Phenolic composition of hydrophilic extract of manna from sicilian Fraxinus angustifolia vahl and its reducing, antioxidant and anti-inflammatory activity in vitro

Manna, a very singular vegetable product derived from the spontaneous solidification of the sap of some Fraxinus species, has long been known for its mild laxative and emollient properties. In this work, a hydro-alcoholic extract of manna (HME) from Sicilian Fraxinus angustifolia Vahl was investigated using HPLC-DAD to find phenol components and using chemical and biological in vitro assays to determine its reducing, antioxidant and anti-inflammatory capacity. We identified elenolic acid, tyrosol, hydroxytyrosol, catechin, fraxetin, verbascoside, gallic acid, procyanidin-B1, and luteolin 3,7 glucoside, in order of abundance. Measurements of total antioxidant activity by Folin-Ciocalteu reaction and ferric reducing ability (FRAP), as well as of scavenger activity towards ABTS•+, DPPH•, and perferryl-myoglobin radicals, showed that the phytocomplex effectively reduced oxidants with different standard potentials. When compared with vitamin E, HME also behaved as an efficient chain-breaking antioxidant against lipoperoxyl radicals from methyl linoleate. In cellular models for oxidative stress, HME counteracted membrane lipid oxidation of human erythrocytes stimulated by tert-butyl hydroperoxide and prevented the generation of reactive oxygen species, as well as the GSH decay in IL-1β–activated intestinal normal-like cells. Moreover, in this in vitro intestinal bowel disease model, HME reduced the release of the pro-inflammatory cytokines IL-6 and IL-8. These findings may suggest that manna acts as an antioxidant and anti-inflammatory natural product in humans, beyond its well-known effects against constipation

The compositional and mineralogical analysis of fired pigments in Nasca pottery from Cahuachi (Peru) by the combined use of the portable PIXE-alpha and portable XRD techniques

Abstract An analytical protocol based on the combined use of the portable PIXE-alpha (Particle Induced X-ray Emission) and XRD (X-ray Diffraction) non destructive techniques developed at the LANDIS laboratory (Laboratorio di Analisi Non Distruttive) of the INFN–CNR (Istituto Nazionale di Fisica Nucleare–Consiglio Nazionale delle Ricerche) in Catania (Italy), was applied for the characterisation of the surface paints of some archaeological fragments of Nasca pottery from the Ceremonial Centre of Cahuachi in Southern Peru. Measurements were carried out on the black, white, red, orange and grey pigments; quantitative information on the chemical composition as well as on the mineralogical phases present on the paints were obtained. Results allowed to make some considerations about the materials and the manufacturing technique used to realise such fired pigments. It should be noted that during firing the precursor minerals composing the pigments undergo a phase transformation and their identification presents some difficulties

Structure of the partial cone conformer of 25,26,27,28-tetrakis[(2-pyridylmethyl)oxy]calix[4]arene

The partial cone conformer of tetrakis[(2-pyridylmethyl)- oxy]pentacyclo[ 19.3.1.13'7.19'13.115,19]octacosa- 1 (25),- 3,5,7 (28),9,11,13 (27), 15,17,19(26),21,23-dodecaene, (I), adopts a conformation in which the pendant OCH2py group of the rotated aryl ring is oriented away from the calixarene cavity produced by the other three aryl rings, with its N atom exo to the calixarene cup. The orientation of the four aromatic rings is such that two rings are almost parallel to each other and the other two are at an angle of 42 ° . This conformation precludes any solvent molecule being enclathrated within the small molecular cavity

Modeling and simulation of cancer immunoprevention vaccine

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Understanding peace through the world news

Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace

Human mobility from theory to practice: Data, models and applications

The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library "scikit-mobility" developed by the presenters of the tutorial

Modeling Adversarial Behavior Against Mobility Data Privacy

Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way

Modeling the competition between lung metastases and the immune system using agents

<p>Abstract</p> <p>Background</p> <p>The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based - ODE model for the simulation of the vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations.</p> <p>Results</p> <p>The model shows that it is possible to obtain "in silico" a 45% reduction in the number of vaccinations. The analysis of the results further suggests that any optimal protocol for preventing lung metastases formation should be composed by an initial massive vaccine dosage followed by few vaccine recalls.</p> <p>Conclusions</p> <p>Such a reduction may represent an important result from the point of view of translational medicine to humans, since a downsizing of the number of vaccinations is usually advisable in order to minimize undesirable effects. The suggested vaccination strategy also represents a notable outcome. Even if this strategy is commonly used for many infectious diseases such as tetanus and hepatitis-B, it can be in fact considered as a relevant result in the field of cancer-vaccines immunotherapy. These results can be then used and verified in future "in vivo" experiments, and their outcome can be used to further improve and refine the model.</p

The WISSH quasars Project: II. Giant star nurseries in hyper-luminous quasars

Studying the coupling between the energy output produced by the central quasar and the host galaxy is fundamental to fully understand galaxy evolution. Quasar feedback is indeed supposed to dramatically affect the galaxy properties by depositing large amounts of energy and momentum into the ISM. In order to gain further insights on this process, we study the SEDs of sources at the brightest end of the quasar luminosity function, for which the feedback mechanism is supposed to be at its maximum. We model the rest-frame UV-to-FIR SEDs of 16 WISE-SDSS Selected Hyper-luminous (WISSH) quasars at 1.8 < z < 4.6 disentangling the different emission components and deriving physical parameters of both the nuclear component and the host galaxy. We also use a radiative transfer code to account for the contribution of the quasar-related emission to the FIR fluxes. Most SEDs are well described by a standard combination of accretion disk+torus and cold dust emission. However, about 30% of them require an additional emission component in the NIR, with temperatures peaking at 750K, which indicates the presence of a hotter dust component in these powerful quasars. We measure extreme values of both AGN bolometric luminosity (LBOL > 10^47 erg/s) and SFR (up to 2000 Msun/yr). A new relation between quasar and star-formation luminosity is derived (LSF propto LQSO^(0.73)) by combining several Herschel-detected quasar samples from z=0 to 4. Future observations will be crucial to measure the molecular gas content in these systems, probe the impact between quasar-driven outflows and on-going star-formation, and reveal the presence of merger signatures in their host galaxies.Comment: 19 pages, 12 figures; Accepted for publication in Astronomy & Astrophysics on June 13, 201