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Literature, as a carrier of researchers’ research results, can objectively reflect the development status of the field. Rural human resource development and management are of great significance and value to China's sustainable development and has received much attention as a research hotspot over the years. This study aims to understand the current situation of rural human resources research in China, help relevant scholars keep abreast of the research dynamics in this field in China, and better engage in rural human resources research. The article selects the China National Knowledge Infrastructure Database (CNKI) as the data source uses “rural human resources” as the keyword to search the relevant literature from 2000 to 2020; a total of 909 documents were searched. On this basis, it uses the bibliometric method to analyze the relevant literature. The results of this study showed the following: 1) There are era differences in the academic circles’ attention to rural human resource development. From 2000 to 2020, the relevant literature showed certain volatility. 2) There is insufficient systematic research on the literature. There are many journal articles in the literature resources, but a lack of dissertations. 3) Author units are mostly concentrated in colleges and universities, particularly agricultural universities. 4) The quantity of excellent literature is insufficient, and there is little funded literature. In future research, it is recommended that relevant scholars increase their research efforts on rural revitalization and the cultivation of emerging professional farmers in light of China's national conditions, and at the same time improve the systematic and targeted research.

Since 2014, highly pathogenic avian influenza (HPAI) H5 viruses of clade 2.3.4.4 have been dominating the outbreaks across Europe, causing massive deaths among poultry and wild birds. However, the factors shaping these broad‐scale outbreak patterns, especially those related to waterbird community composition, remain unclear. In particular, we do not know whether these risk factors differ from those of other H5 clades. Addressing this knowledge gap is important for predicting and preventing future HPAI outbreaks. Using extensive waterbird survey datasets from about 6883 sites, we here explored the effect of waterbird community composition on HPAI H5Nx (clade 2.3.4.4) spatial patterns in the 2016/2017 and 2020/2021 epidemics in Europe, and compared it with the 2005/2006 HPAI H5N1 (clade 2.2) epidemic. We showed that HPAI H5 occurrences in wild birds in the three epidemics were strongly associated with very similar waterbird community attributes, which suggested that, in nature, similar interspecific transmission processes operate between the HPAI H5 subtypes or clades. Importantly, community phylogenetic diversity consistently showed a negative association with H5 occurrence in all three epidemics, suggesting a dilution effect of phylogenetic diversity. In contrast, waterbird community variables showed much weaker associations with HPAI H5Nx occurrence in poultry. Our results demonstrate that models based on previous epidemics can predict future HPAI H5 patterns in wild birds, implying that it is important to include waterbird community factors in future HPAI studies to predict outbreaks and improve surveillance activities. [ABSTRACT FROM AUTHOR]

Wetlands, as a crucial component of terrestrial ecosystems, play a significant role in global ecological services. Aboveground biomass (AGB) is a key indicator of the productivity and carbon sequestration potential of wetland ecosystems. The current research methods for remote-sensing estimation of biomass either rely on traditional vegetation indices or merely perform integer-order differential transformations on the spectra, failing to fully leverage the information complexity of hyperspectral data. To identify an effective method for estimating AGB of mixed-wetland-plant communities, we conducted field surveys of AGB from three typical wetlands within the Crested Ibis National Nature Reserve in Hanzhong, Shaanxi, and concurrently acquired canopy hyperspectral data with a portable spectrometer. The spectral features were transformed by applying fractional-order differentiation (0.0 to 2.0) to extract optimal feature combinations. AGB prediction models were built using three machine learning models, XGBoost, Random Forest (RF), and CatBoost, and the accuracy of each model was evaluated. The combination of fractional-order differentiation, vegetation indices, and feature importance effectively yielded the optimal feature combinations, and integrating vegetation indices with feature bands enhanced the predictive accuracy of the models. Among the three machine-learning models, the RF model achieved superior accuracy using the 0.8-order differential transformation of vegetation indices and feature bands (R2 = 0.673, RMSE = 23.196, RPD = 1.736). The optimal RF model was visually interpreted using Shapley Additive Explanations, which revealed that the contribution of each feature varied across individual sample predictions. Our study provides methodological and technical support for remote-sensing monitoring of wetland AGB. [ABSTRACT FROM AUTHOR]

