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Edge computing provides computing capability at close-user proximity to reduce service latency for end users. To improve the efficiency of edge computing infrastructures, geographically-distributed edge datacenters can co-work with each other and with cloud datacenters, forming a new paradigm referred to as cooperative edge-cloud computing. In this context, applications typically run on a virtual machine (VM) that can be replicated at multiple sites, and thus user traffic can be served at all the sites where corresponding VMs reside. For the performance of many applications, latency is a critical parameter. In this work, taking applications latencies as the primary constraint, we model the problem of VM placement and workload assignment as a mixed integer linear program and develop heuristic algorithms accordingly. The goal is to minimize the consumption of information technology (IT) infrastructures for placing VMs in cooperative edge-cloud computing, while meeting the heterogeneous latency demands of different applications. Some preliminary results indicate that edge datacenters resource efficiency can be optimized by proper cross-site VM placement and workload re-direction.

Objective: With the fast development of economy and culture, medical professionals parents are paying more and more attention to nocturnal enuresis (NE) since it rais ed the negative impact on children's health and imposes a long -term burden on families. The present clinical investigation aims to evaluate the clinical effect of pressing needle on nocturnal enuresis in children through a randomized controlled clinical trial. Materials and Methods: A total of 120 children with nocturnal enuresis were randomly assigned into pressing needle group, desmopressin group and pills Capsule group, 40 cases in each group. The children in pressing needle group were treated with pressing needles, and those in the desmopressin group and Suoquan pills were orally administrated with desmopressin and Suoquan pills, respectively. Combined behavioral therapy was also given in all the three groups during the treatment. Enuresis frequency, degree of arousal, functional bladder capacity (FBC) and postvoid residual urine volume (PRUV) were employed as the indicators to evaluate the clinical efficacy in the three groups. After 6 months of follow-up, the recurrence rate of the three groups was also analyzed for efficacy assessment. Results: The total responsive rate in pressing needle group was 95.0%, which was higher than 87.5% of Suoquan pills group (p < 0.05), and significantly higher than 77.5% of desmopressin group (p < 0.01). The frequency of enuresis was significantly lowered after treatment (p < 0.01), concurrently with an evident improvement in arousal levels in all of the three groups (p < 0.01), and the inter-group difference in post-treatment enuresis frequency and arousal level between pressing needle group and the other two groups was also statistically significant (p < 0.05). The recurrence rate of pressing needle group, desmopressin group and Suoquan pills group was 7.89%, 48.39% and 22.86%, respectively, with a difference which was statistically significant between pressing needle group and desmopressin group (p < 0.01), but not between pressing needle group and Suoquan pills group (p > 0.05). Conclusion: Pressing needle treatment showed a substantial clinical effect on enuresis in children by improving the bladder function, increasing the FBC and reducing PRUV of the bladder, as well as by enhancing the degree of arousal and lowering the recurrence rate, therefore is suggested to be considered as an option for enuresis treatment for children.

Deep reinforcement learning (DRL)-based recommender systems have recently come into the limelight due to their ability to optimize long-term user engagement. A significant challenge in DRL-based recommender systems is the large action space required to represent a variety of items. The large action space weakens the sampling efficiency and thereby, affects the recommendation accuracy. In this article, we propose a DRL-based method called deep hierarchical category-based recommender system (DHCRS) to handle the large action space problem. In DHCRS, categories of items are used to reconstruct the original flat action space into a two-level category-item hierarchy. DHCRS uses two deep Q -networks (DQNs): 1) a high-level DQN for selecting a category and 2) a low-level DQN to choose an item in this category for the recommendation. Hence, the action space of each DQN is significantly reduced. Furthermore, the categorization of items helps capture the users' preferences more effectively. We also propose a bidirectional category selection (BCS) technique, which explicitly considers the category-item relationships. The experiments show that DHCRS can significantly outperform state-of-the-art methods in terms of hit rate and normalized discounted cumulative gain for long-term recommendations.

Background: Upregulation of mitochondrial E3 ubiquitin ligase 1 (Mul1) contributes to brain injury in ischemic stroke due to disturbance of mitochondrial dynamics, and bioinformatics analysis predicts that Mul1 is a potential target of Dipsacoside B. Objective: The aim of the study was to explore whether Dipsacoside B can exert a beneficial effect on brain injury in the ischemic stroke rat via targeting Mul1. Methods: The SD rat brains or PC12 cells were subjected to 2 h-ischemia or 8 h-hypoxia plus 24 h-reperfusion or 24 h-reoxygenation to establish the ischemic stroke rat model in vivo or in vitro, which were treated with Dipsacoside B at different dosages. The brain or PC12 cell injury, relevant protein levels and mitochondrial functions were measured by methods of biochemistry, flow cytometry or Western blot. Results: The neurological dysfunction and brain injury (such as infarction and apoptosis) observed in the ischemic stroke rats were accompanied by increases in Mul1 and dynamin-related protein 1 (Drp1) levels along with decreases in mitofusin 2 (Mfn2) level and ATP production. These effects were attenuated by Dipsacoside B. Consistently, cell injury (necroptosis and apoptosis) occurred in the PC12 cells exposed to hypoxia concomitant with the upregulation of Mul1 and Drp1 along with downregulation of Mfn2 and mitochondrial functions (such as increases in reactive oxygen species production and mitochondrial fission and decreases in mitochondrial membrane potential and ATP production).These phenomena were reversed in the presence of Dipsacoside B. Conclusion: Dipsacoside B can protect the rat brain against ischemic injury via inhibition of Mul1 due to the improvement of mitochondrial function.

Traditional decision making in a vehicle network includes uploading vehicle sensing data to faraway cloud platforms and then returning correlated results to the vehicles. The data have features of large quantity and high redundancy, which causes high communication latency and vehicle applications to deteriorate. Vehicular fog computing (VFC) is a new network paradigm that uses local fog nodes for decision making. However, how to achieve distributed information exchange with low latency is a challenging issue because the connectivity of the vehicle network is low due to vehicle mobility. In this paper, a distributed information exchange scheme with low latency in VFC is proposed. First, considering the frequent changes in vehicle positions and the randomness in driving routes, public transportation facilities with a wider driving range such as buses and taxis are used as fog nodes to increase the probability of uploading data. Then, the fog nodes should dynamically adjust the data sampling frequency according to the time-space correlation of the data to ensure that only nonredundant data are received. To minimize the interruption latency caused by accidents during an exchange, the fog nodes evaluate and predict connection states among them and their neighboring vehicles when establishing exchanges. If a fog node finds that a vehicle cannot complete information exchange because the vehicle may move outside its communication range in a future period, it will recalculate an optimized relay route for the vehicle by using mixed integer programming. Theoretical analysis and simulation results show that compared with the existing work, the proposed scheme can completely exchange all vehicle data with lower latency.

Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by hand-crafted rules which may limit their performance. In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention based deep architecture as the policy network to guide the selection of next solution. We apply our method to two important routing problems, i.e. travelling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art deep learning based approaches. The learned policies are more effective than the traditional hand-crafted ones, and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions and even real-world dataset., Comment: 10 pages, 4 figures

Space robotics has been used extensively in complex space missions. Rigid-manipulator space robots may suffer from rigid-body collisions with targets. This collision is likely to cause damage to the space robot and the target. To overcome such a problem, a novel Continuum-Manipulator Space Robot (CMSR) for performing on-orbit servicing missions is proposed in this paper. Compared with rigid-manipulator space robots, CMSRs are able to perform compliant operations and avoid rigid-body collisions with a target. The CMSR consists of two kinds of flexible components, including solar arrays and continuum manipulators. The elastic vibrations of these flexible components disturb the position and attitude of CMSRs. The beating phenomenon introduced by the energy transfer among these flexible components can cause damage to solar arrays. The complicated dynamic coupling poses enormous challenges in dynamic modeling and vibration analysis. The dynamic model for CMSRs is derived and the mechanism of the beating phenomenon is analyzed in this paper. Simulation results show that an obvious beating phenomenon occurs and the amplitude of the solar arrays increases significantly when the natural frequencies of two kinds of flexible components are close. A method is provided to avoid the beating phenomenon.

Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key challenges in FL is the non-independent and identically distributed (Non-IID) data across the clients, which decreases the efficiency of stochastic gradient descent (SGD) based training process. Moreover, clients with different data distributions may cause bias to the global model update, resulting in a degraded model accuracy. To tackle the Non-IID problem in FL, we aim to optimize the local training process and global aggregation simultaneously. For local training, we analyze the effect of hyperparameters (e.g., the batch size, the number of local updates) on the training performance of FL. Guided by the toy example and theoretical analysis, we are motivated to mitigate the negative impacts incurred by Non-IID data via selecting a subset of participants and adaptively adjust their batch size. A deep reinforcement learning based approach has been proposed to adaptively control the training of local models and the phase of global aggregation. Extensive experiments on different datasets show that our method can improve the model accuracy by up to 30\%, as compared to the state-of-the-art approaches.

Doubling of the optic disc is rare in clinic, which appears as true doubling or pseudo-doubling. Bilateral doubling of the optic discs is even more less seen in clinic. Here, we report the case of a 35-year-old woman who presented to the clinic for a physical examination. The patient's best-corrected visual acuity was 0.6 OD with +2.25 DS/3.50 DC×175°, and 0.9 OS with -3.00 DS/0.50 DC×145°. The intraocular pressure of each eye was normal. Fundoscopy examination revealed an enlarged suspected optic disc in both eyes. Visual field examination revealed an additional blind spot in each eye. Optical coherence tomography (OCT) analysis showed a normal macular thickness and profile. B-scan ultrasound revealed a single optic nerve shadow in each eye. Cranial computed tomography (CT) showed only 1 optic foramen and 1 optic nerve in each eye. Although double-blind spots were demonstrated by visual field examination, and pits in both optic disc regions for both eyes were shown by OCT, it was not enough to support the diagnosis of true doubling of the optic disc. Consequently, the diagnosis of bilateral pseudo-doubling of optic discs was made based on the clinical evidence, leading to the consideration of possible causality of other ophthalmic diseases.

Social networks have proven to be effective for high-quality item recommendation. Most social recommenders, however, are insufficient to capture the user preferences over items, as they largely neglect the multi-facet social relations latent in the social network, such as classmates, colleagues, and families. We, therefore, propose a novel disentangled social recommendation (DSR) framework to exploit the multifacet social relations for enhanced item recommendation. Specifically, DSR explicitly disentangles the social relations into multiple facets, and encodes the social influence under each facet into disentangled user embeddings in the social network. The multiple user embeddings are then aggregated via a facet-level attention mechanism, which distinguishes the effective facets for better inferring user interests over items. Extensive experiments show the superiority of DSR against the state-of-the-art methods and its potential in alleviating the data sparsity issue.

In the new paradigm of blockchain-powered intelligent edge, the key agreement is a significant problem which has not been extensively studied so far. Existing key agreement protocols in the traditional public-key setting are usually too complicated and heavy for edge and end devices. Besides, most protocols in use do not have effective measures to resist side-channel attacks which are increasingly threatening cloud servers, edge devices and end devices. Identity (ID)-based protocols can be conveniently implemented in the blockchain-powered intelligent edge. Several leakage-resilient ID-based protocols which can resist side-channel attacks have been proposed. However, they all involve time-consuming pairing computations. Besides, none of them address side-channel attacks to the key generation center (KGC). This paper designs and realizes two novel ID-based key agreement protocols for the blockchain-powered intelligent edge, including an extended Canetti-Krawczyk (eCK) secure ID-based authenticated key agreement (AKA) protocol and a continuous after-the-fact leakage-resilient eCK (CAFL-eCK) secure ID-based AKA protocol. Both protocols do not involve any heavy pairing computation. Besides, the second one can resist side-channel attacks to the KGC and the communicating parties. A hybrid implementation of the two protocols can achieve high efficiency and strong security at the same time in blockchain-powered intelligent edge environments. This is demonstrated via a use case of a blockchain-powered smart home.

Objective: Functional fecal incontinence in children (FFIC), also known as functional fecal disorder in children, is a type of childhood diseases characterized by excretory dysfunction. In the study, we observed the clinical effect of Changqiang acupoint injection on functional fecal incontinence in children (FFIC) by randomized controlled clinical trials. Materials and methods: 140 children with functional fecal incontinence who met the screening criteria were respectively assigned into the treatment group (100 cases) and the control group (40 cases) according to the completely randomized controlled design. In the treatment group, Chuankezhi injection was administrated at the Changqiang acupoint once a week for three weeks (one course of treatment). In contrast, the subjects in the control group was instructed to receive sphincter exercise and defecation training, followed by an assessment on the therapeutic efficacy after one course of treatment. Results: The overall response rate of the two groups was 93.00% (93/100) and 57.50% (23/40), respectively, with a statistically significant difference (P Conclusion: Changqiang acupoint injection has significant curative effect on FFIC as demonstrated by significant relief in the symptoms associated with fecal incontinence, and thus is considered as a useful approach to be widely applied in clinical practice.

