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Direct hydrogen production from inexhaustible seawater using abundant offshore wind power offers a promising pathway for achieving a sustainable energy industry and fuel economy. Various direct seawater electrolysis methods have been demonstrated to be effective at the laboratory scale. However, larger-scale in situ demonstrations that are completely free of corrosion and side reactions in fluctuating oceans are lacking. Here, fluctuating conditions of the ocean were considered for the first time, and seawater electrolysis in wave motion environment was achieved. We present the successful scaling of a floating seawater electrolysis system that employed wind power in Xinghua Bay and the integration of a 1.2 Nm 3  h −1 -scale pilot system. Stable electrolysis operation was achieved for over 240 h with an electrolytic energy consumption of 5 kWh Nm −3 H 2 and a high purity (>99.9%) of hydrogen under fluctuating ocean conditions (0~0.9 m wave height, 0~15 m s −1 wind speed), which is comparable to that during onshore water electrolysis. The concentration of impurity ions in the electrolyte was low and stable over a long period of time under complex and changing scenarios. We identified the technological challenges and performances of the key system components and examined the future outlook for this emerging technology. Seawater electrolysis shows promising potential toward sustainable energy generation, but large-scale in-situ demonstrations are still lacking. Here, authors report a floating platform integrating a 1.2 Nm 3 h −1 seawater direct electrolysis system with wind power for energy input in the Xinghua Bay.

Background The prognosis of children with Mycoplasma pneumoniae pneumonia (MPP) is significantly impacted by the possibility of a missed or delayed diagnosis during the early stages of the illness. In this study, we assessed the potential of utilizing MP antibody and procalcitonin levels as diagnostic markers in pediatric patients with MPP, and their correlation with drug-resistance gene mutations. Methods A retrospective analysis was conducted on 80 hospitalized children with MPP confirmed by 23 S PCR and 80 healthy controls, focusing on serum MP antibody and procalcitonin (PCT) levels. The diagnostic value was assessed using receiver operating characteristic (ROC) curves. Additionally, PCR combined with TaqMan fluorescent probe technology was utilized to detect drug-resistance gene mutations in the MPP group. Results The observation group exhibited significantly higher positive rates of MP antibody and PCT compared to the control group (28.75% vs. 8.75% and 72.50% vs. 18.75%, respectively; P  < 0.05). ROC analysis revealed areas under the curve (AUCs) of 0.700 (95% CI: 0.623–0.770) and 0.779 (95% CI: 0.707–0.841) for MP antibody and PCT, respectively, with a combined diagnostic AUC of 0.869 (95% CI: 0.806–0.917) ( P  < 0.05). Furthermore, 22.5% of children with MPP exhibited drug-resistance gene mutations, associated with increased MP antibody and PCT levels. Conclusion MP antibody and PCT levels are promising markers for diagnosing MPP in children, offering enhanced diagnostic value when used in combination. Furthermore, the presence of MP drug resistance gene mutations is associated with increased MP antibody and PCT levels, suggesting that these markers may have potential utility in guiding treatment strategies. Further research is needed to confirm their role in improving patient outcomes.

Objective To investigate whether body mass index (BMI) affects the accuracy of vertebral bone quality (VBQ) in determining bone mineral density (BMD) in patients undergoing lumbar fusion surgery. Methods In this retrospective study, patients with preoperative noncontrast T1-weighted MRI were included. Restricted Cubic Spline (RCS) was employed to explore the nonlinear relationship between BMI and VBQ. Then patients were stratified according to the threshold of BMI. Pearson correlation analysis and linear regression were used to analyze the correlation between VBQ and the BMD in different groups. Receiver operating characteristic (ROC) analysis to calculate the area under the curve (AUC) was used to assess diagnostic efficacy according to BMI. Results A total of 328 patients (201 female and 127 male patients) with a mean age of 68.3 ± 3.3 years were included in the study. Significant nonlinear relationship was observed given the results of RCS. In patients with BMI < 23.8 kg/m 2 , the correlation coefficient between VBQ and the lowest BMD was − 0.32 and significant distribution difference of VBQ score was observed between osteoporosis and normal as well as osteopenia subgroups. However, in patients with BMI ≥ 23.8 kg/m 2 , the correlation coefficient between VBQ and the lowest BMD was − 0.39 and significant distribution difference of VBQ score was observed in all three subgroups. In addition, the ROC analysis revealed that the predictive performance in determining low BMD was superior in patients with BMI ≥ 23.8 kg/m 2 (AUC 0.80 vs. AUC 0.66, p  = 0.034). Conclusions In this study, significant nonlinear relationship between BMI and VBQ was observed. Compared with patients with BMI < 23.8 kg/m 2 , VBQ has better discrimination between higher BMI (≥ 23.8 kg/m 2 ) patients with low BMD and those with normal bone density.

