-term natural selection and artificial breeding, have developed the ability to survive and reproduce in hypoxic environments. In this study, we conducted whole-genome resequencing of 30 Tibetan pigs from high-altitude regions and 30 Diannan small-ear pigs from low-altitude areas, to identify candidate genes that support Tibetan pigs’ adaptation to hypoxic conditions through selection signal analysis. Additionally, we performed transcriptome sequencing on five tissues (heart, liver, spleen, lung, and bone marrow) from both Tibetan pigs and Diannan small-ear pigs to identify genes with significant differential expression between the two breeds. We then integrated the genomic and transcriptomic data by examining the expression of candidate genes identified in selection signal analysis across different tissues. The selection signal analysis identified 10 genes—HES4, ANGPT1, HIF3A, SPHK2, PCK2, RCN3, HIGD2A, DNM2, IRF9, and SRF—that were under positive selection in the Tibetan pig population and are associated with hypoxia adaptation. When combined with transcriptome data, we found that five of these genes—HIF3A, RCN3, HIGD2A, PCK2, and IRF9—exhibited differential expression. Through an integrated approach of selection signal and transcriptome analysis, we identified five key functional genes that contribute to the adaptation of Tibetan pigs to hypoxic environments. These findings offer new insights into the adaptability of plateau animals.

Abstract Bacillus subtilis is an industrially important microorganism that is often used as a microbial cell factory for the production of recombinant proteins due to its food safety, rapid growth, and powerful secretory capacity. However, the lack of data on functional genes related to recombinant protein production has hindered the further development of B. subtilis cell factories. Here, a strategy combining genome‐wide CRISPRi screening and targeted CRISPRa activation to enhance recombinant protein expression is proposed. First, a CRISPRi library covering a total of 4225 coding genes (99.7%) in the B. subtilis genome and built the corresponding high‐throughput screening methods is constructed. Twelve key genes for recombinant protein expression are identified, including targets without relevant functional annotations. Meanwhile, the transcription of recombinant protein genes by CRISPRa is up‐regulated. These screened or selected genes can be easily applied to metabolic engineering by constructing sgRNA arrays. The relationship between differential pathways and recombinant protein expression in engineered strains by transcriptome analysis is also revealed. High‐density fermentation and generalisability validation results prove the reliability of the strategy. This method can be extended to other industrial hosts to support functional gene annotation and the design of novel cell factories.

Traumatic brain injury (TBI) is a leading cause of disability and death. Thus, timely and effective secondary brain injury intervention is crucial, with potential to improve the prognosis of TBI. Oxidative stress contributes to post-traumatic secondary cognitive impairment, and the reduction of post-traumatic oxidative stress effectively enhances cognitive function. Phosphoglycerate-mutating enzyme 5 (PGAM5), a member of the phosphoglycerate transporter enzyme family, is upregulated in TBI and induces mitochondrial autophagy. This further exacerbates damage following TBI. The present study focused on the small molecule drug, LFHP-1c, which is a novel inhibitor of PGAM5. The present study used an in vivo mouse model incorporating a controlled cortical impact-induced TBI, to examine the impact of LFHP-1c on oxidative stress and cognitive function. The present study aimed to determine the impact of LFHP-1c on the PGAM5-Kelch-like ECH-associated protein 1 (KEAP1)- nuclear factor erythroid 2-related factor 2 (NRF2) ternary complex within the TBI context. Results of the present study indicated that LFHP-1c suppresses PGAM5 expression and inhibits the development of the PGAM5-KEAP1-NRF2 ternary complex, thereby promoting the release of NRF2 and KEAP1. This in turn promotes the entry of NRF2 into the nucleus following TBI, leading to increased expression of anti-oxidative stress downstream factors, such as heme oxygenase-1, glutathione peroxidase 1 and superoxide dismutase 1. In addition, LFHP-1c also released KEAP1, leading to mitochondrial Rho GTPase 2 degradation and reducing perinuclear aggregation of mitochondria in the cell, which reduced oxidative stress and ultimately improved cognitive function after TBI.

The power battery in an electric vehicle is an essential part, and the damage to the battery will have a substantial impact on its thermal characteristics. In order to improve the reliability of the air-cooled lithium-ion battery packs in the high temperature environments, this paper offers a more useful and general optimization strategy for the design of the thermal management system for the batteries which have a damaged battery. To ensure the optimal heat dissipation and prolong the battery string operation, the damaged batteries should be swapped out with the backup batteries. The relationship between the target and the design variables which was established using the quadratic polynomial response function is examined. The experimental cases are selected using the optimal Latin hypercube design approach. After optimizing through the multi-island genetic approach, the error between the simulation results and the prediction results is only 0.06 %. The maximum temperature and the maximum temperature difference of the ideal structure are reduced by 4.58 % and 28.05 %, respectively. Then, the backup battery is selected as the sole battery that has the highest temperature of the ideal structure, and each broken battery is replaced by the backup battery when the damaged position is determined. Finally, a parametric equation connecting the temperature and the flow rate is created, and the inlet flow rate is tuned to match the optimal heat dissipation state of the battery pack. The results of the battery thermal management system having a damaged battery in various situations have an important significance for adjusting the operating conditions of the cooling system for the batteries.

Aiming at two aircraft conflict scenario in the pre-tactical stage, by converting the uncertain flight trajectory of the target aircraft into a spatio-temporal trajectory under its performance constraints, a conflict detection model based on truncated normal distribution was proposed, and influencing factors affecting the overall conflict probability were analysed by numerical simulation. For the conflict scenario, nonlinear particle swarm optimisation (NPSO) algorithm was applied to solve the optimal separation configuration strategy for the ownship. The simulation results show that, in comparison to conventional PSO algorithm, the improved NPSO algorithm improves the optimal value by 14.88% and decreases the maximum velocity change by 19.84%. The simulation also shows that the algorithm can maintain the minimum interval requirements under different initial parameters, demonstrating its strong adaptability.