Due to the threat of a failure and the discrete distribution of data center users, the research of distributed cloud data center provides real-time cloud services with robustness, reliability and security. Faced with data center interconnection, network failures cause mass services delay and interruption, which do a great damage to cloud computing. Many researchers have studied fault location methods in data center interconnection, which are easy to trap in local optimum limited by search capability and reduce the accuracy of location, especially when confronted with large-scale alarm information. In this paper, the deep neural evolution network is introduced to extract deep-hidden fault features from massive collected alarm information in cloud data center interconnection. It has the prominent capacity of global search without the constraint of gradient to realize the breakthrough of fault location accuracy. The fault location method based on deep neural evolution network (FL-DNEN) is applied which uses the alarm set and suspicious scope of fault getting from fault propagation model as input and export deterministic faults accurately. The emulations demonstrate that the proposed method dramatically improves the accuracy of fault location to 92% with large-scale alarm information, which improves the resilience of cloud data center interconnection dramatically.

In complicated product industry such as aircraft manufacturing, an assembly model contains abundant engineering information for use in design, manufacture, and maintenance. Assembly retrieval can be used to find relevant models for knowledge reuse. However, an assembly with rotatable joints may have many poses, which brings difficulty to assembly retrieval, since there is no pose principle for assembly design. Therefore, focused on rotatable joints in assembly, a skeleton-based descriptor for pose-free assembly retrieval is proposed. The centroid points of part surfaces and contact faces in an assembly are extracted to construct a spatial-contact skeleton. The skeleton-based distance is proposed to measure the distance between two surface points, which is invariant to the rotatable joints. The distribution of skeleton distances between two parts is used to describe the pair. Considering a part paired with all other parts in the assembly, the set of part pairs is used to represent a part, and the modified Hausdorff distance is used to measure the dissimilarity between parts for assembly retrieval. Experiments are conducted to compare the accuracy of the proposed descriptor to holistic and structureless descriptors. The proposed method is shown to retrieve assemblies with similar parts and structures regardless of their rotatable joints.

OBJECTIVE Previous studies have suggested the use of 1.0 g/kg of 20% mannitol at the time of skin incision during neurosurgery in order to improve brain relaxation. However, the incidence of brain swelling upon dural opening is still high with this dose. In the present study, the authors sought to determine a better timing for mannitol infusion. METHODS One hundred patients with midline shift who were undergoing elective supratentorial tumor resection were randomly assigned to receive early (immediately after anesthesia induction) or routine (at the time of skin incision) administration of 1.0 g/kg body weight of 20% mannitol. The primary outcome was the 4-point brain relaxation score (BRS) immediately after dural opening (1, perfectly relaxed; 2, satisfactorily relaxed; 3, firm brain; and 4, bulging brain). The secondary outcomes included subdural intracranial pressure (ICP) measured immediately before dural opening; serum osmolality and osmole gap (OG) measured immediately before mannitol infusion (T0) and at the time of dural opening (TD); changes in serum electrolytes, lactate, and hemodynamic parameters at T0 and 30, 60, 90, and 120 minutes thereafter; and fluid balance at TD. RESULTS The time from the start of mannitol administration to dural opening was significantly longer in the early administration group than in the routine administration group (median 66 [IQR 55–75] vs 40 [IQR 38–45] minutes, p < 0.001). The BRS (score 1/2/3/4, n = 14/26/9/1 vs 3/25/18/4, p = 0.001) was better and the subdural ICP (median 5 [IQR 3–6] vs 7 [IQR 5–10] mm Hg, p < 0.001) was significantly lower in the early administration group than in the routine administration group. Serum osmolality and OG increased significantly at TD compared to levels at T0 in both groups (all p < 0.001). Intergroup comparison showed that serum osmolality and OG at TD were significantly higher in the routine administration group (p < 0.001 and = 0.002, respectively). Patients who had received early administration of mannitol had more urine output (p = 0.001) and less positive fluid balance (p < 0.001) at TD. Hemodynamic parameters, serum lactate concentrations, and incidences of electrolyte disturbances were comparable between the two groups. CONCLUSIONS Prolonging the time interval between the start of mannitol infusion and dural incision from approximately 40 to 66 minutes can improve brain relaxation and decrease subdural ICP in elective supratentorial tumor resection.

Background: Primary biliary cholangitis (PBC) is a complex disease with high heritability. We investigated the association between human leukocyte antigen (HLA)-DRB1 alleles and PBC in families and sporadic cases to evaluate the genetic components of the disease. Methods: We performed whole exome sequencing in three PBC families. We genotyped HLA-DRB1 and calculated the association between HLA-DRB1 alleles and the encoding amino acid sequences with the clinical features. Results: Ten variants harboured the HLA-DRB1 gene associated with PBC. DRB1 x07:01, 14:01 and 14:05 were highly increased in PBC. Ten coding region polymorphisms were associated with PBC that encode the amino acid variants of HLA-DR beta 54, beta 59 and beta 66 located in the peptide-binding site of the MHC molecule. Glutamine at position 54 was confirmed as a risk amino acid, verifying the results of familial aggregation analysis of PBC families. Discussion: Familial aggregation analysis indicated that HLA-DRB1 is a candidate gene for the risk of disease course. Considering that amino acid variations are critical to peptide-binding properties, they underlie the major component of MHC association with PBC. (c) 2021 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Over 80% of wireless traffic already takes place in buildings. Like water, gas, and electricity, wireless communication is becoming one of the most fundamental utilities of a building. It is well known that building structures have a significant impact on in-building wireless networks. If we seek to achieve the optimal network performance indoors, the buildings should be designed with the objective of maximizing wireless performance. So far, wireless performance has not yet been considered when designing a building. In this paper, we introduce a novel and interdisciplinary concept of building wireless performance (BWP) to a wide audience in both wireless communications and building design, emphasizing its broad impacts on wireless network development and deployment, and on building layout/material design. We first give an overview of the BWP evaluation framework proposed in our state-of-the-art works and explain their interconnections. Then, we outline the potential research directions in this exciting research area to encourage further interdisciplinary research., Accepted at IEEE Wireless Communications

