Recent studies have revealed that lung inflammation mediated by CD4+ T cells may contribute to the pathogenesis of acute respiratory distress syndrome (ARDS). The imbalance between CD4 + CD25 + Foxp3 ...+ regulatory T (Treg) cells and T helper (Th)17 cells has been found in a number of different inflammation and autoimmune diseases, while the role of the Th17/Treg balance in ARDS remains largely unknown. The aim of this study was to investigate the Th17/Treg pattern and its impact on disease severity and outcomes in patients with ARDS.
This prospective, observational study enrolled 79 patients who fulfilled the Berlin definition of ARDS and 26 age- and sex-matched healthy controls. Circulation Th17 and Treg cell frequencies were analyzed by flow cytometry, and the expressions of Th17- and Treg-related cytokines in serum were measured by enzyme-linked immunosorbent assay (ELISA). Acute Physiologic and Chronic Health Evaluation (APACHE) II score, Sequential Organ Failure Assessment (SOFA) score, and the Lung Injury Score were also calculated at enrollment.
Within 24 hours after the onset of ARDS, the changes of peripheral circulating Th17 and Treg cell frequencies gradually increased from mild to severe ARDS. Th17/Treg ratio was positively correlated with APACHE II score, SOFA score, and Lung Injury Score, while negatively correlated with PaO₂/FiO₂. The areas under the receiver operating characteristic (AUC) curves of Th17/Treg ratio for predicting 28-day mortality in ARDS patients was higher than that of APACHE II score, SOFA score, Lung injury score, as well as PaO2/FiO2. Using a Th17/Treg ratio cutoff value of >0.79 to determine 28-day mortality, the sensitivity was 87.5% with 68.1% specificity. Multivariate logistic regression showed Th17/Treg ratio >0.79 (odds ratio = 8.68, P = 0.002) was the independent predictor for 28-day mortality in patients with ARDS. Finally, cumulative survival rates at 28-day follow-up also differed significantly between patients with Th17/Treg ratio >0.79 and ≤0.79 (P <0.001).
The Th17/Treg imbalance favoring a Th17 shift represents a potential therapeutic target to alleviate lung injury and a novel risk indicator in patients with early ARDS.
Glucagon-like peptide-1 (GLP-1) has a broad spectrum of biological activity by regulating metabolic processes via both the direct activation of the class B family of G protein-coupled receptors and ...indirect nonreceptor-mediated pathways. GLP-1 receptor (GLP-1R) agonists have significant therapeutic effects on non-alcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH) in animal models. However, clinical studies indicated that GLP-1 treatment had little effect on hepatic steatosis in some NAFLD patients, suggesting that GLP-1 resistance may occur in these patients. It is well-known that the gut metabolite sodium butyrate (NaB) could promote GLP-1 secretion from intestinal L cells. However, it is unclear whether NaB improves hepatic GLP-1 responsiveness in NAFLD. In the current study, we showed that the serum GLP-1 levels of NAFLD patients were similar to those of normal controls, but hepatic GLP-1R expression was significantly downregulated in NAFLD patients. Similarly, in the NAFLD mouse model, mice fed with a high-fat diet showed reduced hepatic GLP-1R expression, which was reversed by NaB treatment and accompanied by markedly alleviated liver steatosis. In addition, NaB treatment also upregulated the hepatic p-AMPK/p-ACC and insulin receptor/insulin receptor substrate-1 expression levels. Furthermore, NaB-enhanced GLP-1R expression in HepG2 cells by inhibiting histone deacetylase-2 independent of GPR43/GPR109a. These results indicate that NaB is able to prevent the progression of NAFL to NASH via promoting hepatic GLP-1R expression. NaB is a GLP-1 sensitizer and represents a potential therapeutic adjuvant to prevent NAFL progression to NASH.
To investigate the correlation between CD133-positive gastric cancer and clinicopathological features and its impact on survival.
