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Abstract Metabolic imbalance leading to inflammatory hypoxia and stabilization of hypoxia-inducible transcription factors (HIFs) is a hallmark of inflammatory bowel diseases. We hypothesize that HIF could be stabilized in CD4+ T cells during intestinal inflammation and alter the functional responses of T cells via regulation of microRNAs. Our assays reveal markedly increased T cell-intrinsic hypoxia and stabilization of HIF protein during experimental colitis. microRNA screen in primary CD4+ T cells points us towards miR-29a and our subsequent studies identify a selective role for HIF-2α in CD4-cell-intrinsic induction of miR-29a during hypoxia. Mice with T cell-intrinsic HIF-2α deletion display elevated T-bet (target of miR-29a) levels and exacerbated intestinal inflammation. Mice with miR-29a deficiency in T cells show enhanced intestinal inflammation. T cell-intrinsic overexpression of HIF-2α or delivery of miR-29a mimetic dampen TH1-driven colitis. In this work, we show a previously unrecognized function for hypoxia-dependent induction of miR-29a in attenuating TH1-mediated inflammation.

Abstract The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

Aiming at the problems of large capacity, narrow transverse width, large excitation, high safety level, and difficulty in accurately grasping the working state of the cable-stayed bridge for the long-span track, this research obtains the structural response data in real time by establishing a health monitoring system. The adaptive filtering method was employed to separate the train load response and the temperature load response. Then, a train load effect analysis method based on the influence line and a temperature load effect analysis method based on the correlation were proposed to assess the operational status of the bridge in real time and objectively. The Chongqing Nanjimen Railway Track Bridge (hereinafter Chongqing Nanjimen track bridge) project was utilized as a case study to demonstrate the application of these methods. The results show that the adaptive filtering method can effectively separate the response of train and temperature loads. The normalized cross-correlation (NCC) results of the measured train load response and the influence line’s finite element calculation show a high degree of fit between the measured values and the theory, proving that no significant anomalies are found in the bridge. There is a strong correlation between the ambient temperature difference and the Pearson correlation coefficient of structural response, which indicates that the Chongqing Nanjimen track bridge is currently in normal working condition.

Aiming at the shortcomings of traditional contact cable force monitoring technology in accuracy, efficiency, and applicability, an assessment method based on microwave radar measurements is proposed to measure a sloping cable with a damper for lengths greater than 200 m in this study. A formula for calculating the cable-stayed force with a damper is derived, and an intelligent cable force monitoring platform is developed based on cloud technology. Based on the Chongqing Nanjimen Railway Bridge, a real bridge test was carried out. It was indicated that the microwave radar method could be used to freely adjust the measurement angle and possessed high applicability and penetration. It significantly improved the measurement accuracy and efficiency of cables with a damper for lengths greater than 200 m. It has good application value for the solution of the problems of complicated operation and high costs in the monitoring of cables with a damper. The formula for calculating the cable force with a damper was proven to be reliable and accurate when compared to the results of direct calculation and the equivalent cable length method. It was able to significantly reduce the calculation error of the cable force caused by the influence of the damper. Additionally, the intelligent cable force monitoring platform was utilized to enhance the level of digitization, providing technical support for the scientific management and maintenance of bridges.

The precise segmentation of the glomerular basement membrane (GBM) can aid pathologists in making accurate pathological diagnoses. However, conventional methods solely focus on segmenting GBM from the background, disregarding the interconnections between GBM and its similar surrounding tissues, which leads to imprecise boundary segmentation of GBM. To address this issue, we employed a multi-category segmentation method to model the distinctions and interconnections between GBM and its similar surrounding tissues. Our experimental results demonstrate that this method can more accurately segment GBM with blurred boundaries. Historically, scholars have primarily used convolution to build models. This approach has limitation that only local information is modeled without effectively extracting global information. To address this issue, we propose a more reasonable structure combining convolution and attention, which we call the Self-Reinforcing Attention Mechanism. Experimental results indicate that the addition of the attention mechanism can help the segmentation of GBM by yielding more continuous boundaries. Finally, we incorporate the feature maps of each layer of the model in the loss function, allowing the model to focus semantic information at varying scales while also providing control over the model’s focus by adjusting the weight. Our experimental results demonstrated that the proposed method has higher performance and better generalization ability than the state-of-the-art approaches.

Respiratory activity is the most important basic life activity of human body, respiratory state detection is of great practical significance. Based on the characteristic of high correlation between the change of tidal volume and the change of abdominal displacement, a method of detecting respiratory status through abdominal displacement data is proposed. The method uses a gas pressure sensor to collect the tidal volume in the steady state of the subject once, which is used as the baseline data. The abdominal displacement data of the subject in the three breathing states of slow breathing, steady breathing and rapid breathing were collected with an acceleration sensor. The warp path distance between the lung and abdominal data in the three different states was calculated, this warp path distance together with the period extracted from the abdominal data is used as a two-dimensional feature and input to the support vector machine classifier. The experiments show that the accuracy of the classification results reaches 90.23%. The method only needs to measure the lung data once in smooth breathing state, and the subsequent continuous detection is achieved by measuring the displacement of the abdomen only. This method has the advantages of stable and reliable acquisition results, low implementation cost and simplified wearing method, and has high practicality.

