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OBJECTIVE To investigate the effectiveness of a circulating tumor cell (CTC)-based classifier in stratifying stage IB lung adenocarcinoma (LUAD). SUMMARY BACKGROUND DATA Stage IB LUADs have an approximately 70% 5-year survival rate. The clinical application of adjuvant chemotherapy (ACT) is controversial due to inconsistent results in a series of trials and few useful guide biomarkers. Thus, there is a pressing need for robust biomarkers to stratify stage IB patients to define which group would most likely benefit from ACT. METHODS 212 stage IB LUAD patients were enrolled and were divided into three independent cohorts. The aptamer-modified NanoVelcro system was used to enrich the CTCs. RESULTS A cutoff of < 4 or ≥ 4 CTCs as the optimal prognostic threshold for stage IB LUAD was generated to stratify the patients in a 70-patient cohort into low-risk and high-risk groups. Patients with ≥ 4 CTCs in the training cohort had shorter progression-free survival (PFS, P < 0.0001) and overall survival (OS, P < 0.0001) than patients with < 4 CTCs. CTC number remained the strongest predictor of PFS and OS even in a multivariate analysis including other clinicopathological parameters. Furthermore, a nomogram based on the CTC count was developed to predict the 3-year and 5-year survival in the training cohort and performed well in the other two validation cohorts (C-index: 0.862, 0.853 and 0.877). CONCLUSION The presence of ≥ 4 CTCs can define a high-risk subgroup, providing a new strategy to make optimal clinical decisions for stage IB LUAD.

Few-shot learning, aiming to learn novel concepts from one or a few labeled examples, is an interesting and very challenging problem with many practical advantages. Existing few-shot methods usually utilize data of the same classes to train the feature embedding module and in a row, which is unable to learn adapting to new tasks. Besides, traditional few-shot models fail to take advantage of the valuable relations of the support-query pairs, leading to performance degradation. In this article, we propose a transductive relation-propagation graph neural network (GNN) with a decoupling training strategy (TRPN-D) to explicitly model and propagate such relations across support-query pairs, and empower the few-shot module the ability of transferring past knowledge to new tasks via the decoupling training. Our few-shot module, namely TRPN, treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intraclass commonality and interclass uniqueness. Through relation propagation, the model could generate the discriminative relation embeddings for support-query pairs. To the best of our knowledge, this is the first work that decouples the training of the embedding network and the few-shot graph module with different tasks, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.

Traditionally, domestic induction heating (IH) is designed to heat the ferromagnetic (FM) pan. However, it is incompetent to heat the non-ferromagnetic (NFM) pan since the small equivalent resistance of the pan will easily result in the overcurrent on the inverter. Inspired by the compensation network in the wireless power transfer (WPT), the series-parallel resonance (SPR) network with a relay switch is newly integrated into the IH technology to develop a dual-resonant topology-reconfigurable inverter for all-metal IH. The proposed system can be purposely configured to a half-bridge series resonant inverter with the pulse width modulation (PWM) to heat the FM pan, or a full-bridge SPR-based inverter with the phase shift control (PSC) to heat the NFM pan, respectively. Moreover, the output-input current gain of the SPR network equals the heating-coil quality factor, thereby readily eliminate the overcurrent issue of the inverter. For exemplification, a 1 kW prototype has been built with system efficiencies of 94.32% and 91.05% for heating FM and NFM pans, respectively. Finally, the proposed system can be evolved to achieve automatic load detection and impedance matching to ensure uninterrupted resonance. Both calculated and measured results are given to validate the flexibility and feasibility of the proposed system.

A Cloud-Assisted Reliable Trust Computing Scheme(CRTC) is proposed to identify the trust of ISDs and ensure secure data collection in IoT. The CRTC scheme includes the following parts: (a) A reliable approach for identifying the trust of ISDs is proposed in which ISDs use a fitting function to fit forwarding packets information to Inspection Information (II) with a small amount of data and then send II to Inspection Nodes (INs), effectively reducing inspection information cost of routing. Then, an effective trust reasoning approach is given to compute the trust of ISDs based on the data and II collected by UAV. Last, the aggregators are selected from high-trust ISDs to ensure secure data collection. (b) A trajectory optimization algorithm for UAV is proposed to collect data as well as II from aggregator and INs. Theoretical analysis shows that the proposed URTC scheme is superior to previous strategies.

Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively. In this paper, we compare the recognition performance and robustness of two multimodal emotion recognition models: deep canonical correlation analysis (DCCA) and bimodal deep autoencoder (BDAE). The contributions of this paper are three folds: 1) We propose two methods for extending the original DCCA model for multimodal fusion: weighted sum fusion and attention-based fusion. 2) We systemically compare the performance of DCCA, BDAE, and traditional approaches on five multimodal datasets. 3) We investigate the robustness of DCCA, BDAE, and traditional approaches on SEED-V and DREAMER datasets under two conditions: adding noises to multimodal features and replacing EEG features with noises. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition results on all five datasets: 94.6% on the SEED dataset, 87.5% on the SEED-IV dataset, 84.3% and 85.6% on the DEAP dataset, 85.3% on the SEED-V dataset, and 89.0%, 90.6%, and 90.7% on the DREAMER dataset. Meanwhile, DCCA has greater robustness when adding various amounts of noises to the SEED-V and DREAMER datasets. By visualizing features before and after DCCA transformation on the SEED-V dataset, we find that the transformed features are more homogeneous and discriminative across emotions.

Contemporarily, fast development of computing, communication and storage technology has revolutionized the way that various data-based applications reach massive data from underlying sensor networks. However, such process also raises two challenging but critical issues: trustworthy and privacy issue for data collectors. Therefore, this paper pro-poses a novel system, which is designed over the distributed mobile edge network to sufficiently exploit advantages of blockchain and differential privacy to collect trustworthy data and protect privacy for data collectors. Firstly, to im-prove trustworthy of data collections, a new consensus mechanism is proposed for blockchain-based data collection structure, which comprehensively incorporates trustworthy, collection contribution, and throughput together to prefer data collectors for the next block. Secondly, with assistance of fully trusted devices, a verifiable trustworthy evaluation strategy is designed to accurately compute the trustworthy for data collec-tors. Thirdly, we enforce differential privacy on the data stored in global blockchain maintained by the cloud server to protect privacy for data collectors without influencing data availability. Finally, both theoretical analyses and experi-mental results prove that the proposed system comprehen-sively improves performance of data collections in distributed network without adding any additional cost for cloud server, compared to other schemes.

