Search

Background: Asthma affects 3% of the global population, leading to over 0.25 million deaths. Due to its complexity, asthma is difficult to cure or prevent, and current therapies have limitations. This has led to a growing demand for alternative asthma treatments. We found rosmarinic acid (RosA) as a potential new drug candidate from natural medicine. However, RosA has poor bioavailability and remains mainly in the gastrointestinal tract after oral administration, suggesting the involvement of gut microbiota in its bioactivity. Purpose: To investigate the mechanism of RosA in alleviating allergic asthma by gut-lung axis. Methods: We used 16S rRNA gene sequencing and metabolites analysis to investigate RosA's modulation of gut microbiota. Techniques of molecular biology and metabolomics were employed to study the pharmacological mechanism of RosA. Cohousing was used to confirm the involvement of gut microbiota in RosA-induced improvement of allergic asthma. Results: RosA decreased cholate levels from spore-forming bacteria, leading to reduced 5-hydroxytryptamine (5HT) synthesis, bronchoconstriction, vasodilation, and inflammatory cell infiltration. It also increased short-chain fatty acids (SCFAs) levels, facilitating the expression of intestinal tight junction proteins to promote intestinal integrity. SCFAs upregulated intestinal monocarboxylate transporters (MCTs), thereby improving their systemic delivery to reduce Th2/ILC2 mediated inflammatory response and suppress eosinophil influx and mucus production in lung. Additionally, RosA inhibited lipopolysaccharide (LPS) production and translocation, leading to reduced TLR4-NF kappa B mediated pulmonary inflammation and oxidative stress. Conclusions: The anti-asthmatic mechanism of oral RosA is primarily driven by modulation of gut microbiotaderived 5-HT, SCFAs, and LPS, achieving a combined synergistic effect. RosA is a safe, effective, and reliable drug candidate that could potentially replace glucocorticoids for ast

Yiyang Li,1,* Jiali Chen,1,* Xingping Quan,1 Ying Chen,2 Yan Han,1 Jinfen Chen,1 Li Yang,1 Youhua Xu,3 Xu Shen,4 Ruibing Wang,1,5 Yonghua Zhao1,5 1Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Taipa, Macau SAR, People’s Republic of China; 2School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China; 3Faculty of Chinese Medicine, Macau University of Science and Technology, Taipa, Macao SAR, People’s Republic of China; 4Jiangsu Key Laboratory of Drug Target and Drug for Degenerative Diseases, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 5Department of Pharmaceutical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yonghua Zhao, Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Taipa, Macao SAR, People’s Republic of China, Tel +853-88224877, Fax +853-28841358, Email yonghuazhao@um.edu.moBackground: Ischemic stroke (IS) causes tragic death and disability worldwide. However, effective therapeutic interventions are finite. After IS, blood–brain barrier (BBB) integrity is disrupted, resulting in deteriorating neurological function. As a novel therapeutic, extracellular vesicles (EVs) have shown ideal restorative effects on BBB integrity post-stroke; however, the definite mechanisms remain ambiguous. In the present study, we investigated the curative effects and the mechanisms of EVs derived from bone marrow mesenchymal stem cells and brain endothelial cells (BMSC-EVs and BEC-EVs) on BBB integrity after acute IS.Methods: EVs were isolated from BMSCs and BECs, and we investigated the therapeutic effect in vitro oxygen-glucose deprivation (OGD) insulted BECs model and in vivo rat

Background: Due to the wide variety of morphology, size, and dynamics, selecting an optimal valve size and location poses great difficulty in percutaneous pulmonary valve implantation (PPVI). This study aimed to report our experience with in vitro bench testing using patient-specific three-dimensional (3D)-printed models for planning PPVI with the Venus P-valve. Methods: Patient-specific 3D soft models were generated using PolyJet printing with a compliant synthetic material in 15 patients scheduled to undergo PPVI between July 2018 and July 2020 in Central China Fuwai Hospital of Zhengzhou University. Results: 3D model bench testing altered treatment strategy in all patients (100%). One patient was referred for surgery because testing revealed that even the largest Venus P-valve would not anchor properly. In the remaining 14 patients, valve size and/or implantation location was altered to avoid valve migration and/or compression coronary artery. In four patients, it was decided to change the point anchoring because of inverted cone-shaped right ventricular outflow tract (RVOT) (n = 2) or risk of compression coronary artery (n = 2). Concerning sizing, we found that an oversize of 2-5 mm suffices. Anchoring of the valve was dictated by the flaring of the in- and outflow portion in the pulmonary artery. PPVI was successful in all 14 patients (absence of valve migration, no coronary compression, and none-to-mild residual pulmonary regurgitation [PR]). The diameter of the Venus P-valve in the 3D simulation group was significantly smaller than that of the conventional planning group (36 [2] vs. 32 [4], Z = -3.77, P <0.001). Conclusions: In vitro testing indicated no need to oversize the Venus P-valve to the degree recommended by the balloon-sizing technique, as 2-5 mm sufficed.

Jiwen Hou,1,2,&ast; Hua Jiang,1,&ast; Yan Han,1 Rong Huang,1 Xiang Gao,1 Wei Feng,1 Zongjun Guo2 1Department of Geriatrics, Affiliated Hospital of Chengdu University, Chengdu, People’s Republic of China; 2Department of Geriatrics, Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zongjun Guo, Department of Geriatrics, Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, Shandong Province, People’s Republic of China, Tel +8618661808736, Email guozjj@163.comPurpose: This study assessed the influences of different lifestyle on mild cognitive impairment (MCI) progression and established a decision tree prediction model to analyse their predictive significance on MCI progression incidence.Patients and Methods: From October 2015 to February 2020,330 patients with MCI were recruited, and demographic and lifestyle information collected. They were followed up for 19.04 ± 10.227 months. Cognitive function was assessed using the Mini-Mental State Examination Scale every 6 months, and they were divided into MCI stable group and MCI progression group.Results: The Kaplan Meier survival analysis showed an overall cohort survival rate of 33.2%; the annual conversion rate of MCI progression was 20%. Physical exercise, social engagement, high-fat diet, age, napping, and tea drinking were decision tree prediction model nodes. Hobbies were the most important factor for predicting MCI progression. The MCI progression probability rates were: with hobbies 26.829% (44 cases), without hobbies 57.831% (96 cases); for those withot hobbies, with physical exercise 43.077% (28 cases) without physical exercise 72.340% (68 cases); for those without hobbies with physical exercise and social engagement 20.000% (4 cases), without social engagement 53.333% (24 cases); for those without hobbies, physical exercises and social engagement and with nap habits 48.485% (16 ca

