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Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach.

Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This paper focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR. Firstly, we propose a heuristic mechanism for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Secondly, we introduce a motion-aware key frame selection mechanism to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

STUDY QUESTION Do women with polycystic ovary syndrome (PCOS) have a greater risk of adverse pregnancy complications (gestational diabetes, preeclampsia, cesarean section, placental abnormalities) and neonatal outcomes (preterm birth, small for gestational age, prolonged delivery hospitalization) compared to women without a PCOS diagnosis and does this risk vary by BMI, subfertility and fertility treatment utilization? SUMMARY ANSWER Deliveries to women with a history of PCOS were at greater risk of complications associated with cardiometabolic function, including gestational diabetes and preeclampsia, as well as preterm birth and prolonged length of delivery hospitalization. WHAT IS KNOWN ALREADY Prior research has suggested that women with PCOS may be at increased risk of adverse pregnancy outcomes. However, findings have been inconsistent possibly due to lack of consistent adjustment for confounding factors, small samples size and other sources of bias. STUDY DESIGN, SIZE, DURATION Massachusetts deliveries among women ≥18 years old during 2013–2017 from state vital records linked to hospital discharges, observational stays and emergency department visits were linked to the Society for Assisted Reproductive Technology Clinic Outcome Reporting System (SART CORS) and the Massachusetts All-Payers Claims Database (APCD). PARTICIPANTS/MATERIALS, SETTING, METHODS PCOS was identified by ICD9 and ICD10 codes in APCD prior to index delivery. Relative risks (RRs) and 95% CI for pregnancy and delivery complications were modeled using generalized estimating equations with a log link and a Poisson distribution to take multiple cycles into account and were adjusted a priori for maternal age, BMI, race/ethnicity, education, plurality, birth year, chronic hypertension and chronic diabetes. Tests for homogeneity investigated differences between maternal pre-pregnancy BMI categories ( MAIN RESULTS AND THE ROLE OF CHANCE Among 91 825 deliveries, 3.9% had a history of PCOS. Women with a history of PCOS had a 51% greater risk of gestational diabetes (CI: 1.38–1.65) and a 25% greater risk of preeclampsia (CI: 1.15–1.35) compared to women without a diagnosis of PCOS. Neonates born to women with a history of PCOS were more likely to be born preterm (RR: 1.17, CI: 1.06–1.29) and more likely to have a prolonged delivery hospitalization after additionally adjusting for gestational age (RR: 1.23, CI: 1.09–1.40) compared to those of women without a diagnosis of PCOS. The risk for gestational diabetes for women with PCOS was greater among women with a pre-pregnancy BMI LIMITATIONS, REASONS FOR CAUTION PCOS was defined by ICD documentation prior to delivery so there may be women with undiagnosed PCOS or PCOS diagnosed after delivery included in the unexposed group. The study population is limited to deliveries within Massachusetts among most private insurance payers and inpatient or observational hospitalization in Massachusetts during the follow-up window, therefore there may be diagnoses and or deliveries outside of the state or outside of our sample that were not captured. WIDER IMPLICATIONS OF THE FINDINGS In this population-based study, women with a history of PCOS were at greater risk of pregnancy complications associated with cardiometabolic function and preterm birth. Obstetricians should be aware of patients’ PCOS status and closely monitor for potential pregnancy complications to improve maternal and infant perinatal health outcomes. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the NIH (R01HD067270). S.A.M. receives grant funding from NIH, AbbVie and the Marriot Family Foundation; payment/honoraria from the University of British Columbia, World Endometriosis Research Foundation and Huilun Shanghai; travel support for attending meetings for ESHRE 2019, IASP 2019, National Endometriosis Network UK meeting 2019; SRI 2022, ESHRE 2022; participates on the data safety monitoring board/advisory board for AbbVie, Roche, Frontiers in Reproductive Health; and has a leadership role in the Society for Women’s Health Research, World Endometriosis Research Foundation, World Endometriosis Society, American Society for Reproductive Medicine and ESHRE. The other authors have no conflicts of interest. TRIAL REGISTRATION NUMBER N/A.

Automatic crack detection is vital for efficient and economical road maintenance. With the explosive development of convolutional neural networks (CNNs), recent crack detection methods are mostly based on CNNs. In this article, we propose a deeply supervised convolutional neural network for crack detection via a novel multiscale convolutional feature fusion module. Within this multiscale feature fusion module, the high-level features are introduced directly into the low-level features at different convolutional stages. Besides, deep supervision provides integrated direct supervision for convolutional feature fusion, which is helpful to improve model convergency and final performance of crack detection. Multiscale convolutional features learned at different convolution stages are fused together to robustly represent cracks, whose geometric structures are complicated and hardly captured by single-scale features. To demonstrate its superiority and generalizability, we evaluate the proposed network on three public crack data sets, respectively. Sufficient experimental results demonstrate that our method outperforms other state-of-the-art crack detection, edge detection, and image segmentation methods in terms of F1-score and mean IU.

Background Before 2010, guidelines recommended adenosine 6, 12, and a repeat dose of 12 mg for paroxysmal supraventricular tachycardia (PSVT). After 2010, these doses were reduced to two. This study aims to outline adenosine using trend from 2000 to 2012 in Taiwan emergency departments (EDs). Methods This was an ecological study. PSVT were drawn from one million individuals of the National Health Insurance Database. The χ2 test was used to determine an association between different adenosine doses and other antiarrhythmic drugs (OADs), including verapamil, diltiazem, amiodarone, digoxin, and labetalol. Results There were 3361 PSVT visits from 2000 to 2012; 834 (24.8%) did not receive an antiarrhythmic drug, and 2527 (75.2%) did, either adenosine with/without OADs or OADs alone. The use of an OAD was significantly different between the adenosine 6–18 mg and 19 + mg groups. Conclusions Most PSVT episodes converted with adenosine within 18 mg, and the success conversion rate was 62.2%. It could be up to 65.2% if they received more. Of the patients who did not have their PSVT reverted with

Fueled by the advances in the Internet of Things, and the growing capacity of smart mobile devices at the edge of the Internet, we have witnessed a growing trend in research and development for edge computing and edge storage, which extends the abilities of single mobile device on the edge through on-demand collaboration among multiple geographically distributed mobile devices. In this paper, we address several technical challenges that are unique for collaborative storage at the edge due to unique characteristics of mobile devices. First, we formalize the collaborative storage problem as an optimization problem. Second, we design an Acceleration Algorithm for Collaborative Storage, called A2CS, based on the architecture of Alternating Direction Method of Multipliers (ADMM). Specifically, we use the Nesterov's Acceleration strategy and the step size rules in the process of updating variables and determining the optimal speed of convergence. We develop a novel collaborative storage policy in order to guide the whole lifecycle of collaborative storage. Finally, we conduct a series of experiments for acceleration performance analysis and validation. We show that A2CS delivers a better convergence performance compared with ADMM and ADMM-OR, and a better utility performance compared with Average Distribution Strategy and Distance Preferred Distribution Strategy.

