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Monocrystalline layered cathode is of particular interest owing to the satisfactory mechanical integrity against long-term cycling. However, the high-temperature calcination, which is perceived as the indispensable reaction condition, favors the formation of Ni2+ that triggers the Li+/Ni2+ disorder and the formation of electrochemically inactive NiO. Herein, we develop a SrO-assistant method to synthesize a monocrystalline ultrahigh-Ni LiNi0.95-Co0.04Mn0.01O2 under a moderate calcination temperature. The addition of an appropriate amount of SrO (0.3 wt %, SCN95-Sr-3) facilitates the formation of monocrystalline LiNi0.95Co0.04Mn0.01O2 (grain size: 2-4 mu m) under 790 degrees C. The obtained LiNi0.95Co0.04Mn0.01O2 represents excellent electrochemical properties with an initial discharge capacity of 240.5 mAh g-1 and a Coulombic efficiency of 90.1 % at 0.1C (1C = 220 mA g-1) in a Li- metal half cell. Upon long-term cycling, the monocrystalline feature with less crystallographic defect effectively enhances the structural stability and inhibits the formation of intergranular cracks, resulting in an enhanced cyclic stability (discharge capacity of 151.0 mAh g-1 maintained after 300 cycles at 1C in a Li-metal half cell). Coupled with a graphite anode, the LiNi0.95Co0.04Mn0.01O2\\ graphite full cell represents a stable cycling at 1C, with a capacity retention of 99.0 % after 100 cycles.

Qian-Qian Liao,1,* Yan-Ju Mo,2,* Ke-Wei Zhu,3,4,* Feng Gao,2 Bin Huang,2 Peng Chen,4 Feng-Tian Jing,5 Xuan Jiang,6 Hong-Zhen Xu,1 Yan-Feng Tang,7 Li-Wei Chu,8 Hai-Ling Huang,1 Wen-Li Wang,8 Fang-Ning Wei,9 Dan-Dan Huang,9 Bin-Jing Zhao,9 Jia Chen,9 Hao Zhang1 1Department of Pharmacy, People’s Hospital of Guilin, Guilin, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, People’s Hospital of Guilin, Guilin, People’s Republic of China; 3GuangZhou BaiYunShan Pharmaceutical Holdings CO.,LTD. BaiYunShan Pharmaceutical General Factory, Guangzhou, People’s Republic of China; 4Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 5Department of Respiratory Medicine, Xing An County People’ Hospital, Guilin, People’s Republic of China; 6Department of Hospital Infection Management, People’s Hospital of Guilin, Guilin, People’s Republic of China; 7Department of Geriatrics, People’s Hospital of Guilin, Guilin, People’s Republic of China; 8Department of Laboratory Medicine, People’s Hospital of Guilin, Guilin, People’s Republic of China; 9School of Clinical Pharmacy, Guilin Medical University, Guilin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ke-Wei Zhu, GuangZhou BaiYunShan Pharmaceutical Holdings CO.,LTD. BaiYunShan Pharmaceutical General Factory, No. 88 Yunxiang Road Tonghe Street, Baiyun District, Guangzhou, Guangdong Province, 510515, People’s Republic of China, Tel +86-020-87062636, Fax +86-020-87095309, Email zhukew2018@outlook.comPurpose: The study comprehensively evaluated the prognostic roles of the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), basophil-to-lymphocyte ratio (BLR), and eosinophil-to-lymphocyte ratio (ELR) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that \gpt has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of \gpt for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://github.com/EnVision-Research/GPT4Affectivity.

Low-dose computed tomography (LDCT) plays a vital role in clinical applications by mitigating radiation risks. Nevertheless, reducing radiation doses significantly degrades image quality. Concurrently, common deep learning methods demand extensive data, posing concerns about privacy, cost, and time constraints. Consequently, we propose a few-shot low-dose CT reconstruction method using Partitioned Hankel-based Diffusion (PHD) models. During the prior learning stage, the projection data is first transformed into multiple partitioned Hankel matrices. Structured tensors are then extracted from these matrices to facilitate prior learning through multiple diffusion models. In the iterative reconstruction stage, an iterative stochastic differential equation solver is employed along with data consistency constraints to update the acquired projection data. Furthermore, penalized weighted least-squares and total variation techniques are introduced to enhance the resulting image quality. The results approximate those of normal-dose counterparts, validating PHD model as an effective and practical model for reducing artifacts and noise while preserving image quality.

Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-based generative models are widely used in sparse-view CT re-construction, performance diminishes significantly with a sharp reduction in projection angles. Therefore, we propose an ultra-sparse view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff), designed to concentrate on the global distribution of information and facilitate the reconstruction of sparse views with local image characteristics. Specifically, the proposed model ingeniously integrates information from both comprehensive sampling and selectively sparse sampling tech-niques. Through precise adjustments in diffusion model, it is capable of extracting diverse noise distribution, furthering the understanding of the overall structure of images, and aiding the fully sampled model in recovering image information more effec-tively. By leveraging the inherent correlations within the projec-tion data, we have designed an equidistant mask, enabling the model to focus its attention more effectively. Experimental re-sults demonstrated that the multi-scale model approach signifi-cantly improved the quality of image reconstruction under ultra-sparse angles, with good generalization across various datasets.

Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation Autonomous Vehicle (AV) perception. However, most existing BEV solutions either require considerable resources to execute on-vehicle inference or suffer from modest performance. This paper proposes a simple yet effective framework, termed Fast-BEV , which is capable of performing faster BEV perception on the on-vehicle chips. Towards this goal, we first empirically find that the BEV representation can be sufficiently powerful without expensive transformer based transformation nor depth representation. Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference. We further introduce (4) a strong data augmentation strategy for both image and BEV space to avoid over-fitting, (5) a multi-frame feature fusion mechanism to leverage the temporal information. Through experiments, on 2080Ti platform, our R50 model can run 52.6 FPS with 47.3% NDS on the nuScenes validation set, exceeding the 41.3 FPS and 47.5% NDS of the BEVDepth-R50 model and 30.2 FPS and 45.7% NDS of the BEVDet4D-R50 model. Our largest model (R101@900x1600) establishes a competitive 53.5% NDS on the nuScenes validation set. We further develop a benchmark with considerable accuracy and efficiency on current popular on-vehicle chips. The code is released at: https://github.com/Sense-GVT/Fast-BEV., Comment: submitted to TPAMI. arXiv admin note: substantial text overlap with arXiv:2301.07870

Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving. However, most existing BEV solutions either suffer from modest performance or require considerable resources to execute on-vehicle inference. This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing real-time BEV perception on the on-vehicle chips. Towards this goal, we first empirically find that the BEV representation can be sufficiently powerful without expensive view transformation or depth representation. Starting from M2BEV baseline, we further introduce (1) a strong data augmentation strategy for both image and BEV space to avoid over-fitting (2) a multi-frame feature fusion mechanism to leverage the temporal information (3) an optimized deployment-friendly view transformation to speed up the inference. Through experiments, we show Fast-BEV model family achieves considerable accuracy and efficiency on edge. In particular, our M1 model (R18@256x704) can run over 50FPS on the Tesla T4 platform, with 47.0% NDS on the nuScenes validation set. Our largest model (R101@900x1600) establishes a new state-of-the-art 53.5% NDS on the nuScenes validation set. The code is released at: https://github.com/Sense-GVT/Fast-BEV., Comment: Accepted by NeurIPS2022_ML4AD on October 22, 2022

Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and their corresponding interactions by using a predictor. Using only one shared predictor cannot differentiate the attentive field of instance-level prediction and relation-level prediction. To solve this problem, we propose a new transformer-based method named Parallel Reasoning Network(PR-Net), which constructs two independent predictors for instance-level localization and relation-level understanding. The former predictor concentrates on instance-level localization by perceiving instances' extremity regions. The latter broadens the scope of relation region to reach a better relation-level semantic understanding. Extensive experiments and analysis on HICO-DET benchmark exhibit that our PR-Net effectively alleviated this problem. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.

Zhou X, Liu Z, Huang B, et al. Drug Des Devel Ther. 2019;13:2703–2713. The Editor and Publisher of Drug Design, Development and Therapy wish to retract the published article. Concerns were raised regarding the alleged duplication of Oil Red O images in Figures 4 and Figure 6. Specifically, Figure 4C, Oil Red O, Ctrl, appears to have been duplicated with the same image for Figure 6D, Oil Red O, OG+ICG-001. The authors cooperated with the investigation and provided data associated with the study. However, despite the authors’ assistance, it was still not clear how the images came to be duplicated, and, as we cannot verify the validity of the published work, we are therefore retracting the article and the authors agree with this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

This paper shows that Masking the Deep hierarchical features is an efficient self-supervised method, denoted as MaskDeep. MaskDeep treats each patch in the representation space as an independent instance. We mask part of patches in the representation space and then utilize sparse visible patches to reconstruct high semantic image representation. The intuition of MaskDeep lies in the fact that models can reason from sparse visible patches semantic to the global semantic of the image. We further propose three designs in our framework: 1) a Hierarchical Deep-Masking module to concern the hierarchical property of patch representations, 2) a multi-group strategy to improve the efficiency without any extra computing consumption of the encoder and 3) a multi-target strategy to provide more description of the global semantic. Our MaskDeep brings decent improvements. Trained on ResNet50 with 200 epochs, MaskDeep achieves state-of-the-art results of 71.2% Top1 accuracy linear classification on ImageNet. On COCO object detection tasks, MaskDeep outperforms the self-supervised method SoCo, which specifically designed for object detection. When trained with 100 epochs, MaskDeep achieves 69.6% Top1 accuracy, which surpasses current methods trained with 200 epochs, such as HCSC, by 0.4% .

OBJECTIVES: To assess the incidence, risk factors, and outcomes of atrial fibrillation (AF) in the ICU and to describe current practice in the management of AF. DESIGN: Multicenter, prospective, inception cohort study. SETTING: Forty-four ICUs in 12 countries in four geographical regions. SUBJECTS: Adult, acutely admitted ICU patients without a history of persistent/permanent AF or recent cardiac surgery were enrolled; inception periods were from October 2020 to June 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We included 1,423 ICU patients and analyzed 1,415 (99.4%), among whom 221 patients had 539 episodes of AF. Most (59%) episodes were diagnosed with continuous electrocardiogram monitoring. The incidence of AF was 15.6% (95% CI, 13.8-17.6), of which newly developed AF was 13.3% (11.5-15.1). A history of arterial hypertension, paroxysmal AF, sepsis, or high disease severity at ICU admission was associated with AF. Used interventions to manage AF were fluid bolus 19% (95% CI 16-23), magnesium 16% (13-20), potassium 15% (12-19), amiodarone 51% (47-55), beta-1 selective blockers 34% (30-38), calcium channel blockers 4% (2-6), digoxin 16% (12-19), and direct current cardioversion in 4% (2-6). Patients with AF had more ischemic, thromboembolic (13.6% vs 7.9%), and severe bleeding events (5.9% vs 2.1%), and higher mortality (41.2% vs 25.2%) than those without AF. The adjusted cause-specific hazard ratio for 90-day mortality by AF was 1.38 (95% CI, 0.95-1.99). CONCLUSIONS: In ICU patients, AF occurred in one of six and was associated with different conditions. AF was associated with worse outcomes while not statistically significantly associated with 90-day mortality in the adjusted analyses. We observed variations in the diagnostic and management strategies for AF.

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.

Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details. However, existing Video LLMs can only provide a coarse description of the entire video, failing to capture the precise start and end time boundary of specific events. In this paper, we solve this issue via proposing VTimeLLM, a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary. Specifically, our VTimeLLM adopts a boundary-aware three-stage training strategy, which respectively utilizes image-text pairs for feature alignment, multiple-event videos to increase temporal-boundary awareness, and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents. Extensive experiments demonstrate that in fine-grained time-related comprehension tasks for videos such as Temporal Video Grounding and Dense Video Captioning, VTimeLLM significantly outperforms existing Video LLMs. Besides, benefits from the fine-grained temporal understanding of the videos further enable VTimeLLM to beat existing Video LLMs in video dialogue benchmark, showing its superior cross-modal understanding and reasoning abilities.

Diffusion models have emerged as potential tools to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Nevertheless, these prevailing diffusion models predominantly focus on the sinogram or image domains, which can lead to instability during model training, potentially culminating in convergence towards local minimal solutions. The wavelet trans-form serves to disentangle image contents and features into distinct frequency-component bands at varying scales, adeptly capturing diverse directional structures. Employing the Wavelet transform as a guiding sparsity prior significantly enhances the robustness of diffusion models. In this study, we present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction. Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with optimization procedure. Furthermore, we perform the low-frequency and high-frequency generative models on wavelet's decomposed components rather than sinogram or image domains, ensuring the stability of model training. Our method rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform. Our experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods both quantitatively and qualitatively.

