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To explore the correlation between the width of the bony cochlear nerve canal (CNC) and long-term auditory rehabilitation after unilateral cochlear implantation (CI) in pediatric patients with congenital deafness and bilateral cochlear nerve canal stenosis (CNCS). A retrospective review was performed on 10 patients with bilateral CNCS and bilateral congenital profound hearing loss who each underwent unilateral cochlear implantation. The width of the CNC was determined on computed tomography (CT) imaging and following CI, auditory and speech performance following CI were graded using categories of auditory performance (CAP), speech intelligibility rating (SIR), and the meaningful auditory integration scale (MAIS) at 24 months following implantation. No correlation was noted between CAP score and CNCS at 24 months post CI ( P > .05). A positive correlation was noted between SIR score and CNC width (ρ = .81, P < .05). Similarly, a positive correlation was noted between MAIS and CNC width (ρ = .71, P < .05). The width of the CNC in patients with CNCS is positively correlated with some long-term auditory and speech outcomes after CI.

Objective To characterize the clinical features of patients with congenital hearing loss and unilateral cochlear nerve canal stenosis (CNCS). Methods A retrospective review of 12 patients with unilateral CNCS diagnosed between January 2018 and December 2019 at a tertiary referral hospital was performed. Results Of the 12 patients identified, there were 6 males and 6 females. All patients presented with hearing loss, with no other chief complaints. Two patients had accessory auricles. Eleven patients had a severe to profound sensorineural hearing loss on the affected side, while 1 patient had an isolated high-frequency hearing loss. Nine patients demonstrated atresia of the cochlear nerve canal (CNC), while three patients had a stenotic, but patent, CNC. Conclusion Prompt radiologic diagnosis of patients with unilateral CNCS is important for patient counseling and appropriate rehabilitation.

The damage evolution study on 3D woven carbon-fiber reinforced composites has always been a challenge to the research community. In this paper, a new in situ experimental method is proposed to realize the non-destructive observation of the failure process of 3D woven carbon-fiber reinforced composites. Synchrotron radiation computer tomography (SRCT), an in situ load frame and new-style specimens are integrated. With the method, the 3D crack initiation and propagation can be continuously recorded by the SRCT scanner. Through analyzing the experimental results, the out-plane failure mechanism under tension and shear loads is found. Particularly, it is shown that the interface property is the leading factor affecting out-plane strengths. Moreover, the effect of braiding angle on the out-plane strengths in different directions is discussed. According to the failure modes, it can be concluded that with the increasing of the braiding angle, the out-plane tension strength will increase but the out-plane shear strength will decrease. The proposed in situ experimental method and the above findings are significant to calibration of failure prediction model and the novel design of woven composites.

Haixin Li,1,* Ying Huang,2,* Xian Wu,3 Ting Wu,1 Ying Cao,1 Qimei Wang,1 Yuchang Qiu,1 Weiming Fu,1 Qun Zhang,4 Jianxin Pang1 1Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Pharmacy, Shenzhen Hospital, Southern Medical University, Shenzhen, People’s Republic of China; 3Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 4Good Clinical Practice Development, The Third Affiliated Hospital, Southern Medical University, Guangzhou, People’s Republic of China *These authors contributed equally to this work Objective: Hemocoagulase agkistrodon (HCA), a thrombin-like enzyme (TLE) from the venom of the Chinese moccasin snake (Deinagkistrodon acutus), has been used in clinical practice as a hemostatic compound. The aim of this study was to further investigate the pharmacological properties of HCA. Materials and methods: Sodium dodecyl sulfate or native polyacrylamide gel electrophoresis (SDS- or N-PAGE) as well as enzyme linked immunosorbent assays (ELISAs) were conducted to study the effects of HCA on the human plasma fibrinogen and prothrombin levels, as well as its in vitro interactions with some coagulation factors. In addition, the bleeding time effects of HCA in the mouse tail-bleeding model as well as its effects on the fibrinogen levels in rabbits were determined in vivo. Results: In vitro results revealed that HCA exerts its procoagulant activities by hydrolyzing fibrinogen into segments that are easier to be absorbed, reducing the risk of thrombus formation. Besides, HCA could significantly inhibit the activation of prothrombin at the concentration of 0.3 μM. Unexpectedly, we also found that HCA was able to strongly bind to factor X/Xa (in a ratio of 1:1) and thus inhibit the acceleration of active factor X to tissue plasminogen activator-catalyzed plasminogen activation, demonstrating that it could be less likely to lead to thrombus formation. Finally, in vivo results indicated that HCA could significantly shorten the bleeding time in the mouse tail-bleeding model and had no effect on the fibrinogen levels in rabbits. Conclusion: In summary, HCA, a unique and new family member of TLEs, may become a new clinical drug for the prevention and treatment of hemorrhage due to its unique and complex interactions with the blood system. Clarification of these features will enable us to further understand the mechanism of action of HCA and then promote its further application in clinical practice as a therapeutic drug. Keywords: thrombin-like enzymes, TLEs, coagulation factors, procoagulant activity, anticoagulant activity, FXa, t-PA

The V-notched beam test (VNB) is recognized as a standard test method to determine the shear properties of composite materials. But it is not easy to make sure that the specimen placed in a predetermined position; the force conditions of the specimen become very complex when the large deformation of the specimen is generated, the friction between the internal components of fixture cannot be ignored when bending moment is generated as well when using traditional V-notched beam test fixtures. A modified V-notched beam test fixture has been proposed and designed based on standardized apparatus. After improvement, specimen with different thicknesses can be positioned easily. Meanwhile, effects of friction and bending moment are greatly reduced, and it has been validated by finite element simulation. Furthermore, some typical of experiments have been carried out by using the proposed fixture to determine the interlaminar shear modulus and shear strength of a type of 3D woven composites. Some new experimental data are presented in this paper, which can provide guidance and help for future study and industrial design of the 3D woven composites.

