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Financial inclusion is an important part of inclusive rural transformation because it provides access to, and use of, capital to underserved rural households. However, with increased entrepreneurial activities, it begs the questio...
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Financial inclusion is an important part of inclusive rural transformation because it provides access to, and use of, capital to underserved rural households. However, with increased entrepreneurial activities, it begs the question as to the extent by which access to credit actually encourages entrepreneurial activities. Using survey data of 988 farm households in state impoverished counties of China, we find that financial inclusion plays an important role in inclusive rural transformation by facilitating farm households' entrepreneurial activities in rural poverty-stricken areas in China. The survey provides evidence that the entrepreneurial decisions of farm households are positively affected by use of credit rather than access to credit. In addition, when it comes to entrepreneurial decisions, use of formal credit and use of informal credit have no statistical relationship, but operate under separate and statistically distinct channels. Moreover, entrepreneurship has a positive and significant causal effect on income of farm households.
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Analysis of genomes in a phylogenetic context reveals a 350-million-year-old homomorphic sex chromosome in molluscs, probably maintained by regulation of reversible sex-biased genes and sex chromosome turnover. Contrary to classic...
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Analysis of genomes in a phylogenetic context reveals a 350-million-year-old homomorphic sex chromosome in molluscs, probably maintained by regulation of reversible sex-biased genes and sex chromosome turnover. Contrary to classic theory prediction, sex-chromosome homomorphy is prevalent in the animal kingdom but it is unclear how ancient homomorphic sex chromosomes avoid chromosome-scale degeneration. Molluscs constitute the second largest, Precambrian-originated animal phylum and have ancient, uncharacterized homomorphic sex chromosomes. Here, we profile eight genomes of the bivalve mollusc family of Pectinidae in a phylogenetic context and show 350 million years sex-chromosome homomorphy, which is the oldest known sex-chromosome homomorphy in the animal kingdom, far exceeding the ages of well-known heteromorphic sex chromosomes such as 130-200 million years in mammals, birds and flies. The long-term undifferentiation of molluscan sex chromosomes is potentially sustained by the unexpected intertwined regulation of reversible sex-biased genes, together with the lack of sexual dimorphism and occasional sex chromosome turnover. The pleiotropic constraint of regulation of reversible sex-biased genes is widely present in ancient homomorphic sex chromosomes and might be resolved in heteromorphic sex chromosomes through gene duplication followed by subfunctionalization. The evolutionary dynamics of sex chromosomes suggest a mechanism for 'inheritance' turnover of sex-determining genes that is mediated by translocation of a sex-determining enhancer. On the basis of these findings, we propose an evolutionary model for the long-term preservation of homomorphic sex chromosomes.
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Ameliorating T cell exhaustion and enhancing effector function are promising strategies for the improvement of immunotherapies. Here, we show that the HPK1-NF kappa B-Blimpl axis mediates T cell dysfunction. High expression of MAP...
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Ameliorating T cell exhaustion and enhancing effector function are promising strategies for the improvement of immunotherapies. Here, we show that the HPK1-NF kappa B-Blimpl axis mediates T cell dysfunction. High expression of MAP4K1 (which encodes HPK1) correlates with increased T cell exhaustion and with worse patient survival in several cancer types. In MAP4K1(KO) mice, tumors grow slower than in wild-type mice and infiltrating T cells are less exhausted and more active and proliferative. We further show that genetic depletion, pharmacological inhibition, or proteolysis targeting chimera (PROTAC)-mediated degradation of HPK1 improves the efficacy of CAR-T cell-based immunotherapies in diverse preclinical mouse models of hematological and solid tumors. These strategies are more effective than genetically depleting PD-1 in CAR-T cells. Thus, we demonstrate that HPK1 is a mediator of T cell dysfunction and an attractive druggable target to improve immune therapy responses.
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Atherosclerosis, a chief pathogenic factor of cardiovascular disease, is associated with many factors including inflammation, dyslipidemia, and oxidative stress. Peroxisome proliferator-activated receptors (PPARs) are nuclear rece...