Immune checkpoint inhibitors are generally contraindicated for post-transplant patients. However, we report two patients with metastatic hepatocellular carcinoma (HCC) treated with camrelizumab (SHR-1210), an anti-programmed cell death 1 (PD-1) agent, after liver transplant. Before undergoing immunotherapy, both patients underwent liver allograft biopsy and obtained negative PD-L1 expression in tumor and liver graft specimens by immunohistochemistry. Then, camrelizumab (200 mg) was administered once every 3 weeks. During immunotherapy, the targeted therapy was continued, and the immunosuppression regimen was adjusted to a low-dose level. No graft rejection or other severe adverse reactions were observed. The disease remained stable (SD, mRECIST) for 3 months in one patient and 10 months in the other. Therefore, camrelizumab may have safety and potential benefits in advanced HCC after liver transplant.

BACKGROUND: To explore the development of central nervous system (CNS) symptoms and clinical application in predicting the clinical outcomes of SARS-COV-2 patients. METHODS: A retrospective cohort study was performed on the hospitalized patients with SARS-COV-2 recruited from four hospitals in Hubei Province, China from 18 January to 10 March 2020. The patients with CNS symptoms were determined. Data regarding clinical symptoms and laboratory tests were collected from medical records. RESULTS: Of 1268 patients studied, 162 (12.8%) had CNS symptoms, manifested as unconsciousness (71, 5.6%), coma (69, 5.4%), dysphoria (50, 3.9%), somnolence (34, 2.7%) and convulsion (3, 0.2%), which were observed at median of 14 (interquartile range 9-18) days after symptom onset and significantly associated with older age (OR = 5.71, 95% confidence interval [CI] 2.78-11.73), male (OR = 1.73, 95% CI 1.22-2.47) and preexisting hypertension (OR = 1.78, 95% CI 1.23-2.57). The presence of CNS symptoms could be predicted by abnormal laboratory tests across various clinical stages, including by lymphocyte counts of

The explosive growth of modern web-scale applications has made cost-effectiveness a primary design goal for their underlying databases. As a backbone of modern databases, LSM-tree based key–value stores (LSM store) face limited storage options. They are either designed for local storage that is relatively small, expensive, and fast or for cloud storage that offers larger capacities at reduced costs but slower. Designing an LSM store by integrating local storage with cloud storage services is a promising way to balance the cost and performance. However, such design faces challenges such as data reorganization, metadata overhead, and reliability issues. In this article, we propose RocksMash , a fast and efficient LSM store that uses local storage to store frequently accessed data and metadata while using cloud to hold the rest of the data to achieve cost-effectiveness. To improve metadata space-efficiency and read performance, RocksMash uses an LSM-aware persistent cache that stores metadata in a space-efficient way and stores popular data blocks by using compaction-aware layouts. Moreover, RocksMash uses an extended write-ahead log for fast parallel data recovery. We implemented RocksMash by embedding these designs into RocksDB. The evaluation results show that RocksMash improves the performance by up to 1.7 \( \times \) compared to the state-of-the-art schemes and delivers high reliability, cost-effectiveness, and fast recovery.

Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multistage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intraclass variations while maintaining interclass separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.

Post-treatment recurrence remains a challenge for the global control of tuberculosis (TB). This study investigated longitudinal data on pulmonary TB recurrence rates and risk factors for recurrence among successfully treated smear-positive tuberculosis cases in China.Between 1st January 2009 and 31st December 2016 we evaluated 33 441 treatment-naïve patients diagnosed with sputum-smear-positive, non-multidrug-resistant TB in Hangzhou, China. We included the data of 9828 patients with TB who were treated successfully.A total of 4.9% of the cases were recurrent (479/9828), identified within a median observation period lasting 1565 days. Altogether, 51.1% (245/479) of the recurrences occurred within 1 year. The cumulative 2- and 5-year recurrence rates were 3.90% (95% confidence interval (CI) 3.3-4.5%) and 5.4% (95%CI 4.8-6.0%), respectively. Prolonged treatment (over 7 months) occurred in 64.7% (6363/9828), with a median treatment duration of 242 days (interquartile range 195-348 days). Male sex (adjusted hazard ratio (aHR) (95%CI) 1.61 (1.30-2.00), p 0.001), age 60 years old or older (aHR (95%CI) 2.03 (1.70-2.44), p 0.001), pulmonary cavity (aHR (95%CI) 1.51 (1.25-1.82), p 0.001) and sputum positivity at 2 months (aHR (95%CI) 1.39 (1.05-1.81), p 0.02) all increased the risk of TB recurrence. Prolonged treatment was associated with reduced TB recurrence (aHR (95%CI) 0.73 (0.61-0.88), p 0.001).Recurrence remains a problem for successfully treated patients with sputum-smear-positive pulmonary TB, especially those with independent risk factors. Further analysis of prolonged treatment is required.

Understanding and forecasting mobility patterns and travel demand are fundamental and critical to efficient transport infrastructure planning and service operation. However, most existing studies focused on deterministic demand estimation/prediction/analytics. Differently, this study provides confidence interval based demand forecasting, which can help transport planning and operation authorities to better accommodate demand uncertainty/variability. The proposed Origin-Destination (OD) demand prediction approach well captures and utilizes the correlations among spatial and temporal information. In particular, the proposed Probabilistic Graph Convolution Model (PGCM) consists of two components: (i) a prediction module based on Graph Convolution Network and combined with the gated mechanism to predict OD demand by utilizing spatio-temporal relations; (ii) a Bayesian-based approximation module to measure the confidence interval of demand prediction by evaluating the graph-based model uncertainty. We use a large-scale real-world public transit dataset from the Greater Sydney area to test and evaluate the proposed approach. The experimental results demonstrate that the proposed method is capable of capturing the spatial-temporal correlations for more robust demand prediction against several established tools in the literature.