Abstract Strain-specific probiotics can present antioxidant activity and reduce damage caused by oxidation. Streptococcus alactolyticus strain FGM (S. alactolyticus strain FGM) isolated from the chicken cecum shows potential probiotic properties which have been previously demonstrated. However, the antioxidant properties of S. alactolyticus strain FGM remain unknown. In this view, cell-free supernatant (CFS), intact cells (IC) and intracellular extracts (CFE) of strain FGM and 3 strains of Lactobacillus (LAB) were prepared, and their scavenging capacities against DPPH, hydroxyl radicals and linoleic acid peroxidation inhibitory were compared in this study. The effects of strain FGM cell-free supernatant (FCFS) on NO production, activity of SOD and GSH-Px in RAW264.7 cells and LPS-induced RAW264.7 cells were analyzed. The metabolites in the supernatant were quantitated by N300 Quantitative Metabolome. It was shown that the physicochemical characteristics of CFS to scavenge DPPH, hydroxyl radicals, and linoleic acid peroxidation inhibitory were significantly stronger than that of IC and CFE in the strain FGM (P

Abstract Movement disorders manifest in various hereditary neurodegenerative diseases. We reported a young man who presented with progressive upper limb dystonia, spastic tetraplegia, and ataxia. Whole‐exome sequencing (WES) revealed a novel variant, c.2357A > G, in the dynamin domain of OPA1. No mtDNA deletion was detected in muscle by long‐range PCR. Atrophy and decreased glucose metabolism of the basal ganglia were discovered. Decreased mtDNA copy number, fragmented mitochondria, slightly impaired oxidative phosphorylation, and increased autophagy were detected in mutant fibroblasts. Evident oxidative phosphorylation impairment and mtDNA deletions were not involved in the pathogenicity of this mutation unlike mutations in the GTPase domain of OPA1.

Abstract Raman spectroscopy is a nondestructive and highly effective technique for analyzing biological tissues and diagnosing diseases by providing detailed spectral information about the specific molecular structures of substances. Its efficacy in these applications has been widely recognized, making it a powerful tool in the field. This article presents a comprehensive overview of the latest developments in Raman spectroscopy and its wide‐ranging applications in the diagnosis of critical diseases, such as cancer, infections, neurodegenerative diseases, and predicting surgical outcomes. It highlights the significant contributions of Raman spectroscopy in these areas, shedding light on its potential as a valuable diagnostic tool. This article delves into the advancements of Raman spectroscopy in biomedical sciences, with a specific focus on state‐of‐the‐art techniques such as surface‐enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip‐enhanced Raman spectroscopy. These techniques have shown great potential in various applications within the field. The article explores their use in ex vivo and in vivo medical diagnosis, covering topics such as sample collection, data processing, and the successful establishment of correlations between Raman spectra and biochemical information in specific diseases. Furthermore, the article discusses the limitations of the current research and offers insights into potential future directions for further exploration in the field of Raman spectroscopy in biomedical sciences.

Study regions: The Wangjiaba (WJB) watershed, located in the upper Huaihe River Basin in China. Study focus: An attributing framework has been proposed combining the Double Mass Curve (DMC) and the Soil and Water Assessment Tools (SWAT) model to identify the contributions of climate variability, Land use (LU) change, and Other Human Activities (OHA) to the variations in runoff-sediment processes within the WJB. New hydrological insights for the region: The studied period was able to be separated into three sub-periods (P1: 1981–1991, P2: 1992–2009, and P3: 2010–2019) using the DMC, and the SWAT model could simulate runoff and Sediment Yields Load (SYL) properly during different sub-periods after calibration. Generally, the runoff, SYL, and Suspended Sediment Concentration (SSC) within the WJB exhibited a decrease trend with a change rate of −1.3 mm a−1, −8.49×104 t a−1, and −0.01 kg m−3 a−1, respectively. Substantially, climate variability decreases runoff, SYL, and SSC from P1 to P3; LU change decreases runoff, SYL, and SSC from P2 to P3; OHA decreases SYL and SSC from P1 to P2, but increases SYL and SSC from P2 to P3. It should be noticed that the OHA has increased the SYL significantly especially over the downstream of WJB from P2 to P3. It is essential to enhance soil erosion prevention measures in the future under the background of global climate change.

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research domain and highlight the machine-learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin-assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM.

Multimedia technology combines various media elements such as text, images, sound, and video, making classroom teaching more vivid, intuitive, and interesting. This article introduces a multimedia courseware assisted physics teaching application based on sensor networks and deep learning technology. This application has two functional modules: user user end and cluster control end. On the user end, obtain user commands through the virtual machine end and share the original multimedia files using the CIFS protocol. The user command is redirected to the user end, and then the corresponding command is executed on the user end to directly transmit and play the original multimedia file. At the same time, the user end will also provide data feedback and recording for subsequent data analysis and evaluation. At the cluster control end, based on the information collected by the sensor network, an adaptive linear regression algorithm is used to predict the reference value. By analyzing and processing the collected information, the cluster control end can reasonably arrange the playback content of multimedia courseware to meet the learning needs of students. This multimedia courseware assisted physics teaching application based on sensor networks and deep learning technology provides new help and support for physics teaching.