Fan Zhang,1,2 Yan Han,1,2 Guojun Zheng,3 Wenjian Li4 1Department of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 2Department of Clinical Nutrition, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 3Clinical Laboratory, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 4Department of Urology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of ChinaCorrespondence: Wenjian Li, Department of Urology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, 300 Lanling North Road, Changzhou, Jiangsu, 213001, People’s Republic of China, Tel +86-0519-82009011, Email bolite@163.comPurpose: This research aims to examine and scrutinize gender variations in the incidence of diabetic nephropathy (DN) and the trajectory of renal function in type 2 diabetes mellitus (T2DM) patients.Patients and Methods: We conducted a retrospective cohort study that enrolled 1549 patients diagnosed with T2DM from May 2015 to July 2023. We separately compared the clinical characteristics of male and female participants with and without DN. We utilized the Kaplan-Meier method to examine the cumulative incidence of DN among T2DM patients of varying genders. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using univariable and multivariable Cox proportional hazards regression analysis to evaluate the correlation between various factors and the risk of DN incidence. Multiple linear regression was utilized to investigate the relationship between ΔeGFR% and each factor. Logistic regression with cubic spline function and smooth curve fitting was employed to analyze the nonlinea

The development of AI-Generated Content (AIGC) has empowered the creation of remarkably realistic AI-generated videos, such as those involving Sora. However, the widespread adoption of these models raises concerns regarding potential misuse, including face video scams and copyright disputes. Addressing these concerns requires the development of robust tools capable of accurately determining video authenticity. The main challenges lie in the dataset and neural classifier for training. Current datasets lack a varied and comprehensive repository of real and generated content for effective discrimination. In this paper, we first introduce an extensive video dataset designed specifically for AI-Generated Video Detection (GenVidDet). It includes over 2.66 M instances of both real and generated videos, varying in categories, frames per second, resolutions, and lengths. The comprehensiveness of GenVidDet enables the training of a generalizable video detector. We also present the Dual-Branch 3D Transformer (DuB3D), an innovative and effective method for distinguishing between real and generated videos, enhanced by incorporating motion information alongside visual appearance. DuB3D utilizes a dual-branch architecture that adaptively leverages and fuses raw spatio-temporal data and optical flow. We systematically explore the critical factors affecting detection performance, achieving the optimal configuration for DuB3D. Trained on GenVidDet, DuB3D can distinguish between real and generated video content with 96.77% accuracy, and strong generalization capability even for unseen types.

The optical characteristics of three types of black holes (BHs) surrounded by a thin accretion disk are discussed, namely the Schwarzschild BH, Bardeen BH, and Hayward BH. We calculate the deflection angle of light as it traverses the vicinity of each BH using numerical integration and semi-analytical methods, revealing that both approaches can effectively elucidate the deflection of light around the BH. We investigate the optical appearance of the accretion disk and its corresponding observational images at various viewing angles, discovering that the luminosity in the region near the BH on the inner side of the accretion disk is higher than that on the outer side owing to higher material density in closer proximity to the BH. We observe a significant accumulation of brightness on the left side of the accretion disk, attributed to the motion of matter and geometric effects. Our findings emphasize the significant influence of the observation inclination angle on the observed outcomes. An increase in the observation inclination angle results in the separation of higher-order images. With the improvement in EHT observation accuracy, we believe that the feature of a minimal distance between the innermost region of the direct image of the Hayward BH and the outermost region of the secondary image can be used as an indicator for identifying Hayward BHs., Comment: 27 pages, 10 figures

Li Chen,1 Wang-Yang Xu,2 Hao Chen,1 Yan-Quan Han,1 Yu-Ting Zhang1 1The First Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, People’s Republic of China; 2The College of Pharmacy, Anhui University of Traditional Chinese Medicine, Hefei, People’s Republic of ChinaCorrespondence: Hao Chen; Yanquan Han, The First Affiliated Hospital, Anhui University of Traditional Chinese Medicine, Hefei, People’s Republic of China, Email chenhao071522@163.com; hyquan2003@163.comPurpose: Gandouling Tablets (GDL), a proprietary Chinese medicine, have shown a preventive effect against Wilson’s disease (WD)-induced neuronal damage in previous studies. However, the potential mechanisms need additional investigation. Combining metabonomics and network pharmacology revealed the GDL pathway against WD-induced neuronal damage.Methods: The WD rat model with a high copper load was developed, and nerve damage was assessed. Total metabonomics was used to identify distinct hippocampus metabolites and enriched metabolic pathways in MetaboAnalyst. The GDL’s possible targets against WD neuron damage were then determined by network pharmacology. Cytoscape constructed compound metabonomics and pharmacology networks. Moreover, molecular docking and Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) validated key targets.Results: GDL reduced WD-induced neuronal injury. Twenty-nine GDL-induced metabolites may protect against WD neuron injury. According to network pharmacology, we identified three essential gene clusters, of which genes in cluster 2 had the most significant impact on the metabolic pathway. A comprehensive investigation identified six crucial targets, including UGT1A1, CYP3A4, CYP2E1, CYP1A2, PIK3CB, and LPL, and their associated core metabolites and processes. Four targets reacted strongly with GDL active components. GDL therapy improved five targets’ expression.Conclusion: This collaborative effort revealed the mechanisms of GDL against WD

Zhen-Yun Wu,1,&ast; Xiang-Min Lu,2,&ast; Rui Liu,1,&ast; Yan-Xia Han,3 Hong-Ying Qian,1 Qian Zhao,1 Mei’e Niu3 1Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China; 2School of Nursing, Suzhou Medical College of Soochow University, Suzhou, People’s Republic of China; 3Department of Nursing, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Mei’e Niu, Department of Nursing, the First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu, 215006, People’s Republic of China, Email meniu_123@163.com Qian Zhao, Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Suzhou, Jiangsu, 215006, People’s Republic of China, Email xujiali1983_01@163.comPurpose: This study was designed to investigate the differences in skeletal-muscle atrophy between patients with stable chronic obstructive pulmonary disease (COPD) and healthy controls; associated factors were also considered. The study comprised selected residents of communities near the First Affiliated Hospital of Soochow University in Suzhou City, East China.Patients and Methods: Included in this study were 123 COPD patients and 60 controls. All patients completed spirometry as well as examinations to determine their functional exercise capacity, body composition, and handgrip strength (HGS).Results: COPD patients had less fat-free mass (FFM), a lower FFM index (FFMI), and a lower 6-min walking distance (6MWD) compared with controls (P = 0.007, P = 0.020, and P < 0.001, respectively) (FFMI: 17.59 ± 1.83 vs 18.34 ± 1.64). The HGS of these patients was also lower compared with that of controls (32.88 ± 7.84 vs 35.48 ± 7.42), and HGS tended toward statistical significance (P = 0.064, respectively). In mult

Fan Zhang,1,2 Yan Han,1,2 Liming Zheng,3 Jianhong Liu,4 Yunfei Wu,4 Zuowei Bao,5 Longgen Liu,6 Wenjian Li7 1Department of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 2Department of Clinical Nutrition, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 3Clinical Laboratory, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 4Department of Pathology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 5Department of Ultrasonography, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 6Department of Liver Diseases, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 7Department of Urology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of ChinaCorrespondence: Longgen Liu, Department of Liver Diseases, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, 300 Lanling North Road, Changzhou, Jiangsu, 213001, People’s Republic of China, Tel +86-0519-82009057, Email ssewllg@163.com Wenjian Li, Department of Urology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, 300 Lanling North Road, Changzhou, Jiangsu, 213001, People’s Republic of China, Tel +86-0519-82009011, Email bolite@163.comPurpose: The identification of significant fibrosis is critical for predicting the prognosis of non-alcoholic fatty liver disease (NAFLD). This study aimed to compare the predictiv

