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Guangda He,1 Runqing Ji,1 Xiqian Huo,1 Xiaoming Su,1 Jinzhuo Ge,1 Wei Li,1 Lubi Lei,1 Boxuan Pu,1 Aoxi Tian,1 Jiamin Liu,1 Lihua Zhang,1 Yongjian Wu2 On behalf of the China PEACE Collaborative Group1National Clinical Research Center for Cardiovascular Diseases, NHC Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, People’s Republic of China; 2Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of ChinaCorrespondence: Lihua Zhang, National Clinical Research Center for Cardiovascular Diseases, NHC Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, 167 Beilishi Road, Beijing, 100037, People’s Republic of China, Email zhanglihua@fuwai.com Yongjian Wu, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, People’s Republic of China, Email wuyongjian@fuwai.comBackground: Inflammation contributes to the progression of heart failure (HF). However, long-term inflammatory trajectories and their associations with outcomes in patients with acute HF remain unclear.Methods: Data was obtained from the China Patient-Centered Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, and high-sensitivity C-reactive protein (hsCRP) was used to reflect the inflammatory level. Only patients who survived over 12-month and had hsCRP data at admission, 1-, and 12-month after discharge were included. The latent class trajectory modeling was used to characterize hsCRP trajectories. Multivariable Cox regression models were used to explore the association between hsCRP trajectories and following mortality.Results: Totally, 1281 patients with a median 4.77 (interquartile range [IQR]: 4.24– 5.07) years follow-up were included. The median age was 64 years (IQR: 54– 73 years); 453 (35.4%) were female. Four distinct inflammatory trajectories were characterized: persistently low (n = 419, 32.7%), very high-marked decrease (n = 99, 7.7%), persistently high (n = 649, 50.7%), and persistently very high (n = 114, 8.9%). Compared with the persistently low trajectory, the all-cause mortality was increased in a graded pattern in the persistently high (hazard ratio [HR]: 1.59, 95% confidence interval [CI]: 1.23– 2.07) and persistently very high (HR: 2.56, 95% CI: 1.83– 3.70) trajectories; nevertheless, the mortality was not significantly increased in very high-marked decrease trajectory (HR: 0.94, 95% CI: 0.57– 1.54).Conclusion: Four distinct inflammatory trajectories were identified among patients with acute HF who survived over 12-month. Patients with persistently high and very high trajectories had significantly higher mortality than those with the persistently low trajectory.Keywords: heart failure, high-sensitivity C-reactive protein, trajectory, latent class trajectory modeling, mortality

Bo Pu,1,2,* Wenyuan Sang,2 Juan Yang,3,* Siyu Ji,2 Zhiwei Tang1 1School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 2School of Business and Tourism, Sichuan Agricultural University, Chengdu, 611830, People’s Republic of China; 3School of Economics and Management, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bo Pu; Wenyuan Sang Email ppuubo@hotmail.com; lynnnnyy@163.comBackground: This study explores the causal relationship between entrepreneurial leadership and employees’ tacit knowledge sharing in start-ups. Construct a moderated mediation model to test the mediating role of affective commitment and the moderating role of career growth opportunities.Methods: A questionnaire was used to collect data, and 485 samples of employees in Chinese start-ups were collected. Regression analysis and structural equation model were used to analyze data and verify hypotheses.Results: The study shows that entrepreneurial leadership has a significant positive effect on employees’ affective commitment and tacit knowledge sharing. Affective commitment plays a mediating effect between entrepreneurial leadership and employees’ tacit knowledge sharing. Career growth opportunities play a positive moderating role in the impact of entrepreneurial leadership on affective commitment and tacit knowledge sharing, and positively moderate the indirect effect of entrepreneurial leadership on tacit knowledge sharing through affective commitment.Conclusion: The research illustrates that the managers of start-ups can improve employees’ affective commitment by giving full play to entrepreneurial leadership and combining the career growth opportunities provided by the organization. Employees’ tacit knowledge sharing behavior is stimulated, providing guiding value for knowledge management and the human resource management of start-ups.Keywords: entrepreneurial leadership, tacit knowledge sharing, affective commitment, career growth opportunities

Abstract Kidney injury from high-altitude polycythemia (HAPC) is common, yet few studies have explored effective treatments. This research assessed the renal benefits of therapeutic erythrocytapheresis (TE) in HAPC patients, analyzing the efficacy of single versus multiple treatments. From 2017 to 2023, 631 patients undergoing TE were included. Findings showed notable improvements in hemoglobin levels (median: 228.00 vs. 169.00 g/L, p

[Objective] Centrifugal compressors encounter challenges in maintaining stable operation due to the occurrence of harmful surges, and the low-voltage ride through(LVRT) function of converters has been utilized to sustain continuous operation against power supply fuctuations. However, electric-driven centrifugal compressors frequently experience compressor surges during the converter LVRT process. Therefore, it is essential to analyze the surge causes and develop improvement measures for problem prevention. [Methods] This study focuses on electric-driven centrifugal compressors at the Turpan Interconnection Compressor Station of the West-East Gas Pipeline II.An investigation was carried out to analyze parameter changes, including compressor speed, pressure, fow, and anti-surge operating margin during the LVRT process. It was observed that the anti-surge control response was delayed as a result of rapid compressor speed drop during the converter LVRT process, leading to compressor surges. Subsequently, the sharp pressure and flow fluctuations following the surges rendered the anti-surge margin control ineffective, resulting in persistent compressor surges. Based on these findings, the following measures were devised to enhance anti-surge control:(1) incorporating additional measures for surge prevention during LVRT and LVRT state judgment, presetting trigger conditions for preemptively opening the anti-surge valve until a specified opening based on the compressor's operating margins and outlet pressure changes during the LVRT process;(2) implementing supplementary measures for compressor surge detection and protection, specifically by detecting noticeable changes in outlet pressure and variations in vibration that occur following compressor surges, and in response to any detected surge, promptly opening the anti-surge valve to eliminate the surge state. [Results] The implementation of the improvement measures effectively resolved surge-related issues during the LVRT process. By setting operating point margins and outlet pressure change thresholds, the frequency of preemptively opening the anti-surge valve was reduced, resulting in a swift restoration of normal compressor operation. [Conclusion] The research findings provide valuable approaches for effectively mitigating surge hazards and improving the operational stability of electric-driven centrifugal compressors. Therefore, they can serve as a reference for optimizing anti-surge control and improving surge detection in electric-driven centrifugal compressors.

