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Ethnic media play important roles in constructing or reconstructing ethnic identity, facilitating the acculturation process, and encouraging political participation among ethnic groups. The current study investigated the moderation effects of ethnic news use on the relationships between political ideology and presidential approval among Chinese Americans during the Covid-19 pandemic when the virus was called the ‘Chinese virus' by some news media and the former U.S. President Donald Trump. Relying on a survey of Chinese Americans conducted between October and December 2020, the study showed that while conservative Chinese Americans were more likely than liberals to approve of the former president Trump, the effects of political ideology on the presidential evaluations were diminishing for people who used ethnic media more frequently to get news. © 2021 Journal of International Communication.

Scholarship on audience fragmentation typically takes one of two approaches: The micro-level analysis of individuals’ selective exposure to partisan news, or the macro-level analysis of audience overlap. To bridge the gap between these levels of analysis, we introduce the concept of attention centrality as a set of macro-to-micro measures that characterize how individual news media selection is situated within networks of public attention. Relying on an online panel survey conducted in the United States (N = 1,493), we examine the relationship between three indicators of respondents’ attention centrality (closeness, betweenness, and reach) and the partisan valence of their news selections. The study finds different patterns of results for the three indicators of attention centrality, indicating that partisan news media are not uniformly isolated to the periphery of public attention. Results are discussed in light of conversations about selective exposure and audience overlap in the United States and around the world.

BACKGROUND Traumatic brain injury (TBI) seriously affects quality of human health and patient’s prognosis, but epidemiological characteristics of TBI may vary within population. Great changes have occurred in the epidemiological characteristics of inpatients with TBI in the fasted-paced city, Shenzhen China; however, little is known on this matter. OBJECTIVE This study aimed to investigate the changes in TBI epidemiology and help clinicians and policymakers improve medical treatment and formulate appropriate social regulations. METHODS In this retrospective cross-sectional analysis, we collected the following data of 4229 TBI inpatients from 20 hospitals in Shenzhen in 2017: age, sex, injury cause, severity, diagnoses, outcomes, and the time from injury to presenting at a department of neurosurgery. Data collection was performed by two neurosurgeons simultaneously. Our results were compared with those of another study performed in Shenzhen during 1994–2003 to elucidate the changes in the epidemiological characteristics of TBI. RESULTS Most respondents were male [2830 (66.9%)]. The mean age was 32.5 ± 21.4 years. 3,947 (93.3%) patients had a favorable outcome, 219 (5.2%) had an unfavorable outcome, and 63 (1.5%) died. The predominant external cause was falls (1,779 [42.1%]), which was the most common cause of TBI in children and older adults. Electric bicycles (423 [29.0%]) were the most vulnerable to traffic crash-related injuries. A time from injury to presenting at a department of neurosurgery greater than 50 hours had a significant effect on prognosis (P < 0.001). CONCLUSIONS Epidemiological characteristics of TBI had changed significantly over the past twenty years, wherein falls, rather than traffic accidents, were the most common cause of TBI. Further research is needed to decrease falls incidence and improve the outcome of TBI.

Promoting public knowledge about climate is important for garnering support for climate-change policy, and researchers have begun to study the effectiveness of new technologies as learning tools. N...

Professional associations’ roles in shaping the journalism field have been understudied in news industry research. Adopting a social population ecology perspective, this study provides an across-ti...

