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Autophagy is a highly conserved process that degrades certain intracellular contents in both physiological and pathological conditions. Autophagy-related proteins ( ) are key players in this pathway, among which is indispensable in both canonical and non-canonical autophagy. Recent studies demonstrate that modulates the immune system and crosstalks with apoptosis. However, our knowledge of the pathogenesis and regulatory mechanisms of autophagy in various immune related diseases is lacking. Thus, a deeper understanding of 's role in the autophagy mechanism may shed light on the link between autophagy and the immune response, and lead to the development of new therapies for autoimmune diseases and autoinflammatory diseases. In this focused review, we discuss the latest insights into the role of in autoimmunity. Although these studies are at a relatively early stage, may eventually come to be regarded as a \"guardian of immune integrity.\" Notably, accumulating evidence indicates that other genes may have similar functions.

Developing urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations. Plain Language Summary Predicting the velocity field in the urban area with fine resolution at the meter scale is computationally expensive. Yet a detailed velocity field is necessary for improving the accuracy of urban land surface representation in weather and climate models. We propose using a convolutional neural network to predict the velocity field from the three‐dimensional (3D) building distribution. The similarity between the predicted velocity fields and LES simulations in the testing geometries illustrates the prediction capability of the trained model. We also investigate the aerodynamic drag coefficient, a key parameter for quantifying the land‐atmosphere momentum exchange. The results indicate that the trained model prediction is much closer to values derived from large‐eddy simulation models than those from the default parameterization scheme, showing the promise of using machine learning to improve urban land surface modeling. Key Points Machine learning (ML) can help develop city‐specific parameterization that fully utilizes urban form data It is a first attempt to develop an ML model for high‐Reynolds number urban canopy flow with multiple bluff‐body obstacles Limitation of the geometry to flow field approach is quantified by accessing the extrapolative capability of the trained model

Polycystic ovary syndrome (PCOS) is a prevalent hormonal disorder of premenopausal women worldwide and is characterized by reproductive, endocrine, and metabolic abnormalities. The clinical manifestations of PCOS include oligomenorrhea or amenorrhea, hyperandrogenism, ovarian polycystic changes, and infertility. Women with PCOS are at an increased risk of suffering from type 2 diabetes; me\\tabolic syndrome; cardiovascular events, such as hypertension, dyslipidemia; gynecological diseases, including infertility, endometrial dysplasia, endometrial cancer, and ovarian malignant tumors; pregnancy complications, such as premature birth, low birthweight, and eclampsia; and emotional and mental disorders in the future. Although numerous studies have focused on PCOS, the underlying pathophysiological mechanisms of this disease remain unclear. Mitochondria play a key role in energy production, and mitochondrial dysfunction at the cellular level can affect systemic metabolic balance. The recent wide acceptance of functional mitochondrial disorders as a correlated factor of numerous diseases has led to the presupposition that abnormal mitochondrial metabolic markers are associated with PCOS. Studies conducted in the past few years have confirmed that increased oxidative stress is associated with the progression and related complications of PCOS and have proven the relationship between other mitochondrial dysfunctions and PCOS. Thus, this review aims to summarize and discuss previous and recent findings concerning the relationship between mitochondrial dysfunction and PCOS.

The extremely weak heterointerface construction of high‐entropy materials (HEM) hinders them being the electromagnetic wave (EMW) absorbers with ideal properties. To address this issue, this study proposes multiphase interfacial engineering and results in a multiphase‐induced interfacial polarization loss in multielement sulfides. Through the selection of atoms with diverse reaction activities, the multiphase interfacial components of CuS (1 0 5), Fe0.5Ni0.5S2 (2 1 0), and CuFe2S3 (2 0 0) are constructed to enhance the interfacial polarization loss in multielement Cu‐based sulfides. Compared with single‐phase high‐entropy Zn‐based sulfides (ZnFeCoNiCr‐S), the multiphase Cu‐based sulfides (CuFeCoNiCr‐S) possess optimized EMW absorption properties (effective absorption bandwidth (EAB) of 6.70 GHz at 2.00 mm) due to the existence of specific interface of CuS (1 0 5)/CuFe2S3 (2 0 0) with proper EM parameters. Furthermore, single‐phase ZnFeCoNiCr‐S into FeNi2S4 (3 1 1)/(Zn, Fe)S (1 1 1) heterointerface through 400 °C heat‐treated is decomposed. The EMW absorption properties are enhanced by strong interfacial polarization (EAB of 4.83 GHz at 1.45 mm). This work reveals the reasons for the limited EMW absorption properties of high‐entropy sulfides and proposes multiphase interface engineering to improve charge accumulation and polarization between specific interfaces, leading to the enhanced EMW absorption properties. This work shows that the weak electron exchange effect induced by high conformational entropy adversely affects microwave absorption. Constructing the heterointerfaces with significantly different work functions in multi‐element sulfides can effectively enhance the phase interface polarization and eliminate these negative effects. This is expected to serve as a new design guideline for microwave absorbers.

