Gériatrie aux urgences: une sélection de publications récentes [Elderly patients in the emergency department: a selection of some recent publications].

Geriatric patients presenting to the ED are at high risk of mortality as well as of cognitive or functional decline. Thus, ED is an ideal spot for interventions that can improve their outcome. In this article, we summarize six recent studies, regarding the utilization of prognostic evaluation scores in geriatric patients presenting to the ED, adverse drug reactions, the significance of elevated troponin in patients who have remained on the ground after a fall, the rationale of performing head CT in patients without focal neurologic findings after a fall, the ideal treatment of a proximal femoral fracture and the excessive use of urinary catheters in the ED.

Nutrition préopératoire en chirurgie viscérale: recommandations et réalité [Preoperative nutrition in abdominal surgery: recommendations and reality].

Malnutrition concerns up to 50% at in-hospital admission. Its diagnosis and treatment are fundamental parts of the surgical approach because nutritional status directly influences the clinical outcome. The Nutritional Risk Score (NRS-2002) represents the recommended screening tool by the European Society of Parenteral and Enteral Nutrition (ESPEN). Patients with a score > or = 3 and aged > 70 years old, should receive a nutritional support during 7-14 day before surgery. Depending on patient's clinical conditions, the enteral route of administration should be preferred. Despite strong evidence in favor of nutritional supplementation, much effort must be done to implement these supportive strategies in the everyday clinical practice.

Heterogeneity, selection and labor market disparities

We study the incentives to acquire skill in a model where heterogeneous firmsand workers interact in a labor market characterized by matching frictions and costlyscreening. When effort in acquiring skill raises both the mean and the variance of theresulting ability distribution, multiple equilibria may arise. In the high-effort equilibrium, heterogeneity in ability is sufficiently large to induce firms to select the bestworkers, thereby confirming the belief that effort is important for finding good jobs.In the low-effort equilibrium, ability is not sufficiently dispersed to justify screening,thereby confirming the belief that effort is not so important. The model has implications for wage inequality, the distribution of firm characteristics, sorting patternsbetween firms and workers, and unemployment rates that can help explaining observedcross-country variation in socio-economic and labor market outcomes.

Library buildings : Selection of an architect ... A paper read before the Iowa Library Association at Grinnell, Iowa. October 30, 1902

This leaflet talks about the architecture of library buildings. It provides information on the selection of an architect and how to design flooring and room arrangements in a practical way.

Selection processes in a citrus hybrid population using RAPD markers

The objective of this work was to evaluate the processes of selection in a citrus hybrid population using segregation analysis of RAPD markers. The segregation of 123 RAPD markers between 'Cravo' mandarin (Citrus reticulata Blanco) and 'Pêra' sweet orange (C. sinensis (L.) Osbeck) was analysed in a F1 progeny of 94 hybrids. Genetic composition, diversity, heterozygosity, differences in chromosomal structure and the presence of deleterious recessive genes are discussed based on the segregation ratios obtained. A high percentage of markers had a skeweness of the 1:1 expected segregation ratio in the F1 population. Many markers showed a 3:1 segregation ratio in both varieties and 1:3 in 'Pêra' sweet orange, probably due to directional selection processes. The distribution analysis of the frequencies of the segregant markers in a hybrid population is a simple method which allows a better understanding of the genetics of citrus group.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Meta-structure correlation in protein space unveils different selection rules for folded and intrinsically disordered proteins

The number of existing protein sequences spans a very small fraction of sequence space. Natural proteins have overcome a strong negative selective pressure to avoid the formation of insoluble aggregates. Stably folded globular proteins and intrinsically disordered proteins (IDP) use alternative solutions to the aggregation problem. While in globular proteins folding minimizes the access to aggregation prone regions IDPs on average display large exposed contact areas. Here, we introduce the concept of average meta-structure correlation map to analyze sequence space. Using this novel conceptual view we show that representative ensembles of folded and ID proteins show distinct characteristics and responds differently to sequence randomization. By studying the way evolutionary constraints act on IDPs to disable a negative function (aggregation) we might gain insight into the mechanisms by which function - enabling information is encoded in IDPs.

Biometrical analysis and selection of tetraploid progenies of Panicum maximum using mixed model methods

The objectives of this work were to estimate the genetic and phenotypic parameters and to predict the genetic and genotypic values of the selection candidates obtained from intraspecific crosses in Panicum maximum as well as the performance of the hybrid progeny of the existing and projected crosses. Seventy-nine intraspecific hybrids obtained from artificial crosses among five apomictic and three sexual autotetraploid individuals were evaluated in a clonal test with two replications and ten plants per plot. Green matter yield, total and leaf dry matter yields and leaf percentage were evaluated in five cuts per year during three years. Genetic parameters were estimated and breeding and genotypic values were predicted using the restricted maximum likelihood/best linear unbiased prediction procedure (REML/BLUP). The dominant genetic variance was estimated by adjusting the effect of full-sib families. Low magnitude individual narrow sense heritabilities (0.02-0.05), individual broad sense heritabilities (0.14-0.20) and repeatability measured on an individual basis (0.15-0.21) were obtained. Dominance effects for all evaluated characteristics indicated that breeding strategies that explore heterosis must be adopted. Less than 5% increase in the parameter repeatability was obtained for a three-year evaluation period and may be the criterion to determine the maximum number of years of evaluation to be adopted, without compromising gain per cycle of selection. The identification of hybrid candidates for future cultivars and of those that can be incorporated into the breeding program was based on the genotypic and breeding values, respectively. The prediction of the performance of the hybrid progeny, based on the breeding values of the progenitors, permitted the identification of the best crosses and indicated the best parents to use in crosses.

Growth, selection and appropriate contracts

We study a dynamic model where growth requires both long-term investmentand the selection of talented managers. When ability is not ex-ante observable and contracts are incomplete, managerial selection imposes a cost, as managers facing the risk ofbeing replaced choose a sub-optimally low level of long-term investment. This generates atrade-off between selection and investment that has implications for the choice of contractualrelationships and institutions. Our analysis shows that rigid long-term contracts sacrificingmanagerial selection may prevail at early stages of economic development and when heterogeneity in ability is low. As the economy grows, however, knowledge accumulation increasesthe return to talent and makes it optimal to adopt flexible contractual relationships, wheremanagerial selection is implemented even at the cost of lower investment. Measures of investor protection aimed at limiting the bargaining power of managers improve selection undershort-term contract. Given that knowledge accumulation raises the value of selection, theoptimal level of investor protection increases with development.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Issue review : school district cash reserve levy.

During the recent economic crisis, school district budgets have been impacted by state school aid funding shortfalls and state aid reductions due to across-the-board general fund reductions in fiscal year 2009 and fiscal year 2010. Additionally, in fiscal year 2011, the state school aid appropriation was capped and was 156.1 million dollars short of fully funding the state portion of the school aid formula. This issue review examines the impact that the reductions in state aid have had on school district cash reserve levies.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.