Circular RNAs (circRNAs) are closely associated with the progression of oral squamous cell carcinoma (OSCC). circRNA_0001971 has been proved to accelerate the OSCC development. Here, we aim to identify the new molecular mechanism of hsa_circRNA_0001971 (circRNA_0001971) in OSCC.The levels of circRNA_0001971, miR-186-5p, and fibronectin type III domain containing 3B (FNDC3B) in tissues and cells were verified by qRT-PCR or Western blotting. The interaction between circRNA_0001971, miR-186-5p, and FNDC3B was identified by bioinformatics analysis, luciferase assay, and RIP assay. The effect of circRNA_0001971/miR-186-5p/FNDC3B axis on OSCC cell proliferation, migration, and invasion by cell functional experiments including CCK8, wound healing, and transwell assays.Our study displayed that circRNA_0001971 and FNDC3B were elevated in OSCC, whereas miR-186-5p was declined in OSCC. Silencing circRNA_0001971 attenuated the malignancy of OSCC cells by suppressing proliferation, migration, and invasion. In OSCC cells, circRNA_0001971 sponged miR-186-5p to enhance FNDC3B. Due to the interaction between circRNA_0001971, miR-186-5p, and FNDC3B, FNDC3B overexpression relieved the negative function of silencing circRNA_0001971 in OSCC cells.Overall, our study discovered that circRNA_0001971 was a tumor promoter in OSCC progression by targeting miR-186-5p/FNDC3B axis.

Without developing dedicated countermeasures, facial biometric systems can be spoofed with printed photos, replay attacks, silicone masks, or even a 3D mask of a targeted person. Thus, the threat of presentation attacks needs to be addressed to strengthen the security of the biometric systems. Since a 2D convolutional neural network (CNN) captures static features from video frames, the camera motion might hinders the performance of modern CNNs for video-based presentation attack detection (PAD). Inspired by the egomotion theory, we introduce an adaptive spatiotemporal global sampling (ASGS) technique to compensate the camera motion and use the resulting estimation to encode the appearance and dynamics of the video sequences into a single RGB image. This is achieved by adaptively splitting the video into small segments and capturing their global motion within each segment. The proposed global motion is estimated based on four key steps: dense sampling, FREAK feature extraction and matching, similarity transformation, and aggregation function. This allows using deep models pre-trained on images for video-based PAD detection. Moreover, the interpretation of ASGS reveals that the most important parts for supporting the decision on PAD are consistent with motion cues associated with the artifacts, i.e., hand movement, material reflection, and expression changes. Extensive experiments on four standard face PAD databases demonstrate its effectiveness and encourage further study in this domain.

On the surface, it seems that journalists are more publicly accessible than ever before, largely thanks to the popular microblogging website/ app, Twitter. But determining who is interacting with j...

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354 $\pm$ 0.1149 to 0.8372 $\pm$ 0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 $\pm$ 0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better ( p $ 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

Image registration is a basic task in biological image processing. Different stained histology images contain different clinical information, which could assist pathologists to diagnose a certain disease. It is necessary to improve the accuracy of image registration. In this paper, we present a robust registration method that consists of three steps: 1) extracting match points; 2) a pre-alignment consisting of a rigid transformation and an affine transformation on the coarse level; 3) an accurate non-rigid registration optimized by the extracted points. The existing methods use the features of the image pair to initial alignment. We proposed a new metric for the non-rigid transformation which adding the part of optimizing extracting points into the original metric. We evaluate our method on the dataset from the ANHIR Registration Challenge and use MrTRE (median relative target registration error) to measure the performance on the training data. The test result illustrates that the presented method is accurate and robust.

The microsporidium

This paper presents a second-order hybrid finite volume method for solving the Stokes equation on a two dimensional domain. The trial function space of the method for velocity is chosen to be a quadratic conforming finite element space with a hierarchical decomposition technique on triangular meshes, and its corresponding test function space consists of piecewise constant functions and piecewise quadratic polynomial functions based on a dual partition of the domain. The trial function space and test function space of the method for pressure are chosen to be a linear finite element space. We derive the inf-sup conditions of the discrete systems of the method on triangular meshes by using a relationship between the finite volume method and the finite element method. The well-posedness of the proposed finite volume method is obtained by using the Babuska–Lax–Milgram theorem. The error estimates of the optimal order are obtained in the H 1 -norm for velocity and in the L 2 -norm for pressure. Numerical experiments are presented to illustrate the theoretical results.