Most current part libraries are created and deployed in specific usage environments or CAD platforms, which inevitably brings obstacles in the share and exchange for part information. To reduce repeated development and provide a uniform interface for designers in different sites, a 3D part library in cloud scheme is presented in this paper. Host programs with predetermined specification can access the part data through an adapter according to their customized requests with uniform interfaces, which constructs a ubiquitous service. To realize host independence, the part models are created in a native ACIS modeler, and then they are converted into 3D files in various formats for practical needs, finally these files are imported into CAD systems or other platforms in real designs. The whole framework can be divided into three components, namely, PLS(Part Library Service) provider, PLS adapters and hosts. PLS provider is the kernel of 3D data access service in cloud scheme, while PLS adapters serve as the bridges that connect PLS provider and hosts, and the PLS can be grafted on various applications including current mainstream CAD systems as a plug-in module or run on the websites or even mobile terminals. The PLS provider is deployed and maintained on cloud and users can acquire remote part information within a local ongoing project. In the detailed construction of this part library, diversiform knowledge for part parameters and structures is implanted to define the geometry and rule constraints in the 3D modeling, with which the backstage has the ability of conveniently editing the information in the part library for better upgrade and contrapuntally services. The concept has been implemented within a PaaS framework to provide the ubiquitous 3D part data access, which has been successfully applied in a large number of manufacturing enterprises, and accumulates considerable practical cases.

An Unmanned Aerial Vehicle (UAV) assisted Trustworthy Code Dissemination (UTCD) framework in 5G-enabled intelligent IIoT systems is proposed to select credible Mobile Vehicles (MVs) to disseminate code with opportunistic routing style. In the UTCD framework, a verifiable trust evaluation scheme is proposed to identify the trust of the MVs by sending the UAV to collect the Code Wait to be Verified (CWV) directly from the selected devices (bedrock devices) as evidence. First, a bedrock devices selection scheme is presented by selecting as few bedrock devices as possible to collect as much the CWV. Then, based on the CWV, a complete trust evaluation scheme is proposed to obtain the trust of MVs. Furthermore, a UAV trajectory optimization algorithm is proposed to obtain as much CWV as possible. Finally, comprehensive experiments demonstrate that the proposed scheme out-performs the existing schemes in terms of the efficiency and security of code dissemination.

This paper proposes and implements a novel wireless power and drive transfer (WPDT) system using orthogonal bipolar couplers and separately excited modulation (SEM). Specifically, two bipolar coils are orthogonally integrated and naturally decoupled to form a compact magnetic coupler which will be utilized in the transmitter and receiver, respectively. The key is to employ the SEM to alternatively energize bipolar coils in the transmitter while self-driven circuits in the receiver can directly extract the drive information to control receiver-side switches. Consequently, the concurrent wireless power transfer (WPT) and wireless drive transfer (WDT) can be skillfully realized via the shared transfer channel at the single resonant frequency. Moreover, the proposed system can effortlessly achieve the wireless DC motor drive with the capability of bidirectional motion, or the wireless AC motor drive with the variable-voltage variable-frequency (VVVF) control. Besides driving the DC and AC motors, a 200-W prototype is constructed with the transmission efficiency of 89.5 % over the transfer distance of 100 mm for a resistive load of 40 . Both theoretical and experimental results are provided to validate the feasibility and flexibility of the proposed system.

Our previous study reported that clinical features, including the lateral/ventral tongue and non-homogeneous lesions, were associated with increased risk of malignant changes in cytological samples from oral potentially malignant disorders (OPMDs). This cross-sectional study aimed to evaluate the frequency and risk of DNA aneuploidy in the series of 748 patients with OPMD. The cut-off value of aneuploidy was defined as DNA index ≥3.5. We found that the frequency of DNA aneuploidy was higher in OPMD patients >60 years old, and in those with lateral/ventral tongue sites, non-homogeneous lesions, and high-grade dysplasia, than in control group (P 60 years old (1.69-fold; P = 0.022), in those with lateral/ventral tongue sites (2.35-fold; P

Due to the existence of multiple magnetization states (MSs) of the low coercive force (LCF) PMs, the losses minimization of variable flux memory machine (VFMM) under different MSs unable obtained simultaneously. This paper proposes a loss-reduction-oriented (LRO) optimization methodology for overall efficiency improvement in VFMM. Firstly, a recently developed hybrid-magnetic-circuit (HMC) VFMM is selected as a design example to investigate the proposed optimization methodology. Subsequently, the architecture and overall flowchart of the proposed LRO optimization methodology are introduced and given, which includes two rounds of optimization, namely dual-operating-mode rough optimization (DOM-RO) and multiple-operating-mode elaborate optimization (MOM-EO). Besides, a computationally efficient multi-mode field-circuit coupled (MMFC) model is established in finite element (FE) software to account for multiple operating modes within two simple case studies. On this basis, the proposed optimization methodology is applied to the HMC-VFMM, in which the LRO objective function is defined in terms of the time proportions of various MSs in an overall driving cycle. According to the optimization results, the electromagnetic characteristics of the machine are investigated, especially the machine loss and its efficiency maps under different MSs are analyzed. Finally, a prototype is manufactured and tested to verify the validity of the proposed optimization strategy.

