Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks

Recognising human actions in untrimmed videos is an important challenging task. An effective three-dimensional (3D) motion representation and a powerful learning model are two key factors influencing recognition performance. In this study, the authors introduce a new skeleton-based representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a colour encoding process. By normalising the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the colour-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. They then design and train different deep convolutional neural networks based on the residual network architecture on the obtained image-based representations to learn 3D motion features and classify them into classes. Their proposed method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches while requiring less computation for training and prediction

Morphology and Magnetic Properties of Sulfonated Poly[styrene-(ethylene/butylene)-styrene]/Iron Oxide Composites

α-Fe2O3 structures were initiated in the sulfonated polystyrene block domains of poly[styrene–(ethylene/butylene)–styrene] (SEBS) block copolymers via a domain-targeted in-situ chemical precipitation method. The crystal structure of these particles was determined using wide-angle X-ray diffraction and selected area electron diffraction using a transmission electron microscope (TEM). TEM revealed that for less sulfonated SEBS (10 mole%), nanoparticles were aggregated with aggregate size range of 100–150 nm whereas for high sulfonation (16 and 20 mole% sSEBS) there were needle-like structures with length and width of 200–250 nm and 50 nm, respectively. Dynamic mechanical analyses suggest that initial iron oxide nanoparticle growth takes place in the sulfonated polystyrene block domains. The magnetic properties of these nanocomposites were probed with a superconducting quantum interference device magnetometer at 5 and 150 K as well as with an alternating gradient magnetometer at 300 K. The materials exhibited superparamagnetism at 150 K and 300 K and ferrimagnetism at 5 K

Exploiting deep residual networks for human action recognition from skeletal data

The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset

Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks

Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeletonbased representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.This research was carried out at the Cerema Research Center (CEREMA) and Toulouse Institute of Computer Science Research (IRIT), Toulouse, France. Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for Research, Technological Development and demonstration under grant agreement N. 600371, el Ministerio de Economia, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

The Antioxidant Potential of the Mediterranean Diet in Patients at High Cardiovascular Risk: An In-Depth Review of the PREDIMED

Cardiovascular disease (CVD) is the leading global cause of death. Diet is known to be important in the prevention of CVD. The PREDIMED trial tested a relatively low-fat diet versus a high-fat Mediterranean diet (MedDiet) for the primary prevention of CVD. The resulting reduction of the CV composite outcome resulted in a paradigm shift in CV nutrition. Though many dietary factors likely contributed to this effect, this review focuses on the influence of the MedDiet on endogenous antioxidant systems and the effect of dietary polyphenols. Subgroup analysis of the PREDIMED trial revealed increased endogenous antioxidant and decreased pro-oxidant activity in the MedDiet groups. Moreover, higher polyphenol intake was associated with lower incidence of the primary outcome, overall mortality, blood pressure, inflammatory biomarkers, onset of new-onset type 2 diabetes mellitus (T2DM), and obesity. This suggests that polyphenols likely contributed to the lower incidence of the primary event in the MedDiet groups. In this article, we summarize the potential benefits of polyphenols found in the MedDiet, specifically the PREDIMED cohort. We also discuss the need for further research to confirm and expand the findings of the PREDIMED in a non-Mediterranean population and to determine the exact mechanisms of action of polyphenols

Apramycin susceptibility of multidrug-resistant Gram-negative blood culture isolates in five countries in South-East Asia

Bloodstream infections (BSIs) are a leading cause of sepsis, a life-threatening condition that contributes significantly to the mortality of bacterial infections. Aminoglycoside antibiotics such as gentamicin or amikacin are essential medicines in the treatment of BSIs, but their clinical efficacy is increasingly compromised by antimicrobial resistance. The aminoglycoside apramycin has demonstrated preclinical efficacy against aminoglycoside- and multidrug-resistant (MDR) Gram-negative bacilli (GNB) and is currently in clinical development for the treatment of critical systemic infections. Here, we collected a panel of 470 MDR GNB isolates from health care facilities in Cambodia, Laos, Singapore, Thailand, and Vietnam for a multi-centre assessment of their antimicrobial susceptibility to apramycin in comparison to other aminoglycosides and colistin by broth microdilution assays. Apramycin and amikacin MICs ≤ 16 µg/mL were found for 462 (98.3%) and 408 (86.8%) GNB isolates, respectively. Susceptibility to gentamicin and tobramycin (MIC ≤ 4 µg/mL) was significantly lower at 122 (26.0%) and 101 (21.5%) susceptible isolates, respectively. Of note, all carbapenem- and third-generation cephalosporin (3GC) resistant Enterobacterales, all Acinetobacter baumannii, and all Pseudomonas aeruginosa isolates tested in this study appeared to be susceptible to apramycin. Of the 65 colistin-resistant isolates tested, only four (6.2%) had an apramycin MIC > 16 µg/mL. Apramycin demonstrated best-in-class activity against a panel of GNB isolates with resistances to other aminoglycosides, carbapenems, 3GC, and colistin, warranting continued consideration of apramycin as a drug candidate for the treatment of multidrug-resistant BSIs. Keywords: Bloodstream infection; Gram negative; aminoglycoside; antimicrobial resistance; apramycin; blood culture isolates

