A systematic literature review of the effect of anthocyanins on gut microbiota populations

Background: Evidence has shown that anthocyanins, a subclass of polyphenol, are metabolised in the gut, modulate bacterial species and exert bioactive effects through this interaction. Methods: A systematic literature review was undertaken to determine the level of current evidence for the association between anthocyanin intake and changes in gut microbiota populations. The studies included were also assessed for the different techniques used in microbiota determination. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, scientific databases, including Scopus, PubMed, ScienceDirect, Web of Science and MEDLINE, were searched up to June 2017. Details on population/sample, study design, intervention/control, dosage and method of microbiota determination were extracted. Results: Six studies (three in vitro, two animal and one human trials) were included in the review, which showed that anthocyanins induced a significant proliferative effect on Bifidobacterium spp., known for their wide use in probiotics and for the treatment of irritable bowel syndrome. There was also an observed inhibition of Clostridium histolyticum, which was shown to be pathogenic in humans. The depth of analysis is an important consideration for the choice of microbiota determination technique with respect to a comprehensive, high-resolution microbiota analysis or analysis of the main microbiota taxa. Conclusions: Very limited research has been carried out in the area of anthocyanins and gut microbiota; beneficial effects have generally been observed, and further clinical trials in humans are needed to confirm changes to gut microbes in relation to dietary anthocyanin intake and potential health benefits. Journal of Human Nutrition and Dietetic

Metastable anions of dinitrobenzene: resonances for electron attachment and kinetic energy release

Attachment of free, low-energy electrons to dinitrobenzene (DNB) in the gas phase leads to DNB as well as several fragment anions. DNB, (DNB-H), (DNB-NO), (DNB-2NO), and (DNB-NO(2)) are found to undergo metastable (unimolecular) dissociation. A rich pattern of resonances in the yield of these metastable reactions versus electron energy is observed; some resonances are highly isomer-specific. Most metastable reactions are accompanied by large average kinetic energy releases (KER) that range from 0.5 to 1.32 eV, typical of complex rearrangement reactions, but (1,3-DNB-H)(-) features a resonance with a KER of only 0.06 eV for loss of NO. (1,3-DNB-NO)(-) offers a rare example of a sequential metastable reaction, namely, loss of NO followed by loss of CO to yield C(5)H(4)O(-) with a large KER of 1.32 eV. The G4(MP2) method is applied to compute adiabatic electron affinities and reaction energies for several of the observed metastable channels. (C) 2010 American Institute of Physics. [doi:10.1063/1.3514931

Enhancing the Behaviorial Fidelity of Synthetic Entities with Human Behavior Models

Human-behavior models (HBMs) and artificial intelligence systems are called on to fill a wide variety of roles in military simulations. Each of the off the shelf human behavior models available today focuses on a specific area of human cognition and behavior. While this makes these HBMs very effective in specific roles, none are single-handedly capable of supporting the full range of roles necessary in an urban military scenario involving asymmetric opponents and potentially hostile civilians. The research presented here explores the integration of three separate human behavior models to support three different roles for synthetic participants in a single simulated scenario. The Soar architecture, focusing on knowledge-based, goal-directed behavior, supports a fire team of U.S. Army Rangers. PMFServ, focusing on a physiologically/stress constrained model of decision-making based on emotional utility, supports civilians that may become hostile. Finally, AI.Implant, focusing on individual and crowd navigation, supports a small group of opposing militia. Due to the autonomy and wide range of behavior supported by the three human behavior models, the scenario is more flexible and dynamic than many military simulations and commercial computer games

Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

BACKGROUND: The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. METHODS: We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20-80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. RESULTS: The predictive accuracy of ML-based algorithms (0.843 </= AU-ROC </= 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as 'near population' risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. CONCLUSION: ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening

Variation of the omega-3 content of Australian food products

Abstract from the 2008 Annual Scientific Meeting of the Nutrition Society of Australia, 30 November - 3 December 2008, Glenelg, Australia

The Sun's position in the sky

We express the position of the Sun in the sky as a function of time and the observer's geographic coordinates. Our method is based on applying rotation matrices to vectors describing points on the celestial sphere. We also derive direct expressions, as functions of date of the year and geographic latitude, for the duration of daylight, the maximum and minimum altitudes of the Sun, and the cardinal directions to sunrise and sunset. We discuss how to account for the eccentricity of the earth's orbit, the precessions of the equinoxes and the perihelion, the size of the solar disk, and atmospheric refraction. We illustrate these results by computing the dates of "Manhattanhenge" (when sunset aligns with the east-west streets on the main traffic grid for Manhattan, in New York City), by plotting the altitude of the Sun over representative cities as a function of time, and by showing plots ("analemmas") for the position of the Sun in the sky at a given hour of the day.Comment: 19 pages, 16 figures. v3: Replaced to match published version and to re-package Mathematica notebook as an ancillary fil

Comparison of the pressure dependences of Tc in the trivalent d-electron superconductors

Whereas dhcp La superconducts at ambient pressure with Tc = 5 K, the other trivalent d-electron metals Sc, Y, and Lu only superconduct if high pressures are applied. Earlier measurements of the pressure dependence of Tc for Sc and Lu metal are here extended to much higher pressures. Whereas Tc for Lu increases monotonically with pressure to 12.4 K at 174 GPa (1.74 Mbar). Tc for Sc reaches 19.6 K at 107 GPa, the 2nd highest value observed for any elemental superconductor. At higher pressures a phase transition occurs whereupon Tc drops to 8.31 K at 111 GPa. The Tc(P) dependences for Sc and Lu are compared to those of Y and La. An interesting correlation is pointed out between the value of Tc and the fractional free volume available to the conduction electrons outside the ion cores, a quantity which is directly related to the number of d electrons in the conduction band