Jia-Wei Liu, Yan Tan, Tian Chen, Wei Liu, Yue-Tong Qian, Dong-Lai Ma Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Skin and Immune Diseases, Beijing, 100730, People’s Republic of ChinaCorrespondence: Dong-Lai MaDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Skin and Immune Diseases, Beijing, 100730, People’s Republic of ChinaTel +86-10-69151543Fax +86-10-69151502Email mdonglai@sohu.comBackground: Vitiligo can cause disfiguration, impair the social function of the patients and induce physiological burdens. However, limited research about the health-related quality of life has been conducted in vitiligo patients’ sleeping conditions.Objective: To evaluate the prevalence, severity, and risk factors of insomnia in vitiligo patients.Methods: This case–control study was performed in March 2021. An online survey questionnaire including baseline information and the sleep-related instrument was sent to 762 vitiligo patients. The vitiligo-related evaluation was conducted by online video interview. According to whether having insomnia or not, patients were grouped and compared their clinical and demographic characteristics. The logistic regression model was conducted to analyze the risk factors for insomnia.Results: A total of 409 patients were included. About 49.9% of patients (204/409) experienced insomnia. About 55.9% (114/204) of the insomnia in vitiligo patients was adjustment sleep disorder caused by vitiligo. Development, aggravation, or recurrence of vitiligo were deemed as the first reason for insomnia in 71.1% of the sample (81/114). There were significant differences in age (32.1± 4.1 vs 27.9± 4.2 years, P < 0.001), the percentage of female (62.8% vs 49.3%, P=0.006) and working in the urban areas (77.0% vs 66.3%, P = 0.017), vitiligo in face and neck (67.2% vs 48.8%, P < 0.001), progression in vitiligo (65.7% vs 49.3%, P=0.001), oral corticosteroids (25.0% vs 16.6%, P=0.036) and depression (5.4% vs 0.5%, P = 0.003) between groups. After adjusting for gender, age and comorbidity, the multivariate logistic regression revealed that vitiligo in face and neck (OR=2.62; P=0.032), progression in vitiligo (OR=2.50; P=0.002), and oral corticosteroids (OR=2.71; P=0.021) remained risk factors for insomnia in vitiligo patients.Conclusion: Insomnia is prevalent in vitiligo patients. Dermatologists should identify this condition carefully, especially humanistic factors in social life, and perform individualized “non-drug” treatment.Keywords: vitiligo, sleep disorder, insomnia, risk factors, insomnia severity index, psychodermatology

BACKGROUND: While sex-related differences in transplant outcomes have been well characterized amongst adults, there are no sex-specific pediatric heart transplant studies over the last decade and none evaluating waitlist outcomes. In a contemporary cohort of children undergoing heart transplantation in the United States, this analysis was performed to determine if there were sex disparities in waitlist and/or post-transplant outcomes. METHODS: Retrospective review of Scientific Registry of Transplant Recipients database from December 16, 2011 to February 28, 2019 to compare male and female children after listing and after transplant. Demographic, clinical characteristics and outcomes were compared unadjusted and after 1:1 propensity matching for selected covariates. RESULTS: Of 4089 patients, 2299 (56%) were males. At listing, males were more likely to be older, have congenital heart disease (58% vs 48%), renal dysfunction (49% vs 44%) and implantable cardioverter defibrillator (9% vs 7%). At transplant, males were more likely to have renal (42 % vs 35%) and liver dysfunction (13% vs 10%), PRA >10% (29% vs 22%) and ischemic time >3.5 hours (p

Reconfigurable wireless network can flexibly provide efficient spectrum access service and keep stable operation in highly dynamic environment. In this paper, a primary-prioritized recurrent deep reinforcement learning algorithm for dynamic spectrum access based on cognitive radio (CR) technology is proposed. The spectrum Markov state is modeled to capture the evolution behavior to achieve the priority queuing of the primary users and the secondary users. According to the spectrum access strategies of the secondary users under different optimal criteria, we can obtain the best tradeoff benefits of spectrum access fairness and throughput. Furthermore, we proposed a learning-based algorithm for dynamic spectrum access, which allows the secondary users to modify their parameters to select the optimal access policy to maximize network throughput utilization. The Dueling Deep Q-Network (Dueling DQN) with prioritized experience replay combined with recurrent neural network is used to improve the convergence speed. Extensive experimental results demonstrate that the proposed RDRL scheme outperforms the existing Dueling DQN and DQN schemes in terms of convergence speed and channel throughput.

This paper proposes and implements a new wireless power and drive transfer (WPDT) system using wireless switches. A wireless piping network is conceived especially for pipeline transportations and in-pipe robots desiring wireless motor drive. Also, a multi-frequency resonant network is newly explored to generate the variable-frequency-modulated magnetic fields where the wireless switches can extract the drive information directly. Differing from single-frequency T-circuits, this multi-frequency network can offer different resonant frequencies which are not only coupling-independent but also large-scale load-independent. Importantly, it can realize both the power self-equalization and zero-phase-angle operation in transmitter and receiver circuits. To conduct the conceived WPDT, the magnetic resonant modulation is used to control the wireless switches automatically at the receiver side, thus flexibly enabling the bidirectional motions of DC motor and the variable-frequency speed control of AC induction motor. The experimental system efficiency can reach 83.5% based on a 200-W prototype at a 130-mm distance. Both theoretical analysis and hardware experimentation are given to verify the feasibility of proposed WPDT system for wireless piping networks.

In this work, we establish a comprehensive spectral model to realize effective design of high-power ytterbium-Raman fiber amplifiers (YRFAs) employing different types of Raman-pump lasers, and propose an intuitive approach for the optimization of high-power YRFAs. Quantitative comparisons with the experimental results are given in two high-power YRFAs employing Raman-pump lasers with different spectral linewidth. The consistency of measured and simulated power ratios and spectral properties of different components in the two YRFAs confirms the fidelity of the spectral model. Further theoretical analysis reveals that the impact of Raman-pump lasers on YRFAs is mainly determined by the relative intensity noise (RIN) of Raman-pump lasers up to the cutoff frequency of the pump RIN transfer function of the YRFA. Notably, the RIN of Raman-pump lasers could provide a well reference for the superior design of Raman-pump lasers in high-power YRFAs.

