Current methods for skeleton-based human action recognition usually work with complete skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy skeletons, which could ...significantly deteriorate the performance of current methods when some informative joints are occluded or disturbed. To improve the robustness of action recognition models, a multi-stream graph convolutional network (GCN) is proposed to explore sufficient discriminative features spreading over all skeleton joints, so that the distributed redundant representation reduces the sensitivity of the action models to non-standard skeletons. Concretely, the backbone GCN is extended by a series of ordered streams which is responsible for learning discriminative features from the joints less activated by preceding streams. Here, the activation degrees of skeleton joints of each GCN stream are measured by the class activation maps (CAM), and only the information from the unactivated joints will be passed to the next stream, by which rich features over all active joints are obtained. Thus, the proposed method is termed richly activated GCN (RA-GCN). Compared to the state-of-the-art (SOTA) methods, the RA-GCN achieves comparable performance on the standard NTU RGB+D 60 and 120 datasets. More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.
Illustrations on the problems of current BBR losses. Each row shows the optimization results in different iterations with certain loss function. The Black denotes the anchor box. The Blue denotes the ...target box. The fist row denotes GIOU. The second row denotes CIOU. The third row denotes the proposed EIOU. EIOU attains more quick convergence speed and more accurate regression results. Display omitted
•We reveal the flaws of ℓn-norm and IOU-based losses for object detection.•We design a regression version of focal loss to emphasize the most promising anchors.•We conduct extensive experiments to validate the superiority of the proposed methods.
In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss functions for BBR have two main drawbacks: (i) Both ℓn-norm and IOU-based loss functions are inefficient to depict the objective of BBR, which leads to slow convergence and inaccurate regression results. (ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR. To mitigate the adverse effects caused thereby, we perform thorough studies to exploit the potential of BBR losses in this paper. Firstly, an Efficient Intersection over Union (EIOU) loss is proposed, which explicitly measures the discrepancies of three geometric factors in BBR, i.e., the overlap area, the central point and the side length. After that, we state the Effective Example Mining (EEM) problem and propose a regression version of focal loss to make the regression process focus on high-quality anchor boxes. Finally, the above two parts are combined to obtain a new loss function, namely Focal-EIOU loss. Extensive experiments on both synthetic and real datasets are performed. Notable superiorities on both the convergence speed and the localization accuracy can be achieved over other BBR losses.
One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints. However, the complexity of the recent State-Of-The-Art (SOTA) models for ...this task tends to be exceedingly sophisticated and over-parameterized. The low efficiency in model training and inference has increased the validation costs of model architectures in large-scale datasets. To address the above issue, recent advanced separable convolutional layers are embedded into an early fused Multiple Input Branches (MIB) network, constructing an efficient Graph Convolutional Network (GCN) baseline for skeleton-based action recognition. In addition, based on such the baseline, we design a compound scaling strategy to expand the model's width and depth synchronously, and eventually obtain a family of efficient GCN baselines with high accuracies and small amounts of trainable parameters, termed EfficientGCN-Bx, where "x" denotes the scaling coefficient. On two large-scale datasets, i.e. , NTU RGB+D 60 and 120, the proposed EfficientGCN-B4 baseline outperforms other SOTA methods, e.g. , achieving 92.1% accuracy on the cross-subject benchmark of NTU 60 dataset, while being 5.82× smaller and 5.85× faster than MS-G3D, which is one of the SOTA methods. The source code in PyTorch version and the pretrained models are available at https://github.com/yfsong0709/EfficientGCNv1 .