Limited background data are available on the Mishmi takin (Budorcas taxicolor taxicolor) and Bhutan takin (Budorcas taxicolor whitei) subspecies in the Eastern Himalayas of China because of the lack of systematic field investigations and research. Therefore, mature-animal ecological methods were used to evaluate these takin subspecies’ phenotypic characteristics, distribution range, activity rhythm, and population size. From 2013 to 2022, 214 camera traps were installed for wild ungulate monitoring and investigation in all human-accessible areas of the Eastern Himalayas, resulting in 4837 distinguishable takin photographs. The external morphological characteristics were described and compared using visual data. Artificial image correction and related technologies were used to establish physical image models based on the differences between subspecies. MaxEnt niche and random encounter models obtained distribution ranges and population densities. Mishmi takins have a distribution area of 17,314 km2, population density of 0.1729 ± 0.0134 takins/km2, and population size of 2995 ± 232. Bhutan takins have a distribution area of 25,006 km2, population density of 0.1359 ± 0.0264 takins/km2, and population size of 3398 ± 660. Long-term monitoring data confirmed that the vertical migration within the mountain ecosystems is influenced by climate. Mishmi takins are active at 500–4500 m, whereas Bhutan takins are active at 1500–4500 m. The two subspecies were active at >3500 m from May to October yearly (rainy season). In addition, surveying combined with model simulation shows that the Yarlung Zangbo River is not an obstacle to migration. This study provides basic data that contribute to animal diversity knowledge in biodiversity hotspots of the Eastern Himalayas and detailed information and references for species identification, distribution range, and population characteristics.

During the operation of the compressor station where the centralized air after-cooling mode is adopted, the uncooled hightemperature natural gas from the discharge of compressor enters the suction of compressor through the anti-surge pipeline after the anti-surge valve is opened, and then the natural gas is subjected to the repeated cyclic pressurization in the compressor, resulting in obvious temperature rise. During the operation of the unmanned station, there are problems of untimely handling of such phenomenon, which affects the normal operation of the compressor and shortens the life of the pipeline coating. Herein, the causes and effects of circulating temperature rise were analyzed, and it was proposed to open the circulation valve in the station by control to an appropriate opening, further to close the anti-surge valve of the compressor and solve the problem of circulating temperature rise. Therefore, the PI control with dead band was proposed to control the opening of the circulation valve in the station, and the conditions for opening the circulation valve in the station, the target value of PI control and the relevant control parameters were also set. It is verified through operation that the circulating temperature rise after the opening of anti-surge valve of the compressor under the condition of centralized air after-cooling process is effectively controlled, capable of ensuring the safe operation of the compressor station. Generally, the research results could provide guidance for the automatic control of the circulation valve in the compressor station in the centralized air after-cooling mode, laying a foundation for the construction of the unmanned station.

Abstract To provide evidence-based medicine references for formulating prevention and control policies in plateau areas, we explore the characteristics of anemia patients in Tibet (the plateau areas of China), especially those located at an altitude above 4500 m. We collected clinical data from 379 Tibetan anemia patients over the age of 18 years. We found those female patients accounted for the majority of Tibetan anemia patients. Almost half of the anemia patients aged from 28 to 47 years. The percentage of severe anemia and extremely severe anemia was 45.4% and 2.4%, respectively. 88.7% of patients are engaged in agriculture and animal husbandry, and 81.5% of patients just graduated from primary school or below. The most common causes of anemia were nutritional anemia, especially iron-deficiency anemia. At high-altitude localities, folic acid-deficiency anemia needs more attention. Overall, this study showed that altitude influences the incidence, severity, and cause of anemia. Peasants and herdsmen, low education levels, young and middle-aged women, and nutrition status should be paid attention to in future anemia control.

Hydrological fluctuations accelerate biodiversity loss and damage riparian ecosystems surrounding dams and reservoirs. Restoration of vegetation is urgently required in such highly impacted buffer zones. However, it is not known how the nonstructural carbohydrate (NSC) and organic acid concentrations of prominent woody plants such as Salix matsudana would alter under the hydrological regime described in the literature. The study aims to investigate the tap and lateral roots of S. matsudana in the Three Gorges Reservoir, as well as the NSC and organic acid metabolism of these roots. Three groups of two-year-old S. matsudana saplings were planted in this experiment: control (SS), moderate submergence (MS), and deep submergence (DS) at altitudes of 175 m, 170 m, and 165 m, respectively. The results indicated that hydrological regimes restrained the height of S. matsudana with less impact on diameter at P tartaric acid > oxalic acid. The tartaric acid content of the lateral roots and the total roots was significantly higher in MS than in SS1. Conversely, the oxalic acid and malic acid levels in tap roots, lateral roots, and total roots were higher in DS than in SS2. There were strong Pearson's correlations between root starch and NSC with tartaric and malic acids; these correlations ranged from −0.898

As an effective and possible method for computing PageRank problem, the inner-outer (IO) iteration has attracted wide interest in the past few years since it was first proposed by Gleich et al. (2010). In this paper, we present a variant of the IO iteration, which is based on multi-step power and multi-step splitting and is denoted by MPMIO. The description and convergence are discussed in detail. Numerical examples are given to illustrate the effectiveness of the proposed method.

