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Despite advances in treatment, prostate cancer remains a leading cause of cancer-related deaths among men, highlighting the urgent need for innovative therapeutic strategies. MicroRNAs (miRNAs) have emerged as key regulatory molecules in cancer biology. In this research, we investigated the tumor-suppressive role of miR-5100 in PCa and its underlying molecular mechanism. By using RT-qPCR, we observed lower miR-5100 expression in PCa cell lines than in benign prostate cells. Functional assays demonstrated that miR-5100 overexpression significantly suppressed PCa cell proliferation, migration, and invasion. By using RNA-sequencing, we identified 446 down-regulated and 806 upregulated candidate miR-5100 target genes overrepresenting cell cycle terms. Mechanistically, E2F7 was confirmed as a direct target of miR-5100 using the reporter gene assay and RIP assay. By conducting flow cytometry analysis, cell cycle progression was blocked at the S phase. E2F7 overexpression partially mitigated the suppressive impact of miR-5100 in PCa cells. In conclusion, miR-5100 is a tumor suppressor in PCa by blocking cell cycle and targeting E2F7.

Abstract The impact of sodium-glucose co-transporter 2 inhibitors (SGLT2-i) on reducing the risk of all-cause mortality and rehospitalization for heart failure (HF) in patients with acute myocardial infarction (AMI) remains unclear. This study aims to evaluate the effect of SGLT2-i on all-cause mortality and rehospitalization for HF in patients diagnosed with AMI. A comprehensive search was conducted in PubMed, Web of Science, the Cochrane Library, and Embase for relevant studies published up to May 2024, following the PICOS principle. Eligible studies included randomized clinical trials and cohort studies comparing SGLT2-i with placebo regarding all-cause mortality, rehospitalization for HF, cardiovascular mortality, and the incidence of nonfatal MI in AMI patients. Patient-level data from each trial were synthesized into a pooled dataset and analyzed using a mixed-effects or random-effects model based on the I2 statistic. Ten clinical trials enrolling 15,748 participants (6913 in the SGLT2-i group and 8835 in the placebo group) were included. The follow-up duration ranged from 12 weeks to 2.1 years. SGLT2-i significantly reduced rehospitalization for HF (RR: 0.69, 95% CI 0.60–0.81, P

Abstract Regulatory T cells (Tregs), an essential component of the human immune system, are a heterogeneous group of T lymphocytes with the ability to suppress immune responses and maintain immune homeostasis. Recent evidence indicates that Tregs may impair antitumor immunity and facilitate cancer progression by weakening functions of effector T cells (Teffs). Consequently, targeting Tregs to eliminate them from tumor microenvironments to improve Teffs’ activity could emerge as an effective strategy for cancer immunotherapy. This review outlines the biology of Tregs, detailing their origins, classification, and crucial markers. Our focus lies on the complex role of Tregs in cancer’s development, progression and treatment, particularly on their suppressive role upon antitumor responses via multiple mechanisms. We delve into Tregs’ involvement in immune checkpoint blockade (ICB) therapy, their dual effect on cancer immunotherapy and their potential biomarkers for ICB therapy effectiveness. We also summarize advances in the therapies that adjust Tregs to optimize ICB therapy, which may be crucial for devising innovative cancer treatment strategies.

Background:Stephania tetrandra has been used for treating rheumatic diseases for thousands of years in rural areas of China. Several studies have found that tetrandrine and fangchinoline can inactivate the PI3K/Akt signaling pathway by reducing the expression and phosphorylation of AKT. However, the mechanism underlying the therapeutic actions of S. tetrandra on RA is not well known.Methods: In this study, we determined the molecular mechanism of the therapeutic effects of the multiple ingredients of S. tetrandra extract (STE) on collagen-induced arthritic (CIA) rats by integrating pharmacometabolomics, proteomics, and PTMomics.Results: In the multi-omics joint analysis, first, the expression signatures of proteins, PTMs, metabolites, and STE ingredients were profiled in CIA rats PBMCs that underwent STE treatment. Bioinformatics analysis were subsequently probed that STE mainly regulated tryptophan metabolism, inflammatory response, and cell adhesion pathways in CIA rats. The interrelated pathways were further constructed, and the findings revealed that STE attenuated the inflammatory response and proliferation of PBMCs in CIA rats by mediating the key targets of the PI3K/Akt pathway, including Hint1, ACP1, FGR, HSP90@157W + dioxidation, and Prkca@220N + 845.4540 Da. The rheumatic functions of Hint1 and ACP1 were further confirmed by applying a transcriptomic data of RA patients who clinically received abatacept therapy. Furthermore, a cross-ome correlation analysis was performed and major in vivo ingredients of STE, including coclaurine-N-glucuronide, Me,coclaurine-O-glc, N-gluA-schefferine, corydamine, corypamine, tetrandrine, and fangchiniline, were found to act on these targerts to inactivate the PI3K/Akt pathway.Conclusion: These results elucidated the molecular mechanism by which the ingredients of STE mediate the expression of the key targets in the PI3K/Akt pathway, leading to anti-rheumatic functions. The findings of this study provided new insights into the synergistic effect of STE against arthritis in rats.

The penetration fuze, as the initiation control component of the penetration weapon, usually experiences an overload of tens of thousands or even hundreds of thousands of g during the penetration process. In order to prevent the fuze from being overloaded and causing the weapon to explode or misfire early, this article introduces the use of internal sealing reinforcement and external energy absorbing buffer materials to protect the internal circuit modules of the fuze. Several kinds of energy absorbing and buffering materials, including foam metal materials and composite sandwich structure materials, as well as metamaterials that have recently attracted the attention of industry and academia, are reviewed. The high overload impact energy absorption characteristics of materials and the mechanical properties of different material structures are emphatically introduced. In addition, this article also evaluates the applicability and limitations of existing buffer materials and methods, and proposes some potential improvement plans, such as the impact of parameters such as viscoelasticity, porosity, surface coating, printing process, heat treatment process on the energy absorption effect of materials, further improving the engineering practicality of buffer protection materials. A summary of the key technologies in the research of penetration fuze protective materials was made, and some mechanical testing methods were proposed, which can better characterize the impact resistance and resilience of materials. Finally, the future development direction of buffer materials for penetration fuzes was explored, which will help promote the research on the concept of buffer materials used on penetration missiles.