The crowdsourcing scheme emerges as a promising solution for data-based application in the Internet of Things (IoT) network by dividing the large-scale complex sensing tasks into simple micro sensing tasks. AI technologies have been widely applied in crowdsourcing applications for IoT security. However, there are still several issues to be stressed. There exist malicious participants aiming at gaining unjust payment. A trust evaluation mechanism can effectively filter the attackers. Nevertheless, the existing trust evaluation mechanisms cannot preclude co-cheating and overlook the conflicts with the privacy exposure of participants. A novel Privacy-protected Intelligent Crowdsourcing scheme based on Reinforcement Learning (PICRL) is proposed. PICRL optimizes the utility of the system considering the data amount, data quality, and costs at the same time. The main innovations of the PICRL are as follows. First, the quality is guaranteed by an effective trust evaluation mechanism. The proposed trust evaluation consists of three parts: privacy trust, crowd trust, and hybrid active trust. Second, the trust evaluation can effectively prevent co-cheating and provide personal privacy exposure choice for the participants. Third, PICRL maximizes the utility based on evaluated trust utilizing the reinforcement method Q-learning without knowing the specific sensing model, which aims at maximizing the cumulated reward by the selection of state–action pair. The effectiveness of the proposed PICRL is verified by the extensive simulation experiments.

Renewable energy generation, as part of the global effort to mitigate climate change, will play a central role in reducing greenhouse gas emissions and achieving China’s goal of carbon emissions peak before 2030 and carbon neutral before 2060. However, the impact of carbon quota auctions on renewable energy generation has not been sufficiently discussed. The main purpose of this study is to investigate whether China can rely on quota auctions to increase renewable energy generation in the short term, and to demonstrate which is more effective in promoting renewable energy development, policy enforcement or auction constraints? The improved neo-trans-log production model, the multi-objective linear programming model and the dispatch heuristic were used to predict additional emission reduction cost, optimized power mix with different auctioning rates, with economic development, technological progress and the unique characteristics of China's power generation industry being taken into consideration. The results show that the auctioning rate will have little influence on the optimized energy production structure, especially on the share of renewable energy resources; when the total on-grid electricity generation ranges from 7625‒7926 billion kW h and the auctioning rate ranges from 0%‒5%, policy enforcement will influence the generation of renewable energy to a greater extent than auctioning in the near future.

As a key mission of the modern traffic management, crowd flow prediction (CFP) benefits in many tasks of intelligent transportation services. However, most existing techniques focus solely on forecasting entrance and exit flows of metro stations that do not provide enough useful knowledge for traffic management. In practical applications, managers desperately want to solve the problem of getting the potential passenger distributions to help authorities improve transport services, termed as crowd flow distribution (CFD) forecasts. Therefore, to improve the quality of transportation services, we proposed three spatiotemporal models to effectively address the network-wide CFD prediction problem based on the online latent space (OLS) strategy. Our models take into account the various trending patterns and climate influences, as well as the inherent similarities among different stations that are able to predict both CFD and entrance and exit flows precisely. In our online systems, a sequence of CFD snapshots is used as the training data. The latent attribute evolutions of different metro stations can be learned from the previous trend and do the next prediction based on the transition patterns. All the empirical results demonstrate that the three developed models outperform all the other state-of-the-art approaches on three large-scale real-world datasets.

Tumor-induced osteomalacia TIO is a rare paraneoplastic syndrome caused by excessive production of fibroblast growth factor 23 FGF23 by a tumor. Previous studies have revealed generalized mineralization defects and low areal bone mineral density aBMD in TIO. However, data on the bone microarchitecture in TIO are limited. In this study, we evaluated the microarchitecture in the peripheral distal radius and tibia and axial lumbar spine skeleton using high-resolution peripheral quantitative computed tomography HR-pQCT and trabecular bone score TBS and investigated related factors in a large cohort of Chinese patients with TIO. A total of 186 patients with TIO who had undergone dual-energy X-ray absorptiometry DXA or HR-pQCT scans were enrolled. Compared with age-, sex- and BMI-matched healthy controls, TIO patients n=113 had lower vBMD, damaged microstructure and reduced bone strength in the peripheral skeleton, especially at the tibia. The average TBS obtained from 173 patients was 1.15 ± 0.16. The proportion of patients with abnormal TBS

PURPOSE: To compare reliability and elucidate clinical application of magnetization-prepared rapid gradient-echo (MPRAGE) with 9-fold acceleration by using wave-controlled aliasing in parallel imaging (Wave-CAIPI 3 × 3) in comparison to conventional MPRAGE accelerated by using generalized autocalibrating partially parallel acquisition (GRAPPA) 2 × 1. METHODS: A total of 26 healthy volunteers and 33 patients were included in this study. Subjects were scanned with two MPRAGEs, GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 acquired in 5 min 21 s and 1 min 42 s, respectively, on a 3T MR scanner. Healthy volunteers underwent additional two MPRAGEs (CAIPI 3 × 3 and GRAPPA 3 × 3). The image quality of the four MPRAGEs was visually evaluated with a 5-point scale in healthy volunteers, and the SNR of four MPRAGEs was also calculated by measuring the phantom 10 times with each MPRAGE. Based on the results of the visual evaluation, voxel-based morphometry (VBM) analyses, including subfield analysis, were performed only for GRAPPA 2 × 1 and Wave-CAIPI 3 × 3. Correlation of segmentation results between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 was assessed. RESULTS: In visual evaluations, scores for MPRAGE GRAPPA 2 × 1 (mean rank: 4.00) were significantly better than those for Wave-CAIPI 3 × 3 (mean rank: 3.00), CAIPI 3 × 3 (mean rank: 1.83), and GRAPPA 3 × 3 (mean rank: 1.17), and scores for Wave-CAIPI 3×3 were significantly better than those for CAIPI 3 × 3 and GRAPPA 3 × 3. Image noise was evident at the center for additional MPRAGE CAIPI 3 × 3 and GRAPPA 3 × 3. The correlation of segmentation results between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 was higher than 0.85 in all VOIs except globus pallidus. Subfield analysis of hippocampus also showed a high correlation between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3. CONCLUSION: MPRAGE Wave-CAIPI 3 × 3 shows relatively better contrast, despite of its short scan time of 1 min 42 s. The volumes derived from automated segmentation of MPRAGE Wave-CAIPI are considered to be reliable measures.

