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The key to restoring arid and semi-arid ecosystems is maintaining soil water and organic carbon contents. Alfalfa (Medicago sativa L.) is a high-yield perennial forage crop and performs ecological functions as a drought-resistance leguminous herb. It has been widely planted for reconstruction of degraded soils in the Loess Plateau in northwestern China, but long-term planting may affect soil carbon–water coupling and lead to crop yield reduction. To maximize the benefits of reconstructed grassland, this study explored the couplings of soil water, organic carbon, and alfalfa productivity along a reconstruction chronosequence in a semi-arid area of the Loess Plateau. Space-for-time substitution approach was used to select different-aged stands of reconstructed grassland (1, 5, 7, 10, 15, 20, 30 years old). Alfalfa above-ground biomass (AGB), soil water storage (SWS), organic carbon storage (SOCS), and carbon–water coupling coordination degree (D) were measured in the 0–100 cm soil profile. Alfalfa AGB reached a peak in the 7th year, and the degradation began in the 10th year. Both SWS and SOCS varied nonlinearly with stand age. The greatest loss of SWS occurred in the 15th year (80–100 cm depth), whereas the largest increase of SOCS occurred in the 30th year (0–20 cm depth). There was a negative feedback relationship between AGB and SWS over the 30-year study period (Pearson r = −0.835, P = 0.098). AGB and SOCS initially showed a trade-off within the first 10 years (Pearson r = −0.7431, P = 0.2569), in contrast to their positive feedback in the 20–30th years (Pearson r = 0.9978, P = 0.0421). A decoupling between SWS and SOCS (D < 0.6) was observed after 12 years of alfalfa planting. For agricultural production, a greater supply of water and organic fertilizer is required from the 7th year of alfalfa planting, and reseeding may be needed around the 10th year to prolong the life of alfalfa community. Alfalfa should be planted for no more than 12 consecutive years in the study area for ecological protection.

Chloro-haloacetonitrile (Cl-HAN), belongs to a group of nitrogenous disinfection by-products (N-DBPs) found in surface water, and are known to pose a major risk to the safety of human drinking water. However, the exact biological toxicity mechanism and the extent of the stress response caused by Cl-HAN remain unclear, resulting in a lack of effective measures to control its presence. Thus, the quantitative toxicological genomics and bioinformatics methods were applied to explore the effects of three chloro-haloacetonitriles (Cl-HANs) on the transcription of fusion genes under varying concentrations of stress in E. coli over 2-hour period. The initial stress response and their toxic mechanism were analyzed. The study also identified the molecular toxicity endpoint, and the core genes that are responsible for the specific toxicity of different Cl-HANs. Cl-HANs exhibited concentration-dependent characteristics of toxic effects, and caused changes in gene expression related oxidative and membrane stress. The stress response results showed that dichloroacetonitrile (dCAN) still caused significant DNA damage under the lowest concentration stress. Chloroacetonitrile (CAN) and trichloroacetonitrile (tCAN) exhibited lower genetic toxicity levels at 513 μg/L and 10.7 μg/L, respectively. The toxic effects of tCAN were widespread. And there was a good correlation between the molecular endpoint (EC-TELI1.5) and the phenotypic endpoint (LD50) with rp=-0.8634 (P=0.0593). In all concentrations of stress in CAN, dCAN, and tCAN, the number of overexpressed genes shared was 15, 2, and 14, respectively. Furthermore, bioinformatics analysis demonstrated that Cl-HANs affected genes associated with general stress pathways, such as cell biochemistry and physical homeostasis, resulting in changes in biological processes. And for CAN-induced DNA damage, polA played a dominant role, while katG, oxyR, and ahpC were the core genes involved in oxidative stress induced by dCAN and tCAN, respectively. These findings provide valuable data for the toxic effect of Cl-HANs.