In order to understand the current research status of human resource management in China, this article takes CNKI as the data source and uses CiteSpace software to sort out relevant literatures on Human resources in Rural China since 2010, and analyzes them from four dimensions: the number of articles published, author contribution, institutional cooperation and keywords. Research indicates that China's research on rural human resources has generally shown a downward trend since 2010. However, with the implementation of China's rural revitalization strategy, targeted poverty alleviation and other national policies, research on rural human resources has begun to rebound in recent years.In addition, the core team of authors in this research field has not yet formed, and there is a lack of cooperation between them.In terms of research institutions, colleges and universities undertake the main task of theoretical research, while government agencies pay more attention to cooperation between different departments.At the same time, through analysis, it is also found that the research hotspots in this field are epochal, and key words with high degree of emergence have appeared in different periods.The value of this article lies in the three-dimensional presentation of the current situation of China's rural human resources research, which provides a better direction for scholars to carry out research in the future.

Objective To investigate the effect of miRNA-495 on the proliferation and apoptosis of acute myeloid leukemia (AML) cells with mixed lineage leukemia (MLL) gene rearrangement. Methods Ten patients diagnosed with AML with MLL-AF9 fusion gene positive (6 males, 4 females, mean age of 39.37±2.71 years), and 10 cases of AML (5 males, 5 females, mean age of 41.12±1.92 years) admitted in our hospital from August 2017 to May 2019 were enrolled in this study, and another 10 patients (6 males, 4 females, mean age of 42.31±3.21 years) who attended the surgical outpatient department during the same period were also collected as the control. THP-1 cells (containing MLL-AF9 fusion gene) were divided into miRNA-495 and control groups based on transfection with miRNA-495 or not, and the expression of miRNA-495 in each group was detected by RT-qPCR; CCK-8 assay was used to determine the proliferation of transfected THP-1 cells in each group; RT-qPCR and Western blotting were performed to measure the expression of proliferation-related factors (Ki-67 and PCNA) and apoptosis-related factors (Bcl-2, Bax and Caspase-3). The target genes of miRNA-495 were predicted by TargetScan and then verified by dual-luciferase reporter assay. Finally, RT-qPCR and Western blotting were adopted to detect the expression of serine/threonine/tyrosine interacting protein (STYX) in groups as follows: miRNA-495 group, control group, STYX group (transfected with STYX) and miRNA-495+STYX group (transfected with miRNA-495 and STYX). Results MiRNA-495 expression was down-regulated in AML patients with MLL-AF9 fusion gene, with relative expression of 0.25±0.08 (95%CI=-0.86~-0.65, P < 0.001); miRNA-495 inhibited cell proliferation (P < 0.001) and induced apoptosis of THP-1 cells, and it targetedly bound to STYX (P < 0.001). In addition, STYX overexpression reversed the effects of miRNA-495 on THP-1 cell proliferation and apoptosis. Conclusion MiRNA-495 regulates the proliferation and apoptosis of AML cells with MLL gene rearrangement by targeting STYX.

In pursuit of sustainable solutions for water pollution mitigation, we have successfully employed electrospinning technology to fabricate a multilayered sodium alginate (SA)/polyethyleneimine (PEI)/polyvinyl alcohol (PVA) nanocomposite fiber membrane, with a focus on enhancing its adsorption capacity for Cu2+ ions in wastewater. Our research underscores the potential of this novel membrane, characterized by its small diameter, high uniformity, and expansive surface area, in effectively filtering heavy metal ions. By optimizing critical electrospinning parameters such as a voltage of 19.5 KV, a collector distance of 8 cm, a specific mass ratio of SA:PEI: PVA (1:2:6), and an injection rate of 8 μL/min, we achieved a nanofiber membrane with an average diameter of 112.5 nm, exhibiting exceptional morphological characteristics and high efficiency. Notably, the membrane exhibited an adsorption capacity of over 85% for Cu2+ during initial testing, maintaining over 80% efficiency throughout four consecutive filtration cycles. This work not only advances the field of nanocomposite membranes for water purification but also contributes significantly to the broader goal of achieving environmental sustainability by mitigating the impact of heavy metal contamination in water bodies. [ABSTRACT FROM AUTHOR]

