The bibliometric and altmetric analysis of chronic traumatic encephalopathy research: how great is the impact?

Background: The study of chronic traumatic encephalopathy (CTE) has received great attention from academia and the general public. This study aims to analyze the research productivity on CTE and investigate the most discussed articles in academia and the general public by conducting bibliometric and altmetric analyses. Methods: Data of articles were obtained from the Web of Science Core Databases and Altmetric Explore. VOSviewer and CiteSpace software were used to analyze and visualize the articles. The correlation between Altmetric attention scores (AAS) and citation counts were assessed by Spearman correlation coefficient. Results: 788 publications of CTE were eventually gathered and analyzed, and 100 articles with highest citation counts (Top-cited) and 100 articles with highest AASs (Top-AAS) were then identified. The keywords density map showed both the general public and the scientists were particularly interested in the risk factors and pathology of CTE, and scientists were interested in the causes and characteristics of neurodegenerative diseases while the public became increasingly concerned about the detection and prevention of CTE. By examining the shared characteristics of the 44 articles (High-High articles) that overlapped between Top-cited and Top-AAS articles, we identified certain traits that may potentially contribute to their high citation rates and high AASs. Besides, significant positive correlations with varied strength between AAS and citation were observed in the 788 articles, Top-cited, Top-AAS and High-High datasets. Conclusion: This study is the first to link bibliometric and altmetric analyses for CTE publications, which may provide deeper understanding of the attention of the scientists and the general public pay to the study of CTE, and offer some guidance and inspiration for future CTE in the selection of research topics and directions.

Ultrasensitive one-pot detection of monkeypox virus with RPA and CRISPR in a sucrose-aided multiphase aqueous system.

IMPORTANCE: The monkeypox virus was declared as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) and continues to cause infection cases worldwide. Given the risk of virus evolution, it is essential to identify monkeypox virus infection in a timely manner to prevent outbreaks. This study establishes a novel one-pot recombinase polymerase amplification-Clustered Regularly Interspaced Short Palindromic Repeats (RPA-CRISPR) assay for monkeypox virus with an ultra-high sensitivity. The assay shows good specificity, accuracy, and the rapidness and convenience important for point-of-care testing. It provides an effective tool for the early diagnosis of monkeypox, which is useful for the prevention of an epidemic.

A recombinase polymerase amplification and Pyrococcus furiosus Argonaute combined method for ultra-sensitive detection of white spot syndrome virus in shrimp.

White spot disease (WSD) in shrimp is an acute infectious disease caused by white spot syndrome virus (WSSV). WSD has seriously threatened the security of shrimp farming, causing huge economic losses worldwide. As there is currently no effective treatment for WSD, developing early detection technologies for WSSV is of great significance for the prevention. In this study, we have established a detection method for WSSV using a combination of recombinase polymerase amplification (RPA) and Pyrococcus furiosus Argonaute (PfAgo). We have achieved a detection sensitivity of single copy per reaction, which is more sensitive than the previously reported detection methods. Additionally, we have demonstrated high specificity. The entire detection process can be completed within 75 min without the need for precise thermal cyclers, making it suitable for on-site testing. The fluorescence signal generated by the reaction can be quantified using a portable fluorescence detector or observed with the naked eye under a blue light background. This study provides an ultrasensitive on-site detection method for WSSV in shrimp aquaculture and expands the application of PfAgo in the field of aquatic disease diagnosis.

Multi-modal recommendation algorithm fusing visual and textual features.

In recommender systems, the lack of interaction data between users and items tends to lead to the problem of data sparsity and cold starts. Recently, the interest modeling frameworks incorporating multi-modal features are widely used in recommendation algorithms. These algorithms use image features and text features to extend the available information, which alleviate the data sparsity problem effectively, but they also have some limitations. On the one hand, multi-modal features of user interaction sequences are not considered in the interest modeling process. On the other hand, the aggregation of multi-modal features often employs simple aggregators, such as sums and concatenation, which do not distinguish the importance of different feature interactions. In this paper, to tackle this, we propose the FVTF (Fusing Visual and Textual Features) algorithm. First, we design a user history visual preference extraction module based on the Query-Key-Value attention to model users' historical interests by using of visual features. Second, we design a feature fusion and interaction module based on the multi-head bit-wise attention to adaptively mine important feature combinations and update the higher-order attention fusion representation of features. We conduct experiments on the Movielens-1M dataset, and the experiments show that FVTF achieved the best performance compared with the benchmark recommendation algorithms.

Scale-Aware Automatic Augmentations for Object Detection With Dynamic Training.

Data augmentation is a critical technique in object detection, especially the augmentations targeting at scale invariance training (scale-aware augmentation). However, there has been little systematic investigation of how to design scale-aware data augmentation for object detection. We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scale-aware search space, where both image- and instance-level augmentations are designed for maintaining scale robust feature learning. Upon this search space, we propose a new search metric, termed Pareto Scale Balance, to facilitate efficient augmentation policy search. In experiments, Scale-aware AutoAug yields significant and consistent improvement on various object detectors (e.g., RetinaNet, Faster R-CNN, Mask R-CNN, and FCOS), even compared with strong multi-scale training baselines. Our searched augmentation policies are generalized well to other datasets and instance-level tasks beyond object detection, e.g., instance segmentation. The search cost is much less than previous automated augmentation approaches for object detection, i.e., 8 GPUs across 2.5 days versus. 800 TPU-days. In addition, meaningful patterns can be summarized from our searched policies, which intuitively provide valuable knowledge for hand-crafted data augmentation design. Based on the searched scale-aware augmentation policies, we further introduce a dynamic training paradigm to adaptively determine specific augmentation policy usage during training. The dynamic paradigm consists of an heuristic manner for image-level augmentations and a differentiable copy-paste-based method for instance-level augmentations. The dynamic paradigm achieves further performance improvements to Scale-aware AutoAug without any additional burden on the long tailed LVIS benchmarks. We also demonstrate its ability to prevent over-fitting for large models, e.g., the Swin Transformer large model. Code and models are available at https://github.com/dvlab-research/SA-AutoAug.