Jian Zhu,1,* Chuan-Yang Jiang,2,* Bin Huang,3,* Ji-Min Hu,4 Si-Zhen Fang,5 Ke Huang,1 Yan-Hong Gao,6 Jiao Yu7 1Department of Thoracic Cardiovascular Surgery, General Hospital of Central Theater Command of People’s Liberation Army, Wuhan, 430070, People’s Republic of China; 2College of Mechanical Engineering, Liaoning Petrochemical University, Fushun, 113001, People’s Republic of China; 3The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 4Science Technology and Standards Center, Jiangsu Testing and Inspection Institute for Medical Devices, Nanjing, 210019, People’s Republic of China; 5Spectris Instrumentation & Systems Shanghai Ltd. Guangzhou Branch, Guangzhou, 510620, People’s Republic of China; 6Department of Ultrasound, General Hospital of Central Theater Command of People’s Liberation Army, Wuhan, 430070, People’s Republic of China; 7College of Science, Liaoning Petrochemical University, Fushun, 113001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jiao Yu, College of Science, Liaoning Petrochemical University, Fushun, 113001, People’s Republic of China, Email yujiaojoy@hotmail.com Yan-Hong Gao, Department of Ultrasound, General Hospital of Central Theater Command of People’s Liberation Army, Wuhan, 430070, People’s Republic of China, Email gaoyh200509@163.comBackground: The COVID-19 pandemic has continued for more than two years since its outbreak. Due to the clinical auscultation needs of doctors when wearing airtight protective clothing, a cylindrical tube stethoscope was proposed to address this problem. However, the idea has been questioned by some experts.Methods: To address these questions, we performed three-part experiments using cylindrical tube stethoscopes. First, we performed laboratory tests to detect the sound intensity from a cylindrical tube stethoscope. Second, we improved the cyli

Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade the image quality. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction. To extract sufficient prior information from single sample, the Hankel matrix formulation is employed. Besides, the penalized weighted least-squares and total variation are introduced to achieve superior image quality. Specifically, we first train a score-based generative model on one sinogram by extracting a great number of tensors from the structural-Hankel matrix as the network input to capture prior distribution. Then, at the inference stage, the stochastic differential equation solver and data consistency step are performed iteratively to obtain the sinogram data. Finally, the final image is obtained through the filtered back-projection algorithm. The reconstructed results are approaching to the normal-dose counterparts. The results prove that OSDM is practical and effective model for reducing the artifacts and preserving the image quality., Comment: 11 pages, 11 figures. arXiv admin note: text overlap with arXiv:2211.13926

Wenjie Wen,1,2,&ast; Bin Huang,1,2,&ast; Shandong Ye1,2 1Department of Endocrinology and Laboratory for Diabetes, The First Affiliated Hospital of University of Science and Technology of China (USTC), Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China; 2Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shandong Ye, University of Science and Technology of China, No. 96 Jinzhai Road, Hefei, Anhui Province, 230026, People’s Republic of China, Email ysd196406@163.comBackground: Metformin is used as a first-line drug for the treatment of type 2 diabetes. Epithelial–mesenchymal transition (EMT) plays a significant role in the development of renal tubular damage in diabetic kidney disease. However, the underlying mechanisms of EMT in diabetic kidney disease are unclear and how to inhibit this process remains to be explored.Methods: C57 mice were randomly divided into four groups, including the normal control group (NC group), the Type 2 diabetes group (T2DM group), the metformin group (MET group), and glibenclamide group (GLIB). Fasting blood glucose (FBG), glycated hemoglobin (HbA1c), urinary albumin, RBP, PCX, and creatinine were measured. Renal pathology was observed with HE staining. Molecular mechanism of VDR expression are regulated by metformin through wound healing assay, and Western blot analysis of VDR, Ecad, and SMA in HK2 cells.Results: In animal experiments, compared with the NC group, the T2DM group showed decreased body weight, increased levels of FBG, HbA1c, UAlb/UCR, URBP/UCR, and UPCX/UCR, decreased levels of VDR protein and mRNA expression in renal tissues (P < 0.05), and significantly increased renal pathological damage in mice in the T2DM group. Compared with the T2DM group, mice in the GLIB and MET groups had higher body

Bin Huang,1,2,&ast; Shengju Yang,1,&ast; Shandong Ye1 1Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China; 2Research Institution of Diabetes, University of Science and Technology of China, Hefei, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shandong Ye, Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China, Email 981257013@qq.comIntroduction: Early detection of bacterial infections associated with adequate antibiotic treatment is key to improving diabetic ketoacidosis (DKA) outcomes. Our study aimed to investigate the different sepsis markers (including procalcitonin to lactic acid ratio, PLR) to diagnose bacterial infection in patients with DKA within one hour after admission.Methods: A total of 165 patients diagnosed with DKA were enrolled between July 2014 and July 2018 and divided into an infection group (N =62) and a non-infection group (N=103) based on the positive aetiological tests such as blood culture, sputum culture, urine culture, or definite focus of pulmonary, soft tissue, kidney, etc.Results: Our findings suggest the following: 1) leucocytes (threshold above 10× 109 /L) and PLR (threshold above 0.438) within one hour after admission can help to identify patients with infection in the context of DKA. 2) A subgroup analysis demonstrated that PLR also has a high diagnostic efficacy for infection in patients with DKA, regardless of the type of diabetes.Conclusion: This study concludes that leucocyte count (threshold > 10× 109/L) and PLR (threshold above 0.438) show a diagnostic value to help distinguish DKA patients with infection. By combining these two markers, the reduction of antibiotic misuse may be possible.Keywor

Cold-formed steel C- and Z-section beams are widely used in light gauge steel building systems as flexural members. In their applications as flexural members, mono-symmetric C-sections and point-symmetric Z-sections are often subjected to transverse loads eccentric to the shear centre, leading to combined bending and torsion actions. However, research and design methods for cold-formed steel beams subjected to combined bending and torsion are limited. Hence this research has investigated the structural behaviour, strength and design of cold-formed steel C- and Z-section beams under the action of combined bending and torsion. Twenty-four tests were conducted on simply-supported beams subjected to a mid-span eccentric load. Two C-sections and two Z-sections were used in the tests with two different spans, each with three loading-eccentricities. A special test set-up was developed and used to simulate the different loading-eccentricities and to provide accurate boundary conditions. Numerical models of tested beams were then developed using ANSYS and nonlinear finite element analyses including the effects of large deformation and material yielding were performed. The numerical results agreed well with the test results in terms of ultimate strengths, failures modes and load-displacement curves. Finally the results from tests were compared with predictions from the current design equation for bending and torsion. This paper presents this investigation of cold-formed steel beams subjected to combined bending and torsion, and the results.