Background: Medical imaging datasets frequently encounter a data imbalance issue, where the majority of pixels correspond to healthy regions, and the minority belong to affected regions. This uneven distribution of pixels exacerbates the challenges associated with computer-aided diagnosis. The networks trained with imbalanced data tends to exhibit bias toward majority classes, often demonstrate high precision but low sensitivity. Method: We have designed a new network based on adversarial learning namely conditional contrastive generative adversarial network (CCGAN) to tackle the problem of class imbalancing in a highly imbalancing MRI dataset. The proposed model has three new components: (1) class-specific attention, (2) region rebalancing module (RRM) and supervised contrastive-based learning network (SCoLN). The class-specific attention focuses on more discriminative areas of the input representation, capturing more relevant features. The RRM promotes a more balanced distribution of features across various regions of the input representation, ensuring a more equitable segmentation process. The generator of the CCGAN learns pixel-level segmentation by receiving feedback from the SCoLN based on the true negative and true positive maps. This process ensures that final semantic segmentation not only addresses imbalanced data issues but also enhances classification accuracy. Results: The proposed model has shown state-of-art-performance on five highly imbalance medical image segmentation datasets. Therefore, the suggested model holds significant potential for application in medical diagnosis, in cases characterized by highly imbalanced data distributions. The CCGAN achieved the highest scores in terms of dice similarity coefficient (DSC) on various datasets: 0.965 ± 0.012 for BUS2017, 0.896 ± 0.091 for DDTI, 0.786 ± 0.046 for LiTS MICCAI 2017, 0.712 ± 1.5 for the ATLAS dataset, and 0.877 ± 1.2 for the BRATS 2015 dataset. DeepLab-V3 follows closely, securing the second-best position with DSC scores of 0.948 ± 0.010 for BUS2017, 0.895 ± 0.014 for DDTI, 0.763 ± 0.044 for LiTS MICCAI 2017, 0.696 ± 1.1 for the ATLAS dataset, and 0.846 ± 1.4 for the BRATS 2015 dataset. [ABSTRACT FROM AUTHOR]

Developing a living prosthetic breast to inhibit potential breast cancer recurrence and simultaneously promote breast reconstruction would be a promising strategy for clinical treatment of breast cancer after mastectomy. Here, a living prosthetic breast in the form of injectable gelatin methacryloyl microspheres is prepared, where they encapsulated zeolitic imidazolate framework (ZIF) nanoparticles loaded with small molecules urolithin C (Uro‐C) and adipose‐derived stem cells (ADSCs). Taking advantage of the acidic tumor microenvironment, the ZIF triggered a pH‐sensitive drug release in situ so that Uro‐C can induce tumor cell apoptosis via reactive oxygen species (ROS) generation. Meanwhile, the ADSCs proliferate in situ to promote tissue regeneration. Using such a design, our data showed that the ADSCs maintained viable and proliferate under the inhibitory effect of Uro‐C in vitro. Through ROS generation, Uro‐C also activated a suppressive tumor microenvironment in mice by both re‐polarizing M2 macrophages to M1 macrophages for elevated inflammatory responses, and increasing the ratio between CD8 and CD4 T cells for tumor recurrence inhibition, significantly promoting new adipose tissue formation. Altogether, our results demonstrate that the prepared living prosthetic breast with bifunctional properties can be a promising candidate in clinic involving tumor treatment and tissue engineering in synergy. [ABSTRACT FROM AUTHOR]