Wild-Type and Non-Wild-Type Mycobacterium tuberculosis MIC Distributions for the Novel Fluoroquinolone Antofloxacin Compared with Those for Ofloxacin, Levofloxacin, and Moxifloxacin.

Antofloxacin (AFX) is a novel fluoroquinolone that has been approved in China for the treatment of infections caused by a variety of bacterial species. We investigated whether it could be repurposed for the treatment of tuberculosis by studying its in vitro activity. We determined the wild-type and non-wild-type MIC ranges for AFX as well as ofloxacin (OFX), levofloxacin (LFX), and moxifloxacin (MFX), using the microplate alamarBlue assay, of 126 clinical Mycobacterium tuberculosis strains from Beijing, China, of which 48 were OFX resistant on the basis of drug susceptibility testing on Löwenstein-Jensen medium. The MIC distributions were correlated with mutations in the quinolone resistance-determining regions of gyrA (Rv0006) and gyrB (Rv0005). Pharmacokinetic/pharmacodynamic (PK/PD) data for AFX were retrieved from the literature. AFX showed lower MIC levels than OFX but higher MIC levels than LFX and MFX on the basis of the tentative epidemiological cutoff values (ECOFFs) determined in this study. All strains with non-wild-type MICs for AFX harbored known resistance mutations that also resulted in non-wild-type MICs for LFX and MFX. Moreover, our data suggested that the current critical concentration of OFX for Löwenstein-Jensen medium that was recently revised by the World Health Organization might be too high, resulting in the misclassification of phenotypically non-wild-type strains with known resistance mutations as wild type. On the basis of our exploratory PK/PD calculations, the current dose of AFX is unlikely to be optimal for the treatment of tuberculosis, but higher doses could be effective.The work was supported by the research funding from Infectious Diseases Special Project, Minister of Health of China (2016ZX10003001-12) and Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX201304). The strains used in this project were obtained from the ‘Beijing Bio-Bank of clinical resources on Tuberculosis’ (D09050704640000), Beijing Chest Hospital. In addition, this study was supported by the Health Innovation Challenge Fund (HICF-T5-342 and WT098600), a parallel funding partnership between the UK Department of Health and Wellcome Trust. T. S. was supported by grants from the Swedish Heart and Lung Foundation and Marianne and Marcus Wallenberg Foundation. The views expressed in this publication are those of the authors and not necessarily those of the Department of Health, Public Health England, or the Wellcome Trust. C. U. K. is a Junior Research Fellow at Wolfson College, Cambridge.This is the author accepted manuscript. The final version is available from American Society for Microbiology at http://dx.doi.org/10.1128/AAC.00393-16

Highly sensitive and ultrastable skin sensors for biopressure and bioforce measurements based on hierarchical microstructures

Piezoresistive microsensors are considered to be essential components of the future wearable electronic devices. However, the expensive cost, complex fabrication technology, poor stability, and low yield have limited their developments for practical applications. Here, we present a cost-effective, relatively simple, and high-yield fabrication approach to construct highly sensitive and ultrastable piezoresistive sensors using a bioinspired hierarchically structured graphite/polydimethylsiloxane composite as the active layer. In this fabrication, a commercially available sandpaper is employed as the mold to develop the hierarchical structure. Our devices exhibit fascinating performance including an ultrahigh sensitivity (64.3 kPa^–1), fast response time (<8 ms), low limit of detection of 0.9 Pa, long-term durability (>100 000 cycles), and high ambient stability (>1 year). The applications of these devices in sensing radial artery pulses, acoustic vibrations, and human body motion are demonstrated, exhibiting their enormous potential use in real-time healthcare monitoring and robotic tactile sensing

Exploring the Influence of Problematic Mobile Phone Use on Mathematics Anxiety and Mathematics Self-Efficacy: An Empirical Study during the COVID-19 Pandemic

Problematic mobile phone use is a pervasive issue globally and has aroused wide public concerns. Prior studies have indicated that problematic mobile phone use has a series of negative effects on individuals&rsquo; physical and mental health. However, the effects on student learning have seldom been investigated. During the COVID-19 pandemic, home quarantine and social distancing have led to individuals&rsquo; greater problematic mobile phone use, and it is essential to have a better understanding of individuals&rsquo; problematic mobile phone use and its negative effects during this unprecedented period. Given this, the present study investigates the effects of university students&rsquo; problematic mobile phone use on mathematics self-efficacy and mathematics anxiety, which play critical roles in mathematics learning. This study collected data from 420 students in March 2022, when a large-scale COVID-19 lockdown took place in Shanghai, China. Structural equation modeling was used to analyze the data. Our findings show that university students&rsquo; problematic mobile phone use can significantly impact mathematics anxiety and indirectly&mdash;yet considerably&mdash;influence mathematics self-efficacy. This study calls for increased public concern regarding students&rsquo; problematic mobile phone use during the COVID-19 pandemic

Exploring the Influence of Problematic Mobile Phone Use on Mathematics Anxiety and Mathematics Self-Efficacy: An Empirical Study during the COVID-19 Pandemic