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Atherosclerosis, a chief pathogenic factor of cardiovascular disease, is associated with many factors including inflammation, dyslipidemia, and oxidative stress. Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors and are widely expressed with tissue- and cell-specificity. They control multiple genes that are involved in lipid metabolism, inflammatory response, and redox homeostasis. Given the diverse biological functions of PPARs, they have been extensively studied since their discovery in 1990s. Although controversies exist, accumulating evidence have demonstrated that PPAR activation attenuates atherosclerosis. Recent advances are valuable for understanding the mechanisms of action of PPAR activation. This article reviews the recent findings, mainly from the year of 2018 to present, including endogenous molecules in regulation of PPARs, roles of PPARs in atherosclerosis by focusing on lipid metabolism, inflammation, and oxidative stress, and synthesized PPAR modulators. This article provides information valuable for researchers in the field of basic cardiovascular research, for pharmacologists that are interested in developing novel PPAR agonists and antagonists with lower side effects as well as for clinicians.
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Floods are great threats to human life and property. Extensive research has investigated the spatiotemporal variation in flood occurrence, while few have studied the heterogeneity in global flood events of different sizes, which m...
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Floods are great threats to human life and property. Extensive research has investigated the spatiotemporal variation in flood occurrence, while few have studied the heterogeneity in global flood events of different sizes, which may require different coping strategies and risk reduction policies. In this study, we analysed the spatiotemporal patterns of global flood events with different affected areas (classified in three levels) during 1985-2019 and examined the contribution of different influencing factors to flood-induced mortality using Geodetector. The results show that (1) the increase in global flood frequency was mainly caused by Level II and Level III floods, and the average area affected by flood events has been increasing yearly since 1985. (2) In America and Africa, the frequency of Level III floods has increased monotonically. At the same time, the frequency of Level I floods in Europe and Level II floods in Asia has increased significantly. (3) For Europe and Asia, most of the deaths occurred with Level II floods; while for America and Africa, Level III floods caused the most mortality. (4) The top three factors contributing to the spatial heterogeneity in flood-induced mortality were the affected population, GDP per capita and flood duration. The contribution of each factor varied among the different types of floods. Topographic factors (percentage of mountainous area) magnified flood-induced mortality during extreme events with heavy rainfall, especially for Level III floods. The heterogeneity in flood frequency and flood-induced mortality indicates that flood protection measures should be more targeted. In addition, the increase in large-scale floods (Level III) highlights the need for transregional cooperation in flood risk management.
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Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based methods have been proposed to address challenges in differen...
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Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy, including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
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Accumulation of toxic protein assemblies and damaged mitochondria are key features of neurodegenerative diseases, which arise in large part from clearance defects in the Macroautophagy/autophagy-lysosome system. The autophagy carg...
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Accumulation of toxic protein assemblies and damaged mitochondria are key features of neurodegenerative diseases, which arise in large part from clearance defects in the Macroautophagy/autophagy-lysosome system. The autophagy cargo receptor SQSTM1/p62 plays a major role in the clearance of ubiquitinated cargo through Ser403 phosphorylation by multiple kinases. However, no phosphatase is known to physiologically dephosphorylate SQSTM1 on this activating residue. RNAi-mediated knockdown and overexpression experiments using genetically encoded fluorescent reporters and defined mutant constructs in cell lines, primary neurons, and brains show that SSH1, the canonical CFL (cofilin) phosphatase, mediates the dephosphorylation of phospho-Ser403-SQSTM1, thereby impairing SQSTM1 flux and phospho-MAPT/tau clearance. The inhibitory action of SSH1 on SQSTM1 is fully dependent on SQSTM1 Ser403 phosphorylation status and is separable from SSH1-mediated CFL activation. These findings reveal a unique action of SSH1 on SQSTM1 independent of CFL and implicate an inhibitory role of SSH1 in SQSTM1-mediated clearance of autophagic cargo, including phospho-MAPT/tau.