Enabling deep learning technology on the mobile web can improve user's experience for achieving web artificial intelligence in various fields. However, heavy DNN models and limited computing resource of the mobile web are now unable to support executing computationally intensive DNNs when deploying in a cloud computing platform. With the help of promising edge computing, we propose a lightweight collaborative deep neural network for the mobile web, named LcDNN, which contributes to three aspects: (1) we design a composite collaborative DNN that reduces the model size, accelerates inference and reduces mobile energy cost by executing a lightweight binary branch on the mobile web. (2) we provide a jointly training method for LcDNN, and implement an energy-efficient inference library for executing the binary branch on the mobile web. (3) to further promote the resource utilization of the edge cloud, we develop a DRL-based online scheduling scheme to obtain an optimal allocation for LcDNN. The experimental results show that LcDNN outperforms the existing approaches for reducing the model size by about 16x to 29x. It also reduces the end-to-end latency and mobile energy cost with an acceptable accuracy, and improves the throughput and resource utilization of the edge cloud.

When a single line-to-ground fault occurs in the ungrounded distribution system, the steady-state fault current is relatively small for fault analysis and the transient fault current is observable, which can be used for faulted feeder identification and location. The principal frequency component retains most of the characteristics of the transient current. The principal frequency is related to the distance from the fault point to the substation and can be used for fault location. This paper analyzes the sequence network model of a single line-to-ground fault in the distribution network, and gives a method for principal frequency calculation. Depending on the characteristics of the maximum amplitude of the principal frequency component of the faulted feeder, the method of faulted feeder identification is given. Based on the complementary characteristics of the phase angle of the principal frequency component of the fault current and the phase angle at the substation bus, the faulted section location is carried out. MATLAB simulation is used to verify the effectiveness of the faulted feeder identification and location method.

Low-power consumption and long-distance transmission are two problems that have to be solved by the application of broadband power line communication for the automatic meter reading system. To reduce the power consumption of the communication module, based on the analysis of the composition of the power consumption, some methods are proposed. From the communication chip level and the module circuit level, the design scheme of low-power consumption is given. To solve the problem of transmission distance, a frequency band of 2.44 MHz~5.6 MHz is used as the main working frequency band. The communication module supports multiple frequency bands. Using this feature, the optimal frequency band is adaptively selected for communication and automatic switching, which further improve the transmission distance. Field application shows that the above methods effectively decrease the power consumption of the communication module and extend the transmission distance.

Takotsubo syndrome (TTS) is characterized by acute and transient dysfunction of the apical segment of the left ventricle. In recent years, there have been increasing numbers of case reports about TTS; however, it is still being neglected and misdiagnosed in many primary hospitals in China. We present a case of a 68-year-old female who presented to our hospital with one month of paroxysmal cough. Here severe cough would sometimes induce headache, chest pain, nausea, and vomiting. She had an unexplained cardiogenic shock approximately 4 months prior and gradually developed orthopnea. Cardiac biomarkers were mildly elevated, and electrocardiogram (ECG) displayed diffuse and deep T-waves inversion in leads I, II, AVL, and V2-V9, like acute myocardial infarction. However, coronary angiography was performed and showed the absence of obstructed coronary atherosclerosis or acute plaque rupture. The patient was successively treated in four hospitals and was eventually diagnosed with TTS in our hospital (the fourth hospital) due to noncardiogenic discomfort. Physicians at some primary hospitals require additional clinical experience to deeply understand TTS. Many doctors could learn about TTS from a medical textbook but remain unfamiliar with the disease. We hope that through analysis of this case, primary doctors will have a deeper understanding of TTS, avoid misdiagnosing the typical cases that occur in their patients.

In view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this paper, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of method consists of three core components. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster. We implement the experiments and the results show that our method achieves 92.3%, 91.2%, and 3679.2 kWh respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

Multi-task optimization aims to solve two or more optimization tasks simultaneously by leveraging inter-task knowledge transfer. However, as the number of tasks increases to the extent of many-task optimization, the knowledge transfer between tasks encounters more uncertainty and challenges, thereby resulting in degradation of optimization performance. To give full play to the many-task optimization framework and minimize the potential negative transfer, this paper proposes an evolutionary many-task optimization algorithm based on a multi-source knowledge transfer mechanism namely EMaTO-MKT. Particularly, in each iteration, EMaTO-MKT determines the probability of using knowledge transfer adaptively according to the evolution experience, and balances the self-evolution within each task and the knowledge transfer among tasks. To perform knowledge transfer, EMaTO-MKT selects multiple highly similar tasks in term of maximum mean discrepancy as the learning sources for each task. Afterward, a knowledge transfer strategy based on local distribution estimation is applied to enable the learning from multiple sources. Compared with the other state-of-the-art evolutionary many-task algorithms on benchmark test suites, EMaTO-MKT shows competitiveness in solving many-task optimization problems.

We report a 3-year city-wide study of an operational indoor sensing system based on Bluetooth Low Energy (BLE) calledaBeacon (short for alibaba Beacon). aBeacon is pilot-studied, A/B tested, deployed, and operated in Shanghai, China to infer the indoor status of Alibaba couriers, e.g., arrival and departure at the merchants participating in the Alibaba Local Services platform. In its full operation stage (2018/01-2020/04),aBeacon consists of customized BLE devices at 12,109 merchants, interacting with 109,378 couriers to infer their status to assist the scheduling of 64 million delivery orders for 7.3 million customers with a total amount of $600 million order values. Although in an academic setting, using BLE devices to detect arrival and departure looks straightforward, it is non-trivial to design, build, deploy, and operateaBeacon from its conception to its retirement at city scale in a metric-based approach by considering the tradeoffs between various practical factors (e.g., cost and performance) during long-term system evolution. We report our study in two phases, i.e., an 8-month pilot study and a 28-month deployment and operation in the wild. We focus on an in-depth reporting on the five lessons learned and provide their implications in other systems with long-term operation and broad geospatial coverage, e.g., Edge Computing.

