Detection and Genomic Characterization of a Morganella morganii Isolate From China That Produces NDM-5

The increasing prevalence and transmission of the carbapenem resistance gene blaNDM–5 has led to a severe threat to public health. So far, blaNDM–5 has been widely detected in various species of Enterobacterales and different hosts across various cities. However, there is no report on the blaNDM–5– harboring Morganella morganii. In January 2016, the first NDM-5-producing Morganella morganii L241 was found in a stool sample of a patient diagnosed as recurrence of liver cancer in China. Identification of the species was performed using 16S rRNA gene sequencing. Carbapenemase genes were identified through both PCR and sequencing. To investigate the characteristics and complete genome sequence of the blaNDM–5-harboring clinical isolate, antimicrobial susceptibility testing, S1 nuclease pulsed field gel electrophoresis, Southern blotting, transconjugation experiment, complete genome sequencing, and comparative genomic analysis were performed. M. morganii L241 was found to be resistant to broad-spectrum cephalosporins and carbapenems. The complete genome of L241 is made up from both a 3,850,444 bp circular chromosome and a 46,161 bp self-transmissible IncX3 plasmid encoding blaNDM–5, which shared a conserved genetic context of blaNDM–5 (ΔIS3000-ΔISAba125-IS5-blaNDM–5-ble-trpF-dsbC-IS26). BLASTn analysis showed that IncX3 plasmids harboring blaNDM genes have been found in 15 species among Enterobacterales from 13 different countries around the world thus far. In addition, comparative genomic analysis showed that M. morganii L241 exhibits a close relationship to M. morganii subsp. morganii KT with 107 SNPs. Our research demonstrated that IncX3 is a key element in the worldwide dissemination of blaNDM-5 among various species. Further research will be necessary to control and prevent the spread of such plasmids

Graphene-Based Nanocomposites for Energy Storage

Since the first report of using micromechanical cleavage method to produce graphene sheets in 2004, graphene/graphene-based nanocomposites have attracted wide attention both for fundamental aspects as well as applications in advanced energy storage and conversion systems. In comparison to other materials, graphene-based nanostructured materials have unique 2D structure, high electronic mobility, exceptional electronic and thermal conductivities, excellent optical transmittance, good mechanical strength, and ultrahigh surface area. Therefore, they are considered as attractive materials for hydrogen (H2) storage and high-performance electrochemical energy storage devices, such as supercapacitors, rechargeable lithium (Li)-ion batteries, Li–sulfur batteries, Li–air batteries, sodium (Na)-ion batteries, Na–air batteries, zinc (Zn)–air batteries, and vanadium redox flow batteries (VRFB), etc., as they can improve the efficiency, capacity, gravimetric energy/power densities, and cycle life of these energy storage devices. In this article, recent progress reported on the synthesis and fabrication of graphene nanocomposite materials for applications in these aforementioned various energy storage systems is reviewed. Importantly, the prospects and future challenges in both scalable manufacturing and more energy storage-related applications are discussed

Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

Imperceptibility and robustness are the two complementary, but fundamental requirements of any digital image watermarking method. To improve the invisibility and robustness of multiplicative image watermarking, a complex wavelet based watermarking algorithm is proposed by using the human visual texture masking and visual saliency model. First, image blocks with high entropy are selected as the watermark embedding space to achieve imperceptibility. Then, an adaptive multiplicative watermark embedding strength factor is designed by utilizing texture masking and visual saliency to enhance robustness. Furthermore, the complex wavelet coefficients of the low frequency sub-band are modeled by a Gaussian distribution, and a watermark decoding method is proposed based on the maximum likelihood criterion. Finally, the effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack

Point completion by a Stack‐Style Folding Network with multi‐scaled graphical features

Abstract Point cloud completion is prevalent due to the insufficient results from current point cloud acquisition equipments, where a large number of point data failed to represent a relatively complete shape. Existing point cloud completion algorithms, mostly encoder‐decoder structures with grids transform (also presented as folding operation), can hardly obtain a persuasive representation of input clouds due to the issue that their bottleneck‐shape result cannot tell a precise relationship between the global and local structures. For this reason, this article proposes a novel point cloud completion model based on a Stack‐Style Folding Network (SSFN). Firstly, to enhance the deep latent feature extraction, SSFN enhances the exploitation of shape feature extractor by integrating both low‐level point feature and high‐level graphical feature. Next, a precise presentation is obtained from a high dimensional semantic space to improve the reconstruction ability. Finally, a refining module is designed to make a more evenly distributed result. Experimental results shows that our SSFN produces the most promising results of multiple representative metrics with a smaller scale parameters than current models

Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF

Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any sacrifice to accuracy, we apply optimization to the original data of the point cloud, allowing the network to handle fewer data. In the experiment, our proposed method is conducted comprehensively through four evaluation indicators, proving the superiority of our method

MMKE: A Multi-Model Knowledge Extraction System from Unstructured Texts

In this work, we present a Multi-Model Knowledge Extraction (MMKE) System which consists of two unstructured text extraction models (RelationSO model and SubjectRO model) based on a multi-task learning framework. Instead of recognizing entity first and then predicting relationships between entity pairs in previous works, MMKE detects subject and corresponding relationships before extracting objects to cope with the diverse object-type problem, overlapping problem and non-predefined relation problem. Our system accepts unstructured text as input, from which it automatically extracts triplets knowledge (subject, relation, object). More importantly, we incorporate a number of user-friendly extraction functionalities, such as multi-format uploading, one-click extractions, knowledge editing and graphical displays. The demonstration video is available at this link: https://youtu.be/HtOPJrGhSxk

Hydrogen-Bonded Organic Frameworks Based Mixed-Matrix Membranes with Low Temperature Antiviral Activity

Hydrogen-bonded organic frameworks (HOFs) are a type of porous molecular crystal consisting of multiple rigid large π-conjugated structures. They hold great potential as photosensitive antiviral materials due to their solution processability. Moreover, HOFs can easily bind to polymeric matrices, making them a flexible and green option for low-temperature antiviral materials. In this study, we fabricated a series of HOF@polymer mixed-matrix membranes (MMMs) by a solution casting technique for low temperature antiviral applications. The incorporation of HOF-101 crystals into poly vinylidene difluoride (PVDF) membranes can enhance their mechanical strength by at least 20%. The unique one-dimensional pore channels of HOF-101 enable the MMMs to have increased exposure to oxygen, providing the potential for enhanced generation of singlet oxygen (1O2). The 1O2 generated by 1 wt % HOF-101@PVDF MMMs at 263 K was observed to be more than 40% higher compared to that at 298 K. The excellent 1O2 generation efficiency allowed the MMMs to maintain their virucidal efficacy by more than 99% and 95% against vesicular stomatitis virus and herpes simplex virus type 1, respectively, at low temperatures