Lysine acetylation regulates many eukaryotic cellular processes, but its function in prokaryotes is largely unknown. We demonstrated that central metabolism enzymes in Salmonella were acetylated extensively and differentially in response to different carbon sources, concomitantly with changes in cell growth and metabolic flux. The relative activities of key enzymes controlling the direction of glycolysis versus gluconeogenesis and the branching between citrate cycle and glyoxylate bypass were all regulated by acetylation. This modulation is mainly controlled by a pair of lysine acetyltransferase and deacetylase, whose expressions are coordinated with growth status. Reversible acetylation of metabolic enzymes ensure that cells respond environmental changes via promptly sensing cellular energy status and flexibly altering reaction rates or directions. It represents a metabolic regulatory mechanism conserved from bacteria to mammals.

Tirzepatide, a dual glucose-dependent insulinotropic peptide and glucagon-like peptide 1 receptor agonist, has been approved by the US Food and Drug Administration for the treatment of type 2 diabetes. The purpose of this meta-analysis is to evaluate the impact of tirzepatide on lipid profile and waist circumference (WC), both of which are risk factors of cardiovascular diseases. The PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov databases were systematically searched for articles published from database inception to July 31, 2022. This meta-analysis included 7 randomized controlled trials with a minimum duration of 12 weeks that compared tirzepatide with placebo or other antidiabetic medications. The random-effects model was used to estimate mean differences in lipid profile and WC from baseline. The Cochrane risk-of-bias tool for randomized trials, version 2 was used to assess the outcome's risk of bias. We evaluated the evidence using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. A total of 8 articles from 7 trials with 7151 participants were included. All 3 eligible maintenance doses of tirzepatide (5, 10, and 15 mg once a week) were effective in increasing total cholesterol (TC) (P < 0.05), HDL-C (P < 0.05), VLDL-C (P < 0.01), triglyceride (TG) (P < 0.01), and WC (P < 0.01) changes from baseline compared with control agents including placebo, semaglutide, dulaglutide, and degludec. Although the evidence for VLDL-C and TGs by GRADE were high or moderate, the evidences for TC, HDL-C, and WC were low or moderate. Only 5mg once-weekly tirzepatide (P < 0.05), not 10 or 15 mg, could induce significant alteration in LDL-C before sensitivity analysis. The evidence by GRADE was moderate. Tirzepatide had superiority over placebo or other antidiabetic agents in controlling lipid and WC levels. However, the levels of evidence by GRADE varied greatly across different outcome indicators. Limitations of the study include evaluating secondary outcomes of original trials for the meta-analyses, not assessing the effect of baseline lipid-lowering therapy on lipid levels, and not exploring the bias induced by glycemic improvement and weight loss.

Recently, nonrevisiting genetic algorithms have demonstrated superior capabilities compared with classic genetic algorithms and other single-objective evolutionary algorithms. However, the search efficiency of nonrevisiting genetic algorithms is currently low for some complex optimisation problems. This study proposes a nonrevisiting genetic algorithm with a multi-region guided search to improve the search efficiency. The search history is stored in a binary space partition (BSP) tree, where each searched solution is assigned to a leaf node and corresponds to a search region in the search space. To fully exploit the search history, several optimal solutions in the BSP tree are archived to represent the most potential search regions and estimate the fitness landscape in the search space. Except for the conventional genetic operations, the offspring can also be generated through multi-region guided search strategy, where the current solution is first navigated to one of the candidate search regions and is further updated towards the direction of the optimal solution in the search history to speedup convergence. Thus, multi-region guided search can reduce the possibility of getting trapped in local optima when solving problems with complex landscapes. The experimental results on different types of test suites reveal the competitiveness of the proposed algorithm in comparison with several state-of-the-art methods.

Testing predictability is known to be an important issue for the balanced predictive regression model. Some unified testing statistics of desirable properties have been proposed, though their validity depends on a predefined assumption regarding whether or not an intercept term nevertheless exists. In fact, most financial data have endogenous or heteroscedasticity structure, and the existing intercept term test does not perform well in these cases. In this paper, we consider the testing for the intercept of the balanced predictive regression model. An empirical likelihood based testing statistic is developed, and its limit distribution is also derived under some mild conditions. We also provide some simulations and a real application to illustrate its merits in terms of both size and power properties.