Graph neural networks (GNNs) have found extensive applications in learning from graph data. However, real-world graphs often possess diverse structures and comprise nodes and edges of varying types. To bolster the generalization capacity of GNNs, it has become customary to augment training graph structures through techniques like graph augmentations and large-scale pre-training on a wider array of graphs. Balancing this diversity while avoiding increased computational costs and the notorious trainability issues of GNNs is crucial. This study introduces the concept of Mixture-of-Experts (MoE) to GNNs, with the aim of augmenting their capacity to adapt to a diverse range of training graph structures, without incurring explosive computational overhead. The proposed Graph Mixture of Experts (GMoE) model empowers individual nodes in the graph to dynamically and adaptively select more general information aggregation experts. These experts are trained to capture distinct subgroups of graph structures and to incorporate information with varying hop sizes, where those with larger hop sizes specialize in gathering information over longer distances. The effectiveness of GMoE is validated through a series of experiments on a diverse set of tasks, including graph, node, and link prediction, using the OGB benchmark. Notably, it enhances ROC-AUC by $1.81\%$ in ogbg-molhiv and by $1.40\%$ in ogbg-molbbbp, when compared to the non-MoE baselines. Our code is publicly available at https://github.com/VITA-Group/Graph-Mixture-of-Experts., Comment: NeurIPS 2023

In existing Video Frame Interpolation (VFI) approaches, the motion estimation between neighboring frames plays a crucial role. However, the estimation accuracy in existing methods remains a challenge, primarily due to the inherent ambiguity in identifying corresponding areas in adjacent frames for interpolation. Therefore, enhancing accuracy by distinguishing different regions before motion estimation is of utmost importance. In this paper, we introduce a novel solution involving the utilization of open-world segmentation models, e.g., SAM (Segment Anything Model), to derive Region-Distinguishable Priors (RDPs) in different frames. These RDPs are represented as spatial-varying Gaussian mixtures, distinguishing an arbitrary number of areas with a unified modality. RDPs can be integrated into existing motion-based VFI methods to enhance features for motion estimation, facilitated by our designed play-and-plug Hierarchical Region-aware Feature Fusion Module (HRFFM). HRFFM incorporates RDP into various hierarchical stages of VFI's encoder, using RDP-guided Feature Normalization (RDPFN) in a residual learning manner. With HRFFM and RDP, the features within VFI's encoder exhibit similar representations for matched regions in neighboring frames, thus improving the synthesis of intermediate frames. Extensive experiments demonstrate that HRFFM consistently enhances VFI performance across various scenes., Comment: Code will be released

Low-Light Enhancement (LLE) is aimed at improving the quality of photos/videos captured under low-light conditions. It is worth noting that most existing LLE methods do not take advantage of geometric modeling. We believe that incorporating geometric information can enhance LLE performance, as it provides insights into the physical structure of the scene that influences illumination conditions. To address this, we propose a Geometry-Guided Low-Light Enhancement Refine Framework (GG-LLERF) designed to assist low-light enhancement models in learning improved features for LLE by integrating geometric priors into the feature representation space. In this paper, we employ depth priors as the geometric representation. Our approach focuses on the integration of depth priors into various LLE frameworks using a unified methodology. This methodology comprises two key novel modules. First, a depth-aware feature extraction module is designed to inject depth priors into the image representation. Then, Hierarchical Depth-Guided Feature Fusion Module (HDGFFM) is formulated with a cross-domain attention mechanism, which combines depth-aware features with the original image features within the LLE model. We conducted extensive experiments on public low-light image and video enhancement benchmarks. The results illustrate that our designed framework significantly enhances existing LLE methods., Comment: code will be released

In this paper we focus on three major task: 1) discussing our methods: Our method captures a portion of the data in DCD flowsheets, kidney perfusion data, and Flowsheet data captured peri-organ recovery surgery. 2) demonstrating the result: We built a comprehensive, analyzable database from 2022 OPTN data. This dataset is by far larger than any previously available even in this preliminary phase; and 3) proving that our methods can be extended to all the past OPTN data and future data. The scope of our study is all Organ Procurement and Transplantation Network (OPTN) data of the USA organ donors since 2008. The data was not analyzable in a large scale in the past because it was captured in PDF documents known as ``Attachments'', whereby every donor's information was recorded into dozens of PDF documents in heterogeneous formats. To make the data analyzable, one needs to convert the content inside these PDFs to an analyzable data format, such as a standard SQL database. In this paper we will focus on 2022 OPTN data, which consists of $\approx 400,000$ PDF documents spanning millions of pages. The entire OPTN data covers 15 years (2008--20022). This paper assumes that readers are familiar with the content of the OPTN data.

Qian Zhou,1,2 Wenqi Xu,1,2 Deju Xia,1,2 Xiaoyu Zhu,1,2 Yan Han,1,2 Kai Chen,1,2 Yueping Yin1,2 1Institute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, People’s Republic of China; 2National Center for Sexually Transmitted Diseases Control, Chinese Center for Disease Control and Prevention, Nanjing, People’s Republic of ChinaCorrespondence: Wenqi Xu; Yueping Yin, Institute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, People’s Republic of China, Email xuwq@ncstdlc.org; yinyp@ncstdlc.orgPurpose: The accurate detection of antibiotic susceptibility of Neisseria gonorrhoeae (N. gonorrhoeae) is of great importance for the treatment of patients with gonorrhea as well as to hinder the progress of drug resistance. To promote the application of gonococcal antibiotic susceptibility monitoring in primary hospitals and remote medical institutions, this study evaluated the effect of alternative growth supplements on the antibiotic susceptibility testing of N. gonorrhoeae isolates.Methods: We divided the antimicrobial-containing media into three groups by adding different growth supplements (sterile defibrinated sheep blood, bovine hemoglobin, and Vitox). We tested the antimicrobial susceptibility of 80 N. gonorrhoeae isolates in different groups against eight antibiotics. Nonparametric signed-rank tests were utilized to compare the minimum inhibitory concentration (MIC) results of each group. Taking the MIC results of Vitox group as expected, the essential agreement (EA) and category agreement (CA) of the other two groups were calculated.Results: For the group using sheep blood as growth supplements, the EA values and CA values of each antibiotic were above 90.00% and minor error rates were less than 7.00%. No very major error and major error were observed. For the group using hemoglobin as growth supplements, th