BackgroundCerebral aneurysm is a high-risk cerebrovascular disease with a poor prognosis, potentially linked to multiple factors. This study aims to explore the association between mitochondrial-associated proteins and the risk of cerebral aneurysms using Mendelian randomization (MR) methods.MethodsWe used GWAS summary statistics from the IEU Open GWAS project for mitochondrial-associated proteins and from the Finnish database for cerebral aneurysms (uIA, aSAH). The association between mitochondrial-associated exposures and cerebral aneurysms was evaluated using MR-Egger, weighted mode, IVW, simple mode and weighted median methods. Reverse MR assessed reverse causal relationship, while sensitivity analyses examined heterogeneity and pleiotropy in the instrumental variables. Significant causal relationship with cerebral aneurysms were confirmed using FDR correction.ResultsThrough MR analysis, we identified six mitochondrial proteins associated with an increased risk of aSAH: AIF1 (OR: 1.394, 95% CI: 1.109–1.752, p = 0.0044), CCDC90B (OR: 1.318, 95% CI: 1.132–1.535, p = 0.0004), TIM14 (OR: 1.272, 95% CI: 1.041–1.553, p = 0.0186), NAGS (OR: 1.219, 95% CI: 1.008–1.475, p = 0.041), tRNA PusA (OR: 1.311, 95% CI: 1.096–1.569, p = 0.003), and MRM3 (OR: 1.097, 95% CI: 1.016–1.185, p = 0.0175). Among these, CCDC90B, tRNA PusA, and AIF1 demonstrated a significant causal relationship with an increased risk of aSAH (FDR q

The preparation parameters of PM6:Y6 non-fullerene organic solar cells (OSCs) have significant influence on the power conversion efficiency (PCE). Herein, machine learning (ML) models are applied for analyzing the quantitative effects of the PCE on PM6:Y6 non-fullerene OSCs from the perspective of fabrication parameters. Random Forest (RF) model has the best evaluation performance and is considered as the best model among the six different algorithms. The Pearson correlation coefficient, coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error of the test set in the RF model are 0.836, 0.668, 0.695, 0.538, and 0.035, respectively. In addition, the most important preparation parameters for PCE are the effective area of OSCs, type of solvent and solvent additives, spin-coating speed, total concentration, annealing temperature, annealing time from SHAP analysis. The optimized PCE can be obtained in PM6:Y6 non-fullerene OSCs when the effective area is between 4 and 6 mm2, the spin-coating speed is 3000 rpm, and the total concentration is 18 mg ml−1, respectively. The contour map further reflects that the optimal annealing temperature are 80 or 105 °C, and the corresponding annealing time are 5 or 9 min, respectively. Based on the above preparation conditions, we verified through the experimental design that the optimal PCE is 16.91%, and the relative error with the ML prediction is only 1.24%. Our research provides low time and cost guidance on preparation parameter matching for the development of high-performance the PM6:Y6 non-fullerene OSCs.

In environmental tort lawsuits, China has been overly focused on “punishing” violators and has neglected the value of ecological environment restoration. The Article 1234 of Civil Code of China in 2021 has provided an important institutional guideline for the restoration of ecological environment and sustainable development in China. This paper analyzes 512 cases of ecological environment restoration liability and identifies five challenges in the judicial context: the lack of sound legal regulation, the lack of liability allocation, the mismatch of liability subjects, the difficulty of identifying damage facts, and the difficulty of effective implementation of restoration. In the face of these difficulties, countries that attach importance to ecological environment restoration, such as the United States, Germany and Japan, have provided experience that can be drawn on for China's ecological environment restoration liability. Based on foreign experience, China's liability for ecological environment restoration should be improved in the following aspects: first, to improve the legal system from basic laws and specific laws; second, to expand the scope of subjects from both litigation request subjects and liability subjects; third, to improve the identification and assessment mechanism and innovate the identification and assessment procedures in accordance with China's national conditions; fourth, to determine the restoration methods based on the criterion of “utilization value and the differences in the objects. The ultimate goal is to help China's Civil Code to be better applied judicially on the one hand, and to contribute to the world ecological protection on the other.

Abstract Background The present study aimed to investigate the prevalence and molecular characterization of extended-spectrum β-lactamase (ESBL)—producing Escherichia coli (E. coli) isolated from dairy cattle with endometritis in China. The prevalence of ESBL-producing E. coli in sample was detected using ChromID ESBL agar, and genotyping of the ESBL producers was performed by PCR and DNA sequencing. Results The results revealed that the proportion of positive pathogens tested was 69.76% (180/258) in samples obtained from cows diagnosed with clinical endometritis, with E. coli accounting for 170 out of the 180 positive samples. The infection rate of isolated E. coli was 39.14% (101/258), and co-infections with other pathogens were prevalent. Furthermore, among the 158 E. coli isolates, 50 strains were identified as ESBL producers, with TEM and CTX-M prevalence rates at 78.00% and 32.00%, respectively. Drug sensitivity experiments indicated that 50 isolates of ESBL- producing E. coli were multidrug resistance (MDR), with 48.0% of them exhibiting positive results for both the class 1 integron gene and five gene cassettes associated with resistance to trimethoprim (dfr1 and dfrA17) and aminoglycosides (aadA1, aadA5, and dfrA1), respectively. Conclusion This investigation demonstrated a substantial prevalence and heightened level of antimicrobial resistance among ESBL-producing E. coli isolates derived from dairy cattle infected with endometritis in China.

This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed double closed-loop control system operates in two distinct phases: (1) The outer loop controller uses a DNN controller to replace the LMPC controller, overcoming the uncertainties in the kinematic model while reducing the computational burden. (2) The inner loop velocity controller is designed using a nonlinear model predictive control (NMPC) algorithm with its closed-loop stability proven. A DNN + ENMPC 3D trajectory tracking method is proposed, integrating a velocity threshold-triggered mechanism into the inner-loop NMPC controller to reduce computational iterations while sacrificing only a small amount of tracking control performance. Finally, simulation results indicate that compared with the ENMPC algorithm, NMPC + ENMPC can better track the desired trajectory, reduce thruster oscillations, and further minimize the computational load.