Plant diseases are a major challenge for the agricultural sector. Accurate and rapid detection of diseases in plants can greatly reduce economic losses. In this paper, we present a method based on Faster RCNN to detect tomato diseases. We combine Faster RCNN with different deep convolutional neural networks, including vgg16, resnet50, and resnet101. We trained and tested the collected tomato diseased dataset, which contained six diseased images and healthy tomato fruit images. The experimental results show that our proposed system can effectively identify different types of tomato diseases.

This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

Image super-resolution has always been a research hotspot in the field of computer vision. Recently, image super-resolution algorithms using convolutional neural networks (CNN) have achieved good performance. But the existing methods based on CNN usually has to many (20–30) convolution layers, which has a large amount of calculations. In response to this problem, this paper proposes a lightweight network model based on parallel convolution, skip connections and ResNet. Parallel convolution means that different sizes of convolution kernels are set in the same convolutional layer to extract image features of different scales. In addition, in order to reduce the loss of image details, we combine the input and output of the previous layer as the input of the next layer, which is the skip connection. We also borrowed the idea of Residual Net. The network learns the residuals between high-resolution images and low-resolution images. Therefore, the algorithm proposed in this paper not only achieves the most advanced performance, but also achieves faster calculations.

This paper introduces a quantum-inspired Qubit neural tree networks with improved Qubit neuron, overlayer connections and different phase operation functions for different neurons to construct disease classification model. A hybrid evolutionary algorithm that combines the modified PIPE algorithm with beetle antennae search is also proposed to optimize the structure and parameters of the Qubit neural tree network. Simulation results on two disease classification problems show that this model has more advantages in classification precision, feature selection and structure simplification, especially for classification with multi-class attributes.

A stroke is a focal cerebral insult that frequently causes severe neurological deficit and mortality. Recent studies have demonstrated that multipotent mesenchymal stromal cells (MSCs) hold great promise for neurovascular remodeling and neurological function recovery following a stroke. Rather than a direct replacement of parenchymal brain cells, the therapeutic mechanism of MSCs is suggested to be the secretion of soluble factors. Specifically, emerging data described MSCs as being able to release extracellular vesicles (EVs), which contain a variety of cargo including proteins, lipids, DNA and various RNA species. The released EVs can target neurocytes and vascular cells and modify the cell's functions by delivering the cargo, which are considered to mediate the neural restoration effects of MSCs. Therefore, EVs may be developed as a novel cell-free therapy for neurological disorders. In the present review, the current advances regarding the components, functions and therapeutic potential of EVs are summarized and the use of MSC-derived EVs as a promising approach in the treatment of stroke are highlighted.

As the basic level of national governance, local governance is deeply influenced by administrative tradition, political system and economic development level of a country. With more than thirty years of reform and opening up, the pattern of local governance has become diversified. The diversity of governance model cannot be managed in the same way, however, it cannot affect the government's ability to play the initiative role of governance. We need to innovate and improve the existing local governance models.

Live bird markets (LBMs), being a potential source of avian influenza virus, require effective environmental surveillance management. In our study, a total of 2865 environmental samples were collected from 292 LBMs during the 2015–2016 human influenza season from 10 cities in Zhejiang province, China. The samples were tested by real-time quantitative polymerase chain reaction (RT-PCR). Field investigations were carried out to investigate probable risk factors. Of the environmental samples, 1519 (53.0%) were contaminated by A subtype. The highest prevalence of the H9 subtype was 30.2%, and the frequencies of the H5 and H7 subtype were 9.3% and 17.3%, respectively. Hangzhou and Jinhua cities were contaminated more seriously than the others. The prevalence of H5/H7/H9 in drinking water samples was highest, at 50.9%, and chopping board swabs ranked second, at 49.3%. Duration of sales per day, types of live poultry, LBM location and the number of live poultry were the main risk factors for environmental contamination, according to logistic regression analysis. In conclusion, LBMs in Zhejiang were contaminated by avian influenza. Our study has provided clues for avian influenza prevention and control during the human influenza season, especially in areas where LBMs are not closed.

When light irradiates to a test part, the change of the position of the test part can make the intensity of the reflecting light alter accordingly, based on this mechanism of reflective light intensity modulation, a new scheme with optical-fiber displacement sensor is put forward to testing the tip clearance of labyrinth seals. By analysis and treatment of the data from calibration test in static condition, the relationship of the optical-fiber displacement sensor's output in static state and the position from the test part to the sensor are obtained, also influence of different environments to the response curves is analyzed. The results of the experiment show that the presented scheme is feasible, and the high measuring accuracy of the optical-fiber sensor can meet the prospective purpose.