Transmission lines are affected by aeolian vibration, sandstorms and acid rain, which cause structural damage and eventually lead to line breaking accidents. In this paper, the structural health monitoring technology of conductors is researched, and a structural monitoring system for conductors is designed. First, based on the structure of the ACSR (Aluminum Conductor Steel Reinforced) conductor and the dynamic equation, the relation between the natural frequencies and the structure of the conductor is obtained. Then, an online health monitoring system of the conductor structure is proposed, and key issues, such as the power supply solution of sensors, data preprocessing technology, multiple‐sensor synchronization method, are analyzed in detail. Finally, the structural health monitoring of overhead transmission lines designed in this paper is tested on 110‐kV experimental transmission lines. The experimental results show that from the system proposed the operation parameters of the conductors can be well obtained.

The inverse synthetic aperture radar (ISAR) imaging of the target above the rough surface based on the time-domain (TD) electromagnetic (EM) scattering algorithms is focused on in this paper. It effectively combines TD ray tracing (TDRT) method for the composite scene and radar imaging model. The scattering echoes for ISAR imaging are solved by TDRT that the TD mutual coupling between the target and rough surface can be considered by the ray bounce strategy. Compared with the frequency domain ray tracing (FDRT) and multilevel fast multi-pole method (MFMM), TDRT algorithm not only ensures the calculation accuracy, but also greatly reduces the calculation cost. Then, we generate the ISAR image of the target above the rough surface according to the echo matrix based on TDRT. In order to improve the imaging quality of the target, the non-linear mapping theory is presented to suppress the virtual image points and noisy points and it is beneficial to the future research of target recognition in the composite scene.

Damage to low-frequency structures caused by recent major earthquakes, such as the 2011 Great East Japan Earthquake, have emphasized the need to protect these structures from low-frequency ground motions. Previous studies have suggested that rate-independent linear damping (RILD) is a viable option for simultaneously reducing excessive displacement and floor response accelerations of low-frequency structures. The majority of previous studies on RILD have focused on the theoretical and mathematical aspects, while the physical implementation of RILD has not yet been extensively investigated. Therefore, this study proposes a novel method based on using a digital filter for causally and physically implementing RILD and examines its feasibility for the protection of low-frequency structures. A feasibility study based on a numerical analysis is conducted to identify the limitations of the proposed method.

In previous studies, researchers have used singing to treat respiratory function in patients with spinal cord injury. However, few studies have examined the way in which vocal training affects respiratory neural plasticity in patients with spinal cord injury. Vocal respiratory training (VRT) is a type of vocal muscle-related treatment that is often a component of music therapy (MT) and focuses on strengthening respiratory muscles and improving lung function. In this randomized controlled study, we analyzed the therapeutic effects of VRT on respiratory dysfunction at 3 months after cervical spinal cord injury. Of an initial group of 37 patients, 26 completed the music therapy intervention, which comprised five 30-minute sessions per week for 12 weeks. The intervention group (n = 13) received VRT training delivered by professional certified music therapists. The control group (n = 13) received respiratory physical therapy delivered by professional physical therapists. Compared with the control group, we observed a substantial increase in respiratory function in the intervention group after the 12-week intervention. Further, the nerve fiber bundles in the respiratory center in the medulla exhibited a trend towards increased diversification, with an increased number, path length, thickness, and density of nerve fiber bundles. These findings provide strong evidence for the effect of music therapeutic VRT on neural plasticity. This study was approved by the Ethics Committee of China Rehabilitation Research Center (approval No. 2020-013-1) on April 1, 2020, and was registered with the Chinese Clinical Trial Registry (registration No. ChiCTR2000037871) on September 2, 2020.

Background CTCs play a critical role in the diagnosis and prognosis of liver cancer. However, there are few studies on whether different types of CTCs can predict the prognosis in patients with HCC following LT. Methods Retrospective data including CTCs detected by the CanPatrolTM platform combined with RNA-ISH were collected and analyzed on 56 patients from December 2016 to December 2019 at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China. Results During the study period, fifty-six patients (51 males, 5 females) were included with an mean age of 52 ± 9 years. The 1-, 2- and 3-year recurrence rates of postoperative interstitial CTC-positive and CTC-negative groups were 21.7% vs 10.8%, 37.5% vs 10.8% and 55.5% vs 10.8%, confirming a statistically significant difference between the 2 groups (p = 0.044). The 1-, 2- and 3-year recurrence rates of the increasing interstitial CTCs group were 25.2%, 36.9% and 66.9%, while 12.6%, 24.4% and 24.4% in the decreasing and unchanged group, indicating a significant difference (p = 0.038). Conclusion CanPatrolTM platform presents a superior analytical sensitivity, and may be used as a dynamic monitoring tool for CTCs. And interstitial CTCs which are more aggressive and metastatic caused by EMT can be regarded as a predictor of post-transplant tumor recurrence after LT for HCC.

In this letter, using the sparse unmixing framework, a weighted collaborative sparse and L1/2 low-rank regularization with superpixel segmentation method is proposed for hyperspectral unmixing. The method outlined here first uses superpixel segmentation to obtain local homogeneous regions. The reason for this approach is that the shape and size of superpixels are adaptive, which are better for obtaining homogeneous regions than square patches. Next, the weighted collaborative sparse term and L1/2 low-rank regularization were utilized to exploit the spatial and spectral correlation of each superpixel. In addition, the smoothness between adjacent pixels is enforced by total variation regularization. Finally, the proposed method and several state-of-the-art methods were tested on two simulated data sets and two real data sets. The results demonstrate the superiority of the method proposed here.