Cordyceps militaris solid medium polysaccharides (CMMPs) were extracted using an ultrasound-assisted enzyme method, and the process conditions were optimized via response surface methodology (RSM). The CMMPs were separated into four components named CMMP−1, CMMP−2, CMMP−3 and CMMP−4 using ethanol fractional precipitation, and their monosaccharide composition and structural properties were analyzed by molecular weight analysis, Fourier-transform infrared spectroscopy (FT–IR), scanning electron microscopy (SEM), Congo red test, ultraviolet–visible spectroscopy (UV-vis), atomic force microscopy (AFM), and thermogravimetric analysis (TGA). RSM could predict the yield of the CMMP (R2 = 0.9928), and the polysaccharide yield was 15.43% under the selected conditions of 3.1% cellulase enzyme addition, a liquid–solid ratio of 42:1, an extraction temperature of 61 °C, and an extraction time of 60 min. Glucose and galactose were the main constituents of the four fractional precipitated polysaccharides. Furthermore, four components exhibited antioxidant activity, and CMMP−1 demonstrated stronger antioxidant activity in vitro. This study demonstrates the possibility of developing a natural antioxidant food from Cordyceps militaris solid medium.

Abstract The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C‐X‐C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single‐cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune‐inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune‐related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune‐related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G‐related genes was developed using multi‐level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune‐active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

Sugar is an indispensable source of energy for plant growth and development, and it requires the participation of sugar transporter proteins (STPs) for crossing the hydrophobic barrier in plants. Here, we systematically identified the genes encoding sugar transporters in the genome of maize (Zea mays L.), analyzed their expression patterns under different conditions, and determined their functions in disease resistance. The results showed that the mazie sugar transporter family contained 24 members, all of which were predicted to be distributed on the cell membrane and had a highly conserved transmembrane transport domain. The tissue-specific expression of the maize sugar transporter genes was analyzed, and the expression level of these genes was found to be significantly different in different tissues. The analysis of biotic and abiotic stress data showed that the expression levels of the sugar transporter genes changed significantly under different stress factors. The expression levels of ZmSTP2 and ZmSTP20 continued to increase following Fusarium graminearum infection. By performing disease resistance analysis of zmstp2 and zmstp20 mutants, we found that after inoculation with Cochliobolus carbonum, Setosphaeria turcica, Cochliobolus heterostrophus, and F. graminearum, the lesion area of the mutants was significantly higher than that of the wild-type B73 plant. In this study, the genes encoding sugar transporters in maize were systematically identified and analyzed at the whole genome level. The expression patterns of the sugar transporter-encoding genes in different tissues of maize and under biotic and abiotic stresses were revealed, which laid an important theoretical foundation for further elucidation of their functions.

With the implementation of the carbon peaking and carbon neutrality strategy, China's heating system has witnessed various transformative trends, including source-network-load integration and the synergistic utilization of fossil and renewable energy sources. The district heating system, serving as a vital intermediary between heat sources and end-users, plays a crucial role in ensuring a stable energy supply. Particularly, as renewable energy integration increases and end-user requirements become more refined, the demand-supply matching aspect of district heating systems becomes increasingly critical. Therefore, it is essential to quantify and analyze user demand while establishing an adaptive pipe network capable of accommodating fluctuations in end-user load. This paper proposes an optimal topology structure method for district heating systems, taking into account the uncertainty of end-user load. Specifically, a quantitative model of end-user load uncertainty is developed, utilizing the Gram-Charlier A expansion method to capture the factors influencing end-user load uncertainty comprehensively. Furthermore, an optimization model for the pipe network topology is formulated, which considers the uncertainty of end-user load as a constraint and aims to minimize pipeline construction costs. This model optimizes the layout, size, and location of heating sources within the district heating system. The proposed method's practical applicability is validated through a case study of a district heating system in a northern Chinese city that involves 21 end-users. The results indicate that the end-user load uncertainty model outperforms the conventional normal distribution fitting model when dealing with bimodal load distributions. Additionally, the proposed method reduces construction costs by 6.24%, decreases heat loss by 5.30%, and eliminates redundant structures within the district heating system. This research significantly contributes to the planning and design of heating pipeline networks and the optimization and refurbishment of existing pipeline networks within district heating systems.

Abstract Background Traditionally, conventional microbiological culture methods have been used to detect pathogenic microorganisms in chronic osteomyelitis. However, these methods have been found to have a low detection rate, complicating the precise guidance of infection treatment. This study employed metagenomic next-generation sequencing (mNGS) to detect these microorganisms in chronic osteomyelitis with three main objectives: 1). Gain a deeper understanding of the composition of pathogenic microorganisms in chronic osteomyelitis. 2). Compare the microbial detection rates between mNGS and the standard culture methods used in laboratories to enhance the effectiveness of the traditional culture methods. 3). Explore the potential of mNGS in etiological diagnosis. Methods Fifty clinically confirmed intraoperative bone tissue samples of chronic osteomyelitis from January 2021 to December 2021 were collected and subjected to mNGS and microbiological testing, respectively. The orthopaedic surgeon combined clinical manifestations and related examinations to determine the causative pathogens. Results The culture method obtained 29 aerobic and parthenogenic anaerobic bacteria, 3 specific anaerobic bacteria, and 1 yeast-like fungus. Thirty-six aerobic and parthenogenic anaerobic bacteria, 11 specific anaerobic bacteria, and 1 yeast-like fungus were obtained by mNGS, and 2 Mycobacterium tuberculosis(MTB) strains were detected. However, there was no significant difference in the overall positive detection rate between mNGS and the culture method (P = 0.07), and the two were not statistically significant in detecting aerobic and partly anaerobic bacteria (P = 0.625). But, mNGS was significantly superior to culture in detecting anaerobic bacteria and Mycobacterium tuberculosis (P