Background and aims C-reactive protein (CRP) is a hepatocyte-produced marker of inflammation yet with undefined function in liver injury. We aimed to examine the role of CRP in acetaminophen-induced liver injury (AILI). Methods The effects of CRP in AILI were investigated using CRP knockout mice and rats combined with human CRP rescue. The mechanisms of CRP action were investigated in vitro and in mice with Fcγ receptor 2B knockout, C3 knockout, or hepatic expression of CRP mutants defective in complement interaction. The therapeutic potential of CRP was investigated by intraperitoneal administration at 2 or 6 hours post–AILI induction in wild-type mice. Results CRP knockout exacerbated AILI in mice and rats, which could be rescued by genetic knock-in, adeno-associated virus–mediated hepatic expression or direct administration of human CRP. Mechanistically, CRP does not act via its cellular receptor Fcγ receptor 2B to inhibit the early phase injury to hepatocytes induced by acetaminophen; instead, CRP acts via factor H to inhibit complement overactivation on already injured hepatocytes, thereby suppressing the late phase amplification of inflammation likely mediated by C3a-dependent actions of neutrophils. Importantly, CRP treatment effectively alleviated AILI with a significantly extended therapeutic time window than that of N-acetyl cysteine. Conclusion Our results thus identify CRP as a crucial checkpoint that limits destructive activation of complement in acute liver injury, and we argue that long-term suppression of CRP expression or function might increase the susceptibility to AILI.

In food processing environments, various microorganisms can adhere and aggregate on the surface of equipment, resulting in the formation of multispecies biofilms. Complex interactions among microorganisms may affect the formation of multispecies biofilms and resistance to disinfectants, which are food safety and quality concerns. This article reviews the various interactions among microorganisms in multispecies biofilms, including competitive, cooperative, and neutral interactions. Then, the preliminary mechanisms underlying the formation of multispecies biofilms are discussed in relation to factors, such as quorum-sensing signal molecules, extracellular polymeric substances, and biofilm-regulated genes. Finally, the resistance mechanisms of common contaminating microorganisms to disinfectants in food processing environments are also summarized. This review is expected to facilitate a better understanding of interspecies interactions and provide some implications for the control of multispecies biofilms in food processing. HIGHLIGHTS

Background\ud The use of accurate prediction tools and early intervention are important for addressing severe coronavirus disease 2019 (COVID-19). However, the prediction models for severe COVID-19 available to date are subject to various biases. This study aimed to construct a nomogram to provide accurate, personalized predictions of the risk of severe COVID-19.\ud \ud Methods\ud This study was based on a large, multicenter retrospective derivation cohort and a validation cohort. The derivation cohort consisted of 496 patients from Jiangsu Province, China, between January 10, 2020, and March 15, 2020, and the validation cohort contained 105 patients from Huangshi, Hunan Province, China, between January 21, 2020, and February 29, 2020. A nomogram was developed with the selected predictors of severe COVID-19, which were identified by univariate and multivariate logistic regression analyses. We evaluated the discrimination of the nomogram with the area under the receiver operating characteristic curve (AUC) and the calibration of the nomogram with calibration plots and Hosmer-Lemeshow tests.\ud \ud Results\ud Three predictors, namely, age, lymphocyte count, and pulmonary opacity score, were selected to develop the nomogram. The nomogram exhibited good discrimination (AUC 0.93, 95% confidence interval [CI] 0.90–0.96 in the derivation cohort; AUC 0.85, 95% CI 0.76–0.93 in the validation cohort) and satisfactory agreement.\ud \ud Conclusions\ud The nomogram was a reliable tool for assessing the probability of severe COVID-19 and may facilitate clinicians stratifying patients and providing early and optimal therapies.

In recent years, distributed machine learning in WANs (DML-WANs), i.e., collaboratively training a high-quality ML model cross geo-distributed micro-clouds or edge devices, has attracted attention and been widely applied. Compared with cloud-centric training, DML-WANs avoids the high cost of transferring large amounts of raw data to a central cloud and privacy concerns. However, performing DML-WANs still faces challenges. Model synchronization, an essential step of DML-WANs, is accompanied by a lot of model communication cross limited-bandwidth WANs, which generates high communication overhead. Moreover, the parameter server system, which has been widely used, performs model synchronization in a centralized manner, resulting in serious communication in-cast problem. Such communication in-cast further raises the communication overhead, leading to the low efficiency of DML-WANs. To alleviate the communication in-cast, existing researches attempt to build tree-based communication overlays over the parameter server and workers. However, we identify that these approaches can not adapt to the dynamic and heterogeneous network of DML-WANs, resulting in insufficient improvements. This paper proposes TSEngine, an adaptive communication scheduler for efficient communication overlay of the parameter server system in DML-WANs. Its core idea is to dynamically schedule the communication logic over the parameter server and workers based on the active network perception. Specifically, we propose novel communication scheduling protocols for model distribution and model aggregation, respectively. We have implemented TSEngine in a mainstream parameter server system and verified its effectiveness in DML-WANs testbeds.

This paper argues that metaphorical formulations around genetic categories have important implications for individuals’ experiences of their at-genetic-risk bodies vis-à-vis the market for prevention. Drawing on Jacques Derrida’s concept of usure, our findings unpack three central biomedical metaphors that shape the ways in which ‘previvor’ women with the BRCA gene mutation manage and experience their (risky) body-in-transition against the market for prevention. These are the metaphors of: the container, the omnipresent danger, and battle and journey. Our discussion unravels the processes of de/re-stabilisation of the (risky) body-in-transition, as well as the reconfiguration of their rights and duties in the market for prevention to become a good genetic citizen. Moving beyond a discussion of ‘consumer sovereignty’, we contribute to developing a contextually nuanced understanding of the complex relations between the lived experiences of ‘losing control’ and the consumption of prevention.