Identifying ecosystem service functions, clarifying the spatiotemporal trade-offs and synergies of terrestrial ecosystem services and their driving mechanisms, and exploring differentiated ecological functional zoning are crucial steps in achieving healthy regional ecosystem management, and are conducive to developing diversified ecological restoration strategies, establishing a robust cross-regional collaborative management mechanism, implementing differentiated ecological management strategies, and contributing to the construction of a Beautiful China. This paper, based on the InVEST model, Pearson correlation, GeoDetector, and Spatiotemporal Geographically Weighted Regression models, constructs a spatial quantification model of the trade-offs and synergies among five key ecosystem service functions—habitat quality, soil retention, water conservation, food supply, and carbon sequestration—of China’s terrestrial ecosystems from 2000 to 2020. It explores the influencing factors of terrestrial ecosystem services in China and their spatiotemporal heterogeneity, thereby investigating the future strategies for ecological functional zoning and management of China’s national land space. The results indicate that: (1) during 2000–2020, China’s food supply and soil conservation have increased. However, the habitat quality, water conservation, and carbon sequestration have decreased. (2) Significant spatial and temporal heterogeneities exist in the key ecosystem services of China’s terrestrial ecosystems. (3) Natural, economic, and social factors all impact China’s terrestrial key ecosystem services. Among them, slope, annual average precipitation, land development intensity, and vegetation coverage are the main influencing factors, and different factors exhibit significant spatial heterogeneity. (4) Significant trade-offs/synergy effects among critical terrestrial ecosystem services exist in China. (5) China’s national territory is divided into four ecological protection functional zones: ecological restoration areas, ecological control areas, resilient development areas, and ecological conservation areas, and explores differentiated zoning optimization control paths.

A large amount of high salinity wastewater rich in dyes is produced in the printing and dyeing industry. Benefiting from the selective separating property，hollow fiber nanofiltration（NF） membranes are expected to achieve the efficient separation of dyes and inorganic salts，which can complete the pollution control of printing and dyeing wastewater as well as the synchronous recovery of valuable resources. Compared to the conventional spiral-wound NF membrane，hollow fiber NF membrane also presents advantages including high packing density and self-supporting ability. Therefore，it has received increasing attention in the field of printing and dyeing wastewater treatment. The recent research progress of hollow fiber NF technology in the dye separation process were reviewed. Specifically，the preparation of loose hollow fiber NF membrane and positively charged hollow fiber NF membrane suitable for dye separation were discussed. The membrane fouling characteristics of hollow fiber nanofiltration membrane in dye separation process and the corresponding membrane fouling control measures were analyzed. Finally，the future research directions were prospected to further promote the application of hollow fiber nanofiltration membrane technology in dye wastewater treatment.

To enhance the impact resistance of penetration fuze, this paper investigates the response of fuze buffer materials to stress waves and develops a model for stress wave transmission inside the fuze. The stress concentration impacts of different cell structures of Imitation Bamboo Type Penetration Fuze Buffer Protection Structure (IBS) under stress wave action are compared and analyzed. The paper elucidates the impact of different cell parameters on stress concentration impacts, establishes nonlinear fitting functions of Stress Concentration Factor (SCF) and cell parameters, and solves the prediction error. Based on the wave function expansion method, an expression for Dynamic Stress Concentration Factor (DSCF) when stress waves interact with the potting material is derived, and numerical results of DSCF around bubbles under different physical parameters are provided. Finally, dynamic impact tests are conducted on the combined buffer scheme of penetration fuze. Impact test results show that, under an initial velocity impact of 50 m/s, the overload peak attenuation rate is 39.42%, and under an initial velocity impact of 70 m/s, the overload peak attenuation rate is 32.87%. IBS can effectively protect the electronic components inside the fuze.

Abstract Background and objectives Sepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine learning into clinical practice, some researchers have attempted to employ machine learning for early mortality risk prediction in sepsis patients. Nevertheless, there is a lack of comprehensive understanding regarding the construction of predictive variables using machine learning and the value of various machine learning methods. Thus, we carried out this systematic review and meta-analysis to explore the predictive value of machine learning for sepsis-related death at different time points. Methods PubMed, Embase, Cochrane, and Web of Science databases were searched until August 9th, 2022. The risk of bias in predictive models was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). We also performed subgroup analysis according to time of death and type of model and summarized current predictive variables used to construct models for sepsis death prediction. Results Fifty original studies were included, covering 104 models. The combined Concordance index (C-index), sensitivity, and specificity of machine learning models were 0.799, 0.81, and 0.80 in the training set, and 0.774, 0.71, and 0.68 in the validation set, respectively. Machine learning outperformed conventional clinical scoring tools and showed excellent C-index, sensitivity, and specificity in different subgroups. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) are the preferred machine learning models because they showed more favorable accuracy with similar modeling variables. This study found that lactate was the most frequent predictor but was seriously ignored by current clinical scoring tools. Conclusion Machine learning methods demonstrate relatively favorable accuracy in predicting the mortality risk in sepsis patients. Given the limitations in accuracy and applicability of existing prediction scoring systems, there is an opportunity to explore updates based on existing machine learning approaches. Specifically, it is essential to develop or update more suitable mortality risk assessment tools based on the specific contexts of use, such as emergency departments, general wards, and intensive care units.

Abstract Background Autonomic remodeling of the atria plays a pivotal role in the development of atrial fibrillation (AF) and exerts a substantial influence on the progression of this condition. Hyperlipidemia is a predisposing factor for AF, but its effect on atrial nerve remodeling is unclear. The primary goal of this study was to explore the possible mechanisms through which the consumption of a high-fat diet (HFD) induces remodeling of atrial nerves, and to identify novel targets for clinical intervention. Methods Cell models were created in vitro by subjecting cells to palmitic acid (PA), while rat models were established by feeding them a high-fat diet. To investigate the interplay between cardiomyocytes and nerve cells in a co-culture system, we utilized Transwell cell culture plates featuring a pore size of 0.4 μm. The CCK-8 assay was employed to determine cell viability, fluorescent probe DCFH-DA and flow cytometry were utilized for measuring ROS levels, JC-1 was used to assess the mitochondrial membrane potential, the Griess method was employed to measure the nitric oxide (NO) level in the supernatant, a fluorescence-based method was used to measure ATP levels, and MitoTracker was utilized for assessing mitochondrial morphology. The expression of pertinent proteins was evaluated using western blotting (WB) and immunohistochemistry techniques. SNAP was used to treat nerve cells in order to replicate a high-NO atmosphere, and the level of nitroso was assessed using the iodoTMT reagent labeling method. Results The study found that cardiomyocytes’ mitochondrial morphology and function were impaired under high-fat stimulation, affecting nitric oxide (NO) production through the CRIF1/SIRT1/eNOS axis. In a coculture model, overexpression of eNOS in cardiomyocytes increased NO expression. Moreover, the increased Keap1 nitrosylation within neuronal cells facilitated the entry of Nrf2 into the nucleus, resulting in an augmentation of P21 transcription and a suppression of proliferation. Atrial neural remodeling occurred in the HFD rat model and was ameliorated by increasing myocardial tissue eNOS protein expression with trimetazidine (TMZ). Conclusions Neural remodeling is triggered by high-fat stimulation, which decreases the production of NO through the CRIF1/eNOS/P21 axis. Additionally, TMZ prevents neural remodeling and reduces the occurrence of AF by enhancing eNOS expression.