In view of the complexity and diversity of hyperspectral images (HSIs), the classification task has been a major challenge in the field of remote sensing image processing. Hyperspectral classification (HSIC) methods based on neural architecture search (NAS) is a current attractive frontier that not only automatically searches for neural network architectures best suited to the characteristics of HSI data, but also avoids the possible limitations of manual design of neural networks when dealing with new classification tasks. However, the existing NAS-based HSIC methods have the following limitations: (1) the search space lacks efficient convolution operators that can fully extract discriminative spatial–spectral features, and (2) NAS based on traditional differentiable architecture search (DARTS) has performance collapse caused by unfair competition. To overcome these limitations, we proposed a neural architecture search method with receptive field spatial–spectral attention (RFSS-NAS), which is specifically designed to automatically search the optimal architecture for HSIC. Considering the core needs of the model in extracting more discriminative spatial–spectral features, we designed a novel and efficient attention search space. The core component of this innovative space is the receptive field spatial–spectral attention convolution operator, which is capable of precisely focusing on the critical information in the image, thus greatly enhancing the quality of feature extraction. Meanwhile, for the purpose of solving the unfair competition issue in the traditional differentiable architecture search (DARTS) strategy, we skillfully introduce the Noisy-DARTS strategy. The strategy ensures the fairness and efficiency of the search process and effectively avoids the risk of performance crash. In addition, to further improve the robustness of the model and ability to recognize difficult-to-classify samples, we proposed a fusion loss function by combining the advantages of the label smoothing loss and the polynomial expansion perspective loss function, which not only smooths the label distribution and reduces the risk of overfitting, but also effectively handles those difficult-to-classify samples, thus improving the overall classification accuracy. Experiments on three public datasets fully validate the superior performance of RFSS-NAS.

Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions.

This research explores the architecture and efficacy of GaN/AlxGa1−xN-based heterojunction phototransistors (HPTs) engineered with both a compositionally graded and a doping-graded base. Employing theoretical analysis along with empirical fabrication techniques, HPTs configured with an aluminum compositionally graded base were observed to exhibit a substantial enhancement in current gain. Specifically, theoretical models predicted a 12-fold increase, while experimental evaluations revealed an even more pronounced improvement of approximately 27.9 times compared to conventional GaN base structures. Similarly, HPTs incorporating a doping-graded base demonstrated significant gains, with theoretical predictions indicating a doubling of current gain and experimental assessments showing a 6.1-fold increase. The doping-graded base implements a strategic modulation of hole concentration, ranging from 3.8 × 1016 cm−3 at the base–emitter interface to 3.8 × 1017 cm−3 at the base–collector junction. This controlled gradation markedly contributes to the observed enhancements in current gain. The principal mechanism driving these improvements is identified as the increased electron drift within the base, propelled by the intrinsic electric field inherent to both the compositionally and doping-graded structures. These results highlight the potential of such graded base designs in enhancing the performance of GaN/AlxGa1−xN HPTs and provide crucial insights for the advancement of future phototransistor technologies.

Maize growth and development are regulated by light quality, intensity and photoperiod. Cryptochromes are blue/ultraviolet-A light receptors involved in stem elongation, shade avoidance, and photoperiodic flowering. To investigate the function of cryptochrome 1 (CRY1) in maize, where it is encoded by ZmCRY1, we obtained two ZmCRY1a genes (ZmCRY1a1 and ZmCRY1a2), both of which share the highest similarity with other gramineous plants, in particular rice CRY1a by phylogenetic analysis. In Arabidopsis, overexpression of ZmCRY1a genes promoted seedling de-etiolation under blue and white light, resulting in dwarfing of mature plants. In seedlings of the maize inbred line Zong 31 (ZmCRY1a-OE), overexpression of ZmCRY1a genes caused a reduction in the mesocotyl and first leaf sheath lengths due to down-regulation of genes influencing cell elongation. In mature transgenic maize plants, plant height, ear height, and internode length decreased in response to overexpression of ZmCRY1a genes. Expression of ZmCRY1a were insensitive to low blue light (LBL)-induced shade avoidance syndrome (SAS) in Arabidopsis and maize. This prompted us to investigate the regulatory role of the gibberellin and auxin metabolic pathways in the response of ZmCRY1a genes to LBL treatment. We confirmed a link between ZmCRY1a expression and hormonal influence on the growth and development of maize under LBL-induced SAS. These results reveal that ZmCRY1a has a relatively conservative function in regulating maize photomorphogenesis and may guide new strategies for breeding high density-tolerant maize cultivars.

Abstract Local integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms.

Glaucoma is the leading cause of irreversible blindness worldwide. In the pathogenesis of glaucoma, activated microglia can lead to retinal ganglion cells (RGCs) apoptosis and death, however, the molecular mechanisms remain largely unknown. We demonstrate that phospholipid scramblase 1 (PLSCR1) is a key regulator promoting RGCs apoptosis and their clearance by microglia. As evidenced in retinal progenitor cells and RGCs of the acute ocular hypertension (AOH) mouse model, overexpressed PLSCR1 induced its translocation from the nucleus to the cytoplasm and cytomembrane, as well as elevated phosphatidylserine exposure and reactive oxygen species generation with subsequent RGCs apoptosis and death. These damages were effectively attenuated by PLSCR1 inhibition. In the AOH model, PLSCR1 led to an increase in M1 type microglia activation and retinal neuroinflammation. Upregulation of PLSCR1 resulted in strongly elevated phagocytosis of apoptotic RGCs by activated microglia. Taken together, our study provides important insights linking activated microglia to RGCs death in the glaucoma pathogenesis and other RGC-related neurodegenerative diseases.

The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here, A combined cohort containing 1,026 stage I–III patients was divided into the learning (n = 678) and validation datasets (n = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms, which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations, and hallmark pathways were comprehensively analyzed. Remarkably, the classifier was tightly linked to immune infiltrations, highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover, the classifier was a valuable predictor for therapeutic responses in patients, and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein–protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore, our signature provides a forceful basis for individualized LUAD management with promising potential implications.