Using thin-section images to estimate core permeability is an economical and less time-consuming method for reservoir evaluation, which is a goal that many petroleum developers aspire to achieve. Although three-dimensional (3D) pore volumes have been successfully applied to train permeability models, it is very expensive to carry out. In this regard, deriving permeability from two-dimensional (2D) images presents a novel approach in which data are fitted directly on the basis of pore-throat characteristics extracted from more cost-effective thin sections. This work proposes a Fluid–MLP workflow for estimating 3D permeability models. We employed DIA technology combined with artificial lithology and pore classification to calculate up to 110 characteristic parameters of the pore-throat structure on the basis of 2D rock cast thin sections. The MLP network was adopted to train the permeability prediction model, utilizing these 110 parameters as input. However, the accuracy of the conventional MLP network only reached 90%. We propose data preprocessing using fluid flow simulations to improve the training accuracy of the MLP network. The fluid flow simulations involve generating a pore network model based on the 2D pore size distribution, followed by employing the lattice Boltzmann method to estimate permeability. Subsequently, six key structural parameters, including permeability calculated by LBM, pore type, lithology, two-dimensional porosity, average pore–throat ratio, and average throat diameter, were fed into the MLP network for training to form a new Fluid–MLP workflow. Comparing the results predicted using this new Fluid–MLP workflow with those of the original MLP network, we found that the Fluid–MLP network exhibited superior predictive performance.

The Cenozoic strata of Yinggehai Basin is rich in natural gas, and exploration breakthroughs have been achieved in the middle and deep strata of the basin in recent years. The discovery of gas fields in the Ledong 10 area had attracted concerning for the migration and accumulation of natural gas in lithologic reservoirs in the middle and deep strata of nondiapir structures. The natural gas reservoir in the Miocene Huangliu Formation in the Ledong 10 block appeared to have a high content of CO2. The characteristics and periods of CO2 migration to natural gas reservoirs were discussed by the approaches including Laser Raman spectroscopy and combined with inclusion petrographic observation. Five types of fluid inclusions were classified, including brine inclusions, CO2 inclusions, gas-phase CH4 inclusions, gas-mixed inclusions and gas-mixed brine inclusions. According to the homogenization temperature and density of CO2 inclusions measured by Laser Raman and microthermometry analysis, and combined with the estimation of the capture conditions of CO2 inclusions, sedimentary characteristics, burial history as well as isotopic composition of CO2, two-stage filling mode for CO2 gas reservoirs was then concluded. The first stage was high-density inorganic-origin CO2 charged between 1.4-0.9 Ma; the second stage was medium-density inorganic CO2 charged between 0.7-0.4 Ma.

Intestinal IL-22 has important regulatory effects on the barrier and intestinal diseases and its production is controlled by the intestinal microbiome. Here the authors show that intestinal immune cell production of IL-22 is regulated by short chain fatty acids via an aryl hydrocarbon receptor and HIF1α-mediated mechanism that protects mice from intestinal inflammation.

Background & Aims: Regenerating islet-derived protein (REG3γ), an antimicrobial peptide, typically expressed by intestinal epithelial cells (IEC), plays crucial roles in intestinal homeostasis and controlling gut microbiota. However, the mechanisms that regulate IEC expression of REG3γ are still largely unclear. In this study, we investigated whether and how interleukin (IL) 33, an alarmin produced by IEC in response to injury, regulates REG3γ expression in IEC, thus contributing to intestinal homeostasis. Methods: IEC were isolated from wild-type and IL33-/- mice to determine expression of REG3γ and other antimicrobial peptides by quantitative real-time polymerase chain reaction and Western blot. IEC cell lines were used for mechanistic studies. 16S rRNA pyrosequencing analysis was used for measuring gut microbiota. Citrobacter rodentium was used for enteric infections. Results: The expression of REG3γ, but not β-defensins, in IECs of IL33-/- mice was significantly lower than wild-type mice. IL33 treatment induced IEC expression of REG3γ in both mice and human cell lines. Mechanistically, IL33 activated STAT3, mTOR, and ERK1/2 in IEC. Inhibition of these pathways abrogated IL33-induction of REG3γ. IL33-/- mice demonstrated higher bacteria loads and altered microbiota composition. IL33 did not directly inhibit bacterial growth, but promoted wild-type, not REG3γKO, IECs to kill bacteria in vitro. Consistently, C rodentium infection induced IEC IL33 expression, and IL33-/- mice demonstrated an impaired bacterial clearance with C rodentium infection. Conclusions: Our study demonstrated that IL33, which is produced by IEC in response to injury and inflammatory stimulation, in turn promotes IEC expression of REG3γ, and controls the gut microbiota of the host. Keywords: IEC, IL33, REG3γ, Microbiota

T cells play a critical role in intestinal homeostasis, with increasing evidence suggesting a role for the microbiome metabolome in modulating this response. Here the authors show short-chain fatty acids promote IL-10 production in Th1 cells.

Alveolar Echinococcosis (AE) caused by the cestode Echinococcus multilocularis, is a severe helminth infection of man, where unrestricted parasite growth will ultimately result in organ failure and fatality. The tissue-infiltrative growth of the larval metacestode and the limited efficacy of available drugs complicate successful intervention in AE; patients often need life-long medication, and if possible, surgical resection of affected tissues and organs. Resistance to AE has been reported, but the determinants which confer protection are not known. ln this study, we analyzed in patients at distinct stages of Alveolar Echirococcosis, that is cured, stable and progressive AE, as well as in infection-free controls, the cellular production and plasma levels of pro-inflammatory cytokines lL-17A, lL-17B, lL-17F and their soluble receptors lL-17RA (slL-17RA) and IL-17RB (sIL-17RB). Significantly elevated levels of IL-17B and slL-17RB were observed, whilst lL-17F and slL-17RA were reduced in patients with AE. Similarly, the cellular production of lL-17F and slL-L7RA in response to E. multilocularis antigens was low in AE patients, while levels of slL-17RB were highly enhanced. These observations suggest immune-modulating properties of E. multitocularis on lL-17 cytokine-mediated pro-inflammatory immune responses; this may facilitate the tissue infiltrative growth of the parasite and its persistence in the human host.