Abstract Since the impressive superior performance demonstrated by deep learning methods is widely used in histopathological image analysis and diagnosis, existing work cannot fully extract the information in the breast cancer images due to the limited high resolution of histopathological images. In this study, we construct a novel intermediate layer structure that fully extracts feature information and name it DMBANet, which can extract as much feature information as possible from the input image by up-dimensioning the intermediate convolutional layers to improve the performance of the network. Furthermore, we employ the depth-separable convolution method on the Spindle Structure by decoupling the intermediate convolutional layers and convolving them separately, to significantly reduce the number of parameters and computation of the Spindle Structure and improve the overall network operation speed. We also design the Spindle Structure as a multi-branch model and add different attention mechanisms to different branches. Spindle Structure can effectively improve the performance of the network, the branches with added attention can extract richer and more focused feature information, and the branch with residual connections can minimize the degradation phenomenon in our network and speed up network optimization. The comprehensive experiment shows the superior performance of DMBANet compared to the state-of-the-art method, achieving about 98% classification accuracy, which is better than existing methods. The code is available at https://github.com/Nagi-Dr/DMBANet-main .

High-energy gas fracturing of shale is a novel, high efficacy and eco-friendly mining technique, which is a typical dynamic perturbing behavior. To effectively extract shale gas, it is important to understand the dynamic mechanical properties of shale. Dynamic experiments on shale subjected to true triaxial compression at different strain rates are first conducted in this research. The dynamic stress-strain curves, peak strain, peak stress and failure modes of shale are investigated. The results of the study indicate that the intermediate principal stress and the minor principal stress have the significant influence on the dynamic mechanical behaviors, although this effect decreases as the strain rate increases. The characteristics of compression-shear failure primarily occur in shale subjected to triaxial compression at high strain rates, which distinguishes it from the fragmentation characteristics observed in shale under dynamic uniaxial compression. Additionally, a numerical three-dimensional Split Hopkinson Pressure Bar (3D-SHPB), which is established by coupling PFC3D and FLAC3D methods, is validated to replicate the laboratory characteristics of shale. The dynamic mechanical characteristics of shale subjected to different confining stresses are systematically investigated by the coupling PFC3D and FLAC3D method. The numerical results are in good agreement with the experimental data.

Few-shot classification addresses the challenge of swiftly enabling a deep learning model to comprehend new classes based on minimal supporting image samples. Despite recent research predominantly focusing on devising increasingly intricate classifiers for gauging similarities between query and support images, there is room for improvement in the critical role of feature embeddings and relational measurements. In this paper, a novel technique called Progressive Multi-Scale Relation Network (PMRNet), is designed for few-shot classification tasks. Our work introduces a feature extraction module that adeptly extracts and interactively fuses multi-scale features to address the problem of insufficient feature extraction. Furthermore, an enhanced attention mechanism is integrated, which amplifies critical local and global features for increased discriminative power. A novel bilinear relation module is also designed specifically to accurately quantify the similarities between the extracted features. The PMRNet has been rigorously evaluated on three benchmark datasets. PMRNet’s empirical performance in few-shot image classification tasks exhibits commendable accuracy, substantiating its efficacy as a robust architectural framework for significantly boosting the precision of such tasks.

Abstract Objectives A genome sequence of a threatened species can provide valuable genetic information that is important for improving the conservation strategies. The white-eared night heron (Gorsachius magnificus) is an endangered and poorly known ardeid bird. In order to support future studies on conservation genetics and evolutionary adaptation of this species, we have reported a de novo assembled and annotated whole-genome sequence of the G. magnificus. Data description The final draft genome assembly of the G. magnificus was 1.19 Gb in size, with a contig N50 of 187.69 kb and a scaffold N50 of 7,338.28 kb. According to BUSCO analysis, the genome assembly contained 97.49% of the 8,338 genes in the Aves (odb10) dataset. Approximately 10.52% of the genome assembly was composed of repetitive sequences. A total of 14,613 protein-coding genes were predicted in the genome assembly, with functional annotations available for 14,611 genes. The genome assembly exhibited a heterozygosity rate of 0.49 heterozygosity per kilobase pair. This draft genome of G. magnificus provides valuable genomic resources for future studies on conservation and evolution.

