Accurate and robust detection of road damage is essential for public transportation safety. Currently, deep convolutional neural networks (CNNs)-based road damage detection algorithms to localize and classify damage with a bounding box have achieved remarkable progress. However, research in this field fails to take into account two key characteristics of road damage weak semantic information and abnormal geometric properties, resulting in inappropriate feature representation and suboptimal detection results. To boost the performance, we propose a CNN-based cascaded damage detection network, called CrdNet. The proposed model has three parts (1) We introduce a novel backbone network, named LrNet, that reuses low-level features and mixes suitable range dependency features to learn high-to-low level feature fusions for road damage weak semantic information representation. (2) We apply multi-scale and multiple aspect ratios anchor mechanism to generate high-quality positive samples regarding the damage with abnormal geometric properties for network training. (3) We designed an adaptive proposal assignment strategy and performed cascade predictions on corresponding branches that can establish different range dependencies.  https://www.selleckchem.com/products/mptp-hydrochloride.html  The experiments show that the proposed method achieves mean average precision (mAP) of 90.92% on a collected road damage dataset, demonstrating the good performance and robustness of the model.Despite the introduction of non-invasive techniques in the study of peripheral neuropathies, sural nerve biopsy remains the gold standard for the diagnosis of several neuropathies, including vasculitic neuropathy and neurolymphomatosis. Besides its diagnostic role, sural nerve biopsy has helped to shed light on the pathogenic mechanisms of different neuropathies. In the present review, we discuss how pathological findings helped understand the mechanisms of polyneuropathies complicating hematological diseases.Efficient ways of decontamination are needed to minimize the risk of infections with Yersinia (Y.) enterocolitica, which causes gastrointestinal diseases in humans, and to reduce the numbers of Brochothrix (B.) thermosphacta to extend the shelf-life of meat. While many studies have focused on a single treatment of peracetic acid (PAA) or UV-C-irradiation, there are no studies about a combined treatment on meat. Therefore, in the present study, pork was inoculated with either Y. enterocolitica or B. thermosphacta, and was treated with a combination of 2040 mJ/cm2 UV-C irradiation followed by a 2000 ppm PAA spray treatment (30 s). Samples were packed under modified atmosphere and stored for 1, 7, or 14 days. The samples were examined for Y. enterocolitica and B. thermosphacta content, chemical and sensory effects, and meat quality parameters. For Y. enterocolitica, a significant reduction of up to 2.16 log10 cfu/cm2 meat and for B. thermosphacta, up to 2.37 log10 cfu/cm2 meat was seen on day 14 after UV-C/PAA treatment compared to the untreated controls.Exploiting the transmission and reception of low frequency ultrasounds in air is often associated with the innate echolocating abilities of some mammals, later emulated with sophisticated electronic systems, to obtain information about unstructured environments. Here, we present a novel approach for the reception of ultrasounds in air, which exploits a piezopolymer broadband sensor and an electronic interface based on a second-generation voltage conveyor (VCII). Taking advantage of its capability to manipulate both voltage and current signals, in this paper, we propose an extremely simple interface that presents a sensitivity level of about -100 dB, which is in line with commercially available references. The presented results are obtained without any filtration stage. The second-generation voltage conveyor active device is implemented through a commercially available AD844, with a supply voltage of ±15 V.Significance analysis of microarrays (SAM) provides researchers with a non-parametric score for each gene based on repeated measurements. However, it may lose certain power in general statistical tests to correctly detect differentially expressed genes (DEGs) which violate homogeneity. Monte Carlo simulation shows that the "half SAM score" can maintain type I error rates of about 0.05 based on assumptions of normal and non-normal distributions. The author found 265 DEGs using the half SAM scoring, more than the 119 DEGs detected by SAM, with the false discovery rate controlled at 0.05. In conclusion, the author recommends the half SAM scoring method to detect DEGs in data that show heterogeneity.Semantic segmentation is one of the most widely studied problems in computer vision communities, which makes a great contribution to a variety of applications. A lot of learning-based approaches, such as Convolutional Neural Network (CNN), have made a vast contribution to this problem. While rich context information of the input images can be learned from multi-scale receptive fields by convolutions with deep layers, traditional CNNs have great difficulty in learning the geometrical relationship and distribution of objects in the RGB image due to the lack of depth information, which may lead to an inferior segmentation quality. To solve this problem, we propose a method that improves segmentation quality with depth estimation on RGB images. Specifically, we estimate depth information on RGB images via a depth estimation network, and then feed the depth map into the CNN which is able to guide the semantic segmentation. Furthermore, in order to parse the depth map and RGB images simultaneously, we construct a multi-branch encoder-decoder network and fuse the RGB and depth features step by step. Extensive experimental evaluation on four baseline networks demonstrates that our proposed method can enhance the segmentation quality considerably and obtain better performance compared to other segmentation networks.Toxin-antitoxin (TA) systems, which are ubiquitously present in plasmids, bacterial and archaeal genomes, are classified as types I to VI, according to the nature of the antitoxin and to the mode of toxin inhibition [...].