Background Neonatal stroke can potentially result in significant neurological sequelae in affected infants. Studies on neurodevelopmental outcomes and the need for rehabilitation therapies in the first two years are limited. We aimed to describe the clinical characteristics, diagnostic evaluation, and neurodevelopmental outcomes of a cohort of infants with neonatal stroke. Methods A retrospective cohort study of infants with neonatal stroke, from 2011 to 2020. Maternal and infant characteristics were described. Placental pathology, echocardiogram results, and prothrombotic evaluations were reported. The neurodevelopmental outcomes using Bayley scale of infant development (BSID III), rates of epilepsy and cerebral palsy, and the need for rehabilitation therapies at two years were described. Results During the study period, 55 infants had neonatal stroke. Majority (93%) were term or late preterm infants. Maternal chorioamnionitis and perinatal HIE were diagnosed in about a third of the infants. Most (66%) of the infants presented with seizures. On brain MRI, the lesions were unilateral in 76% and arterial in origin in 86% of the infants. Meconium exposure (42%), intrauterine inflammation/infection (37%) and fetal vascular malperfusion (16%) were seen on placental histopathology. At two-year BSID III assessment, median (min, max) composite cognitive, language, and motor scores were 100 (55–145), 97 (47–124), and 100 (46–141), respectively. Among this cohort, epilepsy (27%), cerebral palsy (16%) and the need for rehabilitation therapies (physical −24%, occupational −18%, speech −21%) were reported at two years. Conclusion Neonatal stroke presented commonly in term or late preterm infants with seizures. It was unilateral and arterial in origin in most infants. Maternal chorioamnionitis and perinatal HIE were the most commonly associated conditions at birth. About one-fifth of the infants had mild or severe developmental delays at two years. Epilepsy, cerebral palsy, and need for rehabilitation therapies were noted in a significant proportion of infants at two years.

Arthritis and osteoporosis are two common disorders in the world, especially for the elder, but the current treatments have limited efficacy. Herein, we aimed to determine whether the novel technique, neurological training can alleviate osteoporosis complicated with arthritis in rat model. Thirty rats were assigned into normal group, model group, and treatment group (treated with forsythin and neurological training) (n = 10) followed by assessment of chondrocytes and osteoblasts using Mankin score, apoptosis by TUNEL and flow cytometry, and IL-1β, TNF-α, and Aggrecan levels. Apoptotic chondrocytes of treatment group (27.43±1.34) was lower than model group (p < 0.05), whereas amount of osteoblast was increased upon forsythin and neurological training, with lower Mankin’s score (6.38±0.76). Besides, the content of IL-1β and TNF-α of treatment group was significantly lower but Aggrecan mRNA and protein expression was significantly higher. In conclusion, neurological training could protect and alleviate osteoporosis complicated with arthritis.

The intense requirement for bifunctional electromagnetic wave (EMW) absorbing materials is becoming the urgent focus and upgraded exploration for advanced application of absorbing EMW device in complex and extreme environments. Based on high dielectric loss capacity and superior chemistry inertness of graphite nanosheets, a two-dimensional (2D) graphite nanosheet decorated with graphite encapsulating soft-magnetic Ni3Fe nanocapsules was designed and exploited by introducing novel nitrogen catalyst in one-pot non-balance evaporating strategy. Through modulating the impedance matching and further optimal mass ratio, a minimum reflection loss (RL) of −35.8 dB at 7.35 GHz with an absorber thickness of 3.5 mm and a wide effective absorption bandwidth (EAB) of 5.5 GHz (RL ≤ −10 dB), which almost covered whole Ku-band, were realized with an absorber thickness of 2 mm. Combining the prominent EMW absorption, this 2D nanocomposite showed excellent comprehensive anti-corrosion ability to maintain the original microstructure in acidic, neutral and alkaline salty corrosion conditions. The successful exploration of 2D anticorrosive EMW absorber provides a significant insight for rational fabrication of futural bifunctional EMW absorbing devices in complex circumstances.

Severe leakage electromagnetic field (EMF) generated by automatic guided vehicle wireless power transfer (WPT) systems is hazardous to the surrounding objects. To reduce the leakage EMF, a novel compact magnetic coupler is proposed in this paper. The idea of this work is, firstly, adopting the compensation inductors of the LCC-LCC compensated WPT system as EMF canceling coils, and EMF alleviation can be achieved without any additional components; secondly, integrating the power transfer coils and the EMF canceling coil into one magnetic coupler to compact the system. An experimental prototype is fabricated to verify the EMF shielding effect of the proposed magnetic coupler. Experimental results show that compared with that of the general magnetic coupler, the EMF of the proposed magnetic coupler is dramatically reduced by 82.24% at maximum and by 75.18% on average from y=200mm to 350mm. Besides, the transfer efficiency is decreased by 0.32% only with the same output power. That means the proposed magnetic coupler can effectively alleviate the EMF without a significant negative impact on the transfer efficiency.

Sensitive data collected by a wireless sensor network (WSN) should be protected by a secret key shared between two adjacent (or neighboring) sensors. Sensors are mobile devices with limited memory and computational power, and sensors are deployed in a field randomly in which relative positions among sensors cannot be pre‐determined. Key distribution in WSN is a challenging research problem. Random key distribution is one of the most popular key distribution techniques in WSN; but it is a probabilistic key distribution scheme since two neighboring sensors may or may not share any pairwise key. There are many published papers to improve the probability of connectivity of two neighboring sensors. In this paper, a novel design of lightweight key establishment is proposed based on top of random key distribution. The first scheme can increase the probability of connectivity of two neighboring sensors, and the second scheme can also enable sensors to authenticate the established pairwise key. Both schemes need only XOR operation so they are lightweight and especially suitable for WSNs.

Thermoelectric devices enable direct conversion between thermal and electrical energy. Recent studies have indicated that the thin film/substrate heterostructure is effective in achieving high thermoelectric performance via decoupling the Seebeck coefficient and electrical conductivity otherwise adversely inter-dependent in homogenous bulk materials. However, the mechanism underlying the thin film/substrate heterostructure thermoelectricity remains unclear. In addition, the power output of the thin film/substrate heterostructure is limited to the nanowatt scale to date, falling short of the practical application requirement. Here, we fabricated the CrN/SrTiO3−x heterostructures with high thermoelectric output power and outstanding thermal stability. By varying the CrN film thickness and the reduction degree of SrTiO3−x substrate, the optimized power output and the power density have respectively reached 276 μW and 108 mW/cm2 for the 30 nm CrN film on a highly reduced surface of SrTiO3−x under a temperature difference of 300 K. The performance enhancement is attributed to the CrN/SrTiO3−x heterointerface, corroborated by the band bending as revealed by the scanning Kelvin probe microscopy. These results will stimulate further research efforts towards interface thermoelectrics.

