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The fungus Candida albicans frequently colonizes the human gastrointestinal tract, from which it can disseminate to cause systemic disease. This polymorphic species can transition between growing as single-celled yeast and as multicellular hyphae to adapt to its environment. The current dogma of C. albicans commensalism is that the yeast form is optimal for gut colonization, whereas hyphal cells are detrimental to colonization but critical for virulence 1 – 3 . Here, we reveal that this paradigm does not apply to multi-kingdom communities in which a complex interplay between fungal morphology and bacteria dictates C. albicans fitness. Thus, whereas yeast-locked cells outcompete wild-type cells when gut bacteria are absent or depleted by antibiotics, hyphae-competent wild-type cells outcompete yeast-locked cells in hosts with replete bacterial populations. This increased fitness of wild-type cells involves the production of hyphal-specific factors including the toxin candidalysin 4 , 5 , which promotes the establishment of colonization. At later time points, adaptive immunity is engaged, and intestinal immunoglobulin A preferentially selects against hyphal cells 1 , 6 . Hyphal morphotypes are thus under both positive and negative selective pressures in the gut. Our study further shows that candidalysin has a direct inhibitory effect on bacterial species, including limiting their metabolic output. We therefore propose that C. albicans has evolved hyphal-specific factors, including candidalysin, to better compete with bacterial species in the intestinal niche. Both the yeast and hyphal forms of Candida albicans enable colonization of the mammalian gut, with hyphal cells secreting the toxin candidalysin to inihibit bacteria and support fungal commensalism.

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids’ efficient, stable, and economical operation in their initial stages.

Understanding how fungi colonize the GI tract is increasingly recognized as highly relevant to human health. The animal models used to study Candida albicans commensalism commonly rely on altering the host microbiome (via antibiotic treatment or defined diets) to establish successful GI colonization by the C. albicans reference isolate SC5314. Candida albicans is a pathobiont that colonizes multiple niches in the body including the gastrointestinal (GI) tract but is also responsible for both mucosal and systemic infections. Despite its prevalence as a human commensal, the murine GI tract is generally refractory to colonization with the C. albicans reference isolate SC5314. Here, we identify two C. albicans isolates, 529L and CHN1, that stably colonize the murine GI tract in three different animal facilities under conditions where SC5314 is lost from this niche. Analysis of the bacterial microbiota did not show notable differences among mice colonized with the three C. albicans strains. We compared the genotypes and phenotypes of these three strains and identified thousands of single nucleotide polymorphisms (SNPs) and multiple phenotypic differences, including their ability to grow and filament in response to nutritional cues. Despite striking filamentation differences under laboratory conditions, however, analysis of cell morphology in the GI tract revealed that the three isolates exhibited similar filamentation properties in this in vivo niche. Notably, we found that SC5314 is more sensitive to the antimicrobial peptide CRAMP, and the use of CRAMP-deficient mice modestly increased the ability of SC5314 to colonize the GI tract relative to CHN1 and 529L. These studies provide new insights into how strain-specific differences impact C. albicans traits in the host and advance CHN1 and 529L as relevant strains to study C. albicans pathobiology in its natural host niche. IMPORTANCE Understanding how fungi colonize the GI tract is increasingly recognized as highly relevant to human health. The animal models used to study Candida albicans commensalism commonly rely on altering the host microbiome (via antibiotic treatment or defined diets) to establish successful GI colonization by the C. albicans reference isolate SC5314. Here, we characterize two C. albicans isolates that can colonize the murine GI tract without antibiotic treatment and can therefore be used as tools for studying fungal commensalism. Importantly, experiments were replicated in three different animal facilities and utilized three different mouse strains. Differential colonization between fungal isolates was not associated with alterations in the bacterial microbiome but rather with distinct responses to CRAMP, a host antimicrobial peptide. This work emphasizes the importance of C. albicans intraspecies variation as well as host antimicrobial defense mechanisms in defining the outcome of commensal interactions.

Despite previous clinical studies providing some evidence of an association between metformin treatment and polycystic ovary syndrome(PCOS), these findings remain controversial. To investigate whether the association reflect causality, a two-sample Mendelian randomization (MR) method was conducted. Data from genome-wide association studies were analyzed, with the exposure factor being metformin and the outcome variable being PCOS. The inverse variance weighted(IVW) was used as the primary method for MR analysis. In addition, MR-Egger, weighted median, heterogeneity tests, and sensitivity analyses were performed. The initial and validation MR analyses indicated that genetically predicted metformin treatment had no effects on PCOS. Sensitivity analyses provided additional confirmation of the reliability of the MR results. Our two-sample MR analysis did not find genetic evidence supporting a significant association between metformin treatment and PCOS.