The integration of IoT and industry reveals the industrial manufacturing developments, resulting in industry internet of things (IIoT) which is to provide a general interconnect system for the access of various industry devices. However, as the amount and type of terminal increase, the creditability and privacy protection of terminal devices are hard to be guaranteed in IIoT, since the data and digital identity of access devices are nearly transparent for more devices in networks. It is a critical issue for the security of IIoT whether the access and service of device are trustworthy. In this paper, we present a novel private blockchain-enabled trusted anonymous access (BlockTrust) architecture for IIoT, where the distributed cloud radio and optical access networks (C-RON) is considered to provide a risk reduction of privacy leakage. Based on BlockTrust architecture, a blockchain-enabled tripartite anonymous identification trusted service provisioning (TriTrustServ) scheme is further proposed to guarantee a balanced tradeoff among the credibility, confidentiality and efficiency in IIoT, including digital identity generation, anonymous access identification, and trusted resource provisioning. Note that for the sake of a high credibility in IIoT networks, a tripartite authentication is presented in this paper with the first-time among device manufacturer, devices and network operator for the access process of device in IIoT networks. The feasibility and efficiency of BlockTrust architecture are experimentally verified in the realistic testbed, and the performances of TriTrustServ scheme are evaluated by comparing with two benchmark schemes in the terms of average mistrust rate, resource utilization and identification cost.

Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to internet of things devices (IDs) in wireless systems with limited or no infrastructure. In this paper, a UAVs-assisted Fog-enabled Internet of things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs. Besides, there are two types of IDs, namely, Requested-IDs (R-IDs), which has task offloading requirement, and Free-IDs (F-IDs) which could offload tasks for R-IDs with idle computation resources. Considering two offloading links: the Device-to-Device (D2D) link and the Ground-To-Air (G2A) link, which are responsible for both the uplink and downlink offloading procedure. To minimize the total network overhead, we jointly optimize the UAV trajectory, the transmission power and the computation offload radios, while satisfying quality of service (QoS) requirements of R-IDs. The optimization problem is non-convex, the UAV-assisted Task Offloading Optimization algorithm is proposed to obtain the local optimal solutions, which decomposes the original problem into two parallel subproblems and solved alternately. Finally, simulation results demonstrate that the proposed algorithm could achieve superior performance in terms of the network overhead compared with algorithms in the literature.

In the Big Data environment, when green manufacturers invest in the green production technology, to satisfy consumer demand timely and accurately, they may begin to gain consumer performance information (hereafter, CBDI) to design and produce product. However, these will go up their extra costs. Meanwhile, for a green manufacturer who sells the green product through the online channel and the offline channel, the expression of its market demand needs to rethink in the new environment. In these conditions, for a dual-channel green supply chain (hereafter, DGSC), chain members pay more attention on the pricing problems considering the inputs of CBDI and greening R&D. Hence, to resolve this question, a DGSC a green manufacturer selling by the online channel and with one retailer selling by the offline channel was chosen. Afterwards, the demand function of the DGSC was revised, and we analyzed the profits models and its pricing rules in the proposed four common cost-sharing models. Results indicate that whether the retailer bears the CBDI costs or the greening R&D costs, the retailer will not change its retail price. If the retailer can bear some CBDI costs, the alteration tendencies of the best wholesale price are related to the cost-sharing parameter.

Little is known about whether risk classification at diagnosis predicts post-hematopoietic cell transplantation (HCT) outcomes in patients with acute myeloid leukemia (AML). We evaluated 8709 patients with AML from the CIBMTR database, and after selection and manual curation of the cytogenetics data, 3779 patients in first complete remission were included in the final analysis: 2384 with intermediate-risk, 969 with adverse-risk, and 426 with KMT2A-rearranged disease. An adjusted multivariable analysis detected an increased risk of relapse for patients with KMT2A-rearranged or adverse-risk AML as compared to those with intermediate-risk disease (hazards ratio [HR], 1.27; P = .01; HR, 1.71; P < .001, respectively). Leukemia-free survival was similar for patients with KMT2A rearrangement or adverse risk (HR, 1.26; P = .002, and HR, 1.47; P < .001), as was overall survival (HR, 1.32; P < .001, and HR, 1.45; P < .001). No differences in outcome were detected when patients were stratified by KMT2A fusion partner. This study is the largest conducted to date on post-HCT outcomes in AML, with manually curated cytogenetics used for risk stratification. Our work demonstrates that risk classification at diagnosis remains predictive of post-HCT outcomes in AML. It also highlights the critical need to develop novel treatment strategies for patients with KMT2A-rearranged and adverse-risk disease.

Molecules of 12-o-carboranyldodecylphosphonic acid form a novel self-assembled monolayer (SAM) on alumina, which can effectively tune charge carriers in organic field effect transistors (OFETs) with the assembled dipoles of o-carborane at the semiconductor–dielectric interface. This SAM has not only realized p-channel OFETs of pentacene with both low threshold voltage and high field effect mobility (up to 5.2 cm2 V−1 s−1), but also enabled ambipolar charge transport in the solution-processed thin films of a chlorinated tetraazapentacene, which is a typical n-type organic semiconductor. As measured with Kelvin probe force microscopy, the SAM of CBPA has a surface electrostatic potential of −0.40 ± 0.10 V versus gold, which is in agreement with the observed shift of threshold voltage in the OFETs.

With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems. In order to fully report BDA for intelligent manufacturing systems, this paper provides a comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies. The framework, development, key technologies, and applications of BDA for intelligent manufacturing systems are discussed. The challenges and opportunities for future research are highlighted. Through this work, it is hoped to spark new ideas in the effort to realize the BDA for intelligent manufacturing systems.