Objective The beneficial effect of angiotensin(1–7) (Ang(1–7)), via the activation of its receptor, MAS-1, has been noted in diabetes treatment; however, how Ang(1–7) or MAS-1 affects insulin secretion remains elusive and whether the endogenous level of Ang(1–7) or MAS-1 is altered in diabetic individuals remains unexplored. We recently identified an important role of cystic fibrosis transmembrane conductance regulator (CFTR), a cAMP-activated Cl− channel, in the regulation of insulin secretion. Here, we tested the possible involvement of CFTR in mediating Ang(1–7)’s effect on insulin secretion and measured the level of Ang(1–7), MAS-1 as well as CFTR in the blood of individuals with or without type 2 diabetes. Methods Ang(1–7)/MAS-1/CFTR pathway was determined by specific inhibitors, gene manipulation, Western blotting as well as insulin ELISA in a pancreatic β-cell line, RINm5F. Human blood samples were collected from 333 individuals with (n = 197) and without (n = 136) type 2 diabetes. Ang(1–7), MAS-1 and CFTR levels in the human blood were determined by ELISA. Results In RINm5F cells, Ang(1–7) induced intracellular cAMP increase, cAMP-response element binding protein (CREB) activation, enhanced CFTR expression and potentiated glucose-stimulated insulin secretion, which were abolished by a selective CFTR inhibitor, RNAi-knockdown of CFTR, or inhibition of MAS-1. In human subjects, the blood levels of MAS-1 and CFTR, but not Ang(1–7), were significantly higher in individuals with type 2 diabetes as compared to those in non-diabetic healthy subjects. In addition, blood levels of MAS-1 and CFTR were in significant positive correlation in type-2 diabetic but not non-diabetic subjects. Conclusion These results suggested that MAS-1 and CFTR as key players in mediating Ang(1–7)-promoted insulin secretion in pancreatic β-cells; MAS-1 and CFTR are positively correlated and both upregulated in type 2 diabetes.

Highlights • MRI and PSMA PET are complementary for delineating local recurrence after primary RT. • The union of CTVs on MRI and PSMA PET included, Purpose Our purpose was to evaluate intra-prostatic cancer volumes for salvage radiotherapy in men with recurrent prostate cancer confined to the prostate post-primary radiotherapy using mpMRI and 18F-DCFPyL PET/CT (PET). Methods Men with biochemical failure post-primary radiotherapy were enrolled in a multi-centre trial investigating mpMRI and PET. All men with isolated intra-prostatic recurrence are included in this secondary analysis. The intra-prostatic gross tumour volume (GTV) was manually delineated on mpMRI and was also delineated on PET using three methods: 1. manually, 2. using a 30% threshold of maximum intra-prostatic standard uptake value (SUVmax), and 3. using a 67% threshold of this SUVmax. Clinical target volumes (CTV) including expansions on each GTV were generated. Conformity indices were performed between the mpMRI CTV and each PET CTV. Correlation with biopsy and clinical outcomes were performed. Results Of the 36 men included, 30 (83%) had disease in two quadrants or less using the combination of mpMRI and PET. Mean target volume (union of CTV on mpMRI and CTV manually delineated on PET) was 12.2 cc (49% of prostate gland volume). 12/36 (33%) men had a biopsy. Per-patient sensitivity was 91% for mpMRI and 82% for PET. Conclusions mpMRI and PET provide complementary information for delineation of intra-prostatic recurrent disease. Union of CTV on mpMRI and PET is often less than 50% of the prostate, suggesting this imaging could help define a target for focal salvage therapy.

Path to another drug against COVID-19 The rapid development of vaccines has been crucial in battling the ongoing COVID-19 pandemic. However, access challenges remain, breakthrough infections occur, and emerging variants present increased risk. Developing antiviral therapeutics is therefore a high priority for the treatment of COVID-19. Some drug candidates in clinical trials act against the viral RNA-dependent RNA polymerase, but there are other viral enzymes that have been considered good targets for inhibition by drugs. Owen et al . report the discovery and characterization of a drug against the main protease involved in the cleavage of polyproteins involved in viral replication. The drug, PF-07321332, can be administered orally, has good selectivity and safety profiles, and protects against infection in a mouse model. In a phase 1 clinical trial, the drug reached concentrations expected to inhibit the virus based on in vitro studies. It also inhibited other coronaviruses, including severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus, and could be in the armory against future viral threats. —VV

Small stone fragments inevitably resulted from breakage of a large stone are almost impossible to be fully extracted from the collecting system in current RIRS settings. While most of these clinically insignificant residual fragments (CIRFs) < 4 mm may pass spontaneously, increasing evidence suggests that these so called CIRFs may act as an origin for new stone formation and eventually lead to reintervention(1). Therefore, it is of paramount importance to achieve complete stone free status in the aim of improving patient's quality of life not only physically but also psychologically and economically(2).

Background Obesity is associated with impaired immune function and chronic low-grade inflammation. Metabolic surgery is one of the most effective therapies for treating obesity and related metabolic disorders. We aimed to explore the pathophysiological roles of peripheral dendritic cells (DCs) and T lymphocytes in metabolic surgery. Method In this observational cohort study, a total of 106 individuals, including obese participants with or without T2DM, overweight subjects and normal controls, were recruited. All obese participants underwent laparoscopic sleeve gastrectomy surgery and returned for the evaluation of the clinical indicators after surgery. We evaluated the frequencies of circulating DCs subsets (myeloid (mDCs) and plasmacytoid (pDCs)), the pro-inflammatory (Th1 and Th17) and anti-inflammatory (Th2 and Treg) T cell subsets by flow cytometry. Results Compared with normal controls, the frequencies of mDCs, Th1 and Th17 cells increased, while Treg and Th2 cells decreased in the obese participants. The frequency of mDCs and Th1 cells consistently declined after surgery compared with baseline in the obese patients and were restored to the levels observed in the normal controls after surgery. Moreover, the frequency of Treg cells was increased at 6 months after surgery in the obese patients with T2DM, and Th17 cells declined at 6 months after surgery in the severely obese patients without T2DM. Conclusion This study indicates that metabolic surgery can effectively improve imbalanced immune cells in peripheral blood and restore the proportion of immune cells to a normal range during a 12-month follow-up.