ABSTRACT: Phillygenin (PHI) and Baicalin (Bai) are the major chemical ingredients extracted from Forsythia suspensa and Scutellaria baicalensis, respectively. The mixture of Forsythia suspensa and Scutellaria baicalensis according to the theories of Traditional Chinese Veterinary Medicine, compounded formulation can effectively exert heat-clearing and detoxifying effect, but the synergistic anti-IBV activity of PHI combined with Bai was unclear. Here, the protection of PHI combined with Bai on avian infectious bronchitis virus (IBV) M41 infection and the change of respiratory microbiota and metabolomics profiles in broilers that infected with IBV were investigated. According to the experimental findings, the combination of PHI and Bai effectively alleviated broilers’ slowing-growth weight and respiratory symptoms. This was accompanied by a reduction in viral copies and histopathological changes, as well as an increase of antiviral protein (G3BP1) level in tracheas and anti-IBV antibody levels in serum. In addition, 16s RNA sequencing revealed that IBV infection significantly changed respiratory microbiota composition at different taxonomic levels and respiratory metabolism composition in broilers. Interestingly, PHI combined with Bai modulated the composition of respiratory microfloras, especially the abundance of Firmicutes and Lactobacillaceae were upregulated, as well as the abundance of Proteobacteria was downregulated. The metabolomics results indicated that PHI combined with Bai involved in glucose, lipids, amino acids and nucleotide metabolism during IBV infection. In summary, PHI combined with Bai exhibited a synergistic effect on preventing infectious bronchitis (IB), with the protection being closely associated with the composition of respiratory microbiota and metabolites. Therefore, adding the mixture of PHI and Bai to the chicken drinking water is recommended to prevent and control IB in clinical.

Background: In the realm of thoracic surgery, addressing chest wall defects accompanied by infections remains a formidable task. Despite the availability of a spectrum of surgical options, attaining clinical resolution is particularly challenging in intricate cases involving extensive chest wall defects in elderly patients. Thorough debridement followed by the utilization of autologous tissue for repair and reconstruction has emerged as a prevalent approach in current clinical practice. Case presentation: Herein, we present a 72-year-old female patient with a large chest wall defect and infection. She has experienced left breast cancer surgery, multi cycle radiotherapy and chemotherapy. Nine months ago, there was yellow purulent fluid in the left chest wall. She had undergone debridement in other hospital, and the treatment effect was poor. At our hospital, Chest computed tomography (CT) imaging revealed a soft tissue anomaly on the left side of the chest wall, along with partial rib bone deterioration. Considering the patient's clinical presentation and radiological findings, a tentative diagnosis of an infected chest wall defect and chronic osteomyelitis was established. Consequently, daily dressing changes were deemed necessary for the patient's infected chest wound. Surgery for chest wall repair and reconstruction was scheduled once the wound area exhibited cleanliness with emerging granulation tissue. Preoperatively, a myocutaneous flap of an appropriate size was meticulously planned. During the surgical procedure, initial debridement of the infected chest wall area was conducted, followed by the strategic placement of a harvested pedicled latissimus dorsi myocutaneous flap to rectify the defect. Postoperative care involved stringent anti-infective measures, anti-spasmodic treatment, and preventive anticoagulation, accompanied by vigilant monitoring of the myocutaneous flap's viability and the healing progress of the defect site. Conclusions: Utilizing the pedicled latissimus dorsi myocutaneous flap for repairing extensive defects in the chest wall presents a viable and efficient strategy. This technique preserves cardiopulmonary functionality and maintains the thoracic contour. The outcomes observed in the short to medium term postoperatively have been consistently gratifying.

IntroductionThe mismatch negativity (MMN) index has been used to evaluate consciousness levels in patients with disorders of consciousness (DoC). Indeed, MMN has been validated for the diagnosis of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). In this study, we evaluated the accuracy of different MMN amplitude representations in predicting levels of consciousness.MethodsTask-state electroencephalography (EEG) data were obtained from 67 patients with DoC (35 VS and 32 MCS). We performed a microstate analysis of the task-state EEG and used four different representations (the peak amplitude of MMN at electrode Fz (Peak), the average amplitude within a time window −25– 25 ms entered on the latency of peak MMN component (Avg for peak ± 25 ms), the average amplitude of averaged difference wave for 100–250 ms (Avg for 100–250 ms), and the average amplitude difference between the standard stimulus (“S”) and the deviant stimulus (“D”) at the time corresponding to Microstate 1 (MS1) (Avg for MS1) of the MMN amplitude to predict the levels of consciousness.ResultsThe results showed that among the four microstates clustered, MS1 showed statistical significance in terms of time proportion during the 100–250 ms period. Our results confirmed the activation patterns of MMN through functional connectivity analysis. Among the four MMN amplitude representations, the microstate-based representation showed the highest accuracy in distinguishing different levels of consciousness in patients with DoC (AUC = 0.89).ConclusionWe discovered a prediction model based on microstate calculation of MMN amplitude can accurately distinguish between MCS and VS states. And the functional connection of the MS1 is consistent with the activation mode of MMN.

In this study, nitrogen and sulfur heteroatom-doped bacterial cellulose (BC) derived carbon materials have been prepared by a simple, feasible, and environmentally friendly approach that can be easily scaled up for various applications.In the first step, BC was successfully fabricated with chitosan and carrageenan compounds by a layer-by-layer assembly technique without using any template. The resulting composites were then subjected to carbonization to enrich carbon content of the material while incorporation of nitrogen and sulfur heteroatoms into the carbon structure without applying a chemical treatment.A wide variety of BC-derived N- and S-doped carbons were prepared using chitosan and different types of carrageenan precursors. The results showed that the dominant heteroatom on the BC composites was dependent on the last heteroatom precursor used in the layer-by-layer assembly process. The skeleton of the BC composites did not collapse after carbonization and the resulting materials were highly dense and porous having ribbon-like carbon structures. The use of iota-carrageenan as a sulfur precursor with combination of chitosan as a nitrogen precursor resulted in better heteroatom-doped carbons as compared to other carrageenan compounds studied.Synthesized carbons showed great potential as catalyst for hydrothermal conversion of glucose to hydrogen as tested in this study.

