What Are Effective Program Characteristics of Self-Management Interventions in Patients With Heart Failure? : An Individual Patient Data Meta-analysis

Background To identify those characteristics of self-management interventions in patients with heart failure (HF) that are effective in influencing health-related quality of life, mortality, and hospitalizations. Methods and Results Randomized trials on self-management interventions conducted between January 1985 and June 2013 were identified and individual patient data were requested for meta-analysis. Generalized mixed effects models and Cox proportional hazard models including frailty terms were used to assess the relation between characteristics of interventions and health-related outcomes. Twenty randomized trials (5624 patients) were included. Longer intervention duration reduced mortality risk (hazard ratio 0.99, 95% confidence interval [CI] 0.97–0.999 per month increase in duration), risk of HF-related hospitalization (hazard ratio 0.98, 95% CI 0.96–0.99), and HF-related hospitalization at 6 months (risk ratio 0.96, 95% CI 0.92–0.995). Although results were not consistent across outcomes, interventions comprising standardized training of interventionists, peer contact, log keeping, or goal-setting skills appeared less effective than interventions without these characteristics. Conclusion No specific program characteristics were consistently associated with better effects of self-management interventions, but longer duration seemed to improve the effect of self-management interventions on several outcomes. Future research using factorial trial designs and process evaluations is needed to understand the working mechanism of specific program characteristics of self-management interventions in HF patients.

Sustainability or climate change? An ecocritical analysis of Ian McEwan’s Solar :

This essay is an ecocritical literary analysis of the novel Solar. The aim is to investigate the effect on the reader of the main characters, place, symbols and satire with focus on climate change and sustainability. The protagonist Michael Beard, a despicable, selfish antihero, consumes everything and everybody whereas Tom Aldous, the heroic character, is devoted to a life of sustainability. Beard represents the greedy capitalistic Western consumer society and its male leaders who close their eyes to the warnings of a changing climate, and Aldous represents the sustainable and alternative lifestyle. Humor and ridicule exhibits the weaknesses and greed of mankind in a satirical way. Symbols like cancer and the Co2 emitting transport system symbolize overconsumption and a stressed globe. The effect on the reader is a reflection about the ongoing struggle between climate change and sustainability and the demand for a change towards a sustainable lifestyle.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data

Min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) are complementary techniques for analysing short (> 15-25 y), non-stationary, multivariate data sets. We illustrate the two techniques using catch rate (cpue) time-series (1982-2001) for 17 species caught during trawl surveys off Mauritania, with the NAO index, an upwelling index, sea surface temperature, and an index of fishing effort as explanatory variables. Both techniques gave coherent results, the most important common trend being a decrease in cpue during the latter half of the time-series, and the next important being an increase during the first half. A DFA model with SST and UPW as explanatory variables and two common trends gave good fits to most of the cpue time-series. (c) 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.

Identification of deep water lantern sharks (Chondrichthyes : Etmopteridae) using morphometric data and multivariate analysis

Taxonomic distinction to species level of deep water sharks is complex and often impossible to achieve during fisheries-related studies. The species of the genus Etmopterus are particularly difficult to identify, so they often appear without species assignation as Etmopetrus sp. or spp. in studies, even those focusing on elasmobranchs. During this work, the morphometric traits of two species of Etmopterus, E. spinax and E. pusillus were studied using 27 different morphological measurements, relatively easy to obtain even in the field. These measurements were processed with multivariate analysis in order to find out the most important ones likely to separate the two species. Sexual dimorphism was also assessed using the same techniques, and it was found that it does not occur in these species. The two Etmopterus species presented in this study share the same habitats in the overlapping ranges of distribution and are caught together on the outer shelves and slopes of the north-eastern Atlantic.

Multivariate analysis of lifestyle, constitutive and body composition factors influencing bone health in community-dwelling older adults from Madeira, Portugal

info:eu-repo/semantics/publishedVersion

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests.

The seasonal climate drivers of the carbon cy- cle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combina- tion of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measure- ments and 35 litter productivity measurements), their asso- ciated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonal- ity in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rain- fall is < 2000 mm yr-1 (water-limited forests) and to radia- tion otherwise (light-limited forests). On the other hand, in- dependent of climate limitations, wood productivity and lit- terfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosyn- thetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest pro- ductivity in a drier climate in water-limited forest, and in cur- rent light-limited forest with future rainfall < 2000 mm yr-1.

Physico-chemical characterization of banana varieties resistant to black leaf streak disease for industrial purposes.

A cultura da banana tem baixa diversidade genética, tornando a espécie susceptível a doenças dizimadoras como a Sigatoka negra. No entanto, a adoção de novas variedades necessita de avaliações agronômicas e físico-químicas. Neste estudo, as variedades de banana, resistentes à Sigatoka negra, foram caracterizadas e comparadas com a variedade tradicional (Grand Naine). Cada variedade foi avaliada considerando-se critérios relevantes para a agroindústria, como pH, sólidos solúveis totais, acidez total titulável, relação SST/ATT, açúcares totais, açúcares redutores e não redutores, umidade, sólidos totais e rendimento no processamento. A variedade Thap Maeo apresentou-se como a variedade mais potencial para substituição da Gran Naine na indústria, com altos teores de sólidos solúveis totais, açúcares redutores, açúcares totais e umidade. As variedades Caipira e FHIA 2 também podem substituir a Grand Naine. Na análise de agrupamentos, verificou-se que a variedade Grand Naine esteve muito próxima das variedades do subgrupo Gros Michel (Bucaneiro, Ambroisa e Calipso) e também da variedade Caipira, apresentando no seu genoma o grupo AAA. Conclui-se que há opções de variedades resistentes para substituição da variedade tradicional, nas regiões afetadas pela Sigatoka-negra.

Risk analysis of climate change on black sigatoka in banana in Brazil.

2008

Improving multivariate data streams clustering.

Clustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate the attributes without touching upon the correlation among them. In order to overcome this issue, we propose a new framework to cluster multivariate data streams based on their evolving behavior over time, exploring the correlations among their attributes by computing the fractal dimension. Experimental results with climate data streams show that the clusters' quality and compactness can be improved compared to the competing method, leading to the thoughtfulness that attributes correlations cannot be put aside. In fact, the clusters' compactness are 7 to 25 times better using our method. Our framework also proves to be an useful tool to assist meteorologists in understanding the climate behavior along a period of time.

Breed comparison for heat adaptation in rams using multivariate analysis.

2016

Characterization of aromatic profiles in brazilian tropical wines determined by gas chromatography and multivariate statistical analysis.

2011

Effect of residual vanadyl ions on the spectroscopic analysis of humic acids: a multivariate approach.

In a study of the vanadyl (VO2þ)-humic acids system, the residual vanadyl ion suppressed fluorescence and specific electron paramagnetic resonance (EPR) and NMR signals. In the case of NMR, the proton rotating frame relaxation times (T1qH) indicate that this suppression is due to an inefficient H-C cross polarization, which is a consequence of a shortening of T1qH. Principal components analysis (PCA) facilitated the isolation of the effect of the VO2þ ion and indicated that the organic free radical signal was due to at least two paramagnetic centres and that the VO2þ ion preferentially suppressed the species whose electronic density is delocalized over O atoms (greater g-factor). additionally, the newly obtained variables (principal components ? PC) indicated that, as the result of the more intense tillage a relative increase occurred in the accumulation of: (i) recalcitrant structures; (ii) lignin and long-chain alkyl structures; and (iii) organic free radicals with smaller g-factors.

Selection indices and multivariate analysis show similar results in the evaluation of growth and carcass traits in beef cattle.

2016