The method of using seismic information to study the planar distribution characteristics of sedimentary system is illustrated using the lower part of the second member of the Lower Permian Shanxi Formation in the Gaojiahe 3D area of the Ordos Basin as an example. Firstly, based on rock physical properties, time-frequency scanning technology was used to study the seismic reflection characteristics of coal seams in a 90° phase seismic data volume. Secondly, by designing specific filters, seismic waves were filtered to suppress the influence of coal seams on seismic reflection characteristics. A fine (high-frequency) isochronous stratigraphic framework in the 3D seismic data volume was created by combining high-precision sequence stratigraphy with seismic sedimentology. Finally, combining geological features, logging responses and seismic facies analysis, provided the sedimentary analysis of isochronous stratigraphic slices with different frequencies. Mixed frequency display technology was applied to study the spatial distribution characteristics of sedimentary facies. The distributary channels in the lower delta plain sub-facies of the second member of Shanxi Formation in the Gaojiahe 3D area are mainly represented by three trends: (1) Some show north-south, east-west, and then north-south trends, along S229, S228, S232, S208, Y124 well edge. In these distributary channels, a meandering beach with a certain scale may develop near well S231. (2) Some show a northwest-southeast trend, mainly distributed along wells S212, Y127, and S209; (3) Some show a nearly west trend. A large-scale lateral river migration zone develops along wells S210 and S226.

Researchers have proposed a variety of classification schemes for the species in the genus Muntiacus (Artiodactyla: Cervidae) based on morphological, molecular, and other evidence, but disputes remain. The Tibetan Yarlung Zangbo Grand Canyon National Nature Reserve in the Eastern Himalayas is an area with a rich diversity of muntjac species. The habitats of many species overlap in this area, but systematic research in this area is lacking. To clarify the species, population and habitat size of muntjac species in the study area, we used camera-traps to monitor muntjacs in the nature reserve from 2013 to 2021 and described and compared morphological characteristics of the muntjac species. Subsequently, we used the MaxEnt model to simulate the habitats of the muntjac species and the Random Encounter Model to estimate the population density and numbers of muntjacs. Three muntjac species were found in the area, namely Muntiacus vaginalis (n = 7788 ± 3866), Muntiacus gongshanensis (n = 6673 ± 2121), and Muntiacus feae (n = 3142 ± 942). The red muntjac has the largest habitat area, the highest population density, and largest size, followed by Gongshan muntjac and Fea’s muntjac. This study provides basic data for improving the background knowledge of the animal diversity in the Eastern Himalayan biodiversity hotspot, as well as detailed information and references required by wildlife workers for species identification.

A growing body of research has documented the determinants of healthcare expenditure, but no known empirical research has focused on investigating the spatial effects between economic policy uncertainty (EPU) and healthcare expenditure. This study aims to explore the spatial effects of EPU on healthcare expenditure using the panel data of 29 regions in China from 2007 to 2017. Our findings show that healthcare expenditure in China has the characteristics of spatial clustering and spatial spillover effects. Our study also shows that EPU has positive spatial spillover effects on healthcare expenditure in China; that is, EPU affects not only local healthcare expenditure but also that in other geographically close or economically connected regions. Our study further indicates that the spatial spillover effects of EPU on healthcare expenditure only exist in the eastern area. The findings of this research provide some key implications for policymakers in emerging markets.

The goaf left behind after mining has the potential to induce serious geological disasters due to the damaged internal structure of the rock. Estimating the boundary of the underground goaf can effectively control the occurrence of such disasters. However, traditional geophysical methods are inefficient and expensive and are particularly difficult to apply for a wide detection range. This paper proposes a new method for estimating the boundary of underground goaf using the differential interference synthetic aperture radar technique (DInSAR). More specifically, DInSAR is used to obtain the isoline of the subsidence basin above the goaf, and the direction of the two main sections of the goaf is then determined according to the basic law of mining subsidence. Following this, the basic principles of the probability integral and the graphical methods are combined to determine the mining boundary of the strike section and the incline section of the goaf. Finally, six geometric parameters reflecting the boundary of the goaf are obtained. Experiments on simulated and measured data indicate that the proposed method is feasible, with the average relative errors of the simulated and measured data reaching and maintained at 2.2% and 3.7%, respectively.

Abstract 3D‐printing technology is a promising approach for rapidly and precisely manufacturing zeolite adsorbents with desirable configurations. However, the trade‐off among mechanical stability, adsorption capacity, and diffusion kinetics remains an elusive challenge for the practical application of 3D‐printed zeolites. Herein, a facile “3D printing and zeolite soldering” strategy is developed to construct mechanically robust binder‐free zeolite monoliths (ZM‐BF) with hierarchical structures, which can act as a superior configuration for CO2 capture. Halloysite nanotubes are employed as printing ink additives, which serve as both reinforcing materials and precursor materials for integrating ZM‐BF by ultrastrong interfacial “zeolite‐bonds” subjected to hydrothermal treatment. ZM‐BF exhibits outstanding mechanical properties with robust compressive strength up to 5.24 MPa, higher than most of the reported structured zeolites with binders. The equilibrium CO2 uptake of ZM‐BF reaches up to 5.58 mmol g−1 (298 K, 1 bar), which is the highest among all reported 3D‐printed CO2 adsorbents. Strikingly, the dynamic adsorption breakthrough tests demonstrate the superiority of ZM‐BF over commercial benchmark zeolites for flue gas purification and natural gas and biogas upgrading. This work introduces a facile strategy for designing and fabricating high‐performance hierarchically structured zeolite adsorbents and even catalysts for practical applications.

The complexity of algebraic operation methods on Galois fields was analyzed and the operation mechanism of linear network coding was anatomized.Based on deterministic network coding data transmission and random network coding data transmission for single-source multicast connection,the mathematic model had been created to estimate the computation cost of linear network coding and the relationship between operation cost and environmental parameters had been revealed.The key factors of affecting the operation cost were also analyzed.The analysis results indicate that computation cost can be decreased through properly choosing environment parameters.Numerical calculation and simulation results demonstrate the correctness of the proposed model and validate the theoretical analysis.