A search in the Medline and Chinese CNKI (up to 1 Dec 2011) was ...performed using the following keywords gastric cancer, CD133, AC133, prominin-1 etc. Electronic searches were supplemented by hand searching reference lists, abstracts and proceedings from meetings. Outcomes included overall survival and various clinicopathological features.
A total of 773 gastric cancer patients from 7 studies were included. The median rate of CD133 expression by immunohistochemistry (IHC) was 44.8% (15.2%-57.4%) from 5 studies, and that by reverse transcription polymerase chain reaction (RT-PCR) was 91.3% (66.7%-100%) from 4 studies. The accumulative 5-year overall survival rates of CD133-positive and CD133-negative patients were 21.4% and 55.7%, respectively. Meta-analysis showed that CD133-positive patients had a significant worse 5-year overall survival compared to the negative ones (OR = 0.20, 95% CI 0.14-0.29, P<0.00001). With respect to clinicopathological features, CD133 overexpression by IHC method was closely correlated with tumor size, N stage, lymphatic/vascular infiltration, as well as TNM stage.
CD133-positive gastric cancer patients had worse prognosis, and was associated with common clinicopathological poor prognostic factors.
Rotating machinery, such as ventilators and water pumps, are crucial components in modern industry, of which safety monitoring requires intelligent fault diagnosis. Feature representation learning is ...essential in the intelligent fault diagnosis of rotating machinery. In this study, a fast robust capsule network augmented with a dynamic pruning technique and a mutual information loss is proposed. The capsule layer overcomes limitations in pooling layers and scale-invariant feature transformation by learning tensor representations of features. The dynamic pruning method employs a dropout-like strategy to prevent repeated calculations and reduce the scale of parameters to simplify the network topology while increasing robustness. The enhanced agreement function limits the similarity of capsules in the same layer to avoid homogeneous features. The local and global discriminators are designed to learn and obtain mutual information in two aspects. The resulting multiscale mutual information loss for the proposed model successfully increases the model's representation learning capacity by integrating local and global information. The performance of the proposed method is successfully verified on several datasets with various noise levels obtained from a simulation platform.
Multi-stage Tesla valves in the reversed flow state can be applied during the hydrogen decompression process between the high pressure hydrogen storage vessel and the fuel cell. Under high-pressure ...turbulent hydrogen flow, severe aerodynamic noise may be caused and large energy loss inside Tesla valves may be generated, which can cause uncomfortable noise in vehicles. In this paper, the valve stage number and the pressure ratio between the inlet and the outlet are analyzed to investigate the possibility of the occurrence of aerodynamic noise and energy loss inside Tesla valves, and Mach number, turbulent dissipation rate, and exergy loss are used and evaluated as the criterion. The results show that both Mach number and exergy loss increase with the increasing of pressure ratio, but with the decrease of valve stage number, Mach number increases and exergy loss decreases. In addition, large turbulent dissipation rate at each valve stage appears near the bifurcation and the confluence between the straight channel and the bend channel of multi-stage Tesla valves. The correlation between the valve stage number, the pressure ratio, and the maximum Mach number is fitted, which can be used to estimate the possibility of the occurrence of aerodynamic noise.
•Multi-stage Tesla valves are applicable for hydrogen decompression.•Mach number is used to represent the occurrence of aerodynamic noise.•Impacts of valve stage number under different pressure ratios are investigated.•Small valve stage number results in higher Mach number.•Higher pressure ratio and large valve stage number lead to high exergy loss.