Background Inclusion of individuals with diverse or admixed genetic ancestries is crucial to discover novel findings that may be missed by genomics analyses rooted solely in European population. Results Here, we present an analysis framework, SPAmix, which is scalable to a large-scale biobank data analysis including hundreds of thousands of admixed individuals and is universally applicable to various types of complex traits including quantitative traits, time-to-event traits, ordinal traits, and longitudinal traits. Since no alternative model is fitted, SPAmix primarily focuses on association p values. For each genetic variant, SPAmix uses genotype data and genetic principal components to estimate individual-specific allele frequency, which is subsequently used to calibrate p values via a retrospective analysis. A hybrid strategy including saddlepoint approximation (SPA) can greatly increase the accuracy to analyze rare genetic variants, especially if the phenotypic distribution is unbalanced or extremely unbalanced. We also propose SPAmix local to incorporate local ancestry to calculate ancestry-specific p values. To maximize the statistical powers, SPAmix CCT is proposed to combine the p values of SPAmix and SPAmix local via Cauchy combination. Conclusions The SPAmix-based approaches are more accurate than Tractor to address phenotypic variance heterogeneity among ancestries when analyzing quantitative traits and to address an unbalanced case–control ratio when analyzing binary traits. SPAmix CCT is an optimal unified approach for various cross-ancestry genetic architectures. Extensive simulation studies and real data analyses of 369,314 UK Biobank individuals from multiple ancestries demonstrated that SPAmix is scalable and can discover novel hits while controlling type I error rates well.

Chlorogenic acid (CA) is a phenolic compound commonly found in human plant-based diets. CA is the main component of many traditional Chinese medicine preparations, and in recent years, it has been found to have hypoglycemic, hypolipidemic, anti-inflammatory, antioxidant, and other pharmacological properties. Specifically, CA relieves the effects of, and prevents, diabetes mellitus (DM). In addition, CA is also beneficial against complications arising from DM, such as diabetic nephropathy (DN), diabetic retinopathy (DR), and diabetic peripheral neuropathy (DPN). Herein, we review the use of CA in the prevention and treatment of DM and its complications, providing a background for further research and medical uses.

Baicalin is reported as an effective drug for ulcerative colitis (UC). However, its effect on gut microbiota and short-chain fatty acids (SCFAs) remains unknown. In this study, we investigated the role of baicalin on Th17/Treg balance, gut microbiota community, and SCFAs levels in trinitrobenzene sulphonic acid (TNBS)-induced UC rat model. We found the DAI scores were significantly increased in the TNBS-treated rats, while reduced in the baicalin-treated group in a dose-dependent manner, accompanied with the alleviation of mucosal injury, the reduction of ZO-1, Occludin, and MUC2 expression. At the meanwhile, baicalin repressed the increased levels of reactive oxygen species (ROS) and MDA, while deceased the GSH and SOD levels in colon tissue of rats treated with TNBS. On the other hand, administration of baicalin attenuated the TNBS-induced upregulations of Th17/Treg ratio, indicating a strong amelioration in the colorectal inflammation. More importantly, pyrosequencing of the V4 regions of 16S rRNA genes in rat feces revealed a deviation of the gut microbiota in response to baicalin treatment. In particular, the decreased Firmicutes-to-Bacteroidetes ratios and endotoxin-bearing Proteobacteria levels indicated that baicalin reversed TNBS-induced gut dysbiosis OTUs. In addition, we further investigated the fecal levels of major SCFAs in rats and found that baicalin significantly resorted the fecal butyrate levels in rats treated with TNBS. The increased butyrate levels were in consistent with the higher abundance of butyrate-producing species such as Butyricimonas spp., Roseburia spp., Subdoligranulum spp., and Eubacteriu spp. in baicalin-treated group. In conclusion, our findings suggest that baicalin possibly protected rats against ulcerative colitis by regulation of Th17/Treg balance, and modulation of both gut microbiota and SCFAs. Baicalin may be used as a prebiotic agent to treat ulcerative colitis-associated inflammation and gut dysbiosis.

Psoriasis is a chronic inflammatory immune skin disease mediated by genetic and environmental factors. As a bridge between innate and adaptive immunity, mast cells are involved in the initiation, development, and maintenance of psoriasis by interactions and communication with a variety of cells. The current review describes interactions of mast cells with T cells, Tregs, keratinocytes, adipocytes, and sensory neurons in psoriasis to emphasize the important role of mast cell-centered cell networks in psoriasis.

A bstract Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to the traditional jet substructure analysis. In this work, the Mask R-CNN framework is adopted to reconstruct Higgs jets in collider-like events, with the effects of pileup contamination taken into account. This automatic jet reconstruction method achieves higher efficiency of Higgs jet detection and higher accuracy of Higgs boson four-momentum reconstruction than traditional jet clustering and jet substructure tagging methods. Moreover, the Mask R-CNN trained on events containing a single Higgs jet is capable of detecting one or more Higgs jets in events of several different processes, without apparent degradation in reconstruction efficiency and accuracy. The outputs of the network also serve as new handles for the t t ¯ background suppression, complementing to traditional jet substructure variables.