Line commutated converter based high-voltage direct-current (LCC-HVDC) transmissions are prone to harmonic oscillation under weak grids. Impedance modeling is an effective method for assessing interaction stability. Firstly, this paper proposes an improved calculation method for the DC voltage and AC currents of commutation stations to address the complex linearization of the commutation process and constructs an overall harmonic state-space (HSS) model of an LCC-HVDC. Based on the HSS model, the closed-loop AC impedances on the LCC-HVDC sending and receiving ends are then derived and verified. The impedance characteristics of the LCC-HVDC are then analyzed to provide a physical explanation for the harmonic oscillation of the system. The effects of the grid strength and control parameters on system stability are also analyzed. To improve the impedance characteristics and operating stability of the LCC-HVDC system, a virtual impedance based stability enhancement control is proposed, and a parameter design method is considered to ensure satisfactory phase margins at both the sending and receiving ends. Finally, simulation results are presented to verify the validity of the impedance model and virtual impedance based stability enhancement control.

The growth and biological decline of alfalfa may be linked to the rhizosphere microbiome. However, plant–microbe interactions in the rhizosphere of alfalfa and associated microbial community variations with stand age remain elusive. This study explored the successional pattern of rhizosphere microbial communities across different aged alfalfa stands and its relationship with alfalfa decline. Rhizosphere soils were collected from 2- and 6-year-old alfalfa stands. Control soils were collected from interspaces between alfalfa plants in the same stands. Soil bacterial and fungal communities were characterized by 16S and ITS rRNA gene sequencing, respectively. Specific microbial taxa colonized the rhizosphere soils, but not the control soils. The rhizosphere-specific taxa mainly included potentially beneficial genera (e.g., Dechloromonas, Verrucomicrobium) in the young stand and harmful genera (e.g., Peziza, Campylocarpon) in the old stand. Alfalfa roots regulated soil microbial communities by selective promotion or inhibition of distinct taxa. The majority of time-enriched taxa were reported as harmful fungi, whose relative abundances were negatively correlated with plant traits. Time-depleted taxa were mostly known as beneficial bacteria, which had relative abundances positively correlated with plant traits. The relative abundances of functional bacterial genes associated with vancomycin biosynthesis, zeatin biosynthesis, and amino acid metabolism trended lower in rhizosphere soils from the old stand. An upward trend was observed for fungal pathogens and wood saprotrophs with increasing stand age. The results suggest that root activity drives the negative succession of rhizosphere microbial communities during alfalfa decline in old stands.

Background Lung adenocarcinoma (LUAD) is a major pathological subtype of malignant lung cancer with a poor prognosis. Necroptosis is a caspase-independent programmed cell death mode that plays a pivotal role in cancer oncogenesis and metastasis. Here, we explore the prognostic values of different necroptosis-related genes (NRGs) in LUAD. Methods mRNA expression data and related clinical information for LUAD samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. NRGs were identified using the GeneCards database. Least absolute shrinkage and selection operator Cox regression and multivariate Cox analysis were used to construct a prognostic risk model. Time-dependent receiver-operating characteristic curves and a nomogram were constructed to validate the predictive values of the prognostic signatures. A necroptosis-related protein–protein interaction network was visualised using the STRING database and Cytoscape software. Functional analyses, including Gene Ontology, Kyoto Encyclopaedia of Genes and Genomes pathway enrichment, gene set enrichment, and gene set variation analyses, were conducted to explore the underlying molecular mechanisms. Finally, the mRNA expression of the prognostic signatures in LUAD cell lines was assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. Results A prognostic model was established for eight NRGs (CALM1, DDX17, FPR1, OGT, PGLYRP1, PRDX1, TUFM, and CPSF3) based on TCGA-cohort data and validated with the GSE68465 cohort. Patients with low-risk scores had better survival outcomes than those with high-risk scores (p = 0.00013). The nomogram was used to predict the prognosis of patients with LUAD. The prediction curves for 1-, 3-, and 5-year OS showed good predictive performance and the accuracy of the nomograms increased over time. RT-qPCR results demonstrated that these eight genes, especially CALM1, PRDX1, and PGLYRP1, were differentially expressed in LUAD cells. Conclusion We constructed a reliable eight-NRG signature that provides new insights for guiding clinical practice in the prognosis and treatment of LUAD.