Qian Zhou,1,2,&ast; Wenqi Xu,1,2,&ast; Xiaoyu Zhu,1,2 Jin Zhang,1,2 Yan Zhang,1,2 Yan Han,1,2 Kai Chen,1,2 Yueping Yin1,2 1Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China; 2STD Reference Laboratory, National Center for Sexually Transmitted Diseases Control, Chinese Center for Disease Control and Prevention, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yueping Yin, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China, Email yinyp@ncstdlc.orgPurpose: The increasing drug resistance of Neisseria gonorrhoeae has become a serious public health concern. This study investigated N. gonorrhoeae isolates susceptibility to tetracycline antibiotics and the correlation between minimum inhibitory concentrations (MICs) of different antibiotics. The presence of resistance determinants in N. gonorrhoeae strains displaying different levels of tigecycline resistance was also compared.Methods: The minimum inhibitory concentrations (MICs) of tetracycline, minocycline, tigecycline, eravacycline, omadacycline on 412 N. gonorrhoeae isolates were measured by the agar dilution method. The MICs of ceftriaxone and azithromycin were also measured to determine the correlations between antibiotics by the value of the correlation coefficient R. The presence of resistance determinants was identified through polymerase chain reaction (PCR) and sequencing.Results: The MIC90 was 64mg/L for tetracycline, 64mg/L for minocycline, 0.5mg/L for tigecycline, 0.5mg/L for eravacycline, 4mg/L for omadacycline, 0.25 mg/L for ceftriaxone, and 1mg/L for azithromycin. The MIC90 and mode of tigecycline and eravacycline were much lower than those of tetracycline and minocycline. A poor correlation between omadacycline, eravacycline and tetracycline susceptibility was observed. Minocyc

Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a \textit{long-tail} distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known as \textit{disassortative mixing}. To address these two challenges, we go beyond the bipartite graph to take a hypergraph perspective, introducing a new paradigm that leverages \underline{auxiliary} information from anonymized customer engagement sessions to assist the \underline{main task} of query-item link prediction. This auxiliary information is available at web scale in the form of search logs. We treat all items appearing in the same customer session as a single hyperedge. The hypothesis is that items in a customer session are unified by a common shopping interest. With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}. We develop a \textit{\textbf{D}ual-\textbf{C}hannel \textbf{A}ttention-Based \textbf{H}ypergraph Neural Network} (\textbf{DCAH}), which synergizes information from two potentially noisy sources (original query-item edges and item-item hyperedges). In this way, items on the tail are better connected due to the extra hyperedges, thereby enhancing their link prediction performance. We further integrate DCAH with self-supervised graph pre-training and/or DropEdge training, both of which effectively alleviate disassortative mixing. Extensive experiments on three proprietary E-Commerce datasets show that DCAH yields significant improvements of up to \textbf{24.6\% in mean reciprocal rank (MRR)} and \textbf{48.3\% in recall} compared to GNN-based baselines. Our source code is available at \url{https://github.com/amazon-science/dual-channel-hypergraph-neural-network}., Comment: WSDM 2023

Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data. Whilst these algorithms have achieved significant performance gains across a wide plethora of tasks, they have also failed to consider and increase comprehensive similarity constraints; thus learning a sub-optimal metric in the embedding space. Moreover, up until now; there have been few studies with respect to their performance in the presence of noisy labels. Here, we address the concern of learning a discriminative deep embedding space by designing a novel, yet effective Deep Class-wise Discrepancy Loss (DCDL) function that segregates the underlying similarity distributions (thus introducing class-wise discrepancy) of the embedding points between each and every class. Our empirical results across three standard image classification datasets and two fine-grained image recognition datasets in the presence and absence of noise clearly demonstrate the need for incorporating such class-wise similarity relationships along with traditional algorithms while learning a discriminative embedding space.

Amphibious ground-aerial vehicles fuse flying and driving modes to enable more flexible air-land mobility and have received growing attention recently. By analyzing the existing amphibious vehicles, we highlight the autonomous fly-driving functionality for the effective uses of amphibious vehicles in complex three-dimensional urban transportation systems. We review and summarize the key enabling technologies for intelligent flying-driving in existing amphibious vehicle designs, identify major technological barriers and propose potential solutions for future research and innovation. This paper aims to serve as a guide for research and development of intelligent amphibious vehicles for urban transportation toward the future.

Before the recent success of deep learning methods for automated medical image analysis, practitioners used handcrafted radiomic features to quantitatively describe local patches of medical images. However, extracting discriminative radiomic features relies on accurate pathology localization, which is difficult to acquire in real-world settings. Despite advances in disease classification and localization from chest X-rays, many approaches fail to incorporate clinically-informed domain knowledge. For these reasons, we propose a Radiomics-Guided Transformer (RGT) that fuses \textit{global} image information with \textit{local} knowledge-guided radiomics information to provide accurate cardiopulmonary pathology localization and classification \textit{without any bounding box annotations}. RGT consists of an image Transformer branch, a radiomics Transformer branch, and fusion layers that aggregate image and radiomic information. Using the learned self-attention of its image branch, RGT extracts a bounding box for which to compute radiomic features, which are further processed by the radiomics branch; learned image and radiomic features are then fused and mutually interact via cross-attention layers. Thus, RGT utilizes a novel end-to-end feedback loop that can bootstrap accurate pathology localization only using image-level disease labels. Experiments on the NIH ChestXRay dataset demonstrate that RGT outperforms prior works in weakly supervised disease localization (by an average margin of 3.6\% over various intersection-over-union thresholds) and classification (by 1.1\% in average area under the receiver operating characteristic curve). We publicly release our codes and pre-trained models at \url{https://github.com/VITA-Group/chext}.

Modern people live in an environment with ubiquitous food cues, including food advertisements, videos, and smells. Do these food cues change people's eating behavior? Since diet plays a crucial role in maintaining health, it has been researched for decades. As convenient alternatives for real food, food images are widely used in diet research. To date, researchers from Germany, Spain, and other countries have established food photo databases; however, these food pictures are not completely suitable for Chinese studies because of the ingredients and characteristics of Chinese food. The main goal of this research is to create a library of Chinese food images and to provide as complete a data reference as possible for future studies that use food images as experimental material. After standardized processing, we selected 508 common Chinese food pictures with high familiarity and recognizability and attached detailed classifications concerning taste, macronutrients, calories, and participants' emotional responses to the pictures. Additionally, with food pictures as material, we conducted research on how people make dietary decisions in order to identify the variables that may affect a person's food choices. The effects of individual perceived healthiness and palatability, gender, BMI, family income, and levels of emotional and restricted eating were examined using eating decisions based on healthiness and palatability as dependent variables. The results showed that people with low household incomes are more likely to be influenced by food taste in their dietary decision-making process, while individuals with high household incomes are more likely to consider the healthy aspects of food. Moreover, parental BMI affects what children consume, with children who have parents with higher BMIs being more prone to overlook the healthiness value of food.

