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Enterovirus D68 (EV-D68) is a nonpolio enterovirus that is mainly transmitted through respiratory routes and poses a potential threat for large-scale spread. EV-D68 infections mostly cause moderate to severe respiratory diseases in children and potentially induce neurological diseases. However, there are no specific antiviral drugs or vaccines against EV-D68. Herein, through virtual screening and rational design, a series of novel quinoline analogues as anti-EV-D68 agents targeting VP1 were identified. Particularly

To identify the feasibility of deep learning-based diagnostic models for detecting and assessing lower-extremity fatigue fracture severity on plain radiographs.This retrospective study enrolled 1151 X-ray images (tibiofibula/foot: 682/469) of fatigue fractures and 2842 X-ray images (tibiofibula/foot: 2000/842) without abnormal presentations from two clinical centers. After labeling the lesions, images in a center (tibiofibula/foot: 2539/1180) were allocated at 7:1:2 for model construction, and the remaining images from another center (tibiofibula/foot: 143/131) for external validation. A ResNet-50 and a triplet branch network were adopted to construct diagnostic models for detecting and grading. The performances of detection models were evaluated with sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), while grading models were evaluated with accuracy by confusion matrix. Visual estimations by radiologists were performed for comparisons with models.For the detection model on tibiofibula, a sensitivity of 95.4%/85.5%, a specificity of 80.1%/77.0%, and an AUC of 0.965/0.877 were achieved in the internal testing/external validation set. The detection model on foot reached a sensitivity of 96.4%/90.8%, a specificity of 76.0%/66.7%, and an AUC of 0.947/0.911. The detection models showed superior performance to the junior radiologist, comparable to the intermediate or senior radiologist. The overall accuracy of the diagnostic model was 78.5%/62.9% for tibiofibula and 74.7%/61.1% for foot in the internal testing/external validation set.The deep learning-based models could be applied to the radiological diagnosis of plain radiographs for assisting in the detection and grading of fatigue fractures on tibiofibula and foot.• Fatigue fractures on radiographs are relatively difficult to detect, and apt to be misdiagnosed. • Detection and grading models based on deep learning were constructed on a large cohort of radiographs with lower-extremity fatigue fractures. • The detection model with high sensitivity would help to reduce the misdiagnosis of lower-extremity fatigue fractures.

Searching for non-noble-metal cocatalyst for hydrogen evolution in photocatalytic water–splitting has attracted much attention. Herein, molybdenum phosphide (MoP) as an efficient and stable cocatalyst was prepared in a facile phosphorization process at relatively low temperatures under N2 atmosphere, and the effect of preparation temperature (300–500 °C) was studied. Using Eosin Y (EY) and the prepared sample as catalyst (sensitizer) and cocatalyst, respectively, the photocatalytic activity for hydrogen evolution was investigated in aqueous trimethylamine (TMA) solution under visible light irradiation (λ ≥ 420 nm). MoP prepared at 400 °C (MoP-400) exhibits the highest sensitization activity and superior stability, and the maximal apparent quantum yield (AQY) for hydrogen evolution is up to 48.0% at 420 nm, much higher than the most reported data for MoP-based photocatalysts. The highest activity can be attributed to the highest P content in MoP-400 and the rapidest electron transfer between photoexcited EY and MoP-400. The possible mechanism was discussed.

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1,221 COVID-19 and 1,520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

Condensed π-conjugation C-PAN/MS-CN nanotubes were obtained through the facile polyacrylonitrile (PAN)-confined molten salt (MS) thermal condensation of melamine.