In order to alleviate the urban traffic congestion and ensure traffic safety, we need to do a good job in urban road traffic safety planning, make the real-time analysis and forecast of urban traffic flow to detect changes of current traffic flow in time, make scientific planning of roads and improve the road service ability and the transport efficiency of freight vehicles. The data of short-term vehicles volume is characterized by uncertainty and timing correlation series. Given this, the ARIMA seasonal model and the Holt-Winters model are used to establish a forecasting model for the short-term vehicles volume of the city. Finally, we compare the model with predictions.

Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC. The models achieved robust predictive accuracy (test R 2  = 0.82-0.95), except for surface area. Notably, analysis of the optimal models revealed SS characteristics had a significant impact on SSBC properties, particularly total and fixed carbon content (combined importance exceeding 80%). This underscores the needs of source analysis and preparation optimization in targeted SS recovery or SSBC applications. To facilitate this, a graphical user interface was developed for strategic analyzation of sludge sources and pyrolysis settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Identifying effective plasmonic photocatalysts exhibiting robust activities across the entire solar spectrum poses a significant challenge. CuSe, with its local surface plasmon resonance (LSPR) effect, has garnered attention as a prospective plasmonic photocatalyst. However, severe charge recombination and insufficient light absorption limit its photocatalytic performance. To enhance the performance, constructing CuSe-based n-p plasmonic semiconductor heterostructures is a potential strategy. MXene quantum dots (MQDs), a kind of n-type plasmonic semiconductor with metallic conductivity and a high LSPR effect, are a promising candidate to couple with p-type CuSe. According to the complementary principle, we designed a 0D/2D MQDs/CuSe n-p plasmonic semiconductor, achieved by wrapping CuSe nanosheets with MQDs. This n-p plasmonic heterostructure exhibits a synergistic effect on an enhanced electronic field, facilitating charge transfer and separation, thereby enhancing charge excitation, carrier migration, and photothermal effect. Furthermore, optimizing the MQD loading content leads to an ultrafast photocatalytic reaction rate, achieving 100% Cr(VI) reduction efficiency within just 60 min with a reaction kinetics of 0.069 min -1 , surpassing the performance of bare CuSe. Our work presents a promising approach for developing advanced n-p plasmonic heterostructures based on MQDs for wastewater treatment and other photocatalytic applications.

Privacy and security concerns restrict access to original training datasets, posing significant challenges for model compression. Data-Free Knowledge Distillation (DFKD) emerges as a solution, aiming to transfer knowledge from teacher to student networks without accessing original data. Existing DFKD methods struggle to generate high-quality synthetic samples that capture the complexities of real-world data, leading to suboptimal knowledge transfer. Moreover, these approaches often fail to preserve the spatial attributes of the teacher network, resulting in shortcut learning and limited generalization.To address these issues, a novel DFKD strategy is proposed with three innovations: (1) an enhanced DCGAN generator with an attention module for synthesizing samples with improved micro-discriminative features; (2) a Multi-Scale Spatial Activation Region Consistency (MSARC) mechanism to accurately replicate the teacher's spatial attributes; and (3) an adversarial learning framework that creates a dynamic competitive environment between the generative and distillation phases. Rigorous evaluation of the method on several benchmark datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and medical imaging datasets such as PathMNIST, BloodMNIST, and PneumoniaMNIST, demonstrates superior performance compared to existing DFKD methods. Specifically, on CIFAR-100, the student network attains an accuracy of 77.85%, surpassing previous methods like CMI and SpaceshipNet. On BloodMNIST, the method achieves an accuracy of 80.50%, outperforming the next best method by over 5%., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Deep vein thrombosis (DVT) is a serious condition characterized by blood clots in deep veins, posing a significant public health burden. Muscle strength has been implicated as a potential risk factor for DVT due to its influence on venous return. This study aims to investigate the causal association between muscle strength and DVT using a Mendelian randomization (MR) approach, leveraging genetic variants as instrumental variables (IVs). We conducted a 2-sample MR analysis using genome-wide association study (GWAS) data for hand-grip strength and DVT. IVs were selected based on their significant associations with muscle strength and DVT, as well as their linkage disequilibrium patterns. We employed statistical methods including inverse-variance weighting (IVW), MR-Egger, and weighted median to address pleiotropy bias. Sensitivity analyses were conducted to evaluate the robustness of the results. A total of 21 and 14 independent IVs were identified for hand grip strength (EWGSOP) and hand grip strength (FNIH), respectively. IVW analysis revealed a consistent causal and negative association between both definitions of hand grip strength and DVT (EWGSOP: OR = 0.702, 95% CI: 0.511-0.964, P = .029; FNIH: OR = 0.715, 95% CI: 0.570-0.898, P = .004). No directional pleiotropy was detected in MR-Egger and MR-PRESSO analyses for either definition (EWGSOP: MR-Egger Intercept P = .516; MR-PRESSO global test P = .162; FNIH: MR-Egger Intercept P = .569; MR-PRESSO global test P = .371).Sensitivity analyses demonstrated the stability of the causal effect estimates, with little influence from individual IVs. The MR analysis provided evidence of a causal association between muscle strength and DVT risk, suggesting that increasing muscle strength may have a protective effect. These findings have implications for preventive strategies and the promotion of resistance exercises and muscle-strengthening activities. Further research and validation of these results could inform clinical guidelines and interventions for DVT prevention., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

For secure network coding to prevent eavesdropping attacks, a general design is used that puts restrictions on the eavesdropping channel set or encryption technology is embedded, which can cause problems in practical applications. Based on random linear network coding technology, in this paper, a transmission strategy to prevent eavesdropping attacks is proposed for single-source multicast networks, without either precondition of restricting the eavesdropping channel set or embedding encryption technology. The data transmission process is divided into three phases, and the global encoding vectors are separated from the encoded data on the channel. The proposed strategy can enable the sink to decode successfully, making it very difficult for eavesdroppers to meet the conditions for successful decoding by randomly selecting the eavesdropping channel set without obtaining network topology knowledge. The feasibility of the proposed method is strictly demonstrated using layered network technology, and its security is analyzed. The simulation results confirm the validity of the theoretical analysis., (© 2024. The Author(s).)