Wen-Qi Xu,1,2 Xiao-li Zheng,1,2 Jing-Wei Liu,1,2 Qian Zhou,1,2 Xiao-Yu Zhu,1,2 Jin Zhang,1,2 Yan Han,1,2 Kai Chen,1,2 Shao-Chun Chen,1,2 Xiang-Sheng Chen,1,2 Yue-Ping Yin1,2 1STD Reference Laboratory, Institute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China; 2STD Reference Laboratory, National Center for Sexually Transmitted Diseases Control, Chinese Center for Disease Control and Prevention, Nanjing, People’s Republic of ChinaCorrespondence: Yue-Ping YinSTD Reference Laboratory, Institute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of ChinaTel/Fax +86 258 547 8024Email yinyp@ncstdlc.orgPurpose: This study aimed to determine the minimum inhibitory concentrations (MICs) of ertapenem on Neisseria gonorrhoeae collected from eight Chinese provinces in 2018.Methods: The MICs of ertapenem on 503 Neisseria gonorrhoeae isolates (415 isolates selected randomly and 88 isolates selected with preference) were measured using the agar dilution method. For comparison, the MICs of ceftriaxone and azithromycin were detected.Results: Among 415 randomly selected isolates, the MIC range for ertapenem was from ≤ 0.008 mg/L to 0.5 mg/L. The corresponding MIC50 and MIC90 were 0.06 and 0.125 mg/L, respectively. Twelve of 415 isolates (2.9%) exhibited MIC values ≥ 0.25 mg/L, and only one isolate (0.2%) had a MIC of 0.5 mg/L. By comparing all 503 tested isolates, a correlation of r = 0.487 (P < 0.001) between ertapenem and ceftriaxone MIC was observed, and the correlation between MICs of ertapenem and azithromycin was low (r = − 0.12, P = 0.007). In 24 ceftriaxone-decreased susceptibility isolates, four isolates (16.7%) showed a MIC ≥ 0.25 mg/L for ertapenem. In 85 azithromycin resistant isolates, three isolates (3.5%) showed a MIC ≥ 0.25 mg/L for ertapenem.Conclusion: T

Yan Han,1,2 Yue-Ping Yin,1,2 Jing-Wei Liu,1,2 Kai Chen,1,2 Bang-Yong Zhu,3 Ke Zhou,1,2 Mei-Qin Shi,1,2 Wen-Qi Xu,1,2 Tulip A Jhaveri,4 Xiang-Sheng Chen1,2 1Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China; 2National Center for STD Control, Chinese Center for Disease Control and Prevention, Nanjing, People’s Republic of China; 3Institute of Dermatology, Guangxi Autonomous Region, Nanning, People’s Republic of China; 4Division of Medical Microbiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Yue-Ping YinInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, 12 Jiangwangmiao Street, Nanjing, 210042, People’s Republic of ChinaTel/Fax +86 258 547 8024Email yinyp@ncstdlc.orgBackground: Previous studies have investigated rectal Mycoplasma genitalium (MG) in men who have sex with men (MSM), while little is known about the prevalence of rectal MG infection in individuals attending sexually transmitted disease (STD) clinics in China. We aimed to estimate the prevalence of rectal MG infection in this population and identified the potential risk factors for rectal MG infection.Methods: A cross-sectional study was conducted among individuals attending STD clinics located in China from June 2018 to August 2020. Univariate and multivariate logistic regression analyses were conducted to explore the association of different risk factors for rectal MG infection.Results: A total of 1,382 patients were included in the final analyses. A total of 30 of 1377 rectal swabs (2.2%) and 77 of 1374 urogenital samples (5.6%) were positive for MG. In Guangxi, 18 of 47 patients (38.3%) infected with urogenital MG and 5 of 19 patients (26.3%) infected with rectal MG received the recommended treatment. Factors found to be significantly associated with rectal MG infection included: male (adjusted odds

Computer-aided diagnosis plays a salient role in more accessible and accurate cardiopulmonary diseases classification and localization on chest radiography. Millions of people get affected and die due to these diseases without an accurate and timely diagnosis. Recently proposed contrastive learning heavily relies on data augmentation, especially positive data augmentation. However, generating clinically-accurate data augmentations for medical images is extremely difficult because the common data augmentation methods in computer vision, such as sharp, blur, and crop operations, can severely alter the clinical settings of medical images. In this paper, we proposed a novel and simple data augmentation method based on patient metadata and supervised knowledge to create clinically accurate positive and negative augmentations for chest X-rays. We introduce an end-to-end framework, SCALP, which extends the self-supervised contrastive approach to a supervised setting. Specifically, SCALP pulls together chest X-rays from the same patient (positive keys) and pushes apart chest X-rays from different patients (negative keys). In addition, it uses ResNet-50 along with the triplet-attention mechanism to identify cardiopulmonary diseases, and Grad-CAM++ to highlight the abnormal regions. Our extensive experiments demonstrate that SCALP outperforms existing baselines with significant margins in both classification and localization tasks. Specifically, the average classification AUCs improve from 82.8% (SOTA using DenseNet-121) to 83.9% (SCALP using ResNet-50), while the localization results improve on average by 3.7% over different IoU thresholds.

Neural Architecture Search (NAS) is the game changer in designing robust neural architectures. Architectures designed by NAS outperform or compete with the best manual network designs in terms of accuracy, size, memory footprint and FLOPs. That said, previous studies focus on developing NAS algorithms for clean high quality data, a restrictive and somewhat unrealistic assumption. In this paper, focusing on the differentiable NAS algorithms, we show that vanilla NAS algorithms suffer from a performance loss if class labels are noisy. To combat this issue, we make use of the principle of information bottleneck as a regularizer. This leads us to develop a noise injecting operation that is included during the learning process, preventing the network from learning from noisy samples. Our empirical evaluations show that the noise injecting operation does not degrade the performance of the NAS algorithm if the data is indeed clean. In contrast, if the data is noisy, the architecture learned by our algorithm comfortably outperforms algorithms specifically equipped with sophisticated mechanisms to learn in the presence of label noise. In contrast to many algorithms designed to work in the presence of noisy labels, prior knowledge about the properties of the noise and its characteristics are not required for our algorithm., Comment: Accepted to WACV 2022

Building a highly accurate predictive model for classification and localization of abnormalities in chest X-rays usually requires a large number of manually annotated labels and pixel regions (bounding boxes) of abnormalities. However, it is expensive to acquire such annotations, especially the bounding boxes. Recently, contrastive learning has shown strong promise in leveraging unlabeled natural images to produce highly generalizable and discriminative features. However, extending its power to the medical image domain is under-explored and highly non-trivial, since medical images are much less amendable to data augmentations. In contrast, their prior knowledge, as well as radiomic features, is often crucial. To bridge this gap, we propose an end-to-end semi-supervised knowledge-augmented contrastive learning framework, that simultaneously performs disease classification and localization tasks. The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation. Specifically, we first apply an image encoder to classify the chest X-rays and to generate the image features. We next leverage Grad-CAM to highlight the crucial (abnormal) regions for chest X-rays (even when unannotated), from which we extract radiomic features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray. In this way, our framework constitutes a feedback loop for image and radiomic modality features to mutually reinforce each other. Their contrasting yields knowledge-augmented representations that are both robust and interpretable. Extensive experiments on the NIH Chest X-ray dataset demonstrate that our approach outperforms existing baselines in both classification and localization tasks., Comment: Accepted by WACV 2022

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models (> 10% in F1-score) and increases the model's interpretability., Comment: Accepted for ISBI 2021