Problematic mobile phone use is a pervasive issue globally and has aroused wide public concerns. Prior studies have indicated that problematic mobile phone use has a series of negative effects on individuals’ physical and mental health. However, the effects on student learning have seldom been investigated. During the COVID-19 pandemic, home quarantine and social distancing have led to individuals’ greater problematic mobile phone use, and it is essential to have a better understanding of individuals’ problematic mobile phone use and its negative effects during this unprecedented period. Given this, the present study investigates the effects of university students’ problematic mobile phone use on mathematics self-efficacy and mathematics anxiety, which play critical roles in mathematics learning. This study collected data from 420 students in March 2022, when a large-scale COVID-19 lockdown took place in Shanghai, China. Structural equation modeling was used to analyze the data. Our findings show that university students’ problematic mobile phone use can significantly impact mathematics anxiety and indirectly—yet considerably—influence mathematics self-efficacy. This study calls for increased public concern regarding students’ problematic mobile phone use during the COVID-19 pandemic

2012 International Conference on Electrical and Electronics Engineering

Unifying Electrical Engineering and Electronics Engineering is based on the Proceedings of the 2012 International Conference on Electrical and Electronics Engineering (ICEE 2012). This book collects the peer reviewed papers presented at the conference. The aim of the conference is to unify the two areas of Electrical and Electronics Engineering. The book examines trends and techniques in the field as well as theories and applications. The editors have chosen to include the following topics; biotechnology, power engineering, superconductivity circuits, antennas technology, system architectures and telecommunication

High-Resolution Remote Sensing Image Classification with RmRMR-Enhanced Bag of Visual Words

A ReliefF improved mRMR (RmRMR) criterion-based bag of visual words (BoVW) algorithm is proposed to filter the visual words that are generated with high information redundancy for remote sensing image classification. First, the contribution degree of each word to the classification is represented by its weighting parameter, which is assigned using the ReliefF algorithm. Next, the relevance and redundancy of each word are calculated according to the mRMR criterion with the addition of a dictionary balance coefficient. Finally, a novel dictionary discriminant function is established, and the globally discriminative small-scale dictionary subsets are filtered and obtained. Experimental results show that the proposed algorithm effectively reduces the amount of redundant information in the dictionary and better balances the relevance and redundancy of words to improve the feature descriptive power of dictionary subsets and markedly increase the classification precision on a high-resolution remote sensing image

HA-MPPNet: Height Aware-Multi Path Parallel Network for High Spatial Resolution Remote Sensing Image Semantic Seg-Mentation

Semantic segmentation of remote sensing images (RSI) plays a significant role in urban management and land cover classification. Due to the richer spatial information in the RSI, existing convolutional neural network (CNN)-based methods cannot segment images accurately and lose some edge information of objects. In addition, recent studies have shown that leveraging additional 3D geometric data with 2D appearance is beneficial to distinguish the pixels’ category. However, most of them require height maps as additional inputs, which severely limits their applications. To alleviate the above issues, we propose a height aware-multi path parallel network (HA-MPPNet). Our proposed MPPNet first obtains multi-level semantic features while maintaining the spatial resolution in each path for preserving detailed image information. Afterward, gated high-low level feature fusion is utilized to complement the lack of low-level semantics. Then, we designed the height feature decode branch to learn the height features under the supervision of digital surface model (DSM) images and used the learned embeddings to improve semantic context by height feature guide propagation. Note that our module does not need a DSM image as additional input after training and is end-to-end. Our method outperformed other state-of-the-art methods for semantic segmentation on publicly available remote sensing image datasets

Collaborative Learning-Based Network for Weakly Supervised Remote Sensing Object Detection

Existing object detection algorithms rely excessively on instance-level labels, which are both time-consuming and expensive. In particular, for remote sensing images (RSIs) with small and dense objects, the labeling cost is much higher than that of general images. Moreover, the propagation process of the labels over the noisy channel results in blurred and noisy information. To address the problem of obtaining RSI instance-level labels, we aim to propose a collaborative learning-based network for weakly supervised remote sensing object detection (CLN-RSOD). Compared with the state-of-the-art, the proposed model combines the advantages of two object detection sub-networks by jointly training. This improves the model&#x0027;s capability and thereby enhances the detection effect for multiobject in RSI. Moreover, we employ a mask-based proposal refinement algorithm for remote sensing images (MPR-RS) to optimize the candidate boxes. In addition, according to the data distribution characteristics of RSI, we introduce a new joint pooling module in CLN-RSOD to enhance the backbone network&#x0027;s characterization of RSI. Finally, the experimental results on two public remote sensing datasets illustrate that the proposed weakly supervised learning method is superior to other weakly supervised methods and demonstrates the effectiveness of the proposed CLN-RSOD

Strong Spatiotemporal Radar Echo Nowcasting Combining 3DCNN and Bi-Directional Convolutional LSTM

In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directional convolutional long short-term memory. The model first constructs dimensions of input data and gets 3D tensor data with spatiotemporal features, extracts local short-term spatiotemporal features of radar echoes through 3D convolution networks, then utilizes constructed bi-directional convolutional LSTM to learn global long-term spatiotemporal feature dependencies, and finally realizes the forecast of echo image changes by forecasting network. This structure can capture the spatiotemporal correlation of radar echoes in continuous motion fully and realize more accurate forecast of moving trend of short-term radar echoes within a region. The samples of radar echo images recorded by Shenzhen and Hong Kong meteorological stations are used for experiments, the results show that the critical success index (CSI) of this proposed model for eight predicted echoes reaches 0.578 when the echo threshold is 10 dBZ, the false alarm ratio (FAR) is 20% lower than convolutional LSTM network (ConvLSTM), and the mean square error (MSE) is 16% lower than the real-time optical flow by variational method (ROVER), which outperforms the current state-of-the-art radar echo nowcasting methods