AbstractBackground Decreased serum hemoglobin (Hb) level is associated with Immunoglobulin A nephropathy (IgAN) progression. However, whether serum Hb level is an independent prognostic factor of IgAN remains controversial. Herein, we aimed to investigate the prognostic value of serum Hb level in IgAN.Methods The Cochrane Library, Embase, PubMed and Open Grey databases were systematically searched and reviewed. Kidney disease progression of IgAN was defined as a doubling of serum creatinine (SCr), a 30% reduction in estimated glomerular filtration rate (eGFR), end-stage renal disease (ESRD), or death. We evaluated the hazard ratio (HR) between serum Hb level and the incidence of kidney disease progression in IgAN before and after adjusting for relevant covariates.Results We included nine studies with 10006 patients in the meta-analysis. As a continuous variable, we found that serum Hb was an independent prognostic factor of IgAN [unadjusted HR = 0.89, 95% confidence interval (CI) = 0.84–0.95, I2 = 98%; adjusted HR = 0.85, 95% CI = 0.79–0.91, I2 = 0%]. The sensitivity analysis confirmed the stability of these results. Consistently, as a dichotomous variable defined as the below/above cutoff for anemia, we observed a positive correlation between serum Hb and kidney disease progression in IgAN (unadjusted HR = 2.12, 95% CI = 1.44–3.12, I2 = 79%; adjusted HR = 1.65, 95% CI = 1.20–2.27, I2 = 0%).Conclusion Serum Hb level was independently correlated with the incidence of kidney disease progression in IgAN.

Abstract Upgrading of atmospheric CO2 into high‐value‐added acetate using renewable electricity via electrocatalysis solely remains a great challenge. Here, inspired by microbial synthesis via biocatalysts, we present a coupled system to produce acetate from CO2 by bridging inorganic electrocatalysis with microbial synthesis through formate intermediates. A 3D Bi2O3@CF integrated electrode with an ice‐sugar gourd shape was fabricated via a straightforward hydrothermal synthesis strategy, wherein Bi2O3 microspheres were decorated on carbon fibers. This ice‐sugar gourd‐shaped architecture endows electrodes with multiple structural advantages, including synergistic contribution, high mass transport capability, high structural stability, and large surface area. Consequently, the resultant Bi2O3@CF exhibited a maximum Faradic efficiency of 92.4% at −1.23 V versus Ag/AgCl for formate generation in 0.5 M KHCO3, exceeding that of Bi2O3/CF prepared using a conventional electrode preparation strategy. Benefiting from the high formate selectivity, unique architecture, and good biocompatibility, the Bi2O3@CF electrode attached with enriched CO2‐fixing electroautotrophs served as a biocathode. As a result, a considerable acetate yield rate of 0.269 ± 0.009 g L−1 day−1 (a total acetate yield of 3.77 ± 0.12 g L−1 during 14‐day operation) was achieved in the electrochemical–microbial system equipped with Bi2O3@CF.

A total of 320 1-day-old broilers were randomly divided into five groups. The control group (CON) received a basal diet, while the FAP4, FAP2, and FAP1 groups were provided with the basal diet supplemented with 4%, 2%, and 1% fermented Astragalus powder, respectively. The unfermented Astragalus powder (UAP2) group was fed the basal diet supplemented with 2% UAP. Each group contained eight replicates of eight chicks each. The results revealed that the final BW and ADG in the FAP 1 and FAP2 were higher than those in the UAP2 and CON groups, while reducing F/G from day 14 to day 42. On day 42, the thymus index in the UAP and FAP groups as well as the bursa index in the FAP4 group showed significant increases compared to those in the CON group. Supplementation with 2% FAP elevated serum IgA levels in broilers on day 28 and day 42, and it also increased serum IgG levels on day 42. Furthermore, supplementation with 2% FAP elevated serum albumin (ALB) levels in broilers, while supplementation with 4% FAP increased serum (glucose) GLU levels in broilers on day 28. The serum biochemical parameters and pathological observation of the liver and kidney in the groups did not show any adverse effects on broilers’ health. In addition, the serum total antioxidant capacity (T-AOC) level significantly increased in the FAP4 and FAP2 groups on day 28, and the malondialdehyde (MDA) level in both serum and liver tissue decreased in the FAP2 group on day 28 and day 42. Compared to the CON group, 2% FAP and 2% UAP supplementation reduced the relative abundance of Bacteroides and supplementation with 2% FAP increased the relative abundance of Alistipes on day 42. In conclusion, the dietary supplementation of FAP can enhance the growth performance, immune function, and antioxidant capacity and regulate microflora in broilers, of which 2% FAP is more effective. It indicates FAP exhibits significant application potential as a promising feed additive for broilers.

Abstract Online health monitoring of thyristor converter valve is very important for the stable operation of high voltage direct current (HVDC) system. However, in practical projects, only offline annual maintenance can be realized. This paper proposes a method for parameter identification of thyristor valve based on wavelet packet key feature extraction and Elman neural network. By preprocessing the measurable characteristic parameters of thyristor valve in practical engineering, such as wavelet packet time‐frequency domain feature extraction, variation coefficient optimization and so on, the characteristic vector that can characterize the health state of thyristor valve is obtained and sent to Elman neural network for training, which realizes the rapid online identification of multiple electrical parameters of thyristor modules in converter valve. Simulation and comparative experiments show that the key feature extraction method proposed in this paper has significant advantages in identification accuracy, and can better identify the damping capacitance and leakage resistance of all thyristor modules. Among them, the deterioration of the damping capacitance is more easily identified, the identification accuracy is the highest and the relative identification error is less than 5%. The parameter identification method proposed in this paper has the advantages of fast identification speed, strong model generalization ability and wide application. This provides a new idea for the online condition monitoring of thyristor valves.