Jia Ke,1,&ast; Meng-Ting Li,1,&ast; Ya-Jing Huo,1 Yan-Qiong Cheng,2 Shu-Fen Guo,1 Yang Wu,1 Lei Zhang,3 Jianpeng Ma,4 Ai-Jun Liu,2 Yan Han1 1Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Department of Pharmacology, School of Pharmacy, Second Military Medical University, Shanghai, People’s Republic of China; 3Department of Vascular Surgery, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 4Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ai-Jun LiuDepartment of Pharmacology, School of Pharmacy, Second Military Medical University, 800 Xiangyin Road, Yangpu, Shanghai, 200433, People’s Republic of ChinaEmail mrliuaijun@163.comYan HanDepartment of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou, Shanghai, 200437, People’s Republic of ChinaTel +86 21 51322043Email hanyan.2006@aliyun.comPurpose: We aimed to investigate potential synergistic antiplatelet effects of Ginkgo biloba extract (GBE50) in combination with aspirin using in vitro models.Methods: Arachidonic acid (AA), platelet activating factor (PAF), adenosine 5ʹ-diphosphate (ADP) and collagen were used as inducers. The antiplatelet effects of GBE50, aspirin and 1:1 combination of GBE50 and aspirin were detected by microplate method using rabbit platelets. Synergy finder 2.0 was used to analyze the synergistic antiplatelet effect. The compounds in GBE50 were identified by UPLC-Q/TOF-MS analysis and the candidate compounds were screened by TCMSP database. The targets of candidate compounds and aspirin were o

The catechol-O-methyltransferase (COMT) Val158Met polymorphism has been associated with working memory (WM) in many studies, but the results have not been consistent. One plausible explanation is sex-specific effects of this polymorphism as reported in several studies. The current study aimed to explore the sex-specific effects of the COMT Val158Met polymorphism on WM-related brain function in an elderly sample. We found that Val homozygotes outperformed Met allele carriers on the backward digit span subtest for both males and females. The triangular part of the left inferior frontal gyrus and the left inferior temporal gyrus exhibited higher activation in Met allele carriers compared with Val homozygotes during the n-back task, while the background functional connectivity (bFC) between the left angular gyrus (ANG) and the right ANG was enhanced in Val homozygotes as compared to Met allele carriers. Finally, the associations between brain activation, bFC (among various regions), and WM performance were identified only in specific genotype groups of the female participants. These findings provide new insights into the role of COMT Val158Met gene polymorphism in brain function, particularly its female-specific nature.

The catechol-O-methyltransferase (COMT) Val158Met polymorphism has been associated with working memory (WM) in many studies, but the results have not been consistent. One plausible explanation is sex-specific effects of this polymorphism as reported in several studies. The current study aimed to explore the sex-specific effects of the COMT Val158Met polymorphism on WM-related brain function in an elderly sample. We found that Val homozygotes outperformed Met allele carriers on the backward digit span subtest for both males and females. The triangular part of the left inferior frontal gyrus and the left inferior temporal gyrus exhibited higher activation in Met allele carriers compared with Val homozygotes during the n-back task, while the background functional connectivity (bFC) between the left angular gyrus (ANG) and the right ANG was enhanced in Val homozygotes as compared to Met allele carriers. Finally, the associations between brain activation, bFC (among various regions), and WM performance were identified only in specific genotype groups of the female participants. These findings provide new insights into the role of COMT Val158Met gene polymorphism in brain function, particularly its female-specific nature.

We propose that there is a universal picture for different constructions of holographic toy models, and its continuous limit can be described by a gravitylike field theory, namely, the rank-2 U(1) gauge theory. First, we show that two different toy models for holography—the perfect tensor networks and the hyperbolic fracton models—are both equivalent to a picture of evenly distributed bit threads on geodesics in the anti–de Sitter space. We name this picture “geodesic string condensation.” It is actually a natural leading-order approximation to the holographic entanglement structure. Then, we reason that the rank-2 U(1) gauge theory with linearized diffeomorphism as its gauge symmetry, also known as a case of Lifshitz gravity, is the bulk field theory that gives rise to this picture. The Gauss's laws and spatial curvature require the geodesic gauge field lines, instead of the local loops (magnetic fields), to be the fundamental dynamical variables, which lead to geodesic string condensation. These results provide an intuitive way to understand the entanglement structure of gravity in anti–de Sitter/conformal field theory., source:https://journals.aps.org/prb/abstract/10.1103/PhysRevB.102.161119

Xiao-Li Zheng,1,2 Wen-Qi Xu,1,2 Jing-Wei Liu,1,2 Xiao-Yu Zhu,1,2 Shao-Chun Chen,1,2 Yan Han,1,2 Xiu-Qin Dai,1,2 Isabelle Griffin Goodman,3 Christoph Budjan,4,5 Xiang-Sheng Chen,1,2 Yue-Ping Yin1,2 1Institute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China; 2National Center for Sexually Transmitted Diseases Control, Chinese Center for Disease Control and Prevention, Nanjing, People’s Republic of China; 3Boston University, Boston, MA, USA; 4Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; 5Department of Systems Biology, Harvard Medical School, Boston, MA, USACorrespondence: Yue-Ping YinInstitute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 12, Jiangwangmiao Street, Nanjing 210042, People’s Republic of ChinaTel/Fax +86 258 547 8024Email yinyp@ncstdlc.orgPurpose: The study aimed to evaluate meropenem, fosfomycin, berberine hydrochloride, and doxycycline minimum inhibitory concentrations (MICs) of Neisseria gonorrhoeae collected from eight provinces in China in 2018.Methods: The MICs of 540 Neisseria gonorrhoeae isolates (451 isolates selected randomly and 89 isolates selected with preference) were determined to meropenem, fosfomycin, berberine hydrochloride, and doxycycline using the agar dilution method, and the MICs of ceftriaxone and azithromycin were detected for comparison.Results: Among 451 randomly selected isolates, the MIC90 was 0.06 mg/L for meropenem, 64 mg/L for fosfomycin, 64 mg/L for berberine hydrochloride, and 16 mg/L for doxycycline. All isolates showed the MIC ≤ 0.125 mg/L to meropenem, 13 isolates (2.9%) showed MIC > 64 mg/L to fosfomycin, 8 isolates (1.8%) demonstrated MIC > 64 mg/L to berberine hydrochloride, and 271 isolates (60.1%) demonstrated MIC > 1 mg/L to doxycycline. Comparing all

Yan Han,1,2 Yue-Ping Yin,1,2 Wen-Qi Xu,1,2 Xiao-Yu Zhu,1,2 Shao-Chun Chen,1,2 Xiu-Qin Dai,1,2 Li-Gang Yang,3,4 Bang-Yong Zhu,5 Na Zhong,6 Wen-Ling Cao,7 Xiao-Hui Zhang,3,4 Zhi-Zhou Wu,8 Liu-Feng Yuan,9 Zhong-Jie Zheng,10 Jun Liu,11 Xiang-Sheng Chen1,2 1Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, Jiangsu, People’s Republic of China; 2National Center for STD Control, Chinese Center for Disease Control and Prevention, Nanjing, Jiangsu, People’s Republic of China; 3Dermatology Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China; 4Guangdong Provincial Dermatology Hospital, Guangzhou, Guangdong, People’s Republic of China; 5Institute of Dermatology, Nanning, Guangxi Autonomous Region, People’s Republic of China; 6Hainan Provincial Center for STD/Skin Disease Control and Prevention, Haikou, Hainan, People’s Republic of China; 7Guangzhou Institute of Dermatology, Guangzhou, Guangdong, People’s Republic of China; 8Jiangmen Dermatology Hospital, Jiangmen, Guangdong, People’s Republic of China; 9Beijing Ditan Hospital Capital Medical University, Beijing, People’s Republic of China; 10Tianjin Center for Disease Control and Prevention, Tianjin, People’s Republic of China; 11Harvard Medical School, Boston, MA, USACorrespondence: Yue-Ping YinInstitute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, 12 Jiangwangmiao Street, Nanjing 210042, People’s Republic of ChinaTel/ Fax +86 258 547 8024Email yinyp@ncstdlc.orgPurpose: Injectable ceftriaxone and oral cefixime are the last agents effective against Neisseria gonorrhoeae. In vitro antimicrobial-susceptibility testing (AST) is done to identify the most efficacious antibiotic needed to combat the infection in that particular individual. The objective of this study was to evaluate whether Kirby&