In order to guide the geophysical exploration technology of coal field fire area, judge the development stage of coal fire, accurately identify the boundary of hidden fire area, carry out targeted high-temperature electrical property research of coal and coal surrounding rock by analyzing the conductive mechanism of rock and the electrical property characteristics of coal rock mass in a narrow sense, by testing the resistance values of coal and rock mass at different temperatures, the change law of coal and rock resistivity with temperature and its influence mechanism are summarized; by means of numerical simulation method, the geoelectric model of the coal field fire area is established and the forward and reverse calculation of transient electromagnetic (TEM) is carried out. The results show that the resistivity of coal decreases with the increase of temperature, and the carbon content of coal is the dominant factor affecting its resistivity at high temperature stage; the resistivity of the surrounding rock of dry coal decreases with the increase of temperature, and the number of free moving carriers under the thermal dynamic condition is the dominant factor affecting its resistivity. Through numerical simulation, it is found that the transient electromagnetic abnormal response amplitude caused by high-temperature coal is much larger than that caused by high-temperature surrounding rock, the coupling effect of the transient electromagnetic central loop device on the high-temperature fire area is the best, and the shallower the burial depth of the fire area, the more obvious the electrical abnormal effects.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver condition that affects a quarter of the global adult population. To date, only a few NAFLD risk prediction models have been developed for Chinese older adults aged ≥ 60 years. This study presented the development of a risk prediction model for NAFLD in Chinese individuals aged ≥ 60 years and proposed personalised health interventions based on key risk factors to reduce NAFLD incidence among the population. Methods A cross-sectional survey was carried out among 9,041 community residents in Shanghai. Three NAFLD risk prediction models (I, II, and III) were constructed using multivariate logistic regression analysis based on the least absolute shrinkage and selection operator regression analysis, and random forest model to select individual characteristics, respectively. To determine the optimal model, the three models’ discrimination, calibration, clinical application, and prediction capability were evaluated using the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis, and net reclassification index (NRI), respectively. To evaluate the optimal model’s effectiveness, the previously published NAFLD risk prediction models (Hepatic steatosis index [HSI] and ZJU index) were evaluated using the following five indicators: accuracy, precision, recall, F1-score, and balanced accuracy. A dynamic nomogram was constructed for the optimal model, and a Bayesian network model for predicting NAFLD risk in older adults was visually displayed using Netica software. Results The area under the ROC curve of Models I, II, and III in the training dataset was 0.810, 0.826, and 0.825, respectively, and that of the testing data was 0.777, 0.797, and 0.790, respectively. No significant difference was found in the accuracy or NRI between the models; therefore, Model III with the fewest variables was determined as the optimal model. Compared with the HSI and ZJU index, Model III had the highest accuracy (0.716), precision (0.808), recall (0.605), F1 score (0.692), and balanced accuracy (0.723). The risk threshold for Model III was 20%–80%. Model III included body mass index, alanine aminotransferase level, triglyceride level, and lymphocyte count. Conclusions A dynamic nomogram and Bayesian network model were developed to identify NAFLD risk in older Chinese adults, providing personalized health management strategies and reducing NAFLD incidence.

Abstract Background Pheochromocytoma (PCC) crisis is a rare life-threatening endocrine emergency. The diagnosis and treatment of PCC crisis, with acute respiratory distress syndrome (ARDS) as the first manifestation, is highly challenging, and traditional PCC management strategies are no longer suitable for these patients. Case presentation A 46-year-old female patient was admitted to the Intensive Care Unit (ICU) following sudden-onset acute respiratory distress and subsequent initiation of mechanical ventilation via endotracheal intubation. She was initially suspected of having a PCC crisis through the bedside critical care ultrasonic examination protocol. The computed tomography examination revealed a left adrenal neoplasm of 6.5cm × 5.9cm. The plasma-free metanephrine level was 100 times higher than the reference value. These findings were compatible with her PCC diagnosis. Alpha-blockers and fluid intake were started immediately. The endotracheal intubation was removed on the 11th day after admission to the ICU. The patient progressed to severe ARDS again, and invasive ventilation and continuous renal replacement therapy were needed. Despite aggressive therapy, her condition deteriorated. Therefore, she underwent veno-arterial extracorporeal membrane oxygenation (VA-ECMO)-assisted emergency adrenalectomy after multidisciplinary discussion. Postoperatively, the patient was supported by VA-ECMO for 7days. She was discharged from the hospital on day 30 after tumor resection. Conclusions This case highlighted the challenges in diagnosing and managing ARDS associated with PCC crisis. The traditional preoperative preparation protocol and optimal operation timing for patients with PCC are not suitable for patients with PCC crisis. Patients with life-threatening PCC crisis may benefit from early tumor removal, and VA-ECMO could maintain hemodynamic stability during and after surgery.

Abstract Chronic prostatic inflammation promotes cell survival and fibrosis, leading to benign prostatic hyperplasia (BPH) with aggravated urinary symptoms. It is investigated whether yes‐associated protein 1 (YAP1), an organ size controller and mechanical transductor, is implicated in inflammation‐induced BPH. The correlation between YAP1 expression and fibrosis in human and rat BPH specimens is analyzed. Furthermore, the effects of YAP1 activation on prostatic cell survival and fibrosis, as well as the underlying mechanism, are also studied. As a result, total and nuclear YAP1 expression, along with downstream genes are significantly upregulated in inflammation‐associated human and rat specimens. There is a significant positive correlation between YAP1 expression and the severity of fibrosis or clinical performance. YAP1 silencing suppresses cell survival by decreasing cell proliferation and increasing apoptosis, and alleviates fibrosis by reversing epithelial‐mesenchymal transition (EMT) and extracellular matrix (ECM) deposition in prostatic BPH‐1 and WPMY‐1 cells. Mechanistically, inflammatory stimulus and rigid matrix stiffness synergistically activate the RhoA/ROCK1 pathway to provoke cytoskeleton remodeling, thereby promoting YAP1 activation to exacerbate BPH development. Overall, inflammation‐triggered mechanical stiffness reinforcement activates the RhoA/ROCK1/F‐actin/YAP1 axis, thereby promoting prostatic cell survival and fibrosis to accelerate BPH progression.

In dynamic complex environments, it is inevitable for UAVs to be damaged due to their confrontational nature. The challenge to minimize the adverse effects of the damage and reallocate the mission is vital for achieving the operational goal. This paper proposes a distributed Multi-UAV mission reallocation method in the case of UAV damage based on the improved consensus-based bundle algorithm (CBBA). Firstly, a dynamic optimization model for Multi-UAV mission reallocation is established based on an improved resource update model. Secondly, a distributed damage inspection method based on the heartbeat hold mechanism is proposed for real-time monitoring of UAV conditions, which could enable the rapid response to UAV damage events. Furthermore, the CBBA is improved by introducing a timeliness parameter to adjust the bidding strategy and optimizing the mission selection strategy based on the time-order priority insertion principle to generate mission reallocation plans quickly. Through numerical examples, the results show that the proposed method can effectively reallocate Multi-UAV missions under damage events and has superior performance compared with original the CBBA, the particle swarm optimization (PSO) algorithm, and the performance impact (PI) algorithm. The proposed method has a faster solving speed, while the obtained solution has higher mission reallocation effectiveness.