The traditional 2D laser slam back-end graph optimization (LSBGO) is not efficient in some special situations, such as fast scene switching, limited computing time, and limited hardware facilities. This paper presents a partitioned pose vector method (PPVM) to optimize a pose vector by dividing it into two parts: a position term and an angle term. Based on this partition, the traditional graph optimization problem has been transformed into two linear equations. The least square solutions of the two equations help to find the pose vector. Furthermore, the paper applies a Cholesky decomposition (CD) to improve the speed of solving the two linear equations. Cholesky decomposition has great advantages in solving linear equations with symmetric positive definite coefficient matrix. The effectiveness of PPVM-CD is numerically verified by MATLAB simulation. Compared with the traditional LSBGO method, PPVM-CD improves the optimization speed by 26% and the optimization accuracy by 11.1%.

The jasmonic acid (JA) signaling pathway is involved in plant growth, development, and response to abiotic or biotic stresses. MYC2, a bHLH transcription factor, is a regulatory hub in the pathway. The function of ZmMYC7, a putative MYC2 ortholog, in jasmonate-signaled defense responses of maize has not been reported. In this study, we found that ZmMYC7 possesses JID, TAD, bHLH and Zip domains and essential characteristics of transcription factors: a nuclear location and transactivation activity. The ZmMYC7 mutants showed markedly increased sensitivity to Fusarium graminearum and Setosphaeria turcica. The expression levels of the defense-associated genes ZmPR1, ZmPR2, ZmPR3, ZmPR5, ZmPR6, and ZmPR7 in response to F. graminearum infection were downregulated in ZmMYC7 mutants, while ZmPR4 and ZmPR10 were up-regulated. ZmMYC7 interacted with members of the ZmJAZ family, including ZmJAZ8, ZmJAZ11, and ZmJAZ12. ZmMYC7 physically interacted with G-box cis-elements in the ZmERF147 promoter in vitro and transcriptional activation of ZmERF147 by ZmMYC7 was inhibited by ZmJAZ11 and ZmJAZ12. ZmERF147 mutants were more susceptible to F. graminearum infection than inbred line B73 with concomitant down-regulation of all defense-associated ZmPRs except ZmPR4. These findings indicate that ZmMYC7 functions in maize resistance to F. graminearum and sheds light on maize defense responses to pathogenic fungi via the JA signaling pathway.

Self-supervised learning (SSL) has emerged as a promising approach for learning representations from unlabeled data. Momentum-based contrastive frameworks such as MoCo-v3 have shown remarkable success among the many SSL methods proposed in recent years. However, a significant gap in encoder representation exists between the online encoder (student) and the momentum encoder (teacher) in these frameworks, limiting the performance on downstream tasks. We identify this gap as a bottleneck often overlooked in existing frameworks and propose “residual momentum” that explicitly reduces the gap during training to encourage the student to learn representations closer to the teacher’s. We also reveal that a similar technique, knowledge distillation (KD), to reduce the distribution gap with cross-entropy-based loss in supervised learning is useless in the SSL context and demonstrate that the intra-representation gap measured by cosine similarity is crucial for EMA-based SSLs. Extensive experiments on different benchmark datasets and architectures demonstrate the superiority of our method compared to state-of-the-art contrastive learning baselines. Specifically, our method outperforms MoCo-v3 0.7% top-1 in ImageNet, 2.82% on CIFAR-100, 1.8% AP, and 3.0% AP75 on VOC detection pre-trained on the COCO dataset; it also improves DenseCL with 0.5% AP (800ep) and 0.6% AP75 (1600ep). Our work highlights the importance of reducing the teacher-student intra-gap in momentum-based contrastive learning frameworks and provides a practical solution for improving the quality of learned representations.

The hydrogeological settings of bedrock islands in northern China are unique. Climate change and human activities can affect the interaction between groundwater and seawater around the islands to varying degrees, but there is a lack of quantitative understanding of such hydrological processes, including seawater intrusion (SWI) and submarine groundwater discharge (SGD). In this study, we analyzed the precipitation, groundwater levels, and water quality data (2012-2016) for identifying the characteristics and controlling factors of the interaction between groundwater and seawater in bedrock islands by using mathematical statistics, spatial interpolation, and hydraulics methods. The results show that precipitation and groundwater pumping are the main factors affecting the groundwater-seawater interaction. The variations of groundwater levels lag behind the rainfall events by about 10 days. There occurred no SWI in most areas on the northeastern and southern coasts of the South Island, with relative stable marine groundwater discharge. The average SGD velocity was 0.2 m/d, and the average NO3-N flux to the sea was 81.8 mmol/(m2·d). The southeast area of the North Island has been seriously threatened by SWI, and the groundwater levels have been below sea level for a long time and falling year by year. The average SWI rate is 0.3 m/d, and the average NO3-N flux towards the land is 69.6 mmol/(m2·d). The SGD rates of the South and NorthIslands were estimated for dry season (April 2014) and wet season (September 2013), respectively. It was 3.5×104−4.5×104 m3/d for the North Island, and 0.4×104−1.1×104 m3/d for the South Island. The results can provide important reference for coastal groundwater resource management and ecological environment protection in bedrock islands.

Abstract Background Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. This study aimed to explore genes underlying the pathogenesis of IgAN. Methods We conducted the summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are potentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed SMR analysis using Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data and replicated the analysis using Genotype-Tissue Expression (GTEx) eQTL data. Results Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes whose expression were pleiotropically associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, P SMR= 2.10 × 10–18), ILMN_1682717 (tagging IER3, P SMR= 1.07 × 10–16) and ILMN_1661439 (tagging FLOT1, P SMR=1.16 × 10–14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes whose expressions were pleiotropically associated with IgAN, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, P SMR= 1.44 × 10–10), ENSG00000186470.9 (tagging BTN3A2, P SMR= 2.28 × 10–10), and ENSG00000224389.4 (tagging C4B, P SMR= 1.23 × 10 –9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes that are potentially involved in the pathogenesis of IgAN. Conclusion We identified many genetic variants/loci that are potentially involved in the pathogenesis of IgAN. More studies are needed to elucidate the exact mechanisms of the identified genetic variants/loci in the etiology of IgAN.