Shao-Chun Chen,1,2,* Jing-Wei Liu,1,2,* Xing-Zhong Wu,3 Wen-Ling Cao,4 Feng Wang,5 Jin-Mei Huang,3 Yan Han,1,2 Xiao-Yu Zhu,1,2 Bang-Yong Zhu,6 Quan Gan,6 Xiao-Zheng Tang,7 Xing Shen,8 Xiao-Lin Qin,3 Yu-Qi Yu,3 He-Ping Zheng,3 Yue-Ping Yin1,2 1Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China; 2National Center for STD Control, Chinese Center for Disease Control and Prevention, Nanjing, People’s Republic of China; 3Clinical Laboratory, Guangdong Provincial Dermatology Hospital, Guangzhou, Guangdong, People’s Republic of China; 4Clinical Laboratory, Guangzhou Dermatology Hospital, Guangzhou, Guangdong, People’s Republic of China; 5Shenzhen Center for Chronic Diseases Control, Shenzhen, Guangdong, People’s Republic of China; 6Clinical Laboratory, Guangxi Provincial Dermatology Hospital, Nanning, Guangxi, People’s Republic of China; 7Clinical Laboratory, Hainan Provincial Dermatology Hospital, Haikou, Hainan, People’s Republic of China; 8Zhuhai Center for Chronic Diseases Control, Zhuhai, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yue-Ping YinNational Center for STD Control,Chinese Center for Disease Control and Prevention, 12 Jiangwangmiao Street, Nanjing 210042, People’s Republic of ChinaTel +86-25-85478024Email yinyp@ncstdlc.orgHe-Ping ZhengGuangdong Provincial Dermatology Hospital, 2 Lujing Road, Guangzhou 510091, People’s Republic of ChinaEmail zhhpf@hotmail.comIntroduction: Antimicrobial resistance (AMR) of Neisseria gonorrhoeae (N. gonorrhoeae) becomes a grave public health problem in the world. A strengthened Antimicrobial Resistance Surveillance Program is needed to track the trend of AMR development. However, the lack of a proper antimicrobial susceptibility test (AST) method is a barrier to expand the AMR surveillance in Chin

We report upon the results of a research and prototype building project \emph{Worldly~OCR} dedicated to developing new, more accurate image-to-text conversion software for several languages and writing systems. These include the cursive scripts Farsi and Pashto, and Latin cursive scripts. We also describe approaches geared towards Traditional Chinese, which is non-cursive, but features an extremely large character set of 65,000 characters. Our methodology is based on Machine Learning, especially Deep Learning, and Data Science, and is directed towards vast quantities of original documents, exceeding a billion pages. The target audience of this paper is a general audience with interest in Digital Humanities or in retrieval of accurate full-text and metadata from digital images.

In this paper we demonstrate predicting electroencephalograpgy (EEG) features from acoustic features using recurrent neural network (RNN) based regression model and generative adversarial network (GAN). We predict various types of EEG features from acoustic features. We compare our results with the previously studied problem on speech synthesis using EEG and our results demonstrate that EEG features can be generated from acoustic features with lower root mean square error (RMSE), normalized RMSE values compared to generating acoustic features from EEG features (ie: speech synthesis using EEG) when tested using the same data sets.

In this paper we demonstrate speech synthesis using different electroencephalography (EEG) feature sets recently introduced in [1]. We make use of a recurrent neural network (RNN) regression model to predict acoustic features directly from EEG features. We demonstrate our results using EEG features recorded in parallel with spoken speech as well as using EEG recorded in parallel with listening utterances. We provide EEG based speech synthesis results for four subjects in this paper and our results demonstrate the feasibility of synthesizing speech directly from EEG features., Comment: Accepted for publication at IEEE ICASSP 2020

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. In this paper, we develop an end-to-end framework, ChexRadiNet, that can utilize the radiomics features to improve the abnormality classification performance. Specifically, ChexRadiNet first applies a light-weight but efficient triplet-attention mechanism to classify the chest X-rays and highlight the abnormal regions. Then it uses the generated class activation map to extract radiomic features, which further guides our model to learn more robust image features. After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions. We evaluate the ChexRadiNet framework using three public datasets: NIH ChestX-ray, CheXpert, and MIMIC-CXR. We find that ChexRadiNet outperforms the state-of-the-art on both disease detection (0.843 in AUC) and localization (0.679 in T(IoU) = 0.1). We will make the code publicly available at https://github.com/bionlplab/lung_disease_detection_amia2021, with the hope that this method can facilitate the development of automatic systems with a higher-level understanding of the radiological world., Comment: Accepted by AMIA 2021

Wang L, Huang S, Guo H, Han Y, Zheng W, Jiang J. Drug Des Devel Ther. 2016;10:2855– 2867. The authors have advised there are errors in Figures 8 and 10 on pages 2864 and 2865, respectively. During image processing, an automated labelling step resulted in the insertion of incorrect images. As a result, in Figure 8, reporting the drug rectal retention time, an incorrect panel was used for inclusion in the TG-5FU-ME group. In Figure 10, reporting the morphology of rectal tissues after exposure to 5FU thermosensitive gel and TG-5FU-ME, incorrect panels were used for inclusion in the two testing groups. These errors do not affect the interpretation of data or the conclusion of the study. The authors apologize for these errors. Read the original article

In this paper, we have studied the Joule-Thomson expansion of a specific black hole in f(R) gravity coupled with Yang-Mills field. Firstly, 4-dimensional equation of state and the Joule-Thomson coefficient were derived from thermodynamic quantities, the critical temperature and the inversion temperature were obtained, and the isenthalpic curves and the inversion curves have plotted in the T-P plane. Then, by considering the higher dimensions black hole, the inversion temperature was obtained, the isenthalpic curves and the inversion curves also were plotted, and the effect of nonlinear term $\eta$ and dimension for the Joule-Thomson expansion is demonstrated visually by depicting different curves. Finally, we compared the ratio of the inversion temperature to the critical temperatures with the van der Waals fluid and other black hole, which the ratio is not equal to 0.5, and the dimension changes, the ratio also changes., Comment: 18 pages, 12 figures