Colorectal cancer is one of the most common gastrointestinal cancers and the second leading cause of cancer-related death. Although colonoscopy screening has greatly improved the early diagnosis of colorectal cancer, its recurrence and metastasis are still significant problems. Tumour cells usually have the hallmark of metabolic reprogramming, while fatty acids play important roles in energy storage, cell membrane synthesis, and signal transduction. Many pathways of fatty acid metabolism (FAM) are involved in the occurrence and development of colon cancer, and the complex molecular interaction network contains a variety of genes encoding key enzymes and related products. Clinical information and RNA sequencing data were collected from TCGA and GEO databases. The prognosis model of colon cancer was constructed by LASSO-Cox regression analysis among the selected fatty acid metabolism genes with differential expression. Nomogram for the prognosis model was also constructed in order to analyze its value in evaluating the survival and clinical stage of the colon cancer patients. The differential expression of the selected genes was verified by qPCR and immunohistochemistry. GSEA and GSVA were used to analyze the enrichment pathways for high- and low-risk groups. CIBERSORT was used to analyze the immune microenvironment of colon cancer and to compare the infiltration of immune cells in the high- and low-risk groups. The \"circlize\" package was used to explore the correlation between the risk score signature and immunotherapy for colon cancer. We analysed the differential expression of 704 FAM-related genes between colon tumour and normal tissue and screened 10 genes with prognostic value. Subsequently, we constructed a prognostic model for colon cancer based on eight optimal FAM genes through LASSO Cox regression analysis in the TCGA-COAD dataset, and its practicality was validated in the GSE39582 dataset. Moreover, the risk score calculated based on the prognostic model was validated as an independent prognostic factor for colon cancer patients. We further constructed a nomogram composed of the risk score signature, age and American Joint Committee on Cancer (AJCC) stage for clinical application. The colon cancer cohort was divided into high- and low-risk groups according to the optimal cut-off value, and different enrichment pathways and immune microenvironments were depicted in the groups. Since the risk score signature was significantly correlated with the expression of immune checkpoint molecules, the prognostic model might be able to predict the immunotherapy response of colon cancer patients. In summary, our findings expand the prognostic value of FAM-related genes in colon cancer and provide evidence for their application in guiding immunotherapy.

In recent years, neural architecture search (NAS) has achieved unprecedented development because of its ability to automatically achieve high-performance neural networks in various tasks. Among these, the evolutionary neural architecture search (ENAS) has impressed the researchers due to the excellent heuristic exploration capability. However, the evolutionary algorithm-based NAS are prone to the loss of population diversity in the search process, causing that the structure of the surviving individuals is exceedingly similar, which will lead to premature convergence and fail to explore the search space comprehensively and effectively. To address this issue, we propose a novel indicator, named architecture entropy, which is used to measure the architecture diversity of population. Based on this indicator, an effective sampling strategy is proposed to select the candidate individuals with the potential to maintain the population diversity for environmental selection. In addition, an unified encoding scheme of topological structure and computing operation is designed to efficiently express the search space, and the corresponding population update strategies are suggested to promote the convergence. The experimental results on several image classification benchmark datasets CIFAR-10 and CIFAR-100 demonstrate the superiority of our proposed method over the state-of-the-art comparison ones. To further validate the effectiveness of our method in real applications, our proposed NAS method is applied in the identification of lumbar spine X-ray images for osteoporosis diagnosis, and can achieve a better performance than the commonly used methods. Our source codes are available at https://github.com/LabyrinthineLeo/AEMONAS.

Peripheral CD8 + T cell number is tightly controlled but the precise molecular mechanism regulating this process is still not fully understood. In this study, we found that epilepsy patients with loss of function mutation of DEPDC5 had reduced peripheral CD8 + T cells, and DEPDC5 expression positively correlated with tumor-infiltrating CD8 + T cells as well as overall cancer patient survival, indicating that DEPDC5 may control peripheral CD8 + T cell homeostasis. Significantly, mice with T cell-specific Depdc5 deletion also had reduced peripheral CD8 + T cells and impaired anti-tumor immunity. Mechanistically, Depdc5 -deficient CD8 + T cells produced high levels of xanthine oxidase and lipid ROS due to hyper-mTORC1-induced expression of ATF4, leading to spontaneous ferroptosis. Together, our study links DEPDC5 - mediated mTORC1 signaling with CD8 + T cell protection from ferroptosis, thereby revealing a novel strategy for enhancing anti-tumor immunity via suppression of ferroptosis.