Background: Unfavorable social determinants of health (SDoH) are associated with depression. Both depression and SDoH are associated with increased risks of mortality, but their joint impacts on mortality risks remain unclear. This study aims to investigate the joint effects of depression and SDoH on mortality risk., Methods: Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2007-2018, 24,727 adults aged ≥ 20 were included. SDoH was assessed based on the 5 domains outlined in the U.S. Healthy People 2030 initiative. The cumulative number of unfavorable SDoH was calculated and categorized into low and high burden levels. The definition of depression was based on the Patient Health Questionnaire-9 (PHQ-9) scores ≥ 10. The joint associations of depression and SDoH with all-cause, cardiovascular disease (CVD), and cancer mortality were examined using Cox proportional hazard models., Results: We identified 2,377 (6.84%) all-cause deaths (CVD, 717; cancer, 606) during a median follow-up of 7.0 years. Depression was associated with increased mortality risks, and SDoH could explain 32.4% and 28.3% of the associations between depression and all-cause and CVD mortality, respectively. No significant interactions were observed between depression and SDoH on mortality. However, a low burden of unfavorable SDoH reduced the risk of all-cause mortality in depressed patients (hazard ratio [HR], 0.58; 95% confidence interval [CI]: 0.36-0.92). In the joint analysis, individuals with both depression and a high burden of unfavorable SDoH had the highest risks of all-cause and CVD mortality. Specifically, compared with individuals with no depression and a low burden of unfavorable SDoH, those with depression and a high burden of unfavorable SDoH had higher risks of all-cause (HR, 2.52; 95% CI: 2.01-3.18) and CVD mortality (HR, 2.79; 95% CI: 1.95-3.99)., Conclusion: Adults with both depression and a high burden of unfavorable SDoH had the highest risks of all-cause mortality and CVD mortality. The result suggests considering depression and SDoH jointly in developing targeted intervention strategies to improve survival outcomes and calls for larger cohort studies and clinical trials to validate our findings., (© 2024. The Author(s).)

Developing green and efficient preparation strategies is a persistent pursuit in the field of 2D transition metal nitrides and/or carbides (MXenes). Traditional etching methods, such as HF-based or high-temperature Lewis-acid-molten-salt etching route, require harsher etching conditions and exhibit lower preparation efficiency with limited scalability, severely constraining their commercial production and practical application. Here, an ultrafast low-temperature molten salt (LTMS) etching method is presented for the large-scale synthesis of diverse MXenes within minutes by employing NH 4 HF 2 as the etchant. The increased thermal motion and improved diffusion of molten NH 4 HF 2 molecules significantly expedite the etching process of MAX phases, thus achieving the preparation of Ti 3 C 2 T x MXene in just 5 minutes. The universality of the LTMS method renders it a valuable approach for the rapid synthesis of various MXenes, including V 4 C 3 T x , Nb 4 C 3 T x , Mo 2 TiC 2 T x , and Mo 2 CT x . The LTMS method is easy to scale up and can yield more than 100 g Ti 3 C 2 T x in a single reaction. The obtained LTMS-MXene exhibits excellent electrochemical performance for supercapacitors, evidently proving the effectiveness of the LTMS method. This work provides an ultrafast, universal, and scalable LTMS etching method for the large-scale commercial production of MXenes., (© 2024 Wiley‐VCH GmbH.)

Fetal multi-anatomical structure detection in Ultrasound (US) images can clearly present the relationship and influence between anatomical structures, providing more comprehensive information about fetal organ structures and assisting sonographers in making more accurate diagnoses, widely used in structure evaluation. Recently, deep learning methods have shown superior performance in detecting various anatomical structures in ultrasound images but still have the potential for performance improvement in categories where it is difficult to obtain samples, such as rare diseases. Few-shot learning has attracted a lot of attention in medical image analysis due to its ability to solve the problem of data scarcity. However, existing few-shot learning research in medical image analysis focuses on classification and segmentation, and the research on object detection has been neglected. In this paper, we propose a novel fetal anatomical structure fewshot detection method in ultrasound images, TKR-FSOD, which learns topological knowledge through a Topological Knowledge Reasoning Module to help the model reason about and detect anatomical structures. Furthermore, we propose a Discriminate Ability Enhanced Feature Learning Module that extracts abundant discriminative features to enhance the model's discriminative ability. Experimental results demonstrate that our method outperforms the state-of-the-art baseline methods, exceeding the second best method with a maximum margin of 4.8% on 5-shot of split 1 under 4CC. The code is publicly available at: https://github.com/lixi92/TKR-FSOD.