Agaricus bisporus is widely consumed on the world market. The easy browning of mushroom surface is one of the most intuitive factors affecting consumer purchase. A certain cognition on browning ...mechanism has been made after years of research. At present, people slow down the browning of mushrooms mainly by improving preservation methods. In addition, breeding is also a reliable way. In the production practice, we have identified some browning-resistant varieties, and we selected a browning-resistant variety to compare with an ordinary variety to reveal the resistance mechanism. Using transcriptomics and metabolomics, the differences in gene expression and metabolite levels were revealed, respectively. The results showed that differentially expressed genes (DEGs) like AbPPO4, AbPPO3 and AbPPO2 were differently expressed and these DEGs were involved in many pathways related to browning. The expression of AbPPO expression play an important role in the browning of A. bisporus and multiple PPO family members are involved in the regulation of browning. However, the resistance to browning cannot be judged only by the expression level of AbPPOs. For metabolomics, most of the different metabolites were organic acids. These organic acids had a higher level in anti-browning (BT) than easy-browning varieties (BS), although the profile was very heterogeneous. On the contrary, the content of trehalose in BS was significantly higher than that in BT. Higher organic acids decreased pH and further inhibited PPO activity. In addition, the BS had a higher content of trehalose, which might play roles in maintaining PPO activity. The difference of browning resistance between BS and BT is mainly due to the differential regulation mechanism of PPO.
The yak (Bos grunniens) is a large ruminant species that lives in high-altitude regions and exhibits excellent adaptation to the plateau environments. To further understand the genetic ...characteristics and adaptive mechanisms of yak, we have developed a multi-omics database of yak including genome, transcriptome, proteome, and DNA methylation data.
The Yak Genome Database ( http://yakgenomics.com/ ) integrates the research results of genome, transcriptome, proteome, and DNA methylation, and provides an integrated platform for researchers to share and exchange omics data. The database contains 26,518 genes, 62 transcriptomes, 144,309 proteome spectra, and 22,478 methylation sites of yak. The genome module provides access to yak genome sequences, gene annotations and variant information. The transcriptome module offers transcriptome data from various tissues of yak and cattle strains at different developmental stages. The proteome module presents protein profiles from diverse yak organs. Additionally, the DNA methylation module shows the DNA methylation information at each base of the whole genome. Functions of data downloading and browsing, functional gene exploration, and experimental practice were available for the database.
This comprehensive database provides a valuable resource for further investigations on development, molecular mechanisms underlying high-altitude adaptation, and molecular breeding of yak.
Few-shot semantic segmentation uses a few annotated data of a specific class in the support set to segment the target of the same class in the query set. Most existing approaches fail to perform well ...when there are significant intra-class variances. This paper alleviates the problem by concentrating on mining the query image and using the support set as supplementary information. First, it proposes a Query Prototype Generation Module to generate a query foreground prototype from the query features. Specifically, we use both prototype-level and pixel-level similarity matching to generate two complementary initial prototypes, which we then integrate to create a discriminative query foreground prototype. Second, we propose a Support Auxiliary Refinement Module to further guide the final precise prediction of the query image by leveraging the target category information of the support set through step-by-step mining. Specifically, we generate a query-support mixture prototype based on the support prototype representation obtained using the attention mechanism. Then we generate a support supplement prototype to complement the missing information by encoding over the foreground regions that the query-support mixture prototype fails to segment out. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works of few-shot segmentation.
•We propose a Query Prototype Generation Module to alleviate appearance discrepancy.•We propose a Support Auxiliary Refinement Module to mine more class information.•Extensive experiments prove the proposed model outperforms existing competitors.
The advantage of intelligent fault diagnosis (IFD) based on industrial big data lies in the powerful feature extraction ability of machine learning models. However, it has become extremely difficult ...to apply machine learning-based fault diagnosis models to the actual industry due to the problem of labeled data insufficiency and class imbalance. Ensemble learning, which leverages the aggregation of multiple base classifiers to effectively utilize data, is regarded as a promising approach to address this issue. In this study, we propose an ensemble learning framework that integrates multiple stacked capsule autoencoders (SCAEs) for accurate fault diagnosis. The proposed ensemble framework introduces a novel method for evaluating intrinsic templates based on a symmetric graph Laplacian with the aim of selecting capsules that can effectively reduce information redundancy. Finally, a new decision fusion method is proposed to achieve the decoupling of composite fault labels by DS evidence. The proposed method is validated to achieve fault classification accuracy of up to 100% and 91% on datasets with sufficient and insufficient samples. In addition, the accuracy is higher than 94% on four imbalanced datasets. The experimental results demonstrate that the proposed method exhibits enhanced resilience against dataset defects, thereby offering more adaptable and reliable fault diagnosis services in real-world industry.