Candida albicans is a diploid human fungal pathogen that displays significant genomic and phenotypic heterogeneity over a range of virulence traits and in the context of a variety of environmental niches. Here, we show that the effect of Rob1 on biofilm and filamentation virulence traits is dependent on both the specific environmental condition and the clinical strain of C. albicans . The C. albicans reference strain SC5314 is a ROB1 heterozygote with two alleles that differ by a single nucleotide polymorphism at position 946, resulting in a serine- or proline-containing isoform. An analysis of 224 sequenced C. albicans genomes indicates that SC5314 is the only ROB1 heterozygote documented to date and that the dominant allele contains a proline at position 946. Remarkably, the ROB1 alleles are functionally distinct, and the rare ROB1 946S allele supports increased filamentation in vitro and increased biofilm formation in vitro and in vivo , suggesting it is a phenotypic gain-of-function allele. SC5314 is among the most highly filamentous and invasive strains characterized to date. Introduction of the ROB1 946S allele into a poorly filamenting clinical isolate increases filamentation, and conversion of an SC5314 laboratory strain to a ROB1 946S homozygote increases in vitro filamentation and biofilm formation. In a mouse model of oropharyngeal infection, the predominant ROB1 946P allele establishes a commensal state while the ROB1 946S phenocopies the parent strain and invades into the mucosae. These observations provide an explanation for the distinct phenotypes of SC5314 and highlight the role of heterozygosity as a driver of C. albicans phenotypic heterogeneity. Candida albicans is a commensal fungus that colonizes the human oral cavity and gastrointestinal tract but also causes mucosal as well as invasive disease. The expression of virulence traits in C. albicans clinical isolates is heterogeneous and the genetic basis of this heterogeneity is of high interest. The C. albicans reference strain SC5314 is highly invasive and expresses robust filamentation and biofilm formation relative to many other clinical isolates. Here, we show that SC5314 derivatives are heterozygous for the transcription factor Rob1 and contain an allele with a rare gain-of-function SNP that drives filamentation, biofilm formation, and virulence in a model of oropharyngeal candidiasis. These findings explain, in part, the outlier phenotype of the reference strain and highlight the role heterozygosity plays in the strain-to-strain variation of diploid fungal pathogens.

When the direct-drive electro-hydraulic servo system is used for position control of hydraulic actuators, load unbalance or external disturbances can cause flow imbalance, leading to poor speed stability of the actuator and thus reduced position accuracy. To address this issue, a flow stabilizing valve with a special structure is proposed, which suppresses flow fluctuations through a mechanical passive compensation mechanism. This paper elaborates on the structural innovations, working principle, and operation circuit of the flow stabilizing valve, and compares it with traditional flow control valves to highlight its advantages in low complexity and high reliability. Simulation models with and without the flow stabilizing valve are built in AMESim to analyze speed and displacement characteristics under different load disturbances. The results verify that the valve significantly improves speed stability but introduces a response lag of approximately 3 s. A test platform is constructed to test low-frequency (0.1–0.5 Hz) and low-amplitude (≤30 mm)signals, clarifying the system's applicable scenarios. The results show that the flow stabilizing valve can control the speed deviation caused by load disturbances within 0.0016 m/s by quickly adjusting the flow rate of the hydraulic cylinder, effectively improving position accuracy.

The prognosis of renal cell carcinoma (RCC) remains poor due to metastases and resistance to chemotherapy. Salinomycin (Sal) exhibits the potential of antitumor, while the underlying mechanism is not completely clear. Here, we found that Sal induced ferroptosis in RCCs and identified Protein Disulfide Isomerase Family A Member 4 (PDIA4) as a mediator of Sal’s effect on ferroptosis. Sal suppressed PDIA4 by increasing its autophagic degradation. Downregulation of PDIA4 increased the sensitivity to ferroptosis, while ectopic overexpression of PDIA4 conferred ferroptosis resistance to RCCs. Our data showed that downregulation of PDIA4 suppressed activating transcription factor 4 (ATF4) and its downstream protein SLC7A11 (solute carrier family 7 member 11), thereby aggravating ferroptosis. In vivo, the administration of Sal promoted ferroptosis and suppressed tumor progress in the xenograft mouse model of RCC. Bioinformatical analyses based on clinical tumor samples and database indicated a positive correlation exists between PDIA4 and PERK/ATF4/SLC7A11 signaling pathway, as well as the malignant prognosis of RCCs. Together, our findings reveal that PDIA4 promotes ferroptosis resistance in RCCs. Treatment of Sal sensitizes RCC to ferroptosis via suppressing PDIA4, suggesting the potential therapeutical application in RCCs.