Objective To determine the effects of maternal periconceptional supplementation with folic acid or multiple micronutrients containing folic acid on the prevention of fetal congenital heart defects (CHDs). Study design Data were drawn from a Prenatal Health Care System and a Birth Defects Surveillance System in a district of Beijing, China. A total of 63 969 singleton births, live or stillborn, 308 CHDs among them, during 2013 to 2018 were included. Associations between different patterns of supplementation and risk for total CHDs or main types of CHDs were evaluated with risk ratios (RRs). Results For folic acid or multiple micronutrients containing folic acid users compared with nonusers, the adjusted RRs (ARRs) for total CHDs, critical CHD, and ventricular septal defect (VSD) were 0.60 (95% CI, 0.44-0.83), 0.41 (95% CI, 0.26-0.67), and 0.47 (95% CI, 0.30-0.74), respectively. When we compared multiple micronutrients containing folic acid users with folic acid users, the ARRs were 0.84 (95% CI, 0.66-1.09), 0.64 (95% CI, 0.41-1.00), and 0.94 (95% CI, 0.63-1.41) for total CHDs, critical CHD, and VSD, respectively. We also found that, compared with supplementation initiated after conception, supplementation initiated before conception was associated with a lower risk for CHDs: the ARRs were 0.68 (95% CI, 0.48-0.95) for total CHDs and 0.26 (95% CI, 0.10-0.71) for critical CHD, but 1.08 (95% CI, 0.63-1.83) for VSD. Conclusions Maternal periconceptional supplementation with folic acid or multiple micronutrients containing folic acid seems to decrease the risk for CHDs, especially critical CHD, in offspring. Supplementation confers a greater protective effect when it is initiated before conception. We did not find any difference between folic acid and multiple micronutrients containing folic acid in terms of preventing CHDs.

The purpose of this study was to evaluate the effect of intracutaneous pyonex on analgesia and sedation in critically ill patients who underwent mechanical ventilation. A total of 88 critically ill patients were divided into a control group and an intervention group. Critical Care Pain Observation Tool (CPOT) and Richmond Agitation and Sedation Scale (RASS) were used to evaluate pain and agitation. The dosage and treatment period of sedative and analgesic drugs in the intervention group were notably lower than the control group (p < 0.05). Analgesia compliance time in the intervention group was superior to control group (p < 0.05). The shallow sedation compliance rate in the intervention group was significantly higher than the control group (p < 0.01). There was significant difference in blood gas analysis before and after treatment between the two groups (p < 0.05). After 2 h of sedation and analgesia, heart rate in the intervention group was lower than control group, but respiratory rate was higher than the control group (p < 0.05). The traditional analgesia and sedation combined with intracutaneous pyonex reduced the total amount and treatment period of sedative and analgesic drugs in critically ill patients throughout the treatment process, and it also decreased the adverse reactions such as blood pressure drops and respiratory depression.

Background Lipusu is the first commercialized liposomal formulation of paclitaxel and has demonstrated promising efficacy against locally advanced lung squamous cell carcinoma (LSCC) in a small‐scale study. Here, we conducted a multicenter, randomized, phase 3 study to compare the efficacy and safety of cisplatin plus Lipusu (LP) versus cisplatin plus gemcitabine (GP) as first‐line treatment in locally advanced or metastatic LSCC. Methods Patients enrolled were aged between 18 to 75 years, had locally advanced (clinical stage IIIB, ineligible for concurrent chemoradiation or surgery) or metastatic (Stage IV) LSCC, had no previous systemic chemotherapy and at least one measurable lesion as per the Response Evaluation Criteria in Solid Tumors (version 1.1) before administration of the trial drug. The primary endpoint was progression‐free survival (PFS). The secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety profiles. To explore the possible predictive value of plasma cytokines for LP treatment, plasma samples were collected from the LP group at baseline and first efficacy evaluation time and were then subjected to analysis by 45‐Plex ProcartaPlex Panel 1 to detect the presence of 45 cytokines using the Luminex xMAP technology. The correlation between treatment outcomes and dynamic changes in the levels of cytokines were evaluated in preliminary analyses. Results The median duration of follow‐up was 15.4 months. 237 patients in the LP group and 253 patients in the GP group were included in the per protocol set (PPS). In the PPS, the median PFS was 5.2 months versus 5.5 months in the LP and GP group (hazard ratio [HR]: 1.03, P = 0.742) respectively. The median OS was 14.6 months versus 12.5 months in the LP and GP group (HR: 0.83, P = 0.215). The ORR (41.8% versus 45.9%, P = 0.412) and DCR (90.3% versus 88.1%, P = 0.443) were also similar between the LP and GP group. A significantly lower proportion of patients in the LP group experienced adverse events (AEs) leading to treatment interruptions (10.9% versus 26.4%, P < 0.001) or treatment termination (14.3% versus 23.1%, P = 0.011). The analysis of cytokine levels in the LP group showed that low baseline levels of 27 cytokines were associated with an increased ORR, and 15 cytokines were associated with improved PFS, with 14 cytokines, including TNF‐α, IFN‐γ, IL‐6, and IL‐8, demonstrating an overlapping trend. Conclusion The LP regimen demonstrated similar PFS, OS, ORR and DCR as the GP regimen for patients with locally advanced or metastatic LSCC but had more favorable toxicity profiles. The study also identified a spectrum of different cytokines that could be potentially associated with the clinical benefit in patients who received the LP regimen., Cisplatin plus Lipusu (LP) regimen has comparable efficacy and more favorable toxicity profiles compared with cisplatin plus gemcitabine (GP) regimen for patients with advanced LSCC. The study also demonstrated that LP had a significant impact on the levels of plasma cytokines and a spectrum of cytokines were associated with clinical benefit in patients who received LP. Thus, our results provided a new option for patients with advanced LSCC.

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms using simulated voltammograms with one cycle of the potential for training has previously been reported. In this paper, it is shown how valuable additional patterns for mechanism distinction become available when a new DNN with advanced architecture is trained simultaneously on images obtained from three cycles of potential using tensor inputs. Significant improvement relative to the single cycle training in achieving correct classification of E, EC1st and EC2nd mechanisms (E = electron transfer step, C1st and C2nd are first and second order follow up chemical reactions, respectively) is demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish even by an experienced electrochemist. Challenges anticipated in applying the new DNN to classification of experimental data are highlighted. Directions for future development are also discussed.