Dechao Feng,* Facai Zhang,* Ling Liu,* Qiao Xiong, Hang Xu, Wuran Wei, Zhenghua Liu, Lu Yang Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhenghua Liu; Lu YangDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, 37 Guoxue Xiang, Chengdu, 610041, Sichuan, People’s Republic of ChinaTel +86 28-8542-2444Fax +86 28-85422451Email zhliu@scu.edu.cn; wycleflue@163.comBackground: There is a surprising paucity of studies investigating the potential mechanism of SKA3 in the progression and prognosis of kidney renal papillary cell carcinoma (KIRP).Methods: We used TCGA and other databases to analyze the expression, clinical value, and potential mechanisms of SKA3 in KIRP patients. We also explored therapeutic agents for KIRP through GSCALite.Results: SKA3 mRNA expression was significantly upregulated and the area under the curve was 0.792 (95% CI 0.727– 0.856). Increased SKA3 expression was related to shorter overall survival, disease-specific survival and progression-free survival. Hub genes in protein–protein interactions were CDK1, CDC20, CCNB1, CCNA2, BUB1, AURKB, BUB1B, PLK1, CCNB2, and MAD2L1, which were differentially expressed and also associated with KIRP prognosis. Gene-set enrichment analysis indicated that E2F targets, epithelial–mesenchymal transition, glycolysis, the WNT signaling pathway, and other pathways were highly enriched upon SKA3 upregulation. Gene-set variation analysis of SKA3 and its ten hub genes showed that the significant correlation of cancer-related pathways included the cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and Ras/MAPK. In addition, we found that MEK inhibitors, ie, trametinib, selumetinib, PD0325901, and RDEA119, may be feasible targeting agents for KIRP patients.Conclusion: SKA3 might contribute to poor prognosis of KIRP through cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and RAS/MAPK. SKA3 potentially serves as a prognostic biomarker and target for KIRP.Keywords: spindle and kinetochore–associated complex subunit 3, kidney renal papillary cell carcinoma, biomarker, enrichment analysis, targeted therapy

Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API. In providing an in-depth characterization of membership privacy risks against machine learning models, this paper presents a comprehensive study towards demystifying membership inference attacks from two complimentary perspectives. First, we provide a generalized formulation of the development of a black-box membership inference attack model. Second, we characterize the importance of model choice on model vulnerability through a systematic evaluation of a variety of machine learning models and model combinations using multiple datasets. Through formal analysis and empirical evidence from extensive experimentation, we characterize under what conditions a model may be vulnerable to such black-box membership inference attacks. We show that membership inference vulnerability is data-driven and its attack models are largely transferable. Though different model types display different vulnerabilities to membership inferences, so do different datasets. Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning exposes vulnerabilities to membership inference risks when the adversary is a participant. We also discuss countermeasure and mitigation strategies.

High doses BMP-2 have resulted in a series of complications in spinal fusion. We previously established a polyelectrolyte complex (PEC) carrier system that reduces the therapeutic dose of BMP-2 in both rodent and porcine spinal fusion models. This study aimed to evaluate the safety and efficacy of the combination of bone marrow mesenchymal stem cells (BMSCs) and low dose BMP-2 delivered by PEC for bone regeneration in a porcine model of anterior lumbar interbody spinal fusion (ALIF) application. Six Yorkshire pigs underwent a tri-segmental (L2/L3; L3/L4; L4/L5) ALIF in four groups, namely: (1) BMSCs + 25 μg BMP-2/PEC (n = 9), (2) 25 μg BMP-2/PEC (n = 3), (3) BMSCs (n = 3), and (4) 50 μg BMP-2/absorbable collagen sponge (n = 3). Fusion outcomes were evaluated by radiography, biomechanical testing, and histological analysis after 12 weeks. Mean radiographic scores at 12 weeks were 2.7, 2.0, 1.0, and 1.0 for Groups 1 to 4, respectively. µ-CT scanning, biomechanical evaluation, and histological analysis demonstrated solid fusion and successful bone regeneration in Group 1. In contrast, Group 2 showed inferior quality and slow rate of fusion, and Groups 3 and 4 failed to fuse any of the interbody spaces. There was no obvious evidence of seroma formation, implant rejection, or any other complications in all groups. The results suggest that the combination of BMSCs and low dose BMP-2/PEC could further lower down the effective dose of the BMP-2 and be used as a bone graft substitute in the large animal ALIF model. This article is protected by copyright. All rights reserved.

Exposure to metal(loid)s is associated with adverse effects on human health, especially for children and adolescents. This study was designed to evaluate metal(loid)s exposure in 2050 children and adolescents aged 6-18 years from Liuzhou City, Southwest China. The detection rates of 21 elements were all above 99%. We found that age was an important predictor for most elements, and that children exhibited more exposure than adolescents, expect for strontium (p 0.05). Interestingly, urinary levels were higher in girls for 13 of our study elements. Multiple regression models also showed that dietary habits also affected the distribution of elements. Moreover, we estimated exposure risk by generating the hazard quotient (HQ) for single metal and the hazard index (HI) for the co-occurrence of metals. The HQ of cadmium was 1 at the P95 value and that the risk of the mixed effect of cadmium, mercury, and thallium was not negligible, and indicated that the associated risk was of concern. Our results provide basic data on the reference values of urinary metal(loid) levels and an assessment of health risks for children and adolescents that reside in industrial areas.

BACKGROUND: Infectious diarrhea is a serious hazard to children under 5 years old. The causative microorganisms are mostly viruses and bacteria, with different treatment required for each. Currently, early clinical differential diagnosis is difficult with the available testing methods. Therefore, new and more sensitive indicators of viral infection reflect the early stage of infection are needed. METHODS: We collected blood samples and fresh fecal samples from 100 children diagnosed with viral diarrhea who were treated in the outpatient clinic at Changzhou Tumor Hospital Affiliated to Soochow University from January 2018 to December 2020. The levels of interleukin-2 (IL-2), IL-6, tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) in serum, the erythrocyte sedimentation rate (ESR) and enterovirus were measured and compared with those in a matched healthy control group. Patients’ demographic and risk factor data were collected by interviewing parents. RESULTS: The mean levels of IL-2, IL-6, TNF-α, ESR, and CRP in the viral diarrhea group were higher than those in the healthy control group. Except for IL-6, the differences in inflammatory factors between groups were statistically significant (P

Cardiovascular diseases (CVDs) are a group of disorders of the blood vessels and heart, which are considered as the leading causes of death worldwide. The pathology of CVDs could be related to the functional abnormalities of multiple cell types in the heart. Single-cell RNA sequencing (scRNA-seq) technology is a powerful method for characterizing individual cells and elucidating the molecular mechanisms by providing a high resolution of transcriptomic changes at the single-cell level. Specifically, scRNA-seq has provided novel insights into CVDs by identifying rare cardiac cell types, inferring the trajectory tree, estimating RNA velocity, elucidating the cell–cell communication, and comparing healthy and pathological heart samples. In this review, we summarize the different scRNA-seq platforms and published single-cell datasets in the cardiovascular field, and describe the utilities and limitations of this technology. Lastly, we discuss the future perspective of the application of scRNA-seq technology into cardiovascular research.