Abstract Background Postoperative delayed hyponatremia (PDH) is a major cause of readmission after endoscopic transsphenoidal surgery (eTSS) for pituitary adenomas (PAs). However, the risk factors associated with PDH have not been well established, and the development of a dynamic online nomogram for predicting PDH is yet to be realized. We aimed to investigate the predictive factors for PDH and construct a dynamic online nomogram to aid in its prediction. Methods We analyzed the data of 226 consecutive patients who underwent eTSS for PAs at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020. An additional 97 external patients were included for external validation. PDH was defined as a serum sodium level below 137 mmol/L, occurring on the third postoperative day (POD) or later. Results Hyponatremia on POD 1–2 (OR = 2.64, P = 0.033), prothrombin time (PT) (OR = 1.78, P = 0.008), and percentage of monocytes (OR = 1.22, P = 0.047) were identified as predictive factors for PDH via multivariable logistic regression analysis. Based on these predictors, a nomogram was constructed with great discrimination in internal validation (adjusted AUC: 0.613–0.688) and external validation (AUC: 0.594–0.617). Furthermore, the nomogram demonstrated good performance in calibration plot, Brier Score, and decision curve analysis. Subgroup analysis revealed robust predictive performance in patients with various clinical subtypes and mild to moderate PDH. Conclusions Preoperative PT and the percentage of monocytes were, for the first time, identified as predictive factors for PDH. The dynamic nomogram proved to be a valuable tool for predicting PDH after eTSS for PAs and demonstrated good generalizability. Patients could benefit from early identification of PDH and optimized treatment decisions.

Typically, Cu catalysts agglomerate under electrochemical conditions. Here, the authors report a coordination polymer catalyst with neighboring Cu sites which remain isolated and reduce CO2 to C2H4 with high selectivity and stability

Whether the stems and leaves of leaf-vegetable sweet potatoes can be listed ahead of schedule is related to the improvement in economic benefits for farmers, and the key to all of this is to implement the safe overwintering of potato seedlings under the premise of saving production costs. Only in this way can we truly seize the “market opportunity” and achieve the goals of cost saving and increasing economic benefit. In this study, the main leaf-vegetable sweet potato variety Fucai 18 was used as the material, and the L9(34) orthogonal experiment was carried out in a simple solar greenhouse environment for two consecutive years from 2021 to 2022 and from 2022 to 2023, respectively. The effects of nine different combinations of factors on the above-ground and underground agronomic traits of overwintering sweet potato seedlings were studied under the conditions of four factors and three levels: planting density (a); different cutting seedlings (b); rooting agent concentration (c); and transplanting time (d). The methods of principal component analysis, membership function method, cluster analysis, grey correlation degree and stepwise regression analysis were used to evaluate the growth of overwintering seedlings, and try to screen out the key indicators that can be used to identify and evaluate the growth of overwintering sweet potato seedlings. Through range analysis, identify the optimal combination of four factors and three levels, and explore the main factors that have a significant impact on the key indicators for evaluating the growth of overwintering potato seedlings. The results indicate the following: (1) The use of simple sunlight greenhouse in Changsha area can achieve the safe overwintering of vegetable sweet potato seedlings. (2) Stem thickness, root length, and root diameter can be used as three key indicators for identifying and evaluating the growth potential of vegetable sweet potato overwintering seedlings. (3) Under four factors and three levels, the best combination was A3B3C1D1 (planting density of 250,000 plants/ha, stem tip core-plucking seedlings, rooting agent concentration of 50 mg/L, the first batch of transplanting time). (4) The transplanting time (D) is the main factor for the two key evaluation indicators of stem diameter and root diameter, while there is no significant difference in the three other factors. (5) Different cutting seedlings (B) are the main influencing factors for the key evaluation index of root length, while the other three factors have the following impact on root length: transplanting time (D) > rooting agent concentration (C) > planting density (A). The results of this study not only contribute to the construction of a safe overwintering cultivation technology system for vegetable sweet potato seedlings, but also provide a certain theoretical basis for the breeding of new cold-leaf-vegetable sweet potato varieties in the future.

The excellent insulation performance of roof insulators is crucial to the reliability of operating trains. The complex environment encountered by trains may cause roof insulator insulation failure, thus affecting the normal operation of the train. The field operation experience shows that during the operation of Hainan CRHAF-A trains, partial arcs are generated frequently on the roof insulators and even flashover accidents occur. In addition, the insulation failure probability of the insulators in the coastal environment is higher than that in other areas, and pollution is an important factor leading to lower insulation performance. Therefore, the pollution accumulation characteristics of roof insulators are investigated and analyzed in this paper. The accumulated pollution simulation model is built based on the operating environment of the train, and the electric field, flow field, and pressure field around the roof insulators are simulated. This has important engineering application and economic value for the study of insulator anti-pollution.

To ensure the reliable functioning of hard target-penetration fuze on the battlefield, this study focuses on research related to fuze protective pads. The main factors causing fuze functional failure are summarized, and a simplified model of projectile penetration into target plates is established. The design conditions for the yield stress parameter of the fuze casing material are derived based on stress wave propagation theory. Modal analysis of the projectile is conducted using dynamic simulation software ANSYS to determine its vibration modes and low-pass filtering frequency. Static compression experiments are performed on different rubber materials (nitrile rubber, fluorine rubber, silicone rubber, and natural rubber) to obtain stress–strain curves and constitutive model parameters. Marshall hammer tests were carried out on rubber pads of different materials and thicknesses, confirming the validity of the simulation results and the feasibility of rubber filtering. The study indicates that when using a 2 mm thick rubber pad for protection, natural rubber provides the best protection. When using a 6 mm thick rubber pad, nitrile rubber shows the best protective performance. Under a 13-tooth tooling impact load, the best protection is achieved using a 2 mm thick natural rubber pad. When using a 6 mm thick pad, silicone rubber provides the best protection. Under a 15-tooth tooling impact load, fluorine rubber provides the best protection when using a 2 mm thick pad, while silicone rubber offers the best protection when using a 6 mm thick pad. Under a 17-tooth tooling impact load, natural rubber offers the best protection when using a 2 mm thick pad, and fluorine rubber demonstrates the best protection when using a 6 mm thick pad. The obtained research results provide a reference for protective methods of hard target-penetration fuze.

Abstract Objective Acute respiratory distress syndrome (ARDS) leads to life-threatening acute hypoxemic respiratory failure and requires mechanical ventilation. However, there is a lack of certainty regarding recruitment maneuvers (RMs) and positive end-expiratory pressure (PEEP) levels. Therefore, we performed a network meta-analysis to identify the optimal RM and PEEP levels of patients with ARDS. Methods We searched the PubMed, OVID and Cochrane Central Register of Controlled Trials (Central) databases for randomized controlled trials (RCTs). The primary outcome was death on the 28th day, and the secondary outcomes included in-hospital death, ventilator-free days, and barotrauma. The relative effect sizes were estimated by risk ratios (RRs) for binary variables and standard mean difference (SMD) for continuous variables. The efficacy of the interventions was ranked using surface under the cumulative ranking. Multiple intervention comparisons based on the Bayesian framework were performed to integrate the efficacy of all included strategies. Results Thirteen RCTs comprising 4410 patients were included in the network meta-analysis. In terms of death at 28 days, inconsistencies were found globally and locally in the tests. None of the ventilation strategies was significantly superior to the others on any outcomes. According to the relative rank probabilities, RM + lower PEEP levels showed the highest probability of reducing the risk of in-hospital death and reducing ventilator-free days. Lower PEEP levels showed the highest probability of benefitting barotrauma. The overall quality of the evidence per grade was moderate to low. Conclusions The no ventilation strategy is significantly superior to the other strategies. RM + lower PEEP levels has the highest probability of benefitting survival. The evidence has low overall quality and should be further studied.