Very recently the NICER collaboration published the first-ever accurate measurement of mass and radius together for PSR J0030+0451, a nearby isolated quickly rotating neutron star (NS). In this work ...we set the joint constraints on the equation of state (EoS) and some bulk properties of NSs with the data of PSR J0030+0451, GW170817, and some nuclear experiments. The piecewise polytropic expansion method and the spectral decomposition method have been adopted to parameterize the EoS. The resulting constraints are consistent with each other. Assuming the maximal gravitational mass of nonrotating NS MTOV lies between 2.04M and 2.4M , with the piecewise method the pressure at twice nuclear saturation density is measured to be at the 90% level. For an NS with canonical mass of 1.4M , we have the moment of inertia , tidal deformability , radius , and binding energy at the 90% level, which are improved in comparison to the constraints with the sole data of GW170817. These conclusions are drawn for the mass/radius measurements of PSR J0030+0451 by Riley et al. For the measurements of Miller et al., the results are rather similar.
Targeting nucleotide metabolism can not only inhibit tumor initiation and progression but also exert serious side effects. With in-depth studies of nucleotide metabolism, our understanding of ...nucleotide metabolism in tumors has revealed their non-proliferative effects on immune escape, indicating the potential effectiveness of nucleotide antimetabolites for enhancing immunotherapy. A growing body of evidence now supports the concept that targeting nucleotide metabolism can increase the antitumor immune response by (1) activating host immune systems via maintaining the concentrations of several important metabolites, such as adenosine and ATP, (2) promoting immunogenicity caused by increased mutability and genomic instability by disrupting the purine and pyrimidine pool, and (3) releasing nucleoside analogs via microbes to regulate immunity. Therapeutic approaches targeting nucleotide metabolism combined with immunotherapy have achieved exciting success in preclinical animal models. Here, we review how dysregulated nucleotide metabolism can promote tumor growth and interact with the host immune system, and we provide future insights into targeting nucleotide metabolism for immunotherapeutic treatment of various malignancies.
To better inform efforts to treat and control the current outbreak with a comprehensive characterization of COVID-19.
We searched PubMed, EMBASE, Web of Science, and CNKI (Chinese Database) for ...studies published as of March 2, 2020, and we searched references of identified articles. Studies were reviewed for methodological quality. A random-effects model was used to pool results. Heterogeneity was assessed using I2. Publication bias was assessed using Egger's test.
43 studies involving 3600 patients were included. Among COVID-19 patients, fever (83.3% 95% CI 78.4–87.7), cough (60.3% 54.2–66.3), and fatigue (38.0% 29.8–46.5) were the most common clinical symptoms. The most common laboratory abnormalities were elevated C-reactive protein (68.6% 58.2–78.2), decreased lymphocyte count (57.4% 44.8–69.5) and increased lactate dehydrogenase (51.6% 31.4–71.6). Ground-glass opacities (80.0% 67.3–90.4) and bilateral pneumonia (73.2% 63.4–82.1) were the most frequently reported findings on computed tomography. The overall estimated proportion of severe cases and case-fatality rate (CFR) was 25.6% (17.4–34.9) and 3.6% (1.1–7.2), respectively. CFR and laboratory abnormalities were higher in severe cases, patients from Wuhan, and older patients, but CFR did not differ by gender.
The majority of COVID-19 cases are symptomatic with a moderate CFR. Patients living in Wuhan, older patients, and those with medical comorbidities tend to have more severe clinical symptoms and higher CFR.
The carbon dioxide (CO2) methanation reaction not only provides a solution for mitigating the excessive carbon dioxide emissions but also can potentially be employed for the storage and ...transportation of low-grade energies. A supported nickel-based catalyst is the most promising candidate for the CO2 methanation reaction. Additionally, understanding the role of the support is essential for the rational design of nickel-based CO2 methanation catalysts. Herein, we elaborated on the effect of the support on the catalyst structure, CO2 adsorption, CO2 activation, methanation mechanism, and deactivation process. Future directions are suggested to elucidate the fundamental aspects of this catalytic system, including the formation mechanism of preferentially exposed facets, the nature of strong metal–support interactions, the balance between support reducibility and basicity, and the CO2 methanation pathways over nickel-based catalysts with various supports.
Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and other constraints, piecewise-smooth x-ray computed tomography (CT) can be ...reconstructed from sparse-view projection data without introducing notable artifacts. However, due to the piecewise constant assumption for the image, a conventional TV minimization algorithm often suffers from over-smoothness on the edges of the resulting image. To mitigate this drawback, we present an adaptive-weighted TV (AwTV) minimization algorithm in this paper. The presented AwTV model is derived by considering the anisotropic edge property among neighboring image voxels, where the associated weights are expressed as an exponential function and can be adaptively adjusted by the local image-intensity gradient for the purpose of preserving the edge details. Inspired by the previously reported TV-POCS (projection onto convex sets) implementation, a similar AwTV-POCS implementation was developed to minimize the AwTV subject to data and other constraints for the purpose of sparse-view low-dose CT image reconstruction. To evaluate the presented AwTV-POCS algorithm, both qualitative and quantitative studies were performed by computer simulations and phantom experiments. The results show that the presented AwTV-POCS algorithm can yield images with several notable gains, in terms of noise-resolution tradeoff plots and full-width at half-maximum values, as compared to the corresponding conventional TV-POCS algorithm.
Podocyte injury is a major determinant of proteinuric kidney disease and the identification of potential therapeutic targets for preventing podocyte injury has clinical importance. Here, we show that ...histone deacetylase Sirt6 protects against podocyte injury through epigenetic regulation of Notch signaling. Sirt6 is downregulated in renal biopsies from patients with podocytopathies and its expression correlates with glomerular filtration rate. Podocyte-specific deletion of Sirt6 exacerbates podocyte injury and proteinuria in two independent mouse models, diabetic nephropathy, and adriamycin-induced nephropathy. Sirt6 has pleiotropic protective actions in podocytes, including anti-inflammatory and anti-apoptotic effects, is involved in actin cytoskeleton maintenance and promotes autophagy. Sirt6 also reduces urokinase plasminogen activator receptor expression, which is a key factor for podocyte foot process effacement and proteinuria. Mechanistically, Sirt6 inhibits Notch1 and Notch4 transcription by deacetylating histone H3K9. We propose Sirt6 as a potential therapeutic target for the treatment of proteinuric kidney disease.Podocytes are essential components of the renal glomerular filtration barrier and podocyte dysfunction leads to proteinuric kidney disease. Here Liu et al. show that Sirt6 protects podocytes from apoptosis and inflammation by increasing autophagic flux through inhibition of the Notch pathway.
The study on atmospheric polycyclic aromatic hydrocarbons (PAHs) in China has regional and global significance to understand the large scale atmospheric transport of PAHs. In this study, 16 US EPA ...priority PAHs were analyzed in more than 500 pairs of gas and particle phases samples, which were collected on the same schedule on a weekly basis from August 2008 to July 2009 at 11 urban sites (6 northern cities and 5 southern cities) across China. The average concentration was 239 ± 329 ng/m3 and 165 ± 164 ng/m3 for the northern cities and the southern cities, respectively. Different seasonal variations of atmospheric PAHs were observed between northern cities and southern cities, which were mainly caused by the different temperature effects in winter. Identified by principal component analysis, coal combustion and vehicle exhaust were the major sources of atmospheric PAHs in northern and southern cities of China, respectively. The temperature dependences of atmospheric PAHs were also different, which were caused by the different influences of temperature on identified sources. To our knowledge, this is the first comprehensive study to report the difference with concentrations, seasonal variations, sources and temperature dependences of atmospheric PAHs between northern cities and southern cities in China.
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•More than 500 pairs of gas and particle phases were collected for PAHs analysis.•3-ring and 4-ring PAHs were the predominate homologues in atmosphere.•Significant difference between northern and southern cities was found in winter.•Coal combustion and vehicle exhaust were major sources of atmospheric PAHs.•Temperatures dependences of atmospheric PAHs were influenced by sources.
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