Implementing artificial intelligence (AI) in the Internet of Things (IoT) involves a move from the cloud to the heterogeneous and low-power edge, following an urgent demand for deploying complex training tasks in a distributed and reliable manner. This work proposes a self-aware distributed deep learning (DDL) framework for IoT applications, which is applicable to heterogeneous edge devices aiming to improve adaptivity and amortize the training cost. The self-aware design including the dynamic self-organizing approach and the self-healing method enhances the system reliability and resilience. Three typical edge devices are adopted with cross-platform Docker deployment: Personal Computers (PC) for general computing devices, Raspberry Pi 4Bs (Rpi) for resource-constrained edge devices, and Jetson Nanos (Jts) for AI-enabled edge devices. Benchmarked with ResNet-32 on CIFAR-10, the training efficiency of tested distributed clusters is increased by 8.44x compared to the standalone Rpi. The cluster with 11 heterogeneous edge devices achieves a training efficiency of 200.4 images/s and an accuracy of 92.45%. Results prove that the self-organizing approach functions well with dynamic changes like devices being removed or added. The self-healing method is evaluated with various stabilities, cluster scales, and breakdown cases, testifying that the reliability can be largely enhanced for extensively distributed deployments. The proposed DDL framework shows excellent performance for training implementation with heterogeneous edge devices in IoT applications with high-degree scalability and reliability., QC 20210903

Ting Sun,1 Tao Wang,1 Yiwen Qiu,1 Shu Shen,1 Xianwei Yang,1 Yi Yang,1 Bin Huang,2 Wentao Wang1 1Department of Liver Surgery & Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu, People’s Republic of China; 2Department of Vascular Surgery, West China Hospital of Sichuan University, Chengdu, People’s Republic of ChinaCorrespondence: Wentao Wang Email wwtdoctor02@163.comBackground: Sarcopenia and visceral adiposity have been shown to be associated with postoperative complications in numerous diseases. However, their effects on the postoperative complications of end-stage hepatic alveolar echinococcosis (HAE) patients undergoing ex vivo liver resection and autotransplantation (ELRA) remain unclear.Methods: This retrospective study included 101 end-stage HAE patients who underwent ELRA from January 2014 to August 2020. We measured the skeletal muscle and adipose tissue of all patients at the level of the third lumbar vertebra on plain abdominal computed tomography (CT) images and subsequently derived an equation via least absolute shrinkage and selection operator (LASSO) regression analysis to calculate the sarcopenia score. Univariate and multivariate regression were performed to reveal the relationship between major postoperative complications and perioperative clinical data, and the obtained nomogram was validated with the bootstrapping method.Results: The sarcopenia score was constructed as a personalized indicator to evaluate sarcopenia and visceral adiposity in each patient. Logistic regression analysis finally selected duration from primary diagnosis to obvious symptoms (OR=1.024, 95% CI, 1.007– 1.042), surgical time (OR=1.003, 95% CI, 0.999– 1.007) and sarcopenia score (OR=4.283, 95% CI, 1.739– 10.551) as independent risk factors for predicting major postoperative complications following ELRA for end-stage HAE patients. The area under the receiver operating characteristic curve (AUROC) of 0.807 (95% CI, 0.720– 0.895)

Kun Li,1,&ast; Bin Huang,2,&ast; Tao Ji,3 Shao-Gan Xu,3 Wen Jiang4 1Department of Anesthesiology, General hospital of central theater command of the People’s Liberation Army, Wuhan, Hubei Province, 430070, People’s Republic of China; 2The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong Province, 510515, People’s Republic of China; 3Department of Thoracic Cardiovascular Surgery, General Hospital of Central Theater Command of People’s Liberation Army, Wuhan, Hubei Province, 430070, People’s Republic of China; 4Department of Health Medicine, General hospital of central theater command of the People’s Liberation Army, Wuhan, Hubei Province, 430070, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wen Jiang; Shao-Gan XuGeneral hospital of central theater command of the People’s Liberation Army, 627#, Wuluo Road, Wuchangqu, Wuhan, Hubei, 430070, People’s Republic of ChinaTel +86 17720529297Fax +86 27-50772388Email zbzqzyyjw@163.com; whzyyxsg@163.comAbstract: At the moment, the SARS-CoV-19 pandemic is still attacking the health of humanity, and vaccines are the primary health strategy to eradicate this global challenge. So, with the exception of the SARS-CoV-2 vaccine, no vaccine for any disease has been brought to clinical use so quickly. Therefore, even with strict management, it can still bring some special adverse effects. One of the most notable is the adverse cardiovascular reactions to SARS-CoV-2 vaccines. No case reports of individuals with irreversible arrhythmia complications following the SARS-CoV-2 vaccine have been found in the available literature. We report the first case of a postoperative man with Marfan syndrome with atrial fibrillation after receiving the SARS-CoV-2 vaccine.Keywords: SARS-CoV-2, vaccine, adverse reactions, arrhythmology

Zhenhua Li,1,2,* Bin Huang,2,* Wenfeng Yi,2,* Fei Wang,1 Shizhuang Wei,1 Huaixing Yan,1 Pan Qin,1 Donghua Zou,1 Rongguo Wei,3 Nian Chen4 1Department of Emergency Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China; 2Intensive Care Unit, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China; 3Department of Clinical Laboratory, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China; 4Department of Infectious Diseases, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Rongguo Wei; Nian Chen Email unwrong@126.com; chennian1111@163.comObjective: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival.Methods: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples.Results: A t

Background: Atrial fibrillation (AF) is common in intensive care unit (ICU) patients and is associated with poor outcomes. Different management strategies exist, but the evidence is limited and derived from non-ICU patients. This international survey of ICU doctors evaluated the preferred management of acute AF in ICU patients. Method: We conducted an international online survey of ICU doctors with 27 questions about the preferred management of acute AF in the ICU, including antiarrhythmic therapy in hemodynamically stable and unstable patients and use of anticoagulant therapy. Results: A total of 910 respondents from 70 ICUs in 14 countries participated in the survey with 24%–100% of doctors from sites responding. Most ICUs (80%) did not have a local guideline for the management of acute AF. The preferred first-line strategy for the management of hemodynamically stable patients with acute AF was observation (95% of respondents), rhythm control (3%), or rate control (2%). For hemodynamically unstable patients, the preferred strategy was observation (48%), rhythm control (48%), or rate control (4%). Overall, preferred antiarrhythmic interventions included amiodarone, direct current cardioversion, beta-blockers other than sotalol, and magnesium in that order. A total of 67% preferred using anticoagulant therapy in ICU patients with AF, among whom 61% preferred therapeutic dose anticoagulants and 39% prophylactic dose anticoagulants. Conclusion: This international survey indicated considerable practice variation among ICU doctors in the clinical management of acute AF, including the overall management strategies and the use of antiarrhythmic interventions and anticoagulants.