Obesity has become a serious public health problem globally. China has the world's largest number of people with obesity or overweight. More than 50% of adults and about 20% of children and adolescents in China are overweight or obese. Over the past 20 years, China has made many efforts in obesity intervention, while obesity rates continued rising. In recent years, China has taken more actions for obesity prevention. The World Obesity Day (WOD) was established in 2015 by the World Obesity Federation, and is a unified day of action calling for a cohesive, cross-sector response to fight obesity. WOD has drawn increasing attention in recent years. China formally joined the WOD activities this year, promoted by The Obesity Prevention and Control Section of Chinese Nutrition Society, which cooperated closely with other organizations in carrying out a series of activities during the 2022 WOD. These efforts aimed to encourage the whole society to pay attention to the obesity problem and take more practical actions. The activities include: (1) The 2022 “World Obesity Day” China Summit; (2) a specifically developed WOD obesity prevention education video; and (3) the launch of The Expert Consensus on Obesity Prevention and Treatment in China. These can serve as examples for other organizations and countries to consider in future intervention efforts. To fight the global obesity epidemic, all counties and people need to act. Countries can learn from each other's successful practices.

Abstract Elderly patients with nonalcoholic fatty liver disease are at a higher risk of developing. This study established an effective, individualised, early Hypertension risk-prediction model and proposed health management advice for patients over 60 years of age with NAFLD. Questionnaire surveys, physical examinations, and biochemical tests were conducted in 11,136 participants. The prevalence of NAFLD among 11,136 participants was 52.1%. Risk factors were screened using the least absolute shrinkage and selection operator model and random forest model. A risk-prediction model was established using logistic regression analysis and a dynamic nomogram was drawn. The model was evaluated for discrimination, calibration, and clinical applicability using receiver operating characteristic curves, calibration curves, decision curve analysis, net reclassification index (NRI), and external validation. The results suggested that the model showed moderate predictive ability. The area under curve (AUC) of internal validation was 0.707 (95% CI: 0.688–0.727) and the AUC of external validation was 0.688 (95% CI: 0.672–0.705). The calibration plots showed good calibration, the risk threshold of the decision curve was 30–56%, and the NRI value was 0.109. This Hypertension risk factor model may be used in clinical practice to predict the Hypertension risk in NAFLD patients.

ObjectiveThis study aimed to establish a risk prediction model for diabetic retinopathy (DR) in the Chinese type 2 diabetes mellitus (T2DM) population using few inspection indicators and to propose suggestions for chronic disease management.MethodsThis multi-centered retrospective cross-sectional study was conducted among 2,385 patients with T2DM. The predictors of the training set were, respectively, screened by extreme gradient boosting (XGBoost), a random forest recursive feature elimination (RF-RFE) algorithm, a backpropagation neural network (BPNN), and a least absolute shrinkage selection operator (LASSO) model. Model I, a prediction model, was established through multivariable logistic regression analysis based on the predictors repeated ≥3 times in the four screening methods. Logistic regression Model II built on the predictive factors in the previously released DR risk study was introduced into our current study to evaluate the model’s effectiveness. Nine evaluation indicators were used to compare the performance of the two prediction models, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, balanced accuracy, calibration curve, Hosmer-Lemeshow test, and Net Reclassification Index (NRI).ResultsWhen including predictors, such as glycosylated hemoglobin A1c, disease course, postprandial blood glucose, age, systolic blood pressure, and albumin/urine creatinine ratio, multivariable logistic regression Model I demonstrated a better prediction ability than Model II. Model I revealed the highest AUROC (0.703), accuracy (0.796), precision (0.571), recall (0.035), F1 score (0.066), Hosmer-Lemeshow test (0.887), NRI (0.004), and balanced accuracy (0.514).ConclusionWe have built an accurate DR risk prediction model with fewer indicators for patients with T2DM. It can be used to predict the individualized risk of DR in China effectively. In addition, the model can provide powerful auxiliary technical support for the clinical and health management of patients with diabetes comorbidities.

Abstract Background Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792 . Registered November 20th, 2017.

Abstract Background To observe the changes of hepatic function and efficacy of conventional dosage of caspofungin in the treatment of patients with different Child–Pugh scores. Methods In total, 200 patients (Child–Pugh A group: 66 patients, Child–Pugh B group: 83 patients, Child–Pugh C group: 51 patients) treated with caspofungin from May 2018 to March 2021 in the Second Affiliated Hospital of Chongqing Medical University were enrolled. Main investigation items were as follows: sex, age, weight, duration of treatment, dosage, department, underlying diseases, risk factors for fungal infection, albumin, liver enzyme, total bilirubin, serum creatinine, estimated glomerular filtration rate. To investigate the changes of liver, kidney function tests and efficacy during the treatments of caspofungin. Patients were divided into three groups according to the duration of treatment of caspofungin:1-week group, 2-week group and 3-week group, respectively. Results In the three groups, albumin, liver enzyme levels, total bilirubin and serum creatinine, estimated glomerular filtration rate had no significant difference (P > 0.05). The efficacy of different Child–Pugh scores and different duration of treatment was also significantly different (P > 0.05). Conclusions Caspofungin is well tolerated and highly effective. And it will not exacerbate the hepatic and renal function when administered with the not-reducing dose, which indicate the clinical application value of caspofungin. Besides, extending the treatment duration has little effect on improving the efficacy of caspofungin. The drug should be withdrawn timely according to the patients’ clinical condition in order to reduce the adverse reactions and economic burden.

In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell's concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744-0.792) and 0.745 (95% CI, 0.669-0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30-54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.