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This article considers a partially linear panel data model with fixed individual and time effects in a setting where both N and T are large. Based on the within transformation and profile likelihood method, we propose an approach ...
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This article considers a partially linear panel data model with fixed individual and time effects in a setting where both N and T are large. Based on the within transformation and profile likelihood method, we propose an approach to estimating the parametric and non parametric components of the partially linear model. The resultant estimators are shown to be consistent and asymptotically normal. Monte Carlo simulations are also conducted to illustrate the finite-sample performance of the proposed estimators.
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With the expansion of wind power grid integration, the challenges of sharp fluctuations and high uncertainty in preparing the power grid day-ahead plan and short-term dispatching are magnified. These challenges can be overcome thr...
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With the expansion of wind power grid integration, the challenges of sharp fluctuations and high uncertainty in preparing the power grid day-ahead plan and short-term dispatching are magnified. These challenges can be overcome through accurate short-term wind power process prediction based on mining historical operation data and taking full advantage of meteorological forecast information. In this paper, adopting the ERA5 reanalysis dataset as input, a short-term wind power prediction framework is proposed, combining light gradient boosting machine (LightGBM), mutual information coefficient (MIC) and nonparametric regression. Primarily, the reanalysis data of ERA5 provide more meteorological information for the framework, which can help improve the model input features. Furthermore, MIC can identify effective feature subsets from massive feature sets that significantly affect the output, enabling concise understanding of the output. Moreover, LightGBM is a prediction method with a stronger ability of goodness-of-fit, which can fully mine the effective information of wind power historical operation data to improve the prediction accuracy. Eventually, nonparametric regression expands the process prediction to interval prediction, which significantly improves the utility of the prediction results. To quantitatively analyze the prediction results, five evaluation criteria are used, namely, the Pearson correlation coefficient (CORR), the root mean square error (RMSE), the mean absolute error (MAE), the index of agreement (IA) and Kling–Gupta efficiency (KGE). Compared with support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost) models, the present framework can make full use of meteorological information and effectively improve the prediction accuracy, and the generated output prediction interval can also be used to promote the safe operation of power systems.
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For large sparsely gauged basins, it is difficult to estimate long-duration probable maximum precipitation (PMP) and probable maximum flood (PMF) due to insufficient observed data and precipitation spatial distribution uncertainty...
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For large sparsely gauged basins, it is difficult to estimate long-duration probable maximum precipitation (PMP) and probable maximum flood (PMF) due to insufficient observed data and precipitation spatial distribution uncertainty. In this paper, a framework coupling the China Grid Daily Precipitation Datasets (CGDPDs) with Soil and Water Assessment Tool (SWAT) was proposed to estimate the 15-day PMP and PMF for the sparsely gauged Upper Nujiang River Basin (with a drainage area of 73,484 km(2)). CGDPD was tested against the observations and further corrected considering the error distribution characteristics. Results showed that 1-, 3-, 7- and 15-day maximum areal precipitations based on the corrected CGDPD were 17, 7, 4 and 18% larger than those calculated only by six observed stations' precipitation. Then CGDPD was used as the precipitation data to estimate PMP. For the spatial distribution of PMP, the 15-day PMP process on the sub-basin scale (PMPsub-basin) could be obtained with the following procedure. First, the basin's 15-day areal PMP was estimated. Among this estimation, the maximum 3-day PMP was estimated by moisture maximization, while the remaining 12-day PMP was estimated with the combined storm obtained by the similar process substitution method. Second, the model storm amplification approach based on water balance principle was used to distribute the areal PMP to each sub-basin to obtain the PMPsub-basin at all 27 sub-basins. The designed PMF could be finally estimated through inputting PMPsub-basin into SWAT. In comparison with PMF derived from PMP without spatial distribution, different duration PMFs could increase by 3-15% when considering PMP spatial distribution uncertainty. This study could provide a reasonable procedure to estimate long-duration PMP and PMF for similar basins.
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