To improve the quality of narrow overlap welds and reduce cost during the high-strength production, it is essential to detect weld defects promptly by identification of the type of defects to provide solution accordingly. This paper proposes an integrated weld defect identification approach combing eddy current detection with 3D laser scanning based on Convolutional Neural Networks (CNN). The detection principle and equipment of the two detection methods are introduced. To fit the training process of CNN, two set of detection signals are preprocessed: a two-dimensional time-frequency diagram for eddy current signals using continuous wavelet transform and for laser images, weld edges are extracted and divided by region using image convolution and combining with integral graph. CNN model VGG16 is trained afterwards with data collected from one local manufacturer in Shanghai. It is discovered that performance of eddy current and laser image identification on different types of weld defects is different, and the accuracy can be increased with the two methods combined. Last, to achieve real-time detection of narrow overlap welding, a two-stage defect recognition model is built which greatly improves the efficiency of weld defect identification without affecting the accuracy of weld defect identification.

Jointly exploiting spectral information and spatial information, rather than working on individual pixels, is important for hyperspectral target detection. In this letter, we propose a hyperspectral target detection method relying on superpixel structures of the input image. Multiscale superpixels are generated to capture textures of the image, and each superpixel is summarized to its representative, which is the average of all its pixels. The SparseCEM detector is then applied to these representatives. Finally, the detection results from all scales are fused to achieve the final output. Our experiment results show that the multiscale-superpixel-based SparseCEM detector (MSSD) outperforms the compared typical detection methods.

Objective This meta-analysis aimed to investigate the efficacy and safety of poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors in BRCA-mutated advanced breast cancer patients comprehensively. Methods We conducted a systematic literature research through PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, China National Knowledge Infrastructure (CNKI), wanfang, China Biology Medicine disc (CBMdisc), and ClinicalTrials.gov from inception to January 2021. Randomized controlled trials (RCTs) with available data comparing PARP inhibitors versus control therapy in BRCA-mutated advanced breast cancer were eligible for analysis. Statistical analyses were performed with Review Manager (RevMan) version 5.4 and R version 4.0.3. Results 1706 studies were retrieved in total, and 4 RCTs with 1540 patients were eligible for meta-analysis finally. The results showed that progression-free survival (PFS) and overall survival (OS) were significantly improved in germline BRCA-mutated breast cancer patients with PARP inhibitors (HR 0.64, 95% CI [0.56–0.74]; HR 0.86, 95% CI [0.74–0.99], respectively) with no significant heterogeneity across studies (I2 = 22%, χ2 p = 0.28; I2 = 0%, χ2 p = 0.70, respectively). There was no significant difference in the overall adverse events (AEs), grade≥3 AEs and AEs leading to treatment discontinuation between PARP inhibitor arms and control arms (RR 1.01, 95% CI [0.99–1.02]; RR 0.95, 95% CI [0.83–1.09]; RR 1.17, 95% CI [0.87–1.57], respectively). Based on the available data, PARP inhibitors provided comparable or better results than control arms in improving the quality of life in BRCA-mutated advanced breast cancer patients. Conclusions PARP inhibitors prolonged PFS and OS among patients with BRCA-mutated advanced breast cancer with tolerable safety and improved quality of life., Graphical abstract Image 1, Highlights • PARP inhibitors showed encouraging efficacy in BRCA-mutated advanced breast cancer. • The safety of PARP inhibitors was tolerable. • We included both PARP inhibitor monotherapy and combination therapy studies.

Lin Mei,1,&ast; Xiaodan Li,1,&ast; Shengcai Wang,1,&ast; Run Si,2 Tingting Ji,1 Zhifei Xu,3 Yun Peng,4 Yue Liu,4 Hongbin Li,1 Jie Zhang,1 Yongli Guo,5 Jinghong Tian,6 Guifei Zhou,2 Huifang Huang,2 Jun Tai,1,7,&ast; Jiangang Liu,8,9 Xin Ni1 1Department of Otolaryngology, Head and Surgery, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, People’s Republic of China; 2School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People’s Republic of China; 3Department of Respiration, Beijing Children′s Hospital, Capital Medical University, National Center for Children′s Health, Beijing, People’s Republic of China; 4Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, People’s Republic of China; 5Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health (NCCH), Beijing, People’s Republic of China; 6Department of Neurorehabilitation, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, People’s Republic of China; 7Department of Otorhinolaryngology, Children’s Hospital, Capital Institute of Pediatrics, Beijing, People’s Republic of China; 8Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People’s Republic of China; 9Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People’s Republic of China, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jiangang LiuBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, People’s Republic of ChinaEmail jgliu@buaa.edu.cnXin NiDepartment of Otolaryngology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56 Nanlishilu, Beijing, People’s Republic of ChinaTel +86-139-1011-1999Email nixin@bch.com.cnObjective: To investigate the impacts of obstructive sleep apnea (OSA) on white matter (WM) integrity and cognitive functions of pediatric patients with different levels of OSA severity.Methods: Fifty-eight children with OSA and thirty-four healthy controls (HC) were recruited. All participants underwent diffusion tensor imaging (DTI) examination, polysomnography (PSG), and neurocognitive assessments. Patients were divided into mild OSA (MG) and moderate-severe OSA (SG) groups. WM integrity, PSG data, and neurocognitive assessment scores were compared among those groups.Results: For apnea hypopnea index (AHI), obstructive apnea hypopnea index (OAHI), arousal index, SpO2 nadir, and attention, SG was worse than both MG and HC with MG worse than HC. For baseline SpO2 and intelligence, SG was worse than both MG and HC with no significant difference between MG and HC. Impaired WM integrity was observed in bilateral anterior thalamic radiation, bilateral inferior fronto-occipital fasciculus, bilateral inferior longitudinal fasciculus, right superior longitudinal fasciculus, right hippocampus, left cingulate gyrus, right uncinate fasciculus, callosum forceps major, and callosum forceps minor only for SG than for HC. WM integrity was significantly correlated with OSA severity and neurocognitive assessment scores only for SG, but not for MG.Conclusion: Decreased baseline SpO2, WM impairment, and intelligence decline were all observed only for SG, but not for MG, implying an associated relationship among decreased SpO2, WM impairment and WM impairment. Thus, for SG, additional assessments of brain damage and cognitive function decline are needed for prognostic evaluation of OSA.Keywords: diffusion tensor imaging, obstructive sleep apnoea, children, tract-based spatial statistics, white matter

Object segmentation for 3-D point clouds plays a critical role in autonomous driving, robotic navigation, and other computer version applications. In object segmentation, all points are considered to be equal of importance in the literature. However, unequal cases exist and a segmentation boundary is mainly determined by neighbor points. To investigate point inequivalence, in this article, an unequal learning approach is proposed to integrate gene expression programming (GEP) and a deep neural network (DNN). GEP is designed to discover the inequivalent function, which measures the importance of different points according to the distances to the segmentation boundary. A cost sensitive learning method is improved to guide the DNN to obtain the loss of different points unequally with the discovered inequivalent function during model training. The experimental results reveal that point inequivalence with respect to boundary distance exists and is helpful to improve the accuracy of object segmentation.

