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The Vlasov–Poisson–Fokker–Planck (VPFP) system is a fundamental model in plasma physics that describes the Brownian motion of a large ensemble of particles within a surrounding bath. Under the high-field scaling, both collision and field are dominant. This paper introduces two Asymptotic-Preserving Neural Network (APNN) methods within a physics-informed neural network (PINN) framework for solving the VPFP system in the high-field regime. These methods aim to overcome the computational challenges posed by high dimensionality and multiple scales of the system. The first APNN method leverages the micro–macro decomposition model of the original VPFP system, while the second is based on the mass conservation law. Both methods ensure that the loss function of the neural networks transitions naturally from the kinetic model to the high-field limit model, thereby preserving the correct asymptotic behavior. Through extensive numerical experiments, these APNN methods demonstrate their effectiveness in solving multiscale and high dimensional uncertain problems, as well as their broader applicability for problems with long time duration and non-equilibrium initial data.

Global food wastage equates to about 1.3 billion tons per year, which causes serious environmental impacts. The objective of this study was to evaluate the influences of addition of digestate from food waste in comparison to a synthetic liquid urea ammonium nitrate solution on plant growth, rhizosphere bacterial community composition and diversity, and hyphal abundance of arbuscular mycorrhizal (AM) fungi. Plant and soil samples were collected at 25, 50, and 75 days after seedling emergence. Annual ryegrass growth was significantly increased by both liquid urea ammonium nitrate and digestate, and digestate was just as effective as liquid urea ammonium nitrate. Additionally, digestate (50 kg N ha−1) significantly increased AM fungal hyphae density. Liquid urea ammonium nitrate (50 kg N ha−1) significantly decreased AM fungal hyphae density compared with liquid urea ammonium nitrate (25 kg N ha−1) at DAE 75. Digestate and liquid urea ammonium nitrate applications significantly shifted the bacterial community composition and OTU richness and changed the abundance of microbial C and N-cycling genes, while application rates had no significant effect. Structural equation modeling showed that digestate and UAN addition both directly and indirectly affected bacterial, C and N cycling genes community composition; the indirect effects were related to increased soil NO3− content and reduced pH. This study showed that the use of digestate as a soil amendment can be environmentally effective and can provide a sustainable supply of nutrients that increases soil organic C. Moreover, the use of digestate can readily be incorporated into agricultural practices with potentially less impact on soil microflora diversity and function than conventional fertilizers.

Pigeon-inspired optimization (PIO) is a new type of intelligent algorithm. It is proposed that the algorithm simulates the movement of pigeons going home. In this paper, a new pigeon herding algorithm called compact pigeon-inspired optimization (CPIO) is proposed. The challenging task for multiple algorithms is not only combining operations, but also constraining existing devices. The proposed algorithm aims to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. A real-valued prototype vector performs probability and statistical calculations, and then generates optimal candidate solutions for CPIO optimization algorithms. The CPIO algorithm was used to evaluate a variety of continuous multi-model functions and the largest model of hydropower short-term generation. The experimental results show that the proposed algorithm is a more effective way to produce competitive results in the case of limited memory devices.

The integrated stress response manifests with the phosphorylation of eukaryotic initiation factor 2α (eIF2α) on serine residue 51 and plays a major role in the adaptation of cells to endoplasmic reticulum stress in the initiation of autophagy and in the ignition of immune responses. Here, we report that lysosomotropic agents, including azithromycin, chloroquine, and hydroxychloroquine, can trigger eIF2α phosphorylation in vitro (in cultured human cells) and, as validated for hydroxychloroquine, in vivo (in mice). Cells bearing a non-phosphorylatable eIF2α mutant (S51A) failed to accumulate autophagic puncta in response to azithromycin, chloroquine, and hydroxychloroquine. Conversely, two inhibitors of eIF2α dephosphorylation, nelfinavir and salubrinal, enhanced the induction of such autophagic puncta. Altogether, these results point to the unexpected capacity of azithromycin, chloroquine, and hydroxychloroquine to elicit the integrated stress response.

Signal transduction activated by Wingless/Wnt ligands directs cell proliferation and fate specification in metazoans, and its overactivation underlies the development of the vast majority of colorectal cancers. In the conventional model, the secretion and movement of Wingless to cells distant from its source of synthesis are essential for long-range signaling in tissue patterning. However, this model was upended recently by an unanticipated finding: replacement of wild-type Drosophila Wingless with a membrane-tethered form produced viable adults with largely normal external morphology, which suggested that Wingless secretion and movement are dispensable for tissue patterning. Herein, we tested this foundational principle in the adult intestine, where Wingless signaling gradients coincide with all major boundaries between compartments. We find that the critical roles of Wingless during adult intestinal development, which include regulation of target gene activation, boundary formation, stem cell proliferation, epithelial cell fate specification, muscle differentiation, gut folding, and signaling crosstalk with the Decapentaplegic pathway, are all disrupted by Wingless tethering. These findings provide new evidence that supports the requirement for the direct, long-range action of Wingless in tissue patterning, with relevance for animal development, tissue homeostasis and Wnt-driven disease.