Calcium silicate cements have been widely used for pulpotomies in immature permanent teeth with complicated crown fractures due to their superior properties. However, few studies have evaluated the long-term outcomes of white mineral trioxide aggregate (WMTA) and iRoot BP Plus for partial pulpotomies. The aim of this study was to investigate the long-term clinical and radiographic outcomes of WMTA and iRoot BP Plus for partial pulpotomies in immature permanent incisors with complicated crown fractures.Children who had partial pulpotomies of immature permanent incisors with complicated crown fractures using WMTA or iRoot BP Plus as capping agents were enrolled. Eighty immature permanent incisors in 68 children (aged 8-13 years) were included. They were divided into two groups (WMTA and iRoot BP Plus) according to the capping agents. Clinical and radiographic information was collected during a 5-year follow-up period. Study data were analyzed using Chi-square tests or Fisher exact tests.The clinical and radiographic success rates in the WMTA (n = 36) and iRoot BP Plus groups (n = 44) were 94.4% versus 97.7% and 88.9% versus 97.7%, respectively (both p .05). The average observation period was 74.5 ± 13.2 months and 61.9 ± 1.6 months in the WMTA and iRoot BP Plus groups, respectively (p .01). Five cases presented with periapical radiolucencies. The WMTA group had four cases of pulp canal calcification (11.1%), while the iRoot BP Plus group had two cases (4.6%). There was crown discolouration in all cases in the WMTA group, but none in the iRoot BP Plus group.Both WMTA and iRoot BP Plus had favorable outcomes in promoting physiological development and maintaining the basic functions of immature permanent incisors with complicated crown fractures. As a partial pulpotomy material, iRoot BP Plus may be more suitable for the esthetic zone than WMTA.

Solar desalination is one of the most promising strategies to address the global freshwater shortage crisis. However, the residual salt accumulated on the top surface of solar evaporators severely reduces light absorption and steam evaporation efficiency, thus impeding the further industrialization of this technology. Herein, a metal-phenolic network (MPN)-engineered three-dimensional (3D) evaporator composed of photothermal superhydrophilic/superhydrophobic sponges and side-twining hydrophilic threads for efficient desalination with directional salt crystallization and zero liquid discharge is reported. The MPN coatings afford the engineering of alternating photothermal superhydrophilic/superhydrophobic sponges with high heating efficiency and defined vapor escape channels, while the side-twining threads induce site-selective salt crystallization. The 3D evaporator exhibits a high and stable indoor desalination rate (∼2.3 kg m

Unsupervised domain adaption (UDA), which aims to enhance the segmentation performance of deep models on unlabeled data, has recently drawn much attention. In this paper, we propose a novel UDA method (namely DLaST) for medical image segmentation via disentanglement learning and self-training. Disentanglement learning factorizes an image into domain-invariant anatomy and domain-specific modality components. To make the best of disentanglement learning, we propose a novel shape constraint to boost the adaptation performance. The self-training strategy further adaptively improves the segmentation performance of the model for the target domain through adversarial learning and pseudo label, which implicitly facilitates feature alignment in the anatomy space. Experimental results demonstrate that the proposed method outperforms the state-of-the-art UDA methods for medical image segmentation on three public datasets, i.e., a cardiac dataset, an abdominal dataset and a brain dataset. The code will be released soon.

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A series of nickel phosphide were synthesized by a simple solid phosphidation reaction and used as catalysts for H2 production from formaldehyde solution. It is found that by changing the molar ratio of Ni and P in the raw materials, the composition, size and morphology of the catalyst can be adjusted. The bi-phase Ni2P/Ni12P5 composite exhibits enhanced catalytic activity as compared to that of pure Ni2P. When the molar ratio of Ni to P is 1:0.5, Ni2P/Ni12P5 composite exhibits the superior H2 production activity with a rate of 510 mL/h at room temperature, which is 8.5 times as high as that of pure Ni2P. The possible mechanism of the reaction was discussed.

In order to solve the problem of pipeline clogging detection, a mathematical model of pipeline clogging detection is established by applying fluid oscillation theory. The amplitude of each component harmonic in different periods is expressed by Fourier series, and then the clogging attenuation parameters of each harmonic are obtained. According to the ratio of harmonic of two separate components and the plugging attenuation parameter, the plugging position and magnitude are determined respectively. Through the analysis of numerical simulation and experimental results, it can be concluded that the method of using fluid oscillation theory to detect pipeline blockage is correct.

Objective To prepare murine monoclonal antibody (mAb) against enterovirus 71 (EV71) capsid protein VP1. Methods VP1 protein was expressed and purified. BALB/c mice were immunized with inactivated and purified EV71 virus, and mAb specific to EV71 VP1 was generated by hybridoma technique. Indirect ELISA was used to test antibody titer and antibody subclass identification. The expression of VP1 protein was detected by Western blot in EV71-infected RD cells. The expression and distribution of VP1 protein was detected by immunofluorescence cytochemistry in EV71-infected RD cells. Results Six antibody strains were obtained, among which three were IgG2a and three were IgG2b, all of which could be used for ELISA, Western blot and immunofluorescence cytochemical staining. 2D7 exhibited neutralization capacity with 50% inhibitory concentrations(IC