In this letter, the reflection elements based on artificial dielectric layers (ADLs) are proposed for broadband wide-angle bistatic radar cross section (RCS) reduction. ADLs are introduced to improve the interference cancellation bandwidth of the reflection elements, and avoid the dilemma of mixing multiple dielectric mediums. The reflection elements achieve an approximate 180° phase difference over the frequency range of 4.8–17.5 GHz (113.9%) under normal incidence, and the bandwidth under TE 40° incidence and TM 55° is 113.9% and 100%, respectively. To validate this design, a coding scatterer surface is simulated and fabricated. Measured results show that the proposed coding scatterer surface achieves a 10 dB bistatic RCS reduction from 5 to 17.5 GHz under normal incidence for both polarizations. Bistatic RCS reduction under oblique incidence is analyzed and presented as well.

Background Childhood neuromyelitis optica spectrum disorders (NMOSDs) may cause visual impairment and brain or spinal cord damage, and the effects may be permanent if left untreated. Since the incidence of NMOSD cases in children is relatively low, the understanding of NMOSD among children is inadequate. Methods This investigation examined the clinical and neuroimaging characteristics of childhood NMOSD. We retrospectively analyzed the clinical information of 11 NMOSD patients admitted to our centre from 2012 to 2021. The disease status was assessed by the Expanded Disability Status Scale (EDSS) score. Results The two major symptoms observed in the study cohort were optic neuritis (ON) (9/11) and encephalopathy (7/11). Antibody tests were performed on 8 children, 2 of whom showed serum aquaporin 4 (AQP4) antibody positivity, and another 2 presented with serum myelin oligodendrocyte glycoprotein (MOG) antibody positivity. All patients showed white matter hyperintensity on magnetic resonance imaging (MRI) scans. Interestingly, a rare radiological sign, enlarged perivascular space (PVS), which is more commonly observed in the elderly or adults, was found in 4 participants with more severe clinical manifestations. Conclusions While NMOSD in children is less commonly diagnosed through clinical evaluations, the symptoms of ON and encephalopathy should raise the possibility of the disease. As the diagnosis of NMOSD in children is relatively difficult, enlarged PVS may represent a promising biomarker for the diagnosis and evaluation of NMOSD.

Purpose To evaluate the incidence rate and risk factors for status epilepticus (SE) after stroke (PSSE), including ischaemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). Methods A meta-analysis was performed using relevant research from databases such as PubMed, Embase, Cochrane Online Library, and Clinicaltrials.gov. The quality of the studies was evaluated by using the quality evaluation criteria of the Agency for Healthcare Research and Quality (AHRQ). All data were pooled by STATA 12.0 software for meta-analysis. Results The review considered 1650 articles, and 17 articles with 2821 instances of SE among 1088087 instances of stroke were included. The incidence rate of SE after stroke was 6.90 per 1000 total strokes (95% CI: 5.58-8.22). By subgroup analysis of SE, the rates were 33.85‰ (95% CI: 13.77-53.94) for non-convulsive status epilepticus (NCSE) and 2.42‰ (95%CI: 1.66-3.19) for generalized convulsive status epilepticus (GCSE). Age, sex, and presence of atrial fibrillation showed no significant difference between the SE group and the non-SE group after stroke. Hypertension and diabetes are associated with a decreased rate of SE. However, African American race, alcohol abuse, and renal disease are associated with an increased rate of SE. Significance There were approximately 6.9 patients with status epilepticus per 1000 strokes. NCSE is more common after stroke and needs more attention. African American race, alcohol abuse and renal disease may be risk factors for PSSE.

Background:. Circulating tumor DNA (ctDNA) is a promising biomarker for non-invasive epidermal growth factor receptor mutations (EGFRm) detection in lung cancer patients, but existing methods have limitations in sensitivity and availability. In this study, we used the ΔCt value (mutant cycle threshold [Ct] value–internal control Ct value) generated during the polymerase chain reaction (PCR) assay to convert super-amplification-refractory mutation system (superARMS) from a qualitative method to a semi-quantitative method named reformed-superARMS (R-superARMS), and evaluated its performance in detecting EGFRm in plasma ctDNA in patients with advanced lung adenocarcinoma. Methods:. A total of 41 pairs of tissues and plasma samples were obtained from lung adenocarcinoma patients who had known EGFRm in tumor tissue and were previously untreated. EGFRm in ctDNA was identified by using superARMS. Through making use of ΔCt value generated during the detection process of superARMS, we indirectly transform this qualitative detection method into a semi-quantitative PCR detection method, named R-superARMS. Both qualitative and quantitative analyses of the data were performed. Kaplan–Meier analysis was performed to estimate the progression-free survival (PFS) and overall survival (OS). Fisher exact test was used for categorical variables. Results:. The concordance rate of EGFRm in tumor tissues and matched plasma samples was 68.3% (28/41). At baseline, EGFRm-positive patients were divided into two groups according to the cut-off ΔCt value of EGFRm set at 8.11. A significant difference in the median OS (mOS) between the two groups was observed (EGFRm ΔCt ≤8.11 vs. >8.11: not reached vs. 11.0 months; log-rank P = 0.024). Patients were divided into mutation clearance (MC) group and mutation incomplete clearance (MIC) group according to whether the ΔCt value of EGFRm test turned negative after 1 month of treatment. We found that there was also a significant difference in mOS (not reached vs. 10.4 months; log-rank P = 0.021) between MC group and MIC group. Although there was no significant difference in PFS between the two groups, the two curves were separated and the PFS of MC group tended to be higher than the MIC group (not reached vs. 27.5 months; log-rank P = 0.088). Furthermore, EGFRm-positive patients were divided into two groups according to the cut-off of the changes in ΔCt value of EGFRm after 1 month of treatment, which was set at 4.89. A significant difference in the mOS between the two groups was observed (change value of ΔCt >4.89 vs. ≤4.89: not reached vs. 11.0 months; log-rank P = 0.014). Conclusions:. Detecting EGFRm in ctDNA using R-superARMS can identify patients who are more likely sensitive to targeted therapy, reflect the molecular load of patients, and predict the therapeutic efficacy and clinical outcomes of patients.