Significant discrepancies were observed between the experiments and the simulations for ṄH 2 time-histories in monomethylhydrazine pyrolysis with the robust mechanism proposed by Pascal and Catoire. The rate of formation analyses for ṄH 2 indicated the significance of the reaction NH 2 ṄH + ṄH 2 = H 2 NN + NH 3 , which has not been well-defined. In this study , ab initio calculations were performed for the theoretical description of the NH 2 ṄH + ṄH 2 chemistry. Most stationary points on the potential energy surface were quantified at the CCSD(T)/CBS//M06-2X/aug-cc-pVTZ level, and the multireference methods were employed for barrier-less reaction and some transition states. The temperature- and pressure-dependent rate coefficients were determined using classical and microcanonical variational transition state theories. Four primary reaction channels were identified as competitive: 1) The H atom abstraction reaction yielding N 2 H 2 (T) + NH 3 , dominating at 1350-3000 K across the 0.001-100 atm pressure range. 2) The H atom abstraction reaction forming N 2 H 2 (S) + NH 3 , dominating at 800-1350 K and competing with the processes of chemical activation and collisional stabilization below 800 K. 3) The chemical-activated reaction resulting in H 2 NN(S) + NH 3 , dominating below 800 K at 0.001 atm. 4) The collisional-stabilized recombination reaction leading to N 3 H 5 , becoming significant as pressure increases and dominating below 600 and 650 K at 1 and 100 atm, respectively. The implications of newly calculated NH 2 ṄH + ṄH 2 kinetics for the monomethylhydrazine pyrolysis mechanism were evaluated, and updates were implemented. Sensitivity analyses indicated the necessity of additional research efforts to comprehend the dynamics of CH 3 NH 2 unimolecular and N 2 H 2 + ṄH 2 reaction systems. The rate coefficients presented in this study can be employed to develop the chemical kinetic model of nitryl-containing systems.

Congenital heart disease (CHD) is the most common congenital disability affecting healthy development and growth, even resulting in pregnancy termination or fetal death. Recently, deep learning techniques have made remarkable progress to assist in diagnosing CHD. One very popular method is directly classifying fetal ultrasound images, recognized as abnormal and normal, which tends to focus more on global features and neglects semantic knowledge of anatomical structures. The other approach is segmentation-based diagnosis, which requires a large number of pixel-level annotation masks for training. However, the detailed pixel-level segmentation annotation is costly or even unavailable. Based on the above analysis, we propose SKGC, a universal framework to identify normal or abnormal four-chamber heart (4CH) images, guided by a few annotation masks, while improving accuracy remarkably. SKGC consists of a semantic-level knowledge extraction module (SKEM), a multi-knowledge fusion module (MFM), and a classification module (CM). SKEM is responsible for obtaining high-level semantic knowledge, serving as an abstract representation of the anatomical structures that obstetricians focus on. MFM is a lightweight but efficient module that fuses semantic-level knowledge with the original specific knowledge in ultrasound images. CM classifies the fused knowledge and can be replaced by any advanced classifier. Moreover, we design a new loss function that enhances the constraint between the foreground and background predictions, improving the quality of the semantic-level knowledge. Experimental results on the collected real-world NA-4CH and the publicly FEST datasets show that SKGC achieves impressive performance with the best accuracy of 99.68% and 95.40%, respectively. Notably, the accuracy improves from 74.68% to 88.14% using only 10 labeled masks.

Background: While Sodium-glucose cotransporter 2 (SGLT2) inhibitors are effective in managing diabetes and reducing cardiovascular risk, concerns about their association with lower limb complications, including, osteomyelitis, ulcers, and peripheral artery disease (PAD), persist. This study employs Mendelian Randomization (MR) to assess the causal relationship between SGLT2 inhibitors and these lower limb safety outcomes., Methods: A two-sample drug-target MR approach was used, complemented by a one-sample MR and genetic association analysis. Six SNPs were selected as instrumental variables to proxy the effect of SGLT2 inhibition. Primary outcomes were major limb safety outcomes, including osteomyelitis, lower limb ulcers, PAD, and cellulitis. The primary analytical method was the generalized inverse variance-weighted (IVW) approach, along with several sensitivity analyses., Results: The MR analysis indicated no significant causal association between genetically proxied SGLT2 inhibition and most of the studied lower limb safety outcomes. However, a significant association with PAD was observed, necessitating careful interpretation due to discrepancies between IVW and MR-Egger results. Sensitivity analyses supported these findings, showing little evidence of heterogeneity or directional pleiotropy., Conclusion: This study suggests that SGLT2 inhibitors may not be significantly associated with an increased risk of most lower limb safety outcomes, including osteomyelitis, lower limb ulcers, and cellulitis, in patients with type 2 diabetes. However, the complex relationship with PAD highlights the need for further research. These findings contribute to the understanding of the safety profile of SGLT2 inhibitors, supporting their continued use in diabetes management while underlining the importance of continuous safety monitoring., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Huang, Pu and Dong.)

Cytokine-induced apoptosis inhibitor 1 (CIAPIN1) is a protein that regulates apoptosis and programmed cell death. This research aims to evaluate its potential role in inhibiting breast cancer cell proliferation, migration, and glycolysis and uncover its underlying molecular mechanism. We collected breast cancer tissue samples from eight patients between January 2019 and June 2023 in our Hospital to analyse CIAPIN1 expression. We transfected human breast cancer cell lines (MCF7, MDA-MB-231, MDA-MB-453, and MDA-MB-468) with siRNA of CIAPIN1. Finally, we determined protein expression using RT-qPCR and Western blotting. CIAPIN1 expression was elevated in both breast cancer tissue and serum. Overexpression of CIAPIN1 detected in the breast cancer cell lines MCF7 and MDA-MB-468. In addition, CIAPIN1 overexpression increased cell proliferation and migration rate. CIAPIN1 downregulation suppressed cell proliferation while elevated cellular apoptosis, reactive oxygen species (ROS) production and oxidative stress in breast cancer cells. Moreover, CIAPIN1 inhibition remarkably suppressed pyruvate, lactate and adenosine triphosphate (ATP) production and reduced the pyruvate kinase M2 (PKM2) protein expression and phosphorylation of signal transducer and activator of transcription 3 (STAT3) in breast cancer cells. Downregulation of CIAPIN1 suppresses cell proliferation, migration and glycolysis capacity in breast cancer cells by inhibiting the STAT3/PKM2 pathway., (© 2024. The Author(s).)