Quantum Criticality in Heavy Fermion Metals

Quantum criticality describes the collective fluctuations of matter undergoing a second-order phase transition at zero temperature. Heavy fermion metals have in recent years emerged as prototypical systems to study quantum critical points. There have been considerable efforts, both experimental and theoretical, which use these magnetic systems to address problems that are central to the broad understanding of strongly correlated quantum matter. Here, we summarize some of the basic issues, including i) the extent to which the quantum criticality in heavy fermion metals goes beyond the standard theory of order-parameter fluctuations, ii) the nature of the Kondo effect in the quantum critical regime, iii) the non-Fermi liquid phenomena that accompany quantum criticality, and iv) the interplay between quantum criticality and unconventional superconductivity.Comment: (v2) 39 pages, 8 figures; shortened per the editorial mandate; to appear in Nature Physics. (v1) 43 pages, 8 figures; Non-technical review article, intended for general readers; the discussion part contains more specialized topic

Phytochemical studies and antioxidant activity of two South African medicinal plants traditionally used for the management of opportunistic fungal infections in HIV/AIDS patients

<p>Abstract</p> <p>Background</p> <p>It has been observed that perturbations in the antioxidant defense systems, and consequently redox imbalance, are present in many tissues of HIV-infected patients. Hence, the exogenous supply of antioxidants, as natural compounds that scavenge free radicals, might represent an important additional strategy for the treatment of HIV infection. The aim of this study was therefore to analyse the phytochemical constituents and antioxidant potential of <it>Gasteria bicolor </it>Haw and <it>Pittosporum viridiflorum </it>Sims., two South African plants traditionally used for the management of opportunistic fungal infections (OFIs) in AIDS patients.</p> <p>Methods</p> <p>The <it>in vitro </it>antioxidant properties of the two plants were screened through DPPH (1,1-diphenyl-2-picrylhydrazyl), NO (nitric oxide), H₂O₂(hydrogen peroxide) radical scavenging effects and reducing power assays. Phytochemical studies were done by spectrophotometric techniques.</p> <p>Results</p> <p>There were no significant differences in the flavonoid and proanthocyanidins contents between the leaves and bark extracts of <it>Gasteria bicolor </it>and <it>Pittosporum viridiflorum </it>respectively, while the total phenolic content of the bark extract of <it>P. viridiflorum </it>was significantly higher than that of <it>G. bicolor </it>leaf. The acetone extracts of both plants indicated strong antioxidant activities.</p> <p>Conclusion</p> <p>The results from this study indicate that the leaves and stem extracts of <it>Gasteria bicolor </it>and <it>Pittosporum viridiflorum </it>respectively possess antioxidant properties and could serve as free radical inhibitors, acting possibly as primary antioxidants. Since reactive oxygen species are thought to be associated with the pathogenesis of AIDS, and HIV-infected individuals often have impaired antioxidant defenses, the inhibitory effect of the extracts on free radicals may partially justify the traditional use of these plants in the management of OFIs in HIV patients in South Africa.</p

Honey health benefits and uses in medicine

The generation of reactive oxygen species (ROS) and other free radicals during metabolism is an essential and normal process that ideally is compensated through the antioxidant system. However, due to many environmental, lifestyle, and pathological situations, free radicals and oxidants can be produced in excess, resulting in oxidative damage of biomolecules (e.g., lipids, proteins, and DNA). This plays a major role in the development of chronic and degenerative illness such as cancer, autoimmune disorders, aging, cataract, rheumatoid arthritis, cardiovascular, and neurodegenerative diseases (Pham-Huy et al. 2008; Willcox et al. 2004). The human body has several mechanisms to counteract oxidative stress by producing antioxidants, which are either naturally synthetized in situ, or externally supplied through foods, and/or supplements (Pham-Huy et al. 2008).info:eu-repo/semantics/publishedVersio