Funder: Société Française de Biochimie et Biologie Moléculaire ; French Research Ministry, Funder: Cancéropole Grand Ouest ; Région Bretagne ; Société Française d’Hématologie, Funder: Ligue Régionale contre le cancer, ETV6-RUNX1 and RUNX1 directly promote RAG1 expression. ETV6-RUNX1 and RUNX1 preferentially bind to the −1200 bp enhancer of RAG1 and the −80 bp promoter of RAG1 gene respectively, and compete for these bindings. ETV6-RUNX1 and RUNX1 induce an excessive RAG recombinase activity. ETV6-RUNX1 participates directly in two events of the multi-hit ALL leukemogenesis: as an initiating event and as an activator of RAG1 expression.

BackgroundSympathetically maintained pain (SMP) involves an increased excitability of dorsal root ganglion (DRG) neurons to sympathetic nerve stimulation and circulating norepinephrine. The current treatment of SMP has limited efficacy, and hence more mechanistic insights into this intractable pain condition are urgently needed.MethodsA caudal trunk transection (CTT) model of neuropathic pain was established in mice.Immunofluorescence staining, small interfering RNA, pharmacological and electrophysiological studies were conducted to test the hypothesis that norepinephrine increases the excitability of small-diameter DRG neurons from CTT mice through the activation of cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) signaling pathway.ResultsBehavior study showed that CTT mice developed mechanical and heat hypersensitivities, which were attenuated by intraperitoneal injection of guanethidine. CTT mice also showed an abnormal sprouting of tyrosine hydroxylase-positive nerve fibers in DRG, and an increased excitability of small-diameter DRG neurons to norepinephrine, suggesting that CTT is a useful model to study SMP. Importantly, inhibiting cGMP-PKG pathway with small interfering RNA and KT5823 attenuated the increased sympathetic sensitivity in CTT mice. In contrast, cGMP activators (Sp-cGMP, 8-Br-cGMP) further increased sympathetic sensitivity. Furthermore, phosphorylation of ATP-sensitive potassium channel, which is a downstream target of PKG, may contribute to the adrenergic modulation of DRG neuron excitability.ConclusionsOur findings suggest an important role of cGMP-PKG signaling pathway in the increased excitability of small-diameter DRG neurons to norepinephrine after CTT, which involves an inhibition of the ATP-sensitive potassium currents through PKG-induced phosphorylation. Accordingly, drugs targeting this pathway may help to treat SMP.

Background Asthma is a chronic inflammatory disorder of the airways involving many different factors. This study aimed to screen for the critical genes using DNA methylation/CpGs and miRNAs involved in childhood atopic asthma. Methods DNA methylation and gene expression data (Access Numbers GSE40732 and GSE40576) were downloaded from the Gene Expression Omnibus database. Each set contains 194 peripheral blood mononuclear cell (PBMC) samples of 97 children with atopic asthma and 97 control children. Differentially expressed genes (DEGs) with DNA methylation changes were identified. Pearson correlation analysis was used to select genes with an opposite direction of expression and differences in methylation levels, and then Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Protein–protein interaction network and miRNA–target gene regulatory networks were then constructed. Finally, important genes related to asthma were screened. Results A total of 130 critical DEGs with DNA methylation changes were screened from children with atopic asthma and compared with control samples from healthy children. GO and KEGG pathway enrichment analysis found that critical genes were primarily related to 24 GO terms and 10 KEGG pathways. In the miRNA–target gene regulatory networks, 9 KEGG pathways were identified. Analysis of the miRNA–target gene network noted an overlapping KEGG signaling pathway, hsa04060: cytokine-cytokine receptor interaction, in which the gene CCL2, directly related to asthma, was involved. This gene is targeted by eight asthma related miRNAs (hsa-miR-206, hsa-miR-19a, hsa-miR-9,hsa-miR-22, hsa-miR-33b, hsa-miR-122, hsa-miR-1, and hsa-miR-23b). The genes IL2RG and CCl4 were also involved in this pathway. Conclusions The present study provides a novel insight into the underlying molecular mechanism of childhood atopic asthma.

Objective By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes. Methods From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used “TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information” as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models. Results The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%. Conclusion Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.

Objectives To investigate whether and how the long noncoding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) sponges microRNA-96 (miR-96) to achieve the osteogenic differentiation of human bone marrow-derived mesenchymal stem cells (hBMSCs). Methods Protein levels were detected by Western blot. Mineralized bone matrix formation was studied by alizarin red staining. Metastasis-associated lung adenocarcinoma transcript 1, miR-96, and osteogenesis-related Messenger RNA expression was assessed by Quantitative Real-time Polymerase Chain Reaction (qRT-PCR). The interactions between miR-96 and osterix (Osx), MALAT1, and miR-96 were determined by luciferase reporter assay. Results The expression of MALAT1 was upregulated whereas that of miR-96 was downregulated in osteogenic hBMSCs. In addition, the expression of MALAT1 significantly decreased whereas that of miR-96 increased in the hBMSCs of osteoporosis (OP) patients. qRT-PCR and alizarin red staining assays showed that MALAT1 silencing or miR-96 overexpression inhibits hBMSC osteogenic differentiation and vice versa. Overexpression of miR-96 reversed the promotive effect of MALAT1 on the osteogenic differentiation of hBMSCs. Dual luciferase report assay verified that miR-96 is a regulatory target of MALAT1 and that Osx is a gene target of miR-96. Conclusions Taken together, the results demonstrate that MALAT1 promotes the osteogenic differentiation of hBMSCs by regulating the miR-96/Osx axis. Our study provides novel mechanistic insights into the critical role of lncRNA MALAT1 as a microRNA sponge in OP patients and sheds new light on lncRNA-directed diagnostics and therapeutics in OP.