Motion mode (M-mode) echocardiography is essential for measuring cardiac dimension and ejection fraction. However, the current diagnosis is time-consuming and suffers from diagnosis accuracy variance. This work resorts to building an automatic scheme through well-designed and well-trained deep learning to conquer the situation. That is, we proposed RAMEM, an automatic scheme of real-time M-mode echocardiography, which contributes three aspects to address the challenges: 1) provide MEIS, the first dataset of M-mode echocardiograms, to enable consistent results and support developing an automatic scheme; For detecting objects accurately in echocardiograms, it requires big receptive field for covering long-range diastole to systole cycle. However, the limited receptive field in the typical backbone of convolutional neural networks (CNN) and the losing information risk in non-local block (NL) equipped CNN risk the accuracy requirement. Therefore, we 2) propose panel attention embedding with updated UPANets V2, a convolutional backbone network, in a real-time instance segmentation (RIS) scheme for boosting big object detection performance; 3) introduce AMEM, an efficient algorithm of automatic M-mode echocardiography measurement, for automatic diagnosis; The experimental results show that RAMEM surpasses existing RIS schemes (CNNs with NL & Transformers as the backbone) in PASCAL 2012 SBD and human performances in MEIS.

Background: An association between increased aortic root dimensions (ARD) and elevated risk of cardiovascular mortality has been reported in the general population. However, evidence regarding the association between ARD and mortality in patients with acute heart failure (AHF) is limited., Methods: In a nationwide prospective cohort of the China Patient-Centered Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, ARD was measured during diastole using echocardiography and indexed to body mass index (BMI). Cox proportional hazard models were used to validate the association between BMI-indexed ARD and mortality. Additionally, the relationship between BMI-indexed ARD and mortality was presented using restricted cubic spline in all populations, and both sexes., Results: A total of 2125 participants with ARD were included in the final analysis, among of 38.4 % were women, with a median age of 67 years. Over a median follow-up period of 54.4 (interquartile range: 30.1 to 59.7) months, 895 deaths occurred, with 750 attributed to cardiovascular causes and 145 to non-cardiovascular causes. Compared to the highest tertile group of BMI-indexed ARD, the lowest tertile group had a lower risk of cardiovascular mortality (hazard ratio [HR], 0.71; 95 % confidence interval [CI], 0.58 to 0.87; P  < 0.001) and all-cause mortality (HR, 0.68; 95 % CI, 0.56 to 0.81; P  < 0.001). Similarly, the middle tertile group also had a lower risk of cardiovascular mortality (HR, 0.78; 95 % CI, 0.65 to 0.93; P  = 0.007) and all-cause mortality (HR, 0.75; 95 % CI, 0.63 to 0.89; P  < 0.001). Considering the competing risks, the lowest BMI-indexed ARD groups showed a significant mortality risk of cardiovascular mortality in all populations, and both sexes. Moreover, the relationship between BMI-indexed ARD and mortality was linear in males, while a "J" shaped relationship was observed in females., Conclusion: Lower BMI-indexed ARD was associated with a decreased risk of all-cause and cardiovascular mortality than those with higher BMI-indexed ARD in AHF. Additionally, a discrepancy was observed between the sexes in the relationship between BMI-indexed ARD and mortality. These findings contribute to the prompt identification of potential mortality risks in patients with AHF., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Background: Poor cardiovascular-kidney-metabolic (CKM) health is associated with premature mortality and excess morbidity in the United States. Adverse social conditions have a prominent impact on cardiometabolic diseases during the life course. We aim to examine the association between social risk profile (SRP) and CKM multimorbidity among US adults., Methods and Results: We used data from the National Health and Nutrition Examination Survey from 1999 to 2018. The definition of CKM syndrome is the coexistence of subclinical or clinical cardiovascular disease, chronic kidney disease, and metabolic disorders. We classified participants by 4 CKM stages according to the different clinical severity of different forms of CKM syndrome. We calculated the summed number of positive SRP measures, including employed, high-income level, food secure, high education attainment, private insurance, owning a house, and married, as SRP scores and classified them into 4 levels by quartiles: low (0-2), lower-middle (3-4), upper-middle (5-6), and high (7-8). A total of 18 373 US adults, aged 20 to 79 years, were included in our analyses. There were 2567 (9.4%) participants with low SRP score level. Most individual SRP measures and a combined SRP score were associated with CKM stages. Compared with high SRP score level, low SRP level was associated with higher odds of having CKM stage 1 (odds ratio [OR], 1.34 [95% CI, 1.06-1.70]), CKM stage 2 (OR, 2.03 [95% CI, 1.59-2.58]), CKM stage 3 (OR, 5.28 [95% CI, 3.29-8.47]), and CKM stage 4 (OR, 5.97 [95% CI, 4.20-8.49])., Conclusions: Cumulative social disadvantage, denoted by higher SRP burden, was associated with higher odds of CKM multimorbidity, independent of demographic and lifestyle factors.

Background: Patients with acute heart failure (HF) are at high risk of 30-day readmission. Little is known about the characteristics and associated factors of 30-day readmissions among patients with acute HF in China., Methods: We enrolled consecutive patients hospitalized for acute HF and discharged from 52 hospitals in China from August 2016 to May 2018. We describe the rate of 30-day readmission, the time interval from discharge to readmission, and the causes of readmission. We also analyzed the factors associated with readmission risk by fitting multivariate Cox proportional hazards models., Results: We included 4875 patients with a median age of 67 years (interquartile range, 57-75), 3045 (62.5%) of whom were male. Within 30 days after discharge, 613 (12.6%) patients were readmitted for all causes, with a median from discharge to readmission of 12 (6-21) days. Most readmissions were attributed to cardiovascular causes (71.1%) and 60.0% to HF-related causes. Readmission occurred within 14 days of discharge in more than half of the patients (56.4%). Diabetes (hazard ratio [HR]: 1.25, 95% confidence interval [95% CI]: 1.06-1.50), anemia (HR: 1.26, 95% CI: 1.03-1.53), high New York Heart Association classification (HR: 1.48, 95% CI: 1.08-2.01), elevated N-terminal pro-B type natriuretic peptide (HR: 1.67, 95% CI: 1.24-2.25), and high-sensitivity cardiac troponin T (HR: 1.26, 95% CI: 1.01-1.58) were associated with increased risks of readmission. High systolic blood pressure (HR: 0.56, 95% CI: 0.38-0.81) and Kansas City Cardiomyopathy Questionnaire-12 scores (HR: 0.64, 95% CI: 0.44-0.94) were associated with decreased risk of readmission., Conclusions: In China, almost one in eight patients with acute HF were readmitted within 30 days after discharge, mainly due to cardiovascular reasons, and approximately three-fifths of the readmissions occurred in the first 14 days. Both clinical and patient-centered characteristics were associated with readmission., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2024 The Author(s). Published by IMR Press.)