BACKGROUND Research on predictors and risk of recurrence after suicide attempt from China is lacking. This study aims to identify risk factors and develop prediction models for recurrent suicidal behavior among suicide attempters using Cox proportional hazard (CPH) and machine learning methods. METHODS The prospective cohort study included 1103 suicide attempters with a maximum follow-up of 10 years from rural China. Baseline characteristics, collected by face-to-face interviews at least 1 month later after index suicide attempt, were used to predict recurrent suicidal behavior. CPH and 3 machine learning algorithms, namely, the least absolute shrinkage and selection operator, random survival forest, and gradient boosting decision tree, were used to construct prediction models. Model performance was accessed by concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve (AUC) value for discrimination, and time-dependent calibration curve along with Brier score for calibration. RESULTS The median follow-up time was 7.79 years, and 49 suicide attempters had recurrent suicidal behavior during the study period. Four models achieved comparably good discrimination and calibration performance, with all C-indexes larger than 0.70, AUC values larger than 0.65, and Brier scores smaller than 0.06. Mental disorder emerged as the most important predictor across all four models. Suicide attempters with mental disorders had a 3 times higher risk of recurrence than those without. History of suicide attempt (HR = 2.84, 95% CI: 1.34-6.02), unstable marital status (HR = 2.81, 95% CI: 1.38-5.71), and older age (HR = 1.51, 95% CI: 1.14-2.01) were also identified as independent predictors of recurrent suicidal behavior by CPH model. CONCLUSIONS We developed four models to predict recurrent suicidal behavior with comparable good prediction performance. Our findings potentially provided benefits in screening vulnerable individuals on a more precise scale.

BACKGROUND: Accurate assessment of lymph node status in gastric cancer (GC) patients can help to select appropriate treatment strategies for GC, but the diagnostic accuracy of conventional methods needs to be improved. The aim of this study was to investigate the predictive value of preoperative hemoglobin and albumin levels and lymphocyte and platelet counts (HALP) on lymph node status in GC patients and to construct a risk prediction model. METHODS: This study retrospectively analyzed the clinicopathological characteristics of 349 patients with GC who underwent radical gastrectomy, among which 250 patients were recruited in the training cohort and 99 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified as independent variables in multivariate logistic regression analysis, which were then incorporated and presented in a nomogram. Receiver operating characteristic (ROC) curves, Calibration curve and decision curve analysis (DCA) curves were used to evaluate the discrimination, prediction accuracy and clinical effectiveness of the model RESULTS: Multifactorial logistic regression analysis showed that alcohol use (OR =2.203, P=0.036), Depth of invasion (OR =7.756, P

Hypertension is a common comorbid condition in patients with diabetes. The pathogenesis of hypertension in diabetes has not been fully clarified. Primary tubular hyperreabsorption may contribute, which may be counteracted by glomerular hyperfiltration in the early diabetic kidney. In this study, we hypothesize that in early diabetes, the macula densa neuronal nitric oxide synthase (NOS1)-derived nitric oxide (NO) production is enhanced, which blunts tubuloglomerular feedback (TGF) response, promotes glomerular hyperfiltration, and maintains normal blood pressure; conversely, insufficient NO generation by the macula densa induces hypertension by lowering glomerular filtration rate and thus inhibiting natriuresis. To test this hypothesis, we examined the changes of macula densa NOS1 expression and phosphorylation as well as NO production, TGF response, glomerular filtration rate, sodium excretion, and blood pressure in a murine model of leptin receptor-deficient (db/db) diabetes with or without macula densa-specific NOS1 deletion. We found that db/db mice presented reduced fractional renal sodium excretion and only a small increase in blood pressure, associated with upregulated expression and activity of macula densa NOS1, inhibited TGF response, and glomerular hyperfiltration. Genetic knockout of macula densa NOS1 restored the TGF response and attenuated glomerular hyperfiltration in db/db mice but also further reduced fractional renal sodium excretion and substantially increased blood pressure. In conclusion, the present study demonstrates that in the early stage of leptin receptor-deficient diabetes, the upregulation of macula densa NOS1 inhibits TGF and increases glomerular filtration rate, which counteracts renal sodium retention and limits the rise in blood pressure.

OBJECTIVE: Although His bundle pacing (HBP) has shown an improved therapeutic effect than conventional pacing in terms of reducing cardiovascular mortality, the basic characteristics of HBP has not been defined systematically. Therefore, a systematical review and meta-analysis on the HBP characteristics can be timely and favorable. In this study, we aimed to clarify the electrocardiogram and echocardiographic characteristics of the pre- and post-operation of HBP. METHODS: Patients with HBP were exclusively included in this study. By evaluating their electrocardiogram characteristics, echocardiographic parameters, and cardiac function, the therapeutic effect of HBP was assessed. RESULTS: A total of 23 studies were included in the analysis. The overall implant success rate for HBP was 83.64%. After HBP treatment, the paced QRS duration dropped from 147.73±19.46 ms to 116.84±17.32 ms (p

Stereo matching is the key technology in stereo vision. Given a pair of rectified images, stereo matching determines correspondences between the pair images and estimate depth by obtaining disparity between corresponding pixels. The current work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task with an end-to-end frame based on convolutional neural networks (CNNs). However, 3D CNN puts a great burden on memory storage and computation, which further leads to the significantly increased computation time. To alleviate this issue, atrous convolution was proposed to reduce the number of convolutional operations via a relatively sparse receptive field. However, this sparse receptive field makes it difficult to find reliable corresponding points in fuzzy areas, e.g., occluded areas and untextured areas, owing to the loss of rich contextual information. To address this problem, we propose the Group-based Atrous Convolution Spatial Pyramid Pooling (GASPP) to robustly segment objects at multiple scales with affordable computing resources. The main feature of the GASPP module is to set convolutional layers with continuous dilation rate in each group, so that it can reduce the impact of holes introduced by atrous convolution on network performance. Moreover, we introduce a tailored cascade cost volume in a pyramid form to reduce memory, so as to meet real-time performance. The group-based atrous convolution stereo matching network is evaluated on the street scene benchmark KITTI 2015 and Scene Flow and achieves state-of-the-art performance.

1. Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. 2. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. 3. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. 4. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs.

Atrial fibrillation (AF) leads to a high risk of recurrent stroke, and the insertable cardiac monitor (ICM), as a new kind of electrocardiographic monitoring device, has been proven to enhance the recognition rate of AF. The aim of this systematic review was to evaluate the efficacy and safety of the ICM use in AF detection of patients with stroke. We pooled 1233 patients from three randomized controlled trials (RCTs). The detection rate of AF was superior in the ICM group to that in the control group at 6 months (risk ratio [RR], 4.63; P

In the present work, the impact of ZrO2 gate dielectric thickness on the electrical performance of TiO2 thin film transistors (TFTs) is systematically investigated. Exhaustive electrical measuremen...

Digital pedagogy was used as a contingency plan to achieve teaching and learning during the COVID-19 pandemic. This study aimed to observe digital pedagogy in lecturers’ online teaching practices and instructional supervisors’ evaluations of teaching outcomes. A sample of 32 lecturers and instructional supervisors from three Chinese universities participated in the in-depth interviews. The thematic analysis approach of grounded theory was used in data analysis. The results show that pedagogical dimensions were extended. Besides technology, innovative pedagogy, and learning environments, this study added two constructs: sustained support and cyber safety. Collaboration is required at different levels;meanwhile, cyber safety is highlighted to ensure healthy digital learning environments. [ABSTRACT FROM AUTHOR] Copyright of Innovations in Education & Teaching International 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.)

To compare survival among patients with head and neck cancer before and after implementing a weekly multidisciplinary clinic and case conference.A retrospective cohort study with chart review was conducted of 3081 patients (1431 preimplementation, 1650 postimplementation) diagnosed with stage I-IVB tumors in the oral cavity, oropharynx, hypopharynx, nasopharynx, or larynx. Pre- and postimplementation differences in overall and disease-specific survival 1, 2, and 3 years after diagnosis were assessed with unadjusted Kaplan-Meier curves and multivariable Cox proportional hazard regression models adjusted for demographic characteristics, comorbidity burden, smoking status, tumor site and stage, p16 status for oropharyngeal squamous cell cancer, and initial treatment modality.Patients less commonly presented with oropharyngeal squamous cell cancer and advanced tumors (III-IVB) and received primary treatment with surgery alone or with adjuvant therapy preimplementation than postimplementation. Overall survival at 3 years was 77.1% and 79.9% (A multidisciplinary clinic and case conference were associated with improved outcomes among patients with head and neck cancer, especially those with advanced tumors.All patients with head and neck cancer should receive multidisciplinary team management, especially those with advanced tumors.

Summary Due to the nonrenewable nature of coal and uncompetitive market value, developing advanced FTS techniques for industrialization remains one of the research frontiers in heterogeneous catalysis. In this review, we present a brief introduction of the reaction pathways that directly convert syngas to olefins/aromatics via Fischer-Tropsch synthesis. Based on the mechanism of olefin formation, four key factors controlling the nature of catalyst functions were reviewed, including active metal/phase, transition metal promoter, alkali promoter, and catalyst support, for improving olefins or aromatics selectivity, while the photocatalytic form as a novel reaction system is also introduced. In the last part, we discussed the challenges in improving product selectivity and catalyst stability, the strategy in catalyst design and catalytic system from a new perspective, the method for understanding the relationship between catalyst structure and catalytic performance, and the potential industrialization for FTO/FTA.

Edge Artificial Intelligence (AI) is a timely complement of cloud-based AI. By introducing intelligence to the edge, it alleviates privacy concerns of streaming and storing data to the cloud, enables real-time operations where milliseconds matter, and brings AI services to remote areas with poor networking infrastructures. Security is a significant problem in Edge AI applications such as self-driving cars and intelligent healthcare. Since the edge devices are empowered to process data and take actions, attacking and compromising them can cause serious damage. However, the wide deployment of computationally limited devices in edge environments and the increasing happening of side-channel (or leakage) attacks pose critical challenges to security. This article thereby aims to enhance the security for Edge AI by designing and developing lightweight and leakage-resilient authenticated key exchange (LRAKE) protocols. Compared with available LRAKE protocols, the proposed protocols in this article can be effortless applied in some mainstreaming security and communication standards. Moreover, this article realizes prototypes and presents implementation details; and a use case of applying the proposed protocol in Bluetooth 5.0 is illustrated. The theoretical design and implementation details will provide a guidance of applying the LRAKE protocols in Edge AI applications.

Background We aimed to explore the risk profiles attributable to psychosocial and behavioural problems during the coronavirus disease 2019 pandemic. To this end, we created a risk-prediction nomogram model. Methods A national multicentre study was conducted through an online questionnaire involving 12,186 children (6-11 years old) and adolescents (12-16 years old). Respondents’ psychosocial and behavioural functioning were assessed using the Achenbach Child Behaviour Checklist (CBCL). Data were analysed using STATA software and R-language. Results The positive detection rate of psychological problems within Wuhan was greater than that outside Wuhan for schizoid (P = 0.005), and depression (P = 0.030) in children, and for somatic complaints (P = 0.048), immaturity (P = 0.023), and delinquent behaviour (P = 0.046) in adolescents. After graded multivariable adjustment, seven factors associated with psychological problems in children and adolescents outside Wuhan were parent-child conflict (odds ratio (OR): 4.94, 95% confidence interval (95% CI): 4.27-5.72), sleep problems (OR: 4.05, 95% CI: 3.77-4.36), online study time (OR: 0.41, 95% CI: 0.37-0.47), physical activity time (OR: 0.510, 95% CI: 0.44-0.59), number of close friends (OR: 0.51, 95% CI: 0.44-0.6), time spent playing videogames (OR: 2.26, 95% CI: 1.90-2.69) and eating disorders (OR: 2.71, 95% CI: 2.35-3.11) (all P Limitation A cross-sectional study and self-reported measures. Conclusions Besides the four significant risk factors within and outside Wuhan, the three additional factors outside Wuhan deserve special attention. The prediction nomogram models constructed in this study have important clinical and public health implications for psychosocial and behavioural assessment.