Quali-quantitative analysis of flavonoides of Cornus mas L. (Cornaceae) fruts.

The methanol extract obtained from the ripe fruits of Cornus mas L. (Cornaceae) have been phytochemically studied. On the basis of HPLC–PDA–MS/MSn analysis eight compounds have been identified as quercetin, kaempferol, and aromadendrin glycosilated derivatives. Three major compounds have been also isolated by Sephadex LH-20 column chromatography followed by HPLC and characterised by NMR spectroscopy. Moreover, LC–PDA–MS analysis of the red pigment revealed the presence of three anthocyanins. The quantitative analysis of all compounds was reported

Gill histopathology in zebrafish model following exposure to aquacultural disinfectants

The effect of acute exposure of four disinfectants commonly used in aquacultural practice (formalin, potassium permanganate, benzalkonium chloride and malachite green) was studied on the histological structure of adult zebrafish (Danio rerio) gills. Groups of 8 individuals were exposed to a dose of each disinfectant corresponding to the therapeutic dose (TD) and five folds of the therapeutic dose (5xTD). Gills of all exposed zebrafish showed a higher occurrence of histopathological changes. These alterations included a slightly focal proliferation of interlamellar cells with obliteration of interlamellar spaces, mild infiammatory reaction with leucocyte infiltration and lifting of the epithelial layer from gill lamellae. Fish exposed to potassium permanganate showed more severe histopathological changes consisting of necrotic change of lamellar cells, distorsion and apical necrosis of secondary lamellae

Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figure

Ziziphus lotus (L.) Lam. as a source of health promoting products: metabolomic profile, antioxidant and tyrosinase inhibitory activities

The methanolic extract of the stem bark of a wild species of jujube, Ziziphus lotus (L.) Lam., growing in Sicily, was chemically and biologically investigated. The chemical profile was defined by UHPLC-HR-ESI-Orbitrap/MS analysis whereas antioxidant and tyrosinase inhibitory activities were investigated by in vitro assays. The extract showed a high total phenolic and flavonoid content (TPC = 271.65 GAE/g and TFC = 188.11 RE/g extract). Metabolomic analysis revealed a rich phytocomplex characterized by phenols, cyclopeptide alkaloids, and triterpenoid saponins, some of which here detected for the first time. The mushroom tyrosinase inhibition assay displayed that the methanolic extract efficiently inhibits the monophenolase and diphenolase activity. Furthermore, the extract showed a strong ability to scavenge DPPH, a good Fe3+ reducing antioxidant power, in addition to a Fe2+ chelating activity. Taken together, these results suggest possible novel applications of wild jujube stem bark as a source of potential skin-care agents with several uses in pharmaceutical and cosmetic industries

Effect of Tomato Peel Extract Grown under Drought Stress Condition in a Sarcopenia Model

Tomatoes and their derivates represent an important source of natural biologically active components. The present study aims to investigate the protective effect of tomato peel extracts, grown in normal (RED-Ctr) or in drought stress (RED-Ds) conditions, on an experimental model of sarcopenia. The phenolic profile and total polyphenols content (TPC) of RED-Ctr and RED-Ds were determined by Ultra High-Performance Liquid Chromatography (UHPLC) analyses coupled to electrospray ionization high-resolution mass spectrometry (ESI-HR-MS). Human skeletal muscle myoblasts (HSMM) were differentiated in myotubes, and sarcopenia was induced by dexametha- sone (DEXA) treatment. Differentiation and sarcopenia were evaluated by both real-time PCR and immunofluorescent techniques. Data show that myosin heavy chain 2 (MYH2), troponin T (TNNT1), and miogenin (MYOG) were expressed in differentiated myotubes. 5 μg Gallic Acid Equivalent (GAE/mL) of TPC from RED-Ds extract significantly reduced muscle atrophy induced by DEXA. Moreover, Forkhead BoxO1 (FOXO1) expression, involved in cell atrophy, was significantly decreased by RED-Ds extract. The protective effect of tomato peel extracts depended on their qualitative polyphenolic composition, resulting effectively in the in vitro model of sarcopenia

Phytochemical data parallel morpho-colorimetric variation in Polygala flavescens DC

Phytochemical data, integrated with other sources of information, represent a valuable tool helping to solve different kinds of taxonomic problems in plant systematics. In the present study, a comparative investigation, in order to clarify the systematic relationships of the three subspecies currently recognized within the Italian endemic Polygala flavescens, was carried out. Preliminarily, a morphometric and colorimetric analysis, in order to test the degree of morphological distinctiveness among the taxa, was performed. Then, a phytochemical analysis based both on volatile and non-volatile compounds was obtained. Concerning the morpho-colorimetric analysis, our results confirm most of the characters as useful to discriminate the three subspecies. In addition, some volatile and non-volatile compounds are good taxonomic markers. Morpho-colorimetric variation is clearly paralleled by phytochemical results, confirming the value of this kind of data to infer relationships in plant systematics. Based on these results, we support a taxonomic treatment at subspecific level for the involved taxa. Finally, based on the most significant morphological characters, a revision of herbarium specimens allowed to redefine the distribution pattern of the three subspecies. Accordingly, the range of P. flavescens subsp. maremmana is limited to Mt. Argentario (southern Tuscany) only. A key is also reported for the identification of the three subspecies

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

We study the performance -- and specifically the rate at which the error probability converges to zero -- of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say $\sim \exp\left(-n\,I + o(n) \right)$, where $n$ is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and $I$ is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate $I$, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for $n$ large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of $n$. Furthermore, approximate error probability curves $\sim \zeta_n \exp\left(-n\,I \right)$ are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where $\zeta_n$ represents the most representative sub-exponential terms of the error probabilities