Intestinal stem cell (ISC) self-renewal and proliferation are directed by Wnt/β-catenin signaling in mammals, whereas aberrant Wnt pathway activation in ISCs triggers the development of human colorectal carcinoma. Herein, we have utilized the Drosophila midgut, a powerful model for ISC regulation, to elucidate the mechanisms by which Wingless (Wg)/Wnt regulates intestinal homeostasis and development. We provide evidence that the Wg signaling pathway, activation of which peaks at each of the major compartment boundaries of the adult intestine, has essential functions. Wg pathway activation in the intestinal epithelium is required not only to specify cell fate near compartment boundaries during development, but also to control ISC proliferation within compartments during homeostasis. Further, in contrast with the previous focus on Wg pathway activation within ISCs, we demonstrate that the primary mechanism by which Wg signaling regulates ISC proliferation during homeostasis is non-autonomous. Activation of the Wg pathway in absorptive enterocytes is required to suppress JAK-STAT signaling in neighboring ISCs, and thereby their proliferation. We conclude that Wg signaling gradients have essential roles during homeostasis and development of the adult intestine, non-autonomously controlling stem cell proliferation inside compartments, and autonomously specifying cell fate near compartment boundaries.

Many plants have multiple chemical components and multiple targets, and their potential effects on diseases are the integrative effects of multiple targets. How to systematically reveal the integrated multi‐targets effect of plants on diseases is not only a challenge, but also an innovation. This study developed a novel research method based on artificial intelligence and took hawthorn as an example; a deep auto‐encoding neural network model was used to encode the expression levels of multiple common targets between hawthorn and atherosclerosis in each cell of the single‐cell transcriptome of atherosclerotic perivascular adipose tissue (PVAT) as an integrated value (MTIS). The landscape and quantitative mapping of multi‐targets potential integrated effect of plants on disease at the single‐cell level would be achieved based on this innovative approach, and in‐depth analysis such as MTIS comparisons, MTIS‐pseudotime difference analysis, cell communication analysis, and immune infiltration analysis, was performed to reveal the potential mechanism and landscapes of hawthorn on the PVAT microenvironment of atherosclerotic. Due to many plants for disease having multiple chemical compositions and multiple targets, the novel method proposed in this study may have a wide range of applications.

BackgroundPharmacological autophagy enhancement constitutes a preclinically validated strategy for preventing or treating most major age-associated diseases. Driven by this consideration, we performed a high-content/high-throughput screen on 65 000 distinct compounds on a robotized fluorescence microscopy platform to identify novel autophagy inducers.ResultsHere, we report the discovery of picropodophyllin (PPP) as a potent inducer of autophagic flux that acts on-target, as an inhibitor of the tyrosine kinase activity of the insulin-like growth factor-1 receptor (IGF1R). Thus, PPP lost its autophagy-stimulatory activity in cells engineered to lack IGF1R or to express a constitutively active AKT serine/threonine kinase 1 (AKT1) mutant. When administered to cancer-bearing mice, PPP improved the therapeutic efficacy of chemoimmunotherapy with a combination of immunogenic cytotoxicants and programmed cell death 1 (PDCD1, better known as PD-1) blockade. These PPP effects were lost when tumors were rendered PPP-insensitive or autophagy-incompetent. In combination with chemotherapy, PPP enhanced the infiltration of tumors by cytotoxic T lymphocytes, while reducing regulatory T cells. In human triple-negative breast cancer patients, the activating phosphorylation of IGF1R correlated with inhibited autophagy, an unfavorable local immune profile, and poor prognosis.ConclusionAltogether, these results suggest that IGF1R may constitute a novel and druggable therapeutic target for the treatment of cancer in conjunction with chemoimmunotherapies.

This study explores the link between smartphone addiction in senior high-school students, parent–child relationship, loneliness, and self-efficacy on the basis of the theory of planned behavior (TPB) and social cognitive theory (SCT). A survey of 2172 students (1205 female students, 966 male students; mean age = 16.58 years, SD = 0.78) from 32 senior high schools in Taiwan was conducted. Moderation mediation analysis was performed using Model 14 of SPSS PROCESS-macro to test the hypotheses of this study. The result showed that the parent–child relationship was negatively related both to smartphone addiction and loneliness, which mediated the link between parent–child relationship and smartphone addiction. Self-efficacy was also found to moderate the level of loneliness related to smartphone addiction. Specifically, loneliness will ease when the parent–child relationship improves, and smartphone addiction will accordingly lessen. It was also discovered that the elevation of self-efficacy could mitigate the level of addiction. Lastly, this study provided parents, education agencies, and other policymakers in the education sector with implications based on these findings. Preventive measures for smartphone addiction and recommendations for future investigations are also given.

The Pigeon-Inspired Optimization (PIO) algorithm is an intelligent algorithm inspired by the behavior of pigeons returned to the nest. The binary pigeon-inspired optimization (BPIO) algorithm is a binary version of the PIO algorithm, it can be used to optimize binary application problems. The transfer function plays a very important part in the BPIO algorithm. To improve the solution quality of the BPIO algorithm, this paper proposes four new transfer function, an improved speed update scheme, and a second-stage position update method. The original BPIO algorithm is easier to fall into the local optimal, so a new speed update equation is proposed. In the simulation experiment, the improved BPIO is compared with binary particle swarm optimization (BPSO) and binary grey wolf optimizer (BGWO). In addition, the benchmark test function, statistical analysis, Friedman’s test and Wilcoxon rank-sum test are used to prove that the improved algorithm is quite effective, and it also verifies how to set the speed of dynamic movement. Finally, feature selection was successfully implemented in the UCI data set, and higher classification results were obtained with fewer feature numbers.