Background: Atrial fibrillation (AF) is common in intensive care unit (ICU) patients and is associated with poor outcomes. Different management strategies exist, but the evidence is limited and derived from non-ICU patients. This international survey of ICU doctors evaluated the preferred management of acute AF in ICU patients. Method: We conducted an international online survey of ICU doctors with 27 questions about the preferred management of acute AF in the ICU, including antiarrhythmic therapy in hemodynamically stable and unstable patients and use of anticoagulant therapy. Results: A total of 910 respondents from 70 ICUs in 14 countries participated in the survey with 24%–100% of doctors from sites responding. Most ICUs (80%) did not have a local guideline for the management of acute AF. The preferred first-line strategy for the management of hemodynamically stable patients with acute AF was observation (95% of respondents), rhythm control (3%), or rate control (2%). For hemodynamically unstable patients, the preferred strategy was observation (48%), rhythm control (48%), or rate control (4%). Overall, preferred antiarrhythmic interventions included amiodarone, direct current cardioversion, beta-blockers other than sotalol, and magnesium in that order. A total of 67% preferred using anticoagulant therapy in ICU patients with AF, among whom 61% preferred therapeutic dose anticoagulants and 39% prophylactic dose anticoagulants. Conclusion: This international survey indicated considerable practice variation among ICU doctors in the clinical management of acute AF, including the overall management strategies and the use of antiarrhythmic interventions and anticoagulants.

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper., Comment: Mobile AI 2021 Workshop and Challenges: https://ai-benchmark.com/workshops/mai/2021/. arXiv admin note: substantial text overlap with arXiv:2105.07809, arXiv:2105.07825

Graph neural networks (GNNs) have been demonstrated as a powerful tool for analyzing non-Euclidean graph data. However, the lack of efficient distributed graph learning systems severely hinders applications of GNNs, especially when graphs are big and GNNs are relatively deep. Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions. This system supports multiple training strategies and enables efficient and scalable big-graph learning on distributed (virtual) machines with low memory. To facilitate graph convolutions, GraphTheta puts forward a new graph learning abstraction named NN-TGAR to bridge the gap between graph processing and graph deep learning. A distributed graph engine is proposed to conduct the stochastic gradient descent optimization with a hybrid-parallel execution, and a new cluster-batched training strategy is supported. We evaluate GraphTheta using several datasets with network sizes ranging from small-, modest- to large-scale. Experimental results show that GraphTheta can scale well to 1,024 workers for training an in-house developed GNN on an industry-scale Alipay dataset of 1.4 billion nodes and 4.1 billion attributed edges, with a cluster of CPU virtual machines (dockers) of small memory each (5$\sim$12GB). Moreover, GraphTheta can outperform DistDGL by up to $2.02\times$, with better scalability, and GraphLearn by up to $30.56\times$. As for model accuracy, GraphTheta is capable of learning as good GNNs as existing frameworks. To the best of our knowledge, this work presents the largest edge-attributed GNN learning task in the literature., Comment: 19 pages, 13 figures, 8 tables

Ping-Xin Qi,1,2,* Jia-Ni Zou,3,* Jian Zhu,2 Bin Huang,4 Er-Ping Xi,2 Xu-Hui Gao2 1School of Medicine, Wuhan University of Science and Technology, Wuhan 430065, People’s Republic of China; 2Department of Thoracic Cardiovascular Surgery, General Hospital of Central Theater Command of the People’s Liberation Army, Wuhan 430070, People’s Republic of China; 3Department of Radiology, General Hospital of Central Theater Command of the People’s Liberation Army, Wuhan 430070, People’s Republic of China; 4The First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xu-Hui Gao; Er-Ping XiDepartment of Thoracic Cardiovascular Surgery, General Hospital of Central Theater Command of the People’s Liberation Army, 627#, Wuluo Road, Wuchang District, Wuhan, Hubei 430070, People’s Republic of ChinaTel +86-13307195757Fax +86-27-50772388Email whzyygxh@126.com; wo5351@126.comAbstract: A 55-year-old man was treated at the village hospital with six months medical history of recurrent chills and fever. Due to the lack of imaging examination, antipyretic and anti-infective medications were given. Although symptomatic treatment can relieve fever symptoms, symptoms easily flare up again two to three days after taking the drug. Later, the patient suffered from fever again during the COVID-19 epidemic and was sent to our hospital for isolation and treatment. During this hospitalization, chest CT examination is mandatory for all patients in order to meet the requirements of epidemic prevention and control. This led to the inadvertent discovery of a large cystic solid mass in the right thoracic cavity communicating with the esophageal lumen. The patient was preliminarily diagnosed as giant midesophageal diverticulum after three-dimensional CT image reconstruction of the chest was reviewed. Considering the

Prior to the accomplishment of this research, the compatibility of the classic e-servicescape conceptual model in the context Taobao and TMall in China were rarely found among existing e-servicescape literatures due to the standardization taken by both online platforms towards the establishment of individual webshops. Meanwhile, the conceptualization of social factors as social dimension of e-servicescape construct was mostly absent in the existing e-servicescape literatures. Hence, this research served as a preliminary research towards the subjects, and some practical insights were generated for facilitating the similar research in the future. The aim of this research was to explore if the classic e-servicescape conceptual model as well as with the addon of social factors were compatible with the context of Taobao and TMall in China, and this aim was addressed by three designated research questions. In order to address three research questions, exploratory factor analysis (EFA) as one form of factor analysis was adopted to test the assumptions, which those measured variables would be derived and clumping underlying constructs accordingly. Bryman (2012) mentioned that the technique of factor analysis was occasionally used by the researchers to confirm the dimensions of a measure which they expected to exist. Also, an online self-completion questionnaire was designed to collect the empirical data mainly via a Chinese social media application (WeChat). The computerized statistic program (IBM SPSS Statistics Version 26) was utilized to conduct exploratory factor analysis (EFA) and generate relevant tables and figures. The results revealed that the assumptions were not supported by this preliminary research, and also the conclusion and recommendations were drawn at the end of this research.