Low visibility weather easily leads to traffic accidents, posing threats to human life and property. To accurately forecast visibility, we conduct an empirical study focusing on Jiangsu Province. Firstly, we collect the monitoring data from meteorological stations and environmental stations for 2017-2018. Secondly, we analyze the changes in visibility from both spatial and temporal perspectives. Next, the maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select factors affecting visibility, finding that humidity and P M 2.5 concentrations are the primary factors. Finally, we propose GCN-GRU (Graph Convolutional Network and Gated Recurrent Unit) model for short-term visibility forecasting, which employs GCN to capture the interactions between stations and uses GRU to learn the interactions between times. Experimental results indicate that GCN-GRU outperforms the standalone GRU model and three machine learning models regarding 6-hour visibility forecasting. Compared to the best competitor, GCN-GRU achieves an average increase of 3.32% in Correlation Coefficient (CORR), a decrease of 17.52% in Root Mean Square Error (RMSE), a reduction of 26.62% in Mean Absolute Percentage Error (MAPE), and a decline of 16.53% in Mean Absolute Error (MAE).

The drug resistance and low response rates of immunotherapy limit its application. This study aimed to construct a new nanoparticle (CaCO -polydopamine-polyethylenimine, CPP) to effectively deliver interleukin-12 (IL-12) and suppress cancer progress through immunotherapy. The size distribution of CPP and its zeta potential were measured using a Malvern Zetasizer Nano-ZS90. The morphology and electrophoresis tentative delay of CPP were analyzed using a JEM-1400 transmission electron microscope and an ultraviolet spectrophotometer, respectively. Cell proliferation was analyzed by MTT assay. Proteins were analyzed by Western blot. IL-12 and HMGB1 levels were estimated by ELISA kits. Live/dead staining assay was performed using a Calcein-AM/PI kit. ATP production was detected using an ATP assay kit. The xenografts in vivo were estimated in C57BL/6 mice. The levels of CD80 /CD86 , CD3 /CD4 and CD3 /CD8 were analyzed by flow cytometry. CPP could effectively express EGFP or IL-12 and increase ROS levels. Laser treatment promoted CPP-IL-12 induced the number of dead or apoptotic cell. CPP-IL-12 and laser could further enhance CALR levels and extracellular HMGB1 levels and decrease intracellular HMGB1 and ATP levels, indicating that it may induce immunogenic cell death (ICD). The tumors and weights of xenografts in CPP-IL-12 or laser-treated mice were significantly reduced than in controls. The IL-12 expression, the CD80 /CD86 expression of DC from lymph glands, and the number of CD3 /CD8 T or CD3 /CD4 T cells from the spleen increased in CPP-IL-12-treated or laser-treated xenografts compared with controls. The levels of granzyme B, IFN-γ, and TNF-α in the serum of CPP-IL-12-treated mice increased. Interestingly, CPP-IL-12 treatment in local xenografts in the back of mice could effectively inhibit the growth of the distant untreated tumor. The novel CPP-IL-12 could overexpress IL-12 in melanoma cells and achieve immunotherapy to melanoma through inducing ICD, activating CD4 T cell, and enhancing the function of tumor-reactive CD8 T cells.

Landslides are highly destructive geological disasters that pose a serious threat to the safety of people’s lives and property. In this study, historical records of landslides in Yunnan Province, along with eight underlying factors of landslide (elevation, slope, aspect, lithology, land cover type, normalized difference vegetation index (NDVI), soil type, and average annual precipitation (AAP)), as well as historical rainfall and current rainfall data were utilized. Firstly, we analyzed the sensitivity of each underlying factor in the study area using the frequency ratio (FR) method and obtained a landslide susceptibility map (LSM). Then, we constructed a regional rainfall-induced landslides (RIL) probability forecasting model based on machine learning (ML) algorithms and divided warning levels. In order to construct a better RIL prediction model and explore the effects of different ML algorithms and input values of the underlying factor on the model, we compared five ML classification algorithms: extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms and three representatives of the input values of the underlying factors. The results show that among the obtained forecasting models, the LSM-based RF model performs the best, with an accuracy (ACC) of 0.906, an area under the curve (AUC) of 0.954, a probability of detection (POD) of 0.96 in the test set, and a prediction accuracy of 0.8 in the validation set. Therefore, we recommend using RF-LSM model as the RIL forecasting model for Yunnan Province and dividing warning levels.