Anemia status, hemoglobin concentration and outcome after acute stroke: a cohort study

<p>Abstract</p> <p>Background</p> <p>In the setting of an acute stroke, anemia has the potential to worsen brain ischemia, however, the relationship between the entire range of hemoglobin to long-term outcome is not well understood.</p> <p>Methods</p> <p>We examined the association between World Health Organization-defined admission anemia status (hemoglobin<13 in males, <12 g/dl in women) and hemoglobin concentration and 1-year outcome among 859 consecutive patients with acute stroke (ischemic or intracerebral hemorrhage).</p> <p>Results</p> <p>The mean baseline hemoglobin concentration was 13.8 ± 1.7 g/dl (range 8.1 - 18.7). WHO-defined anemia was present in 19% of patients among both women and men. After adjustment for differences in baseline characteristics, patients with admission anemia had an adjusted OR for all-cause death at 1-month of 1.90 (95% CI, 1.05 to 3.43) and at 1-year of 1.72 (95% CI, 1.00 to 2.93) and for the combined end-point of disability, nursing facility care or death of 2.09 (95% CI, 1.13 to 3.84) and 1.83 (95% CI, 1.02 to 3.27) respectively. The relationship between hemoglobin quartiles and all-cause death revealed a non-linear association with increased risk at extremes of both low and high concentrations. In logistic regression models developed to estimate the linear and quadratic relation between hemoglobin and outcomes of interest, each unit increment in hemoglobin squared was associated with increased adjusted odds of all-cause death [at 1-month 1.06 (1.01 to 1.12; p = 0.03); at 1-year 1.09 (1.04 to 1.15; p < 0.01)