In this paper, we have studied the Joule-Thomson expansion of a specific black hole in f(R) gravity coupled with Yang-Mills field. Firstly, 4-dimensional equation of state and the Joule-Thomson coefficient were derived from thermodynamic quantities, the critical temperature and the inversion temperature were obtained, and the isenthalpic curves and the inversion curves have plotted in the T-P plane. Then, by considering the higher dimensions black hole, the inversion temperature was obtained, the isenthalpic curves and the inversion curves also were plotted, and the effect of nonlinear term $\eta$ and dimension for the Joule-Thomson expansion is demonstrated visually by depicting different curves. Finally, we compared the ratio of the inversion temperature to the critical temperatures with the van der Waals fluid and other black hole, which the ratio is not equal to 0.5, and the dimension changes, the ratio also changes., Comment: 18 pages, 12 figures

Shan-Shan Su,1,* Sheng-Nan Zhang,1,* Jun-Ru Ye,1 Ling-Na Xu,1 Peng-Cheng Lin,1 Han-Yan Xu,1 Qing Wu,2 Yu-Ping Li1 1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325015, People’s Republic of China; 2The Center of Laboratory and Diagnosis, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325015, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yu-Ping LiDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou, Zhejiang Province 325015, People’s Republic of ChinaTel/fax +86 577 5557 9273Email wzliyp@163.comBackground: Patients with high-titer anti-IFN-γ autoantibodies present disseminated non-tuberculous mycobacterial (NTM) and other opportunistic infections. Due to its rare occurrence and non-specific symptoms, this syndrome is difficult to diagnose during early disease stages. Here, we report a case with high-concentrations of serum anti-IFN-γ autoantibodies who presented with disseminated Talaromyces marneffei and NTM disease accompanied Sweet’s syndrome.Case presentation: A 62-year-old Chinese woman with no previous history was admitted to our hospital in August 2016 due to intermittent fever for 2 years, left chest wall redness, and swelling for 3 months. During hospitalization, the patient was confirmed with disseminated T. marneffei and successfully treated with antifungal therapy. In July 2017, upon second admission, Mycobacterium avium intracellular (MAC) pulmonary infection was established after positive cultures from the right lung tissue. The patient failed treatment after 1 month of anti-NTM therapy due to side effects. In May 2018, she was confirmed as having disseminated MAC disease accompanied by hand rashes, which was considered as Sweet&r

Jing-Wei Liu,1,2 Wen-Qi Xu,1,2 Xiao-Yu Zhu,1,2 Xiu-Qin Dai,1,2 Shao-Chun Chen,1,2 Yan Han,1,2 Jun Liu,3 Xiang-Sheng Chen,1,2 Yue-Ping Yin1,21Institute of Dermatology and Hospital for Skin Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China; 2National Center for Sexually Transmitted Diseases Control, Chinese Center for Disease Control and Prevention, Nanjing, People’s Republic of China; 3SYNAPSE Center, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAPurpose: Gentamicin is a promising antimicrobial for the treatment of gonorrhea. The study aimed to evaluate gentamicin minimum inhibitory concentrations (MICs) of Neisseria gonorrhoeae isolates in China.Methods: In this study, the agar dilution method was used to determine the MICs of 470 isolates collected in 2016 to four effective antimicrobials (gentamicin, azithromycin, ceftriaxone, and spectinomycin).Results: Gentamicin MICs ranged from 1 to 8 mg/L. No isolate was resistant to gentamicin. Of seven isolates simultaneously resistant to azithromycin and ceftriaxone, 6 isolates demonstrated MICs of 4 mg/L or less to gentamicin. No cross relationships were found between MICs of gentamicinand susceptibility profiles of azithromycin, ceftriaxone, and spectinomycin.Conclusion: The in vitro results suggest that gentamicin can be a promising treatment option for gonococcal infections in China. Clinical trials to evaluate the therapeutic efficacy of gentamicin are required.Keywords: Neisseria gonorrhoeae, agar dilution method, gentamicin, antimicrobial surveillance

In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems. A connectionist temporal classification (CTC) based automatic speech recognition (ASR) system was implemented for performing recognition. We introduce techniques to initialize the weights of the recurrent layers in the encoder of the CTC model with more meaningful weights rather than with random weights and we make use of an external language model to improve the beam search during decoding time. We finally study the problem of predicting articulatory features from EEG features in this paper., Comment: On preparation for submission to EUSIPCO 2020. arXiv admin note: text overlap with arXiv:1911.04261, arXiv:1906.08871

In this paper we demonstrate that performance of voice activity detection (VAD) system operating in presence of background noise can be improved by concatenating acoustic input features with electroencephalography (EEG) features. We also demonstrate that VAD using only EEG features shows better performance than VAD using only acoustic features in presence of background noise. We implemented a recurrent neural network (RNN) based VAD system and we demonstrate our results for two different data sets recorded in presence of different noise conditions in this paper. We finally demonstrate the ability to predict whether a person wish to continue speaking a sentence or not from EEG features., Comment: On preparation for submission to EUSIPCO 2020. arXiv admin note: text overlap with arXiv:1906.08871, arXiv:1909.09132

In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model, gated recurrent unit (GRU) regression based model, temporal convolutional network (TCN) regression model and finally using a mixed TCN GRU regression model. We compare our EEG based speech enhancement results with traditional log minimum mean-square error (MMSE) speech enhancement algorithm and our proposed methods demonstrate significant improvement in speech enhancement quality compared to the traditional method. Our overall results demonstrate that EEG features can be used to clean speech recorded in presence of background noise. To the best of our knowledge this is the first time a spoken speech enhancement is demonstrated using EEG features recorded in parallel with spoken speech.

In this paper we first demonstrate continuous noisy speech recognition using electroencephalography (EEG) signals on English vocabulary using different types of state of the art end-to-end automatic speech recognition (ASR) models, we further provide results obtained using EEG data recorded under different experimental conditions. We finally demonstrate decoding of speech spectrum from EEG signals using a long short term memory (LSTM) based regression model and Generative Adversarial Network (GAN) based model. Our results demonstrate the feasibility of using EEG signals for continuous noisy speech recognition under different experimental conditions and we provide preliminary results for synthesis of speech from EEG features.

In this paper we demonstrate continuous noisy speech recognition using connectionist temporal classification (CTC) model on limited Chinese vocabulary using electroencephalography (EEG) features with no speech signal as input and we further demonstrate single CTC model based continuous noisy speech recognition on limited joint English and Chinese vocabulary using EEG features with no speech signal as input. We demonstrate our results using various EEG feature sets recently introduced in [1] as well as we propose a new deep learning architecture in this paper which can perform continuous speech recognition using raw EEG signals on limited joint English and Chinese vocabulary., Comment: arXiv admin note: text overlap with arXiv:1906.08871

In this paper we demonstrate that performance of a speaker verification system can be improved by concatenating electroencephalography (EEG) signal features with speech signal features or only using EEG signal features. We use state-of-the-art end-to-end deep learning model for performing speaker verification and we demonstrate our results for noisy speech. Our results indicate that EEG signals can improve the robustness of speaker verification systems, especially in noiser environment., Comment: Accepted for EUSIPCO 2020