PURPOSE Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. METHODS Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation. RESULTS Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. CONCLUSIONS DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

BACKGROUND Health-related risky behaviors generally refer to behaviors that have a negative impact on health and quality of life. Health-related risky behaviors in adolescents with high-functioning autism (HFA) have not been well understood so far. Adolescents with HFA may have more health-related risky behaviors than neurotypical adolescents. AIM To investigate health-related risky behaviors and their risk factors with HFA. METHODS This is an observational study. Our study enrolled 110 adolescents aged 12-19-years-old meeting Diagnostic and Statistical Manual of Mental Disorders 4th edition criteria for HFA. They were recruited from Peking University Sixth Hospital. There were also 110 age, sex and nationality matched controls enrolled who came from a public school in Beijing, China. Both groups completed the Adolescents Health-related Risky Behavior Inventory. Nonparametric tests were carried out for comparison of the Adolescents Health-related Risky Behavior Inventory scores between the two groups. Expression recognition, the Inventory of Subjective Life Quality for Child and Adolescent, Chinese Wechsler Intelligence Scale for Children, Wechsler Intelligence Scale for Adult-Chinese Revised, Theory of Mind test and Autism Spectrum Screening Questionnaire were assessed in the autism group to explore factors associated with health-related risky behaviors. Multivariate regression analysis was conducted to explore the risk factors of health-related risky behaviors in the HFA group. RESULTS The results showed that the total score of the Adolescents Health-related Risky Behavior Inventory and scores of “aggression and violence,” “suicide and self-injury,” “health compromising behavior” and “unprotected sex” subscales in the HFA group were significantly higher than those in the control group (Z range -4.197 to -2.213, P < 0.05). Among the associated factors, poor emotional experience (B = -0.268, P < 0.001), depression (B = -0.321, P < 0.001), low score of intelligence (B = -0.032, P = 0.042), low score of Theory of Mind test (B = -1.321, P = 0.003) and poor adaptation to school life (B = -0.152, P = 0.006) were risk factors. These risky behaviors may promote the occurrence of health-related risky behaviors in adolescents with HFA. CONCLUSION This study showed that adolescents with HFA were more likely to be involved in health-related risky behaviors. Different health-related risky behaviors have different reasons.

OBJECTIVE To characterize the features of thalamocortical functional connectivity during seizure recurrence at the time of antiseizure medication (ASM) withdrawal. METHODS Patients with chronic epilepsy who attempted to discontinue medications were prospectively registered and followed up; 19 patients remained seizure-free (SF-group), 18 patients had seizure relapses (SR-group) after ASM withdrawal, and 28 healthy controls were recruited. Resting-state functional magnetic resonance imaging was performed before ASM withdrawal. Thalamus subdivisions were set as seeds to calculate voxelwise functional connectivity. Partial correlation analysis between functional connectivity and clinical variables was performed. A support vector machine was used to assess the predictive ability of the specific functional connectivity for seizure relapse. RESULTS The within-group comparison indicated that the SR-group had more extensive functional connectivity than the SF-group; the left inferior pulvinar, left medial pulvinar, and right anterior pulvinar showed a significantly stronger functional connection with the precuneus in the SR-group than in the SF-group (Gaussian random field correction, voxel-level p

Development of high-performance and cost-effective catalysts for electrocatalytic hydrogen evolution reaction (HER) play crucial role in the growing hydrogen economy. Recently, the atomically dispersed metal catalysts have attracted increasing attention due to their ultimate atom utilization and great potential for highly cost-effective and high-efficiency HER electrocatalyst. Herein, we propose a high-temperature treatment strategy to furtherly improve the HER performance of atomically dispersed Pt-based catalyst. Interestingly, after appropriate high-temperature treatment on the atomically dispersed Pt0.8@CN, the Pt species on the designed N-doped porous carbon substrate with rich defect sites can be re-dispersed to single atom state with new coordination environment. The obtained Pt0.8@CN-1000 shows superior HER performance with overpotential of 13 mV at 10 mA cm−2 and mass activity of 11,284 mA/mgPt at −0.1 V, much higher than that of the pristine Pt0.8@CN and commercial Pt/C catalyst. The experimental and theoretical investigations indicate that the high-temperature treatment induces the restructuring of coordination environment and then the optimized Pt electronic state leads to the enhanced HER performances. This work affords new strategy and insights to develop the atomically dispersed high-efficiency catalysts.

Background: Poor well-being among health care workers, often observed as professional burnout, is a well-documented phenomenon. The coronavirus disease 2019 (COVID-19) pandemic has further stressed the health care workforce, but its specific effects on this workforce remain unknown. This study examined well-being and resilience among health care workers during the COVID-19 pandemic.Methods: The study used a cross-sectional survey design. Data collection took place through anonymous surveys of nurses (LPNs and RNs), advanced practice providers (NPs, physician assistants, and certified nurse midwives), certified registered nurse anesthetists, respiratory therapists, health care technicians, and therapy service professionals (physical, occupational, and speech therapists). Of the 6,120 health care workers recruited to participate in the study, data from 2,459 participants were analyzed using logistic regression and linear regression.Results: The study found that several factors significantly increased the likelihood of at-risk well-being. These included having a lower level of resilience, using support resources, feeling the organization lacked understanding of the emotional support needs of health care workers during the pandemic, believing the workload had increased, believing there was insufficient personal protective equipment, believing there was inadequate staffing to safely care for patients, and having a lower degree of psychological safety. After controlling for health care workers' role and employment location, several factors were found to be significantly associated with higher levels of resilience. These included having positive perceptions about the organization's understanding of the emotional support needs of health care workers during the pandemic, believing sufficient educational resources were available regarding the care of COVID-19 patients, having positive perceptions of leadership support from direct managers, having positive perceptions of the redeployment policy, and having a higher degree of psychological safety.Conclusions: This study identified several work environment factors that have significantly affected health care workers' well-being and resilience during the COVID-19 pandemic. This knowledge has practical relevance for health care leaders who aim to better understand and address the well-being and resilience of the health care workforce during this pandemic and beyond.

