Search

Obesity is a metabolic disorder characterized by excess adipose tissue, macrophages infiltration, and inflammation which in turn lead to insulin-resistance. Epidemiological evidences reported that anthocyanins possess not only high antioxidant and antiinflammatory activities, but also improve metabolic complications associated with obesity. The aim of this work was to evaluate the in vitro beneficial effects of cyanidin-3-O-glucoside (C3G) in counteracting inflammation and insulin-resistance in 3T3-L1 hypertrophic adipocytes exposed to palmitic acid (PA). In the present study murine 3T3-L1 adipocytes were pretreated with C3G for 24 h and then exposed to palmitic acid (PA) for 24 h. Real-time PCR, western blotting analysis and Oil Red O staining were applied for investigating the mechanism involved in adipocytes dysfunction. C3G pretreatment reduced lipid accumulation, PPAR? pathway and NF-?B pathway induced by PA in murine adipocytes. In addition, our data demonstrated that PA reduced insulin signaling via IRS-1 Ser(307)phosphorylation while C3G dose-dependently improved insulin sensitivity restoring IRS-1/PI3K/Akt pathway. Furthermore, C3G improved adiponectin mRNA levels altered by PA in 3T3-L1 murine and SGBS human adipocytes. Herein reported data demonstrate that C3G ameliorated adipose tissue dysfunction, thus suggesting new potential roles for this compound of nutritional interest in the prevention of pathological conditions linked to obesity.

Element-based textures are a kind of texture formed by nameable elements, the texels [1], distributed according to specific statistical distributions; it is of primary importance in many sectors, namely textile, fashion and interior design industry. State-of-theart texture descriptors fail to properly characterize element-based texture, so we present Texel-Att to fill this gap. Texel-Att is the first fine-grained, attribute-based representation and classification framework for element-based textures. It first individuates texels, characterizing them with individual attributes; subsequently, texels are grouped and characterized through layout attributes, which give the Texel-Att representation. Texels are detected by a Mask-RCNN, trained on a brand-new element-based texture dataset, ElBa, containing 30K texture images with 3M fully-annotated texels. Examples of individual and layout attributes are exhibited to give a glimpse on the level of achievable graininess. In the experiments, we present detection results to show that texels can be precisely individuated, even on textures "in the wild"; to this sake, we individuate the element-based classes of the Describable Texture Dataset (DTD), where almost 900K texels have been manually annotated, leading to the Element-based DTD (E-DTD). Subsequently, classification and ranking results demonstrate the expressivity of Texel-Att on ElBa and E-DTD, overcoming the alternative features and relative attributes, doubling the best performance in some cases; finally, we report interactive search results on ElBa and E-DTD: with Texel-Att on the E-DTD dataset we are able to individuate within 10 iterations the desired texture in the 90% of cases, against the 71% obtained with a combination of the finest existing attributes so far. Dataset and code is available at https://github.com/godimarcovr/Texel-Att, Comment: Accepted as oral at BMVC 2019

The genus Hypericum (family Hypericaceae) contains 484 species, one of which, Hypericum perforatum, is largely used in medicine as antidepressant, as well as antinflammatory, and antimicrobial agent. Hypericin, together with the other naphthodianthrone derivatives named hypericins (the red pigments recognized as partially responsible for biological activities of this plant), has been identified largely in more than 300 Hypericum species (Skalkos et al., 2006). Particularly, hypericins are one of the most potent naturally occurring photodynamic agents; indeed, upon light irradiation, they very effectively induce apoptosis and/or necrosis of cancer cells (Karioti et al., 2010) and thus might be valued for the photodynamic therapy of cancer. The aim of this study was to evaluate the in vitro phototoxic activity and antioxidant efficacy of 11 different (wild or cultivated) Hypericum species (perforatum L., aegypticum L., androsaemum L., calycinum L., hircinum L., hirsutum L., montanum L., patulum Thunb., perfoliatum L., pubescens Boiss., tetrapterum Fr.) also in correlation with their phytochemical profiles. Plant samples were extracted with ethanol in dark conditions and analyzed by HPLC-DAD-MS in order to determine the content of the main polyphenols, phloroglucinols, and naphtodiantrones. The extracts were also evaluated for their photocytotoxicity using murine fibroblasts NIH/3T3 exposed to either white light (5.2 J/cm2) or dark conditions. The photocytotoxic effects of the Hypericum extracts were determined by means of the sulforhodamine B assay and compared to hypericin and pseudohypericin as positive control. Moreover, the antioxidant/radical scavenging capacity of the Hypericum ethanolic extracts was investigated by means of 3 redox-based assays (Folin-Ciocalteau assay, DPPH assay, and ORAC-FL assay). We found that H. perforatum and H. perfoliatum are the two species with the highest content of hypericin and its biosynthetic precursors, being significant amounts of these compounds present also in H. montanum, H. pubescens and H. tetrapterum. Five flavonoids (quercetin-3-O-glucoside, quercetina-3-O-galattoside, quercitrin, quercetin and biapigenin) due their presence in all samples are possible genus markers, while myricitrin (found only in H. perfoliatum) and quercetin-3-O-rutinoside (recovered only in H. calycinum and H. aegypticum) are possible species chemotaxonomic markers. Furthermore we demonstrated a relevant dose-dependent photocytotoxicity of Hypericum extracts under light exposure conditions and the activity was significantly stronger for the species with the higher amount of naphtodianthrones such as H. perfoliatum and H. tetrapterum. Finally, all Hypericum extracts exhibited a good antioxidant radical scavenger activity. Our study represents a further contribute to the knowledge of the chemical composition and biological activity of Hypericum species. Results show that, in addition to the well known H. perforatum, at least other three species (H. tetrapterum, H. pubescens and H. montanum) are potential sources of biologically active compounds, and at least other two (H. perfoliatum and H. tetrapterum), due their phototoxicity, may be good candidates for their application in photodynamic therapy.

In deep learning, visualization techniques extract the salient patterns exploited by deep networks for image classification, focusing on single images; no effort has been spent in investigating whether these patterns are systematically related to precise semantic entities over multiple images belonging to a same class, thus failing to capture the very understanding of the image class the network has realized. This paper goes in this direction, presenting a visualization framework which produces a group of clusters or summaries, each one formed by crisp salient image regions focusing on a particular part that the network has exploited with high regularity to decide for a given class. The approach is based on a sparse optimization step providing sharp image saliency masks that are clustered together by means of a semantic flow similarity measure. The summaries communicate clearly what a network has exploited of a particular image class, and this is proved through automatic image tagging and with a user study. Beyond the deep network understanding, summaries are also useful for many quantitative reasons: their number is correlated with ability of a network to classify (more summaries, better performances), and they can be used to improve the classification accuracy of a network through summary-driven specializations., Comment: Project page and code available at http://marcocarletti.altervista.org/publications/understanding-visual-summaries/

Human activity recognition and forecasting can be used as a primary cue for scene understanding. Acquiring details from the scene has vast applications in different fields such as computer vision, robotics and more recently smart lighting. This work brings together advanced research in computer vision and the most modern technology in lighting. The goal of this work is to eliminate the need for any switches for lighting, which means that each person in the office perceives the entire office as all lit, while lights, which are not visible by the person, are switched off by the system. This can be achieved by combining lighting with presence detection and smart light control.