Abstract Sepsis is a potentially fatal condition caused by infection. It is frequently difficult to distinguish sepsis from systemic inflammatory response syndrome (SIRS), often resulting in poor prognoses and the misuse of antibiotics. Hence, highly sensitive and specific biomarkers are needed to differentiate sepsis from SIRS. Urine samples were collected and segregated by group (a sepsis group, a SIRS group, and a healthy control group). iTRAQ was used to identify the differentially expressed proteins among the three groups. The identified proteins were measured by ELISA in urine samples. Finally, all the acquired data were analyzed in SPSS. C-reactive protein, leucine-rich alpha glycoprotein-1 and serum amyloid A (SAA) protein were differentially expressed among the three groups. The adjusted median concentrations of urinary C-reactive protein were 1337.6, 358.7, and 2.4 in the sepsis, SIRS, and healthy control groups, respectively. The urinary leucine-rich alpha glycoprotein-1 levels in these three groups were 1614.4, 644.5, and 13.6, respectively, and the levels of SAA were 6.3, 2.9, and 0.07, respectively. For all three of these measures, the sepsis group had higher levels than the SIRS group (P

In this study, the Discrete Element Method (DEM) was employed to investigate numerically the effects of hydrate cementation and intermediate principal stress on the stress-dilatancy relation of grain-cementing type methane hydrate-bearing sediment (MHBS) by conducting a series of conventional and true triaxial tests. A novel 3D thermo-hydro-mechanical-chemical (THMC) contact model for MHBS was employed. The numerical results show that with increasing hydrate saturation and back pressure, or decreasing confining pressure, temperature and salinity, the stress-dilation relation of grain-cementing type MHBS evolves from dilation-dominant to bond-dominant. For the clean sand samples, the relationship between the normalized stress ratio η/Mcr and the dilatancy rate d is close under different intermediate principal stress coefficients. However, for the MHBS samples, this relationship is still affected by the intermediate principal stress coefficient b, due to the effect of hydrate cementation.

In the context of low-carbon globalization, green development has become the common pursuit of all countries and the theme of China’s development in the new era. Fine particulate matter (PM2.5) is one of the main challenges affecting air quality, and how to accurately predict PM2.5 plays a pivotal role in environmental governance. However, traditional data-driven approaches and deep learning methods for prediction rarely consider spatiotemporal features. Furthermore, different regions always have various implicit or hidden states, which have rarely been considered in the off-the-shelf model. To solve these problems, this study proposed a novel Spatial-Temporal Matrix Factorization Generative Adversarial Network (ST MFGAN) to capture spatiotemporal correlations and overcome the regional diversity problem at the same time. Specifically, Generative Adversarial Network (GAN) composed of graph Convolutional Network (GCN) and Long-Short-Term Memory (LSTM) network is used to generate a large amount of reliable spatiotemporal data, and matrix factorization network is used to decompose the vector output by GAN into multiple sub-networks. PM2.5 are finally combined and jointly predicted by the fusion layer. Extensive experiments show the superiority of the newly designed method.

BackgroundCognitive impairment commonly occurs in aneurysmal subarachnoid hemorrhage (aSAH) survivors. Cerebrospinal fluid (CSF) biomarkers have been proven useful in several central neurological disorders. No such diagnostic biomarkers are available for predicting cognitive impairment after aSAH to date. Here, we aimed to identify novel CSF biomarkers for cognitive deficits after aSAH using an in-depth proteomic approach.MethodsWe applied mass spectrometry with data independent acquisition (DIA) quantification to identify biomarker candidates in CSF samples from a well-characterized cohort comprising patients with impaired cognition (n = 9) and patients with intact cognition (n = 9). The potential biological processes and signaling pathways associated with differential proteins were analyzed using R software. The candidates were further validated in a larger independent cohort (n = 40) using ELISA. The diagnostic utility of these proteins was investigated by using receiver operating characteristic curve analysis.ResultsIn total, we identified 628 proteins. The discovery cohort revealed that 115 proteins were differentially expressed in cognitive impairment patients compared to patients with intact cognition (P < 0.05). Independent cohort replication confirmed NCAM2, NPTXR, NRXN2, RELN, and CNTN2 as sensitive and specific candidate biomarkers for disorders of cognition. Lower CSF levels of all biomarker candidates, except RELN, were associated with more pronounced cognitive decline.ConclusionWe identified and validated five CSF biomarkers for cognitive impairment in aSAH patients. These particular proteins have important predictive and discriminative potential for cognitive impairment in aSAH and could be potential targets for early disease intervention.

Abstract Background Sweetpotato (Ipomoea batatas [L.] Lam.) is an important food crop. However, the genetic information of the nuclear genome of this species is difficult to determine accurately because of its large genome and complex genetic background. This drawback has limited studies on the origin, evolution, genetic diversity and other relevant studies on sweetpotato. Results The chloroplast genomes of 107 sweetpotato cultivars were sequenced, assembled and annotated. The resulting chloroplast genomes were comparatively analysed with the published chloroplast genomes of wild species of sweetpotato. High similarity and certain specificity were found among the chloroplast genomes of Ipomoea spp. Phylogenetic analysis could clearly distinguish wild species from cultivars. Ipomoea trifida and Ipomoea tabascana showed the closest relationship with the cultivars, and different haplotypes of ycf1 could be used to distinguish the cultivars from their wild relatives. The genetic structure was analyzed using variations in the chloroplast genome. Compared with traditional nuclear markers, the chloroplast markers designed based on the InDels on the chloroplast genome showed significant advantages. Conclusions Comparative analysis of chloroplast genomes of 107 cultivars and several wild species of sweetpotato was performed to help analyze the evolution, genetic structure and the development of chloroplast DNA markers of sweetpotato.

As one of the most essential parts of the military operation, the ability of weapon systems to achieve the operation mission is vital to the success of a military operation. Focusing on the problem of weapon system effectiveness assessment (WSEA) under interval uncertainty, this paper proposed WSEA method that utilized the interval-valued evidential reasoning (ER) algorithm, the analytical hierarchy process (AHP), and the two-grade interval ranking method to properly deal with interval uncertainty in the assessment as well as provide reliable assessment results. Firstly, the AHP is used to determine the weight of different attributes of the weapon system based on experts’ knowledge, which could enhance the subjectiveness of the assessment result. Then, the interval-valued ER algorithm is applied to combine the assessment of different attributes under interval uncertainty and provide the overall assessment result. Finally, the two-grade interval ranking method is utilized to compare and rank the assessment results of different weapons. Case study shows that the proposed method could provide reliable and accurate results for weapon effectiveness assessment. Comparison results and sensitivity analysis results further confirm that the proposed method is not only as effective as existing methods with precise data, but also has the ability to provide reliable results under uncertainty. In conclusion, by utilizing the interval-valued ER algorithm, the AHP and the two-grade interval ranking method, the proposed method provides a novel and effective way for weapon effectiveness assessment under uncertainty, especially interval uncertainty.

Early retention or initial threshold voltage shift (IVS) is one of the key reliability challenges in charge trapping memory (CTM) based 3D NAND flash. Re-program scheme was introduced in quad-level-cell (QLC) NAND (Shibata et al., 2007, Lee et al., 2018, Shibata et al., 2019, and Khakifirooz et al., 2021), and the IVS improvement by re-program scheme was reported. In this work, it is found that re-program can suppress ~81% of IVS in 3D NAND, which is much more significant than that of 2D NAND ~50% (Chen et al., 2010). The mechanisms of IVS improvement by re-program scheme in 3D NAND are investigated. Both vertical de-trapping in the BE-tunneling oxide and charge lateral migration (LM) in the charge-trap layer are suppressed in re-program. Re-program is effective in vertical de-trapping suppression both in checker-board pattern (C/P) and solid-board pattern (S/P) cases, and is effective in LM suppression only in C/P case. Furthermore, the LM improvement by re-program scheme is more pronounced with gate length (Lg) and inter-gate space (Ls) scaling down, showing potential in the reliability improvement of advanced 3D NAND technologies.