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes. In this work, we propose a Fetal Anatomy Reasoning Network (FARN) that incorporates two kinds of relationship forms: a global context semantic block summarized with visual similarity and a local topology relationship block depicting structural pair constraints. Specifically, by designing the Adaptive Relation Graph Reasoning (ARGR) module, anatomical structures are treated as nodes, with two kinds of relationships between nodes modeled as edges. The flexibility of the model is enhanced by constructing the adaptive relationship graph in a data-driven way, enabling adaptation to various data samples without the need for predefined additional constraints. The feature representation is further refined by aggregating the outputs of the ARGR module. Comprehensive experimental results demonstrate that FARN achieves promising performance in detecting 37 anatomical structures across key US planes in tertiary obstetric screening. FARN effectively utilizes key relationships to improve detection performance, demonstrates robustness to small-scale, similar, and indistinct structures, and avoids some detection errors that deviate from anatomical norms. Overall, our study serves as a resource for developing efficient and concise approaches to model inter-anatomy relationships.

Ciliated protozoa (ciliates) are an ecologically important group of microeukaryotes that play roles in the flow of energy and nutrients in aquatic and terrestrial ecosystems. The community distribution and diversity of soil ciliates in the Nianchu River Basin were investigated by sampling four major habitats, i.e., grassland, farmland, wetland and sea buckthorn forest during May, August and October 2020. Cultivation identification and enumeration of soil ciliates were performed by the non-submerged culture method, in vivo observations and protargol silver staining, and direct counting methods, respectively. A total of 199 species were identified representing, 89 genera, 67 families, 31 orders and 11 classes. Haptorida was the dominant group with 35 species, accounting for 17.59% of the total. The results showed that the α and β diversity indices of soil ciliate communities in the Nianchu River Basin varied significantly in spatial distribution, but not in temporal distribution. Mantel test showed that soil water content, total nitrogen and organic matter were significantly correlated with soil ciliates. Soil water content was the main environmental factor driving the spatial distribution of soil ciliates. Co-occurrence network analysis showed that soil ciliate species in the Nianchu River Basin depend on each other in the relationship of solidarity and cooperation or ecological complementarity. Thus maintaining or enhancing the diversity and stability of the community. Community assembly shows that randomness process was an important ecological process driving soil ciliate community construction in the Nianchu River Basin., Competing Interests: The authors declare no competing interests., (Copyright: © 2024 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: To examine the association between cumulative cognitive function and subsequent mortality among patients hospitalized for acute heart failure (AHF)., Methods: Based on a prospective cohort of patients hospitalized for AHF, cognitive function was measured using Mini-Cog test at admission, 1- and 12-month following discharge. Cumulative cognitive function was interpreted by cumulative Mini-Cog score and cumulative times of cognitive impairment. Outcomes included subsequent all-cause and cardiovascular mortality., Results: 1 454 patients hospitalized for AHF with median follow-up of 4.76 (interquartile range [IQR]: 4.18-5.07) years were included. Tertile 1 of cumulative Mini-Cog score had the highest risk of all-cause (hazard ratio [HR]: 1.52, 95% confidence interval [CI]: 1.14-2.03) and cardiovascular mortality (HR: 1.40, 95% CI: 1.02-1.93) compared with Tertile 3; patients with ≥2 times of cognitive impairment had the highest risk of all-cause (HR: 1.34, 95% CI: 1.03-1.73) and cardiovascular mortality (HR: 1.25, 95% CI: 0.93-1.67) compared with patients without any cognitive impairment. Cumulative Mini-Cog score provided the highest incremental prognostic ability in predicting all-cause (C-statistics: 0.64, 95% CI: 0.61-0.66) and cardiovascular mortality (C-statistics: 0.63, 95% CI: 0.60-0.67) risk on the basis of Get With The Guidelines-Heart Failure score., Conclusions: Poor cumulative cognitive function was associated with increased risk of subsequent mortality and provided incremental prognostic ability for the outcomes among patients with AHF. Longitudinal assessment and monitoring of cognitive function among patients with AHF would be of great importance in identifying patients at greater risk of self-care absence for optimizing personal disease management in clinical practice., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth parameters and diagnosing CHD. Currently, most obstetricians perform segmentation tasks manually, but the pixel-level operation is labor-intensive and requires extensive anatomical knowledge and clinical experience. As such, efficiently and accurately detecting structures from real-world fetal ultrasound images is a key challenge. In this paper, we propose a YOLOX-based deep instance segmentation neural network (i.e., IS-YOLOX) for cardiac anatomical structure location and segmentation in fetal ultrasound images. Specifically, we reconstruct a new instance segmentation branch based on a multi-task deep learning framework. We then design a new multi-level non-maximum suppression (NMS) mechanism to further improve the segmentation performance that consists of three levels of selection. Moreover, unlike two-stage instance segmentation approaches, our method does not rely on object detection results. To the best of our knowledge, this is the first study regarding instance segmentation on 13 types of anatomical structures in the fetal four-chamber view. Extensive experiments were carried out on clinical datasets, and the experimental results show that our method outperforms nine competitive baselines.