A meta-analysis of oncology papers from around the world revealed that cancer patients who lived more than 50 miles away from hospital centers routinely presented with more advanced stages of disease at diagnosis, exhibited lower adherence to prescribed treatments, presented with poorer diagnoses, and reported a lower quality of life than patients who lived nearer to care facilities. Connected health approaches-or the use of broadband and telecommunications technologies to evaluate, diagnose, and monitor patients beyond the clinic-are becoming an indispensable tool in medicine to overcome the obstacle of distance.

A meta-analysis of oncology papers from around the world revealed that cancer patients who lived more than 50 miles away from hospital centers routinely presented with more advanced stages of disease at diagnosis, exhibited lower adherence to prescribed treatments, presented with poorer diagnoses, and reported a lower quality of life than patients who lived nearer to care facilities. Connected health approaches-or the use of broadband and telecommunications technologies to evaluate, diagnose, and monitor patients beyond the clinic-are becoming an indispensable tool in medicine to overcome the obstacle of distance.

Prior to the accomplishment of this research, the compatibility of the classic e-servicescape conceptual model in the context Taobao and TMall in China were rarely found among existing e-servicescape literatures due to the standardization taken by both online platforms towards the establishment of individual webshops. Meanwhile, the conceptualization of social factors as social dimension of e-servicescape construct was mostly absent in the existing e-servicescape literatures. Hence, this research served as a preliminary research towards the subjects, and some practical insights were generated for facilitating the similar research in the future. The aim of this research was to explore if the classic e-servicescape conceptual model as well as with the addon of social factors were compatible with the context of Taobao and TMall in China, and this aim was addressed by three designated research questions. In order to address three research questions, exploratory factor analysis (EFA) as one form of factor analysis was adopted to test the assumptions, which those measured variables would be derived and clumping underlying constructs accordingly. Bryman (2012) mentioned that the technique of factor analysis was occasionally used by the researchers to confirm the dimensions of a measure which they expected to exist. Also, an online self-completion questionnaire was designed to collect the empirical data mainly via a Chinese social media application (WeChat). The computerized statistic program (IBM SPSS Statistics Version 26) was utilized to conduct exploratory factor analysis (EFA) and generate relevant tables and figures. The results revealed that the assumptions were not supported by this preliminary research, and also the conclusion and recommendations were drawn at the end of this research.

Jianjun Zhu,1,2,* Yong Fei,2,* Jiajia Deng,2 Bin Huang,2 Ming Yao2 1The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 2Department of Anesthesiology and Pain Research Center, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ming YaoDepartment of Anesthesiology and Pain Research Center, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, No. 1882 South Zhonghuan Road, Nanhu District, Jiaxing City, Zhejiang Province, People’s Republic of ChinaTel +86 573 13456218632Email jxyaoming666@163.comPurpose: In CT-guided dorsal root ganglion puncture, especially T1–T3, it is often difficult to reach the target due to obstruction of the lamina, transverse process, and ribs. Therefore, a safe and effective puncture method with high success rate is urgently needed to guide us in our clinical work.Patients and Methods: A total of 44 patients with dorsal root ganglion underwent pulsed radiofrequency therapy for pain T1–T3 herpes zoster neuralgia at the pain department of Affiliated Hospital of Jiaxing University from January 2019 to February 2020 were retrospectively reviewed. Each patient underwent the same surgical method. The patient’s operation time, CT filming times, nerve electrophysiological tests, the NRS scores before and after operation at one, four, eight, and 12 weeks, Pittsburgh Sleep Disorder Index (PSQI), dosage of gabapentin capsules and tramadol hydrochloride sustained-release tablets, surgical complications and incidence of postherpetic neuralgia (PHN) were recorded.Results: The success rate of 44 patients who underwent puncturing of the costal transverse process to T1 target was 88.46%, to T2 target was 90.68%, and to T3 target was 90.68%, respectively. The NRS score of patients before surg

A reasonable node selection criterion (NSC) is crucial for the network reduction in power systems. In contrast to the previous works that only consider structure property, this paper proposes a comprehensive and quantitative NSC considering both structural and electrical properties. The proposed NSC is developed by employing the evidential reasoning approach, in which the quasi-one-hot encoding is used to determine the evaluation grades of different criteria or attributes. Then, different criteria are combined through the multi-evidence reasoning. Eventually, the utility evaluation is used to derive the quantitative NSC. Besides, the ER can be readily extended to multiple criteria while considering the uncertainty in the evaluation process simultaneously. The reduced models with higher accuracy can be built by combining the proposed NSC with the existing model reduction algorithms. The case studies on a 30-node power grid substantiate the practicality of the proposed NSC.

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field., Comment: 20 pages, 2 figures. Chinese Physics B, Aug. 2020

Chalcogenide glass (ChG) thin films have a wide range of applications in planar photonics that rely on the stability of their optical properties. However, most methods do not provide quantitative optical property data at sufficiently high resolution. We have employed a resonant cavity refractometry technique capable of detecting refractive index changes down to 10[superscript -6] refractive index unit (RIU) to study the aging, or sub-T[subscript g] structural relaxation kinetics, of Ge[subscript 23]Sb[subscript 7]S[subscript 70]ChG. Our study reveals that the refractive index (RI) change due to aging tends to follow stretched exponential behavior, with stretch exponents and rate of index change dependent on initial glass treatment. Thermally annealed devices show the best stability, suggesting that thermal annealing is the appropriate post-deposition treatment method for obtaining stable ChG films., National Science Foundation (Grant 1506605)