The development of civil aviation has led to flight operations generating massive datasets. Derived from Automatic Dependent Surveillance-Broadcast technology, an eigengap-based automatic hierarchical clustering algorithm is proposed, aiming to overcome the human intervention requirement of hierarchical clustering to determine the number of track clusters in the terminal area. First, the trajectory pair similarity matrix is calculated based on the Euclidean distance and an adaptive scale parameter. Second, the Laplace transformation is applied on the similarity matrix and the eigenvalues are obtained in order to determine the number of clusters. Finally, the hierarchical clustering algorithm divides the terminal area trajectory into several sub-categories. Taking a terminal approach trajectory as an example, simulation analysis is performed, with results revealing that the algorithm divides 404 north-south-oriented aircraft flight trajectories into 2 and 3 categories. Furthermore, the new-index is used to evaluate the clustering results, demonstrating its effectiveness is better than the automatic k-means algorithm. Our study provides support for reliability analysis of air traffic network in the terminal area.

Inflammation is an important driver of atherosclerosis. Succinate is a new extracellular inflammatory alarm released by activated macrophages. Succinate is sensed by succinate receptor 1 (Sucnr1) and then transferred to effector cells. It is worth exploring whether succinate is capable of facilitating the inflammatory response in atherosclerosis. In this study, we firstly found that arterial serum of Coronary Heart Disease (CHD) patients contained significantly higher succinate and interleukin (IL)-1β than Health control (HC) subjects, and succinate was positively correlated with IL-1β. As demonstrated by the in vitro study, succinate/hypoxia-inducible factor 1α (Hif)-1α/IL-1β signal axis existed and significantly facilitated the inflammatory program in human umbilical vein endothelial cells (HUVECs). Under the coculture, activated macrophages released succinate, which would be transferred to HUVECs via Sucnr1 and then activate Hif-1α to produce a greater amount of IL-1β. Likewise, the aortic sinus’s inflammatory phenotype was found to be more significant within Apoe-/- mice that were injected with succinate. Furthermore, Sucnr1 inhibitor (NF-56-EJ40) could significantly interrupt succinate/IL-1β signal in HUVECs and macrophages. As revealed by this study, glycolytic metabolism following the release of succinate could be found in atherosclerotic pathology, and succinate would drive succinate/IL-1β signal dependent on Sucnr1 and then exacerbate inflammatory responses. Sucnr1 might be a novel target for cutting off the transduction of succinate signal to prevent the inflammation of atherosclerosis.

The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

In this study, CeO2@Zn0.5Cd0.5S heterostructure (Ce@ZCS) is synthesized via a simple two-step hydrothermal method. The effect of CeO2 loading on the visible-light photoactivity of Zn0.5Cd0.5S is mainly investigated. It is found that Ce@ZCS shows a 1.9 times activity as high as ZCS for the MB degradation. The improved activity mainly results from the significant enhanced charge separation by CeO2, in which the electron transfer is obviously promoted by the facile Ce(IV)/Ce(III) cycle. The excited electrons of ZCS is easy to transfer to CeO2, thus obviously increasing the charge separation of ZCS. The accepted electrons by CeO2 may easily be captured by the adsorbed O2 to form O2·−, and then O2·− could combine with H+/H2O to form HO2·, and ·OH. Finally, O2·−, h+ and ·OH are confirmed as the major oxidative species in photocatalytic reaction for Ce@ZCS, and a possible photocatalytic mechanism is proposed. The cheap, efficient Ce@ZCS photocatalyst could be applied for practical waste water treatment. Keywords: Ce(IV)/Ce(III) cycle, CeO2@Zn0.5Cd0.5S, Electron transfer, Photocatalysis

As the 3D NAND technology developing toward more and more stack layers, it is essential to shrink the gate length (Lg) and inter-gate space (Ls). However, one of key concerns of scaling Lg/Ls 3D NAND flash is post-cycling data retention characteristics. The impact of cycling induced intercell trapped charge on two primary charge loss mechanisms (vertical and lateral charge loss) was studied in this work. According to experimental analysis and TCAD simulation, it is found that, in vertically scaled 3D NAND, the vertical charge loss is deteriorated not only by the cycling induced tunnel oxide degradation (introducing interface/oxide traps), but also by the cycling induced intercell trapped charge (enhancing word-lines edge electric field), on account of the enhanced Poole-Frenkel effect and tunneling effect. On the other hand, the cycling induced intercell trapped charge can also suppress lateral charge migration. Therefore, the vertical charge loss, rather than the lateral charge migration, still can be the dominant factor for post-cycling retention characteristics in scaling Lg/Ls 3D NAND flash memory.

Dependence assessment, which is to assess the influence of the operator's failure of a task on the failure probability of subsequent tasks, is an important part in Human reliability analysis (HRA). The technique for human error rate prediction (THERP) has been widely applied to assess the dependence in HRA. However, due to the complexity of the real world, various kinds of uncertainty could occur in dependence assessment problem, and how to properly express and deal with uncertainty especially interval uncertainty remains a pressing issue. In this article, a novel method based on the interval evidential reasoning (IER) algorithm is proposed to assess dependence in HRA under interval uncertainty. First, dependence influential factors are identified and their functional relationship is determined. Then, judgments on these factors provided by the analysts are represented using interval belief distributions. Next, the interval evidential reasoning algorithm is employed to aggregate interval belief distributions of different factors according to their functional relationship while considering the credibility of the interval belief distribution. Finally, the conditional human error probability (CHEP) is calculated based on the fused interval belief distribution, where the upper and lower values are determined by assigning belief degree to the highest and lowest grade of the corresponding grade interval, respectively. Two numerical examples demonstrate that the proposed method not only properly deals with interval uncertainty using interval belief distribution and IER algorithm, but also provides a novel and effective way for dependence assessment in HRA.

ObjectiveTo evaluate the efficiency of amiodarone in preventing the occurrence of reperfusion ventricular fibrillation (RVF) after aortic cross-clamp (ACC) release in patients undergoing open-heart surgery.MethodsWe searched the Web of Science, Cochrane Library, EMBASE, and PubMed databases through January 2021 for relevant studies addressing the efficacy of amiodarone in preventing RVF after ACC release in patients undergoing cardiac surgery. A complete statistical analysis was performed using RevMan 5.3. Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated to express the results of dichotomous outcomes using random or fixed-effect models. The chi-square test and I2 test were used to calculate heterogeneity.ResultsSeven studies (856 enrolled patients; 311 in the amiodarone group, 268 in the lidocaine group, and 277 in the placebo group) were selected for the meta-analysis. The incidence of RVF was significantly decreased in the amiodarone group compared to the placebo group (RR = 0.69, 95%CI: 0.50–0.94, P = 0.02). However, amiodarone and lidocaine did not confer any significant difference (RR = 0.98, 95%CI: 0.61–1.59, P = 0.94). The percentage of patients requiring electric defibrillation counter shocks (DCSs) did not confer any significant difference between patients administered amiodarone and lidocaine or placebo (RR = 1.58, 95%CI: 0.29–8.74, P = 0.60; RR = 0.55, 95%CI: 0.27–1.10, P = 0.09; respectively).ConclusionsAmiodarone is more effective than a placebo in preventing RVF after ACC release in cardiac surgery. However, the amiodarone group required the same number of electrical DCSs to terminate RVF as the lidocaine or placebo groups.