An increasing number of studies have shown how feedback interactions between plants and soil can influence primary and secondary succession. However, very little is known about the patterns and ...mechanisms of such plant–soil feedbacks on stressed mine tailings ecosystem, which can be severely contaminated by a range of toxic elements.
In a two‐phase plant–soil feedback experiment based on the rare earth element (REE) mine tailing soil, we investigated biotic (changes in bacterial and fungal communities) and abiotic (changes in chemical properties) legacies of three pioneer grass species, and examined feedback effects of three grasses, two legumes and two woody plants with different root traits.
Positive plant–soil feedbacks were found in Miscanthus sinensis, Paspalum thunbergii and Tephrosia candida, and neutral feedbacks were observed in the other four plants. These effects corresponded with an increase in nutrients and total organic carbon, as well as a decrease in acidity and extractable aluminium and REEs. There were less signs of biotic changes in the conditioned tailings.
The correlation analysis suggested a relationship between plants' responses to soil legacies and root traits, as well as root economics spectrum. On the mine tailings, acquisitive species with higher specific root length appeared to have greater potential for positive feedback.
Synthesis and application. Our study shows that early succession on contaminated rare earth element mine tailings may lead to more positive plant–soil feedback than predicted based on results of non‐contaminated soils, mainly due to the alleviation of abiotic stress in tailings. Therefore, the improvement of specific abiotic soil stress and the trait‐based selection of acquisitive plants should be preferentially considered to promote the primary restoration of degraded land.
摘要
越来越多的研究揭示了植物和土壤之间的反馈相互作用如何影响原生和次生演替。然而, 在受到一系列有毒元素严重污染的胁迫尾矿生态系统中, 人们对植物‐土壤反馈的模式和机制还知之甚少。
基于稀土(rare earth element, REE)尾矿土壤, 我们开展了双阶段植物‐土壤反馈实验。我们研究了三种耐性先锋草本植物的土壤生物(细菌和真菌群落的变化)和非生物遗留产物(化学性质的变化), 并探究了后续具有不同根系性状的三种草本植物、两种豆科植物和两种木本植物的生长反馈效应。
植物‐土壤反馈效应结果表明, 芒草、雀稗和山毛豆为正反馈, 其他4种植物为中性反馈。这些效应与土壤营养元素和总有机碳的增加以及酸度、可提取态铝和稀土元素的减少相对应。但经过草本调节后的尾矿土壤中生物群落的变化较少。
相关性分析表明, 植物对土壤遗产的生长响应与根系性状和经济策略之间存在相关关系。在尾矿中, 具有较高比根长的获取型植物似乎具有更大的正反馈潜力。
综合与应用。我们的研究表明, 受污染的稀土矿尾矿的早期演替可能会呈现更正向的植物‐土壤反馈(若根据以往未受污染土壤中的研究结果预测, 演替早期的植物往往是呈现负反馈), 而这主要是由于尾矿中非生物土壤胁迫得到耐性先锋植物的缓解。因此, 为了促进退化尾矿地的早期生态恢复, 应优先考虑改善特定的非生物土壤胁迫以及基于性状选择获取策略的植物。
Our study shows that early succession on contaminated rare earth element mine tailings may lead to more positive plant–soil feedback than predicted based on results of non‐contaminated soils, mainly due to the alleviation of abiotic stress in tailings. Therefore, the improvement of specific abiotic soil stress and the trait‐based selection of acquisitive plants should be preferentially considered to promote the primary restoration of degraded land.
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