Rural collectively owned commercial construction land marketization (RCCCLM), a land policy that is still being piloted despite being allowed by law, has been regarded as an indispensable policy tool that can meet the land demand for rural development. This study aims to analyze the typical modes and key paths of RCCCLM for rural transformation theoretically and practically. We developed an innovative theoretical framework of “Situation-Structure-Implementation-Outcome” to illustrate the relationship between RCCCLM and rural transformation. By tracing the process of the impact of RCCCLM on rural transformation in typical areas, this study fills the gap in existing research that has not yet explored the theoretical mechanisms between RCCCLM and rural transformation. The results show that a combination of such factors as geographical location and rural land use status brings about three major ways of RCCCLM, namely, local marketization, adjustive marketization and consolidation-based marketization, to promote rural transformation. The influencing mechanism of RCCCLM on rural transformation mainly concerns rights granting, rural land use structure reshaping, the transformation of farmers’ livelihood and improvement in rural governance capacity. Rural transformation follows a basic rule and phased characteristics of evolving from single function to multiple functions, and RCCCLM displays a differentiated driving effect on the evolution of rural functions in different stages of rural development. These findings will provide new insights into rural land use and rural transformation in China and other developing countries and help solve the bottlenecks in rural transformation.

Eliminating poverty and achieving social justice are global concerns. China has focused on common prosperity. Comprehensive land consolidation is a potential policy tool that can contribute to common prosperity, but its effectiveness and implementation methods are yet to be verified and discussed. Therefore, we construct an analytical framework to understand how comprehensive land consolidation promotes common prosperity. The pilot area for comprehensive land consolidation in Ningbo City is used as a qualitative case study. The research results indicate that comprehensive land consolidation focuses on development and sharing to promote high-quality socio-economic development, social fairness, and justice. The paths for achieving development included the following: (1) a network governance structure consisting of multiple entities to enhance land governance; (2) various consolidation activities were conducted at the town scale to optimize the functionality and spatial pattern of public spaces; and (3) enabling the flow of urban and rural factors for improved resource allocation efficiency and providing an impetus for industrial development. The paths for achieving sharing included clarifying collective land ownership and promoting land transactions to provide diverse land values and ensure a shared distribution. This research provides new insights applicable to other Chinese cities and numerous developing countries engaged in land consolidation to address social distribution issues.

Background: Osteoporosis is a common chronic disease in older persons. Physical activity can prevent chronic diseases as well as many diseases associated with aging. Older persons often suffer from depression and other psychological problems. However, there are few studies on the correlation between depression and physical activity in older persons with osteoporosis in China. Methods: This cross-sectional study was conducted from June 1 to December 28, 2021. A total of 207 subjects who met the inclusion and were exclusion criteria were selected from the outpatient department of West China Hospital and evaluated using a self-designed demographic data questionnaire, the Self-rating Depression Scale (SDS), and the Physical Activity Scale for the Elderly (PASE). Multivariate linear regression was performed to analyze the factors affecting physical activity, and multivariate logistic regression analysis was performed to analyze the factors affecting depression. Spearman’s correlation coefficients were calculated to analyze the correlation between depression and physical activity in older persons with osteoporosis. Results: A total of 173 valid questionnaires were statistically analyzed. A total of 122 (70.5%) participants were identified as having depression (SDS ≥ 50 points). The median depression score was 62.5 (24.38), and the median PASE score was 69.29 (116.64). Multivariate logistic regression model results showed that physical activity and pain were the main risk factors for depression in older persons with osteoporosis (p < 0.05). Multivariate linear regression model results showed that gender, participation in social activities, activities of daily living status (ADLs), and depression status were the main risk factors associated with physical activity in older persons with osteoporosis (p < 0.05). The results of the correlation analysis showed that physical activity was negatively correlated with depression (R = −0.510, p = 0.000). Conclusion: We found that depression was negatively correlated with physical activity in older persons with osteoporosis in China. This means that the less physical activity there is, the more serious the depression status and having depression may result in reduced physical activity in older persons with osteoporosis. To better help older persons with osteoporosis, medical staff should give more attention to influencing factors of depression and physical activity. [ABSTRACT FROM AUTHOR]