The liver is vulnerable to sepsis, and sepsis-induced liver injury is closely associated with poor survival of sepsis patients. Studies have found that the overproduction of reactive oxygen species (ROS) is the major cause of oxidative stress, which is the main pathogenic factor for the progression of septic liver injury. The mitochondria are a major source of ROS. Mito-TEMPO is a mitochondria-specific superoxide scavenger. The aim of this study was to investigate the effect of Mito-TEMPO on lipopolysaccharide- (LPS-) induced sepsis mice. We found that Mito-TEMPO pretreatment inhibited inflammation, attenuated LPS-induced liver injury, and enhanced the antioxidative capability in septic mice, as evidenced by the decreased MDA content and the increased SOD activity. In addition, Mito-TEMPO restored mitochondrial size and improved mitochondrial function. Finally, we found that the levels of pyroptosis-related proteins in the liver of LPS-treated mice were lower after pretreatment with Mito-TEMPO. The mechanisms could be related to Mito-TEMPO enhanced antioxidative capability and improved mitochondrial function, which reflects the ability to neutralize ROS.

In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method.

To elucidate nutritional components in sweet potato cultivars for table use and to compare the phytochemicals of cultivars from different countries, ‘Kokei No. 14′ and ‘Xinxiang’ were selected. The physiological parameters and metabolites were determined using the colorimetric method and widely targeted metabolomics, respectively. Transcriptomic analysis was performed to explain the mechanism that resulted in phytochemical differences. ‘Xinxiang’ showed higher flavonoid and carotenoid contents. Metabolomics showed five upregulated flavonoids. Two essential amino acids (EAAs) and one conditionally essential amino acid (CEAA) were upregulated, whereas four EAAs and two CEAAs were downregulated. Unlike lipids, in which only one of thirty-nine was upregulated, nine of twenty-seven differentially accumulated phenolic acids were upregulated. Three of the eleven different alkaloids were upregulated. Similarly, eight organic acids were downregulated, with two upregulated. In addition, three of the seventeen different saccharides and alcohols were upregulated. In ‘other metabolites,’ unlike vitamin C, 6′-O-Glucosylaucubin and pantetheine were downregulated. The differentially accumulated metabolites were enriched to pathways of the biosynthesis of secondary metabolites, ABC transporters, and tyrosine metabolism, whereas the differentially expressed genes were mainly concentrated in the metabolic pathway, secondary metabolite biosynthesis, and transmembrane transport functions. These results will optimize the sweet potato market structure and enable a healthier diet for East Asian residents.

Apparent temperature (AT) is used to evaluate human comfort and is of great importance for studies on the effects of environmental factors on human health. This study used the daytime heat index (HI) calculated by national surface meteorological stations in China as the AT dependent variable, with August 2020 employed as an example. The daytime fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) data and multi-source data extracted from the stations were used as the independent variables. Due to the presence of multicollinearity among the independent variables, we implemented a multiple stepwise regression model and developed a daily near-surface 1 km HI estimation model. The correlation analysis using the model showed that the coefficient of determination (R2) was 0.89; the mean absolute error (MAE) was 1.49 °C, and the root mean square error (RMSE) was 2.08 °C. We also used 10-fold cross-validation to calculate the error between the parameter and predicted values. The R2 of the model was 0.96; the MAE was 1.80 °C, and the RMSE was 2.40 °C. In this month, the mean daily daytime HI was 20.51 °C. According to the Universal Thermal Climate Index (UTCI), the areas with more than 20 days of heat stress for one month were largely distributed in the desert areas of northwest China and the coastal areas in southeast China, accounting for 29.98% of the total land area of China. This study improves the spatial resolution and accuracy of HI prediction, thus providing a scientific reference for studying residential environments and the urban heat island effect.

Due to its high efficiency, cold metal transfer (CMT) arc additive manufacturing presents considerable potential in the aluminium alloy additive manufacturing industry. However, during CMT arc additive manufacturing, the surrounding air environment promotes the lateral flow of liquid aluminium and the instability of the molten pool, reduces the surface quality and material utilisation of deposition walls, and causes internal hydrogen pores and coarse columnar grains, which negatively affect the structure and mechanical properties of the deposition walls. This study developed a CMT arc additive die manufacturing process to control the substrate material and deposition path to improve the physical properties of the deposition wall. The experimental results indicated that the copper plates can affect molten pool flow and material formation in the additive process, minimise hydrogen pores, and refine columnar grains. The porosity dropped from 2.03% to 0.93%, and the average grain size decreased from 16.2 ± 1.4 to 13.6 ± 1.3 μm, thereby enhancing the structure and mechanical properties of the deposition wall to attain standard additive manufacturing products.

This paper deals with finite-time formation control problems of unmanned aerial vehicle swarm system with time-delay and input saturation. Using precise feedback linearization, first, the nonlinear time-delay model of unmanned aerial vehicle is transformed into a linear second-order time-delay system. Based on the neighbors’ states, a fixed-time distributed observer is constructed to estimate the leader’s state for each follower unmanned aerial vehicle quickly and accurately. Moreover, by utilizing Artstein’s transformation, the delayed second-order system is transformed into a delay-free system and a saturation function is used to tackle the input saturation problem. Then, a finite-time formation control protocol is designed based on the Artstein’s transformation and saturation function. Rigorous proof shows that all control inputs are bounded and the formation control with time-delay is achieved in finite time. Finally, a simulation example is given to verify the effectiveness of the proposed protocol.

Ozone pollution is a prominent public health issue, but there are few studies on the effect of ozone on the ultrastructure of respiratory system; we conducted this research. Exposed to 1.1 ppm O3 4 h per day, the mice lungs and bronchi were taken on the 15th or 30th day. The sections stained with HE and immunohistochemical streptavidin–peroxidase methods for NQO1, Nrf2, and Keap1 were observed and measured under the optical microscope. TEM was used for ultrastructure observation. The animals’ serums were detected for CRP and IL-6 levels. The HE-stained sections showed no obvious micromorphological changes in the O3 exposure, but the NQO1 average optical density was higher than the control on the 15th day (p < 0.05). The ultrastructural changes were found in the O3 exposure group, such as bulges and vacuoles in type I alveolar cells, the increased evacuation of substance from lamellar bodies in the type II alveolar cells, the increased space around the goblet nucleus, binuclear goblet, and columnar cells. CRP and IL-6 levels increased compared with the control (p < 0.05). Although inhaling 1.1 ppm O3 had no significant effect on the micromorphology of the mice lungs and bronchi, it did affect the ultrastructure with oxidative stress and inflammatory responses.