Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Computer science & Population. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study in 27 patients

Abstract: Background Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. Our research aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT, relieve working pressure of radiologists and contribute to the control of the epidemic. Methods For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University (Wuhan, Hubei province, China) were retrospectively collected and processed. Twenty-seven consecutive patients undergoing CT scans in Feb, 5, 2020 in Renmin Hospital of Wuhan University were prospectively collected to evaluate and compare the efficiency of radiologists against 2019-CoV pneumonia with that of the model. Findings The model achieved a per-patient sensitivity of 100%, specificity of 93.55%, accuracy of 95.24%, PPV of 84.62%, and NPV of 100%; a per-image sensitivity of 94.34%, specificity of 99.16%, accuracy of 98.85%, PPV of 88.37%, and NPV of 99.61% in retrospective dataset. For 27 prospective patients, the model achieved a comparable performance to that of expert radiologist. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. Conclusion The deep learning model showed a comparable performance with expert radiologist, and greatly improve the efficiency of radiologists in clinical practice. It holds great potential to relieve the pressure of frontline radiologists, improve early diagnosis, isolation and treatment, and thus contribute to the control of the epidemic.

Chitosan- metal complexes as antimicrobial agent: Synthesis, characterization and Structure-activity study

Abstract: Chitosan (CS) metal complexes with bivalent metal ions, including Cu(II), Zn(II), Fe(II) were prepared, and characterized by FT-IR, XRD, AAS and elemental analysis. The crystalline and structural properties of chitosan-metal complexes were different from those of chitosan, and the -NH2, -OH groups in chitosan molecule were considered as the dominating reactive sites. In vitro antimicrobial activities of the obtained chitosan-metal complexes, which were found to be much better than free chitosan and metal salts, were examined against two gram-positive bacteria (S. aureus and S. epidermidis), two gram-negative bacteria (E. coli and P. aeruginosa) and two fungi (C. albicans and C. parapsilosis). Results indicatd that the inhibitory effects of chitosan-metal complexes were dependent on the property of metal ions, the molecular weight and degree of deacetylation of chitosan and environmental pH values. Electro microscopy confirmed that the exposure of S. auresus to the chitosan-Cu(II) complex resulted in the disruption of cell envelop. Based on the discussion upon the antimicrobial mechanism of chitosan-metal complexes and their molecular structures, the structure-activity correlation for the antimicrobial activities was elucidated. All the results show that chitosan-metal complexes are a promising candidate for novel antimicrobial agents that can be used in cosmetic, food, textile et al.

Electrochemical sensors based on graphene materials

Abstract: Single–layered graphene, emerging as a true two–dimensional nanomaterial, has tremendous potential for electrochemical catalysis and biosensing as a novel electrode material. Considering the excellent properties of graphene, such as large surface–to–volume ratio, high conductivity and electron mobility at room temperature, low energy dynamics of electrons with atomic thickness, robust mechanical and flexibility, we give a general view of recent advances in electrochemical sensors based on graphene. We are highlighting here important applications of graphene and graphene nanocomposites, and the assay strategies in electrochemical sensors for DNA, proteins, neurotransmitters, phytohormones, pollutants, metal ions, gases, hydrogen peroxide, and in medical, enzymatic and immunosensors.

Millimeter-Size Single-Crystal Graphene by Suppressing Evaporative Loss of Cu During Low Pressure Chemical Vapor Deposition

Abstract: Millimeter-size single-crystal monolayer graphene is synthesized on polycrystalline Cu foil by a method that involves suppressing loss by evaporation of the Cu at high temperature under low pressure This significantly diminishes the number of graphene domains, and large single crystal domains up to ∼2 mm in size are grown

Camouflaged Object Detection

Abstract: We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are “seamlessly” embedded in their surroundings. The high intrinsic similarities between the target object and the background make COD far more challenging than the traditional object detection task. To address this issue, we elaborately collect a novel dataset, called COD10K, which comprises 10,000 images covering camouflaged objects in various natural scenes, over 78 object categories. All the images are densely annotated with category, bounding-box, object-/instance-level, and matting-level labels. This dataset could serve as a catalyst for progressing many vision tasks, e.g., localization, segmentation, and alpha-matting, etc. In addition, we develop a simple but effective framework for COD, termed Search Identification Network (SINet). Without any bells and whistles, SINet outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Finally, we conduct a large-scale COD study, evaluating 13 cutting-edge models, providing some interesting findings, and showing several potential applications. Our research offers the community an opportunity to explore more in this new field. The code will be available at https://github.com/DengPingFan/SINet/.