Effect of physical activity during pregnancy on mode of delivery.

OBJECTIVE: The purpose of this study was to evaluate the effect of structured physical exercise programs during pregnancy on the course of labor and delivery. STUDY DESIGN: We conducted a systematic review and metaanalysis using the following data sources: Medline and The Cochrane Library. In our study, we used randomized controlled trials (RCT) that evaluated the effects of exercise programs during pregnancy on labor and delivery. The results are summarized as relative risks. RESULTS: In the 16 RCTs that were included there were 3359 women. Women in exercise groups had a significantly lower risk of cesarean delivery (relative risk, 0.85; 95% confidence interval [CI], 0.73-0.99). Birthweight was not significantly reduced in exercise groups. The risk of instrumental delivery was similar among groups (relative risk, 1.00; 95% CI, 0.82-1.22). Data on Apgar score, episiotomy, epidural anesthesia, perineal tear, length of labor, and induction of labor were insufficient to draw conclusions. With the use of data from 11 studies (1668 women), our analysis showed that women in the exercise groups gained significantly less weight than women in control groups (mean difference, -1.13 kg; 95% CI, -1.49 to -0.78). CONCLUSION: Structured physical exercise during pregnancy reduces the risk of cesarean delivery. This is an important finding to convince women to be active during their pregnancy and should lead the physician to recommend physical exercise to pregnant women, when this is not contraindicated.

Correction of through-plane deformation artifacts in stimulated echo acquisition mode cardiac imaging.

Attempts to use a stimulated echo acquisition mode (STEAM) in cardiac imaging are impeded by imaging artifacts that result in signal attenuation and nulling of the cardiac tissue. In this work, we present a method to reduce this artifact by acquiring two sets of stimulated echo images with two different demodulations. The resulting two images are combined to recover the signal loss and weighted to compensate for possible deformation-dependent intensity variation. Numerical simulations were used to validate the theory. Also, the proposed correction method was applied to in vivo imaging of normal volunteers (n = 6) and animal models with induced infarction (n = 3). The results show the ability of the method to recover the lost myocardial signal and generate artifact-free black-blood cardiac images.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

L 'évolution des réserves énergétiques chez les ouvrières et les sexués de la fourmi d 'Argentine (Iridomyrmex humilis) (Mayr): une stratégie en relation avec le mode de fondation

Le poids sec, le rapport corps gras/poids sec et le contenu énergétique ou dernier stade larvaire. de le nymphe et de l'adulte ont été mesurés chez les reines. Les mâles et les ouvrières de la fourmi d'Argentine, une espèce caractérisée par une fondation de type dépendant. Deux principaux résultats ont été obtenus. En ce qui concerne les reines, nous avons montré qu'il existe une accumulation du corps gras entre l'émergence et l'accouplement toutefois cette augmentation du contenu énergétique se révèle moins importante chez cette espèce que chez celles qui possèdent une fondation de type indépendant. L'intérêt adaptatif de cette différence est discuté. En ce qui concerne les ouvrières adultes, c'est au moment de l'émergence que nous avons rencontré le plus de corps gras. En vieillissant les ouvrières perdent de l'énergie. On peut penser que ces variations sont en relation avec les tâches accomplies par les ouvrières, à l'rintéheur et à l'extérieur du nid.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Physiologie des sexués femelles de fourmis (Hymenoptera; Formicidae) en relation avec le mode de fondation

A study was undertaken to determine if there was a relation between the mode of colony founding in ants and the physiology of the new queens produced, in which mature gynes of 24 ant species were examined. Gynes of species utilizing independent colony founding had a far higher relative fat content (X±SD; 54±6%)(g fat/g dry weight) than gynes of species employing dependent colony founding(19±8%). Dimorphism between queens and workers was significantly higher in species employing independent colony founding. Thus independent colony founding not only results in production of queens with a relatively higher fat content and therefore with a higher energy content per g, but also results in the production of larger queens (in comparison with worker size). Of species employing independent colony founding, 80% were monogynous, whereas only 11% of the species employing dependent colony founding were monogynous. These results are discussed with regard to the social structure and life-history of ant species.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.