Fully Convolutional Networks for Panoptic Segmentation With Point-Based Supervision.

In this paper, we present a conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline, which can be optimized with point-based fully or weak supervision. In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly. With this approach, instance-aware and semantically consistent properties for things and stuff can be respectively satisfied in a simple generate-kernel-then-segment workflow. Without extra boxes for localization or instance separation, the proposed approach outperforms the previous box-based and -free models with high efficiency. Furthermore, we propose a new form of point-based annotation for weakly-supervised panoptic segmentation. It only needs several random points for both things and stuff, which dramatically reduces the annotation cost of human. The proposed Panoptic FCN is also proved to have much superior performance in this weakly-supervised setting, which achieves 82% of the fully-supervised performance with only 20 randomly annotated points per instance. Extensive experiments demonstrate the effectiveness and efficiency of Panoptic FCN on COCO, VOC 2012, Cityscapes, and Mapillary Vistas datasets. And it sets up a new leading benchmark for both fully- and weakly-supervised panoptic segmentation.

PointINS: Point-Based Instance Segmentation.

In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features. Differentiating multiple potential instances within a single PoI feature is challenging, because learning a high-dimensional mask feature for each instance using vanilla convolution demands a heavy computing burden. To address this challenge, we propose an instance-aware convolution. It decomposes this mask representation learning task into two tractable modules as instance-aware weights and instance-agnostic features. The former is to parametrize convolution for producing mask features corresponding to different instances, improving mask learning efficiency by avoiding employing several independent convolutions. Meanwhile, the latter serves as mask templates in a single point. Together, instance-aware mask features are computed by convolving the template with dynamic weights, used for the mask prediction. Along with instance-aware convolution, we propose PointINS, a simple and practical instance segmentation approach, building upon dense one-stage detectors. Through extensive experiments, we evaluated the effectiveness of our framework built upon RetinaNet and FCOS. PointINS in ResNet101 backbone achieves a 38.3 mask mean average precision (mAP) on COCO dataset, outperforming existing point-based methods by a large margin. It gives a comparable performance to the region-based Mask R-CNN K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," in Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 2980-2988 with faster inference.

Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein-protein interaction network.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer worldwide and one of the deadliest cancers in Asia. But at present, effective targets for HCC clinical therapy are still limited. The "guilt by association" rule suggests that interacting proteins share the same or similar functions and hence may be involved in the same pathway. This assumption can be used to identify disease related genes from protein association networks constructed from existing PPI data. Given the close association between Hepatitis B virus and Hepatitis B which may lead to HCC, here we develop a computational method to identify hepatocellular carcinoma related genes based on k-th shortest paths in the protein-protein interaction (PPI) network (we set k=1, 2 in this study). Finally, we found 33 genes whose p-values were less than 0.05, and most of them have been reported to be involved in HCC tumorigenesis and development. The results also provide a new reference for research into HCC oncogenesis and for development of new strategies for HCC clinical therapies.

Effects of flange size on dividing streamlines of exterior hoods in cross drafts.

Effects of flange size on the flow patterns of an exterior circular hood subject to the influence of various uniform cross drafts were studied in an apparatus consisting of hood-model/wind-tunnel assembly. The cross draft to suction velocity ratio covered the range from 0.056 to 0.792. The flange width to hood diameter ratio spanned from 1.2 to 3.0. The hood equipped without flange was also probed. A two-component laser Doppler anemometer was used to measure the velocity field on the symmetry plane. The streamline patterns were obtained from the measured velocity data. The cross draft caused a capture envelope. The boundary of the envelope was described by a dividing streamline. It was found that the flange width presented complex effects on the capture envelope and the dividing streamline. Corresponding to a cross draft to suction velocity ratio, a critical flange width existed. If the flange width was smaller than the critical value, the dividing streamline would terminate at the downstream tip of the flange. The behaviors of the capture envelope and the dividing streamline of the hood in cross draft under different flange widths are presented and discussed.

Development and characterization of a wake-controlled exterior hood.

A wake-controlled exterior hood was developed to overcome the negative influence of cross draft on an exterior hood and avoid the operation inconvenience caused by the enclosure of an airflow capture booth. This new type of local exterior hood used the hood suction flow to stabilize the dynamic vortex shedding that was induced when a crossflow passed over a blockage plate, and therefore formed a hydrodynamics-stabilized local isolation area for efficient removing of the contaminant. The development process was performed in a test section of an open-circuit wind tunnel. The blockage plate and the exterior hood model were placed in a wind-tunnel test section so that the crossflow could be freely supplied by the airstream of the wind tunnel. The laser light sheet flow visualization method and the laser Doppler velocimeter were employed to reveal the characteristics of the flow field. Primary influential parameters were factored out of the measured velocity results so that a design procedure was proposed. Experiments using hot-wire type alcohol sensors to measure the toluene vapor concentration distributions showed that the capture efficiency of this type of actively controlled hood was remarkably higher than that of an uncontrolled hood.