BACKGROUND: Previous studies have shown that non-fasting lipids have similar values in cardiovascular risk estimation as fasting, but it is not clear whether this could also be applicable to Chinese participants. METHODS: A total of 127 (76 men, 51 women) participants without atherosclerotic cardiovascular diseases (ASCVD) were enrolled in the study. Serum levels of blood lipids were monitored at 0 h, 2 h and 4 h after a daily breakfast. Ten-year cardiovascular disease (CVD) risk was estimated with China ASCVD risk estimator and European SCORE risk charts. Kappa statistic was used to determine agreement among estimators. RESULTS: China ASCVD risk estimator assessed half of the participants as low risk, while European risk charts assessed half of the participants as moderate risk in the same participants. Reliability analysis in China ASCVD risk estimator and Europe SCORE risk charts based on fasting and or non-fasting lipids profile were relatively high (Kappa =0.731 or 0.718, P

As the COVID-19 pandemic continues to alter daily life and its long-term impact remains unknown, research is needed to examine the effects of this unprecedented pandemic on public health and well-being. Leisure has the potential to facilitate personal growth and to serve as a vehicle for personal transformation under stressful circumstances. The purpose of the study is two-fold: (1) to explore leisure experiences and participation during the COVID-19 global pandemic;and (2) to examine how leisure experiences and participation contribute to stress-related growth during this public health crisis. A total of 470 U.S. residents completed an online survey in July 2020. The results indicated a decline in participation for all types of leisure activities. Changes in leisure activities varied by age and income but social activities had the most substantial decline, followed by outdoor and physical activities. Leisure needs, satisfaction, and social activities were statistically significant predictors of stress-related growth during the pandemic. These findings provide further understanding of how the COVID-19 pandemic has impacted engagement in leisure activities, and additional evidence that participating in leisure can facilitate personal growth during periods of adversity. [ABSTRACT FROM AUTHOR] Copyright of Leisure Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background/Purpose This study aims at investigating the epidemiological profile of chronic hepatitis C (CHC) regarding hepatitis C virus (HCV) genotype in Taiwan. Methods A total of 29,087 CHC patients with advanced fibrosis who received direct-acting antivirals (DAAs) therapy under Taiwan's National Health Insurance (NHI) during 2017–2018 were recruited. The HCV genotype distribution and its association with patients' demographic factors including age, gender, and geographical areas were examined. Results The most common genotypes were 1b (59.5%) and 2 (30.1%) with characteristics of older age (mean ± standard deviation (SD): 66.5 ± 10.7 years and 67.3 ± 10.9 years) and female gender predominant (57.1% and 59.4%), which were associated with iatrogenic infection decades ago. Most of patients with genotype 1a (5.9%) and 6 (3.7%) infection were relatively younger (59.2 ± 12.0 years and 60.0 ± 13.8 years) and male gender predominant (59.1% and 61.1%), except Liujia and Liuying districts in southern Taiwan. The youngest group (53.2 ± 11.8 years) and most male gender predominant (74.3%) was genotype 3 (0.37%). These genotypes with characteristics of being younger and male gender predominant were highly related to injection drug use in recent years. The number of genotype 4 patients were extremely rare (n = 25) and efficacy of genotype-4-specific-DAA was significantly poorer than non-genotype-4-specific DAA (P value = 0.0411). Conclusions The significant differences in demographic characteristics among CHC patients with different HCV genotypes found in this study suggest HCV genotype was highly associated with transmission pattern and may be used as a reference for HCV control.

Road Tunnels are an important part of the current road transportation infrastructure. As the main form of tunnel lining diseases, cracks are easy to interact with other areas, which seriously affects the safe operation of the tunnel. Due to the similarity of brightness and linearity between surface cracks and lining cracks, the existing crack detection algorithms can not extract cracks accurately and quickly. An algorithm of lining seam crack elimination with rectangular mark is proposed here. First, the line segments in the image are detected by the Line Segment Detector algorithm based on the coarse percolation detection of the crack. Second, the distribution directions are calculated, and cracks from the lining seams are distinguished by the adaptive threshold judgment method. Third, by using the distribution characteristics of pixels, the line segments are extended to form rectangular marks perpendicular to the direction of lining seams. Finally, the marking information is used to remove the lining joints and obtain the real surface cracks of tunnel lining. Experimental results show that the algorithm can quickly and effectively remove any shape distribution of lining seam. The algorithm fills in the blank of concrete tunnel lining surface crack detection technology.

Coronary artery fistula (CAF) is characterized by abnormal communication between the main coronary artery and the cardiac chambers, pulmonary arteries, vena cava, and systemic arteries and veins, and is a rare cardiac malformation. Bronchiectasis involves remodeling and persistent dilatation of the bronchial airways. It can either be localized to one lung segment or diffused throughout the whole lung. The incidence of bronchiectasis has increased to 566 per 100,000 over the last decade. There is a 33% possibility that CAF and lung-related disease will co-occur. However, the merging mechanism of these two diseases has not been explored. We report a case of a 61-year-old female with bronchiectasis who was admitted to our hospital with dyspnea and ankle edema. The patient was diagnosed with coronary artery-pulmonary artery fistula by coronary angiography (CAG), which was performed to rule out the possibility of coronary artery stenosis. Using the keywords "coronary artery fistula" and "bronchiectasis" to search the inpatient system of our hospital, PubMed, and Web of Science, we found that these two diseases often occur due to abnormal cardiovascular and pulmonary airway connection. Previous studies also support that CAF can co-occur with bronchiectasis. Based on these findings, we explain the possible mechanism of this co-occurrence with the aim of improving diagnostic techniques and therapeutic methods.

Coronavirus disease 2019 (COVID-19) may rapidly worsen respiratory failure, thereby leading to death. COVID-19-induced respiratory failure exhibits some atypical characteristics, silent hypoxemia, and high lung compliance. Some histopathological changes associated with COVID-19-induced respiratory failure differ from those of classic acute respiratory distress syndrome (ARDS). However, compared with classical ARDS, COVID-19-induced respiratory failure has a similar timing of onset, clinical syndromes, radiological profile, and mortality rate in the intensive care unit (ICU). Respiratory failure induced by COVID-19 is a type of ARDS and is currently underdiagnosed. This condition stretches the definition of classic ARDS; therefore, an updated definition is warranted.