Zezheng Liu,1,* Wenquan Liang,1,* Dawei Kang,1 Qingjing Chen,1 Zhicong Ouyang,1 Huibo Yan,1 Bin Huang,1 Dadi Jin,1 Yinkui Chen,2 Qingchu Li1 1Academy of Orthopedics, Guangdong Province, Department of Spine Surgery, The Third Affiliated Hospital of Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Oncology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qingchu LiDepartment of Spine Surgery, The Third Affiliated Hospital of Southern Medical University, Guangzhou, People’s Republic of ChinaEmail lqc16@263.netYinkui ChenDepartment of Oncology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, People’s Republic of ChinaEmail clytze2@126.comIntroduction: Estrogen deficiency leads to bone loss in postmenopausal osteoporosis, because bone formation, albeit enhanced, fails to keep pace with the stimulated osteoclastic bone resorption. The mechanism driving this uncoupling is central to the pathogenesis of postmenopausal osteoporosis, which, however, remains poorly understood. We previously found that Cxcl9 secreted by osteoblasts inhibited osteogenesis in bone, while the roles of Cxcl9 on osteoclastic bone resorption and osteoporosis are unclear.Materials and Methods: Postmenopausal osteoporosis mouse model was established by bilateral surgical ovariectomy (OVX). In situ hybridization was performed to detect Cxcl9 mRNA expression in bone. ELISA assay was conducted to assess Cxcl9 concentrations in bone and serum. Cxcl9 activity was blocked by its neutralizing antibody. Micro-CT was performed to determine the effects of Cxcl9 neutralization on bone structure. Cell Migration and adhesion assay were conducted to evaluate the effects of Cxcl9 on osteoclast activity. TRAP staining and Western blot were performed to assess osteoclast differentiation. CXCR3 antagonist NBI-7

Chen Qiu,1– 3,* Xin Zhang,3,4,* Bin Huang,3,4 Shuai Wang,3,4 Wenjing Zhou,3,4 Chao Li,3,4 Xingang Li,3,4 Jian Wang,3– 5 Ning Yang3,4,6 1Department of Radiation Oncology, Qilu Hospital of Shandong University, Jinan, People’s Republic of China; 2School of Medicine, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 3Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, People’s Republic of China; 4Shandong Key Laboratory of Brain Function Remodeling, Jinan, People’s Republic of China; 5K. G. Jebsen Brain Tumor Research Center, Department of Biomedicine, University of Bergen, Bergen, Norway; 6Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ning Yang Email yangning@sdu.edu.cnIntroduction: Disulfiram (DSF), a drug used in the treatment of alcoholism since 1948, has been shown to have antitumor properties in various tumor types possibly due to the induction of a type cell death, ferroptosis, and the sensitization of cells to chemo- and radiotherapy. In this study, we explored the antitumor properties of DSF in glioblastoma (GBM) and investigated the underlying molecular mechanisms.Methods: GBM cell lines U251 and LN229 were treated with DSF to assess cytotoxicity and activity of the molecule in vitro. Response of cells to treatment was examined using cell viability, flow cytometry, LDH release assay, immunofluorescence and Western blot analysis.Results: DSF inhibited cell growth of GBM U251 and LN229 cell lines in vitro in a concentration-dependent manner. Flow cytometry demonstrated that DSF caused G0-G1 growth arrest. DSF treatment led to increased ROS and lipid peroxidation levels relative to controls indicating the involvement of ferrop

Xu-Qiong Huang,1,* Shuai Hao,1,* Zi-Qi Zhou,2 Bin Huang,1 Jia-Ying Fang,1 Yan Tang,2 Jian-Hua Zhang,3 Jian-Chuan Xia2 1Huadu District People’s Hospital of Guangzhou, Southern Medical University, Guangzhou, Guangdong Province, People’s Republic of China; 2State Key Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong Province, People’s Republic of China; 3Department of Health Service Management, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian-Chuan XiaState Key Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, 651 Dong-Feng Road East, Guangzhou 510060, People’s Republic of ChinaTel +86-20-87343173Fax +86-20-87343392Email xiajch@mail.sysu.edu.cnJian-Hua ZhangDepartment of Health Service Management, Guangzhou University of Chinese Medicine, 232 Wai-Huan Road East, Guangzhou 510006, People’s Republic of ChinaTel/Fax +86-20-39358999Email zhangjh1962@hotmail.comObject: This study aimed at investigating the clinical significance and biological function of ubiquitination factor E4B (UBE4B) in human renal cell carcinoma (RCC).Methods: 19 paired clear cell renal cell carcinoma (ccRCC) tumor samples and the matched neighboring non-tumor samples were used to detect the expression of UBE4B in RCC tumor by Western blotting and RT-qPCR. UBE4B expression was also detected in 151 ccRCC paraffin-embedded tumor samples by using immunohistochemistry. Overall survival (OS) in different UBE4B expression groups were compared with Log rank test. The prognostic value of UBE4B expression in OS was evaluated with the univariate and multivariate Cox regression models. UBE4B was knocked down by small interfering RNA (siRNA) technology, and the effect of UBE4B on cell proliferation, colony formation, metastasis, apoptosis and cell cycle of RCC

The synthesis of pyrrolidono[3,4-d]pyridones of relevance to putative genotoxic colibactin structures featuring a doubly conjugated 1,6-Michael acceptor system is reported. We investigated and implemented a highly selective Pd-catalyzed C(sp3)–H activation reaction as a key step and further functionalized the pyridone core. Evaluating the role of this structural unit of relevance to colibactin, we found that this structure displayed a high degree of stability toward both acidic conditions and nucleophiles.

Visibility affects all forms of traffic: roads, sailing, and aviation. Visibility prediction is meaningful in guiding production and life. Different from weather prediction, which relies solely on atmosphere factors, the factors that affect meteorological visibility are more complicated, such as the air pollution caused by factory exhaust emission. However, the current prediction of visibility is mostly based on the numerical prediction method similar to the weather prediction. We proposed a method using multimodal fusion to build a weather visibility prediction system in this paper. An advanced numerical prediction model and a method for emission detection were used to build a multimodal fusion visibility prediction system. We used the most advanced regression algorithm, XGBoost, and LightGBM, to train the fusion model for numerical prediction. Through the estimation of factory emission by the traditional detector in the satellite image, we propose to add the result of estimation based on Landsat-8 satellite images to assist the prediction. By testing our numerical model in atmosphere data of various meteorological observation stations in Beijing-Tianjin-Hebei region from 2002 to 2018, our numerical prediction model turns out to be more accurate than other existing methods, and after fusing with emission detection method, the accuracy of our visibility prediction system has been further improved., QC 20190805

Space-division multiplexing (SDM), whereby multiple spatial channels in multimode1 and multicore2 optical fibres are used to increase the total transmission capacity per fibre, is being investigated to avert a data capacity crunch3,4 and reduce the cost per transmitted bit. With the number of channels employed in SDM transmission experiments continuing to rise, there is a requirement for integrated SDM components that are scalable. Here, we demonstrate a cladding-pumped SDM erbium-doped fibre amplifier (EDFA) that consists of six uncoupled multimode erbium-doped cores. Each core supports three spatial modes, which enables the EDFA to amplify a total of 18 spatial channels (six cores × three modes) simultaneously with a single pump diode and a complexity similar to a single-mode EDFA. The amplifier delivers >20 dBm total output power per core and <7 dB noise figure over the C-band. This cladding-pumped EDFA enables combined space-division and wavelength-division multiplexed transmission over multiple multimode fibre spans.

Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential for the many practical tasks, such as face recognition, face tracking, and face animation. In this work, we present a fast bottom-up multi-face alignment approach, which can simultaneously localize multi-person facial landmarks with high precision.In more detail, our bottom-up architecture maps the landmarks to the high-dimensional space with which landmarks of all faces are represented. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously.Extensive experiments show that our method can achieve high performance in the multi-face landmark alignment task while our model is extremely fast. Moreover, we propose a new multi-face dataset to compare the speed and precision of bottom-up face alignment method with top-down methods. Our dataset is publicly available at https://github.com/AISAResearch/FoxNet, Comment: Accepted by the 26th IEEE International Conference on Image Processing(ICIP2019)

This paper has been retracted. Zhou X, Liu Z, Huang B, et al. Drug Des Devel Ther. 2019;13:2703–2713. The Editor and Publisher of Drug Design, Development and Therapy wish to retract the published article. Concerns were raised regarding the alleged duplication of Oil Red O images in Figures 4 and Figure 6. Specifically, Figure 4C, Oil Red O, Ctrl, appears to have been duplicated with the same image for Figure 6D, Oil Red O, OG+ICG-001. The authors cooperated with the investigation and provided data associated with the study. However, despite the authors’ assistance, it was still not clear how the images came to be duplicated, and, as we cannot verify the validity of the published work, we are therefore retracting the article and the authors agree with this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

We demonstrate widely wavelength-tunable continuous-wave (CW) and Q-switched Er3+-doped ZBLAN fluoride fiber lasers operating around 3 μm enabled by a volume Bragg grating (VBG). In the CW operation regime, a total wavelength tuning range of over 160-nm spanning from 2694 to 2854 nm has been achieved. For the Q-switched mode of operation, a slightly modified resonator configuration, incorporating a passive Q-switcher, topological insulator Bi2Te3 nanosheets, can produce stable pulse trains with a pulse width of 880 ns at a repetition rate of 81 kHz, while maintaining a wavelength tuning range of 62 nm from 2762 to 2824 nm through adjusting the VBG. In both operation regimes, the output spectral width is measured to be <;0.3 nm (full-width at half-maximum) over the whole tuning range. Our work both demonstrates the great wavelength-tuning potential of the Er3+ -doped fluoride fiber laser, and also paves a way for the development of a range of high-performance midinfrared laser sources.

We demonstrate widely wavelength-tunable continuous-wave (CW) and Q-switched Er3+-doped ZBLAN fluoride fiber lasers operating around 3 μm enabled by a volume Bragg grating (VBG). In the CW operation regime, a total wavelength tuning range of over 160-nm spanning from 2694 to 2854 nm has been achieved. For the Q-switched mode of operation, a slightly modified resonator configuration, incorporating a passive Q-switcher, topological insulator Bi2Te3 nanosheets, can produce stable pulse trains with a pulse width of 880 ns at a repetition rate of 81 kHz, while maintaining a wavelength tuning range of 62 nm from 2762 to 2824 nm through adjusting the VBG. In both operation regimes, the output spectral width is measured to be <;0.3 nm (full-width at half-maximum) over the whole tuning range. Our work both demonstrates the great wavelength-tuning potential of the Er3+ -doped fluoride fiber laser, and also paves a way for the development of a range of high-performance midinfrared laser sources.

Using a prefecture-level panel dataset from the 1840s-1920s, this paper explores the effect of historical concessions on the rise of the modern economy in China. By applying a DID method, it is found that concessions in Chinese history have a significantly positive effect on the rise of China's modern economy, measured by modern native firms and native banks. More specifically, it shows that the treaty ports with concession(s) perform better in boosting modern economic development than the treaty ports without concession. This effect is still significant after controlling for the effects of infrastructure conditions, conflict, and foreign investment. I argue that the effect of concession partly comes from the fact that concessions could provide better protection over property safety, especially during the Warlord Period, when the political environment was not stable. In addition, concessions may also have positive influences on the legal system and improvement of administration. The administrative institutions favor the merchant elites in many aspects, such as a reasonable tax burden. The effect of concessions with a common law system surpasses that of those with a civil law system. Keywords: Concession, Modern Economy, Property Safety, Legal System, China

Using a prefecture-level panel dataset from the 1840s-1920s, this paper explores the effect of historical concessions on the rise of the modern economy in China. By applying a DID method, it is found that concessions in Chinese history have a significantly positive effect on the rise of China's modern economy, measured by modern native firms and native banks. More specifically, it shows that the treaty ports with concession(s) perform better in boosting modern economic development than the treaty ports without concession. This effect is still significant after controlling for the effects of infrastructure conditions, conflict, and foreign investment. I argue that the effect of concession partly comes from the fact that concessions could provide better protection over property safety, especially during the Warlord Period, when the political environment was not stable. In addition, concessions may also have positive influences on the legal system and improvement of administration. The administrative institutions favor the merchant elites in many aspects, such as a reasonable tax burden. The effect of concessions with a common law system surpasses that of those with a civil law system. Keywords: Concession, Modern Economy, Property Safety, Legal System, China

Multifunctional emitting materials are scarce and need to be further explored. Now, a newly anthraquinone derivative, 2-(phenothiazine-10-yl)-anthraquinone (PTZ-AQ) was designed and synthesized and found to demonstrate polymorphism, multi-color emission, aggregation-induced emission (AIE), mechanochromic luminescence (MCL), and thermally activated delayed fluorescence (TADF) in its different solid forms. It is shown for the first time that TADF properties of a compound can be systematically tuned via its aggregation state. The optimized PTZ-AQ crystal shows a small singlet-triplet energy splitting of 0.01 eV and exhibits red TADF with a photoluminescence quantum yield as high as 0.848. This study shows that the unique multiple functions can be integrated into one single compound through controlling the aggregation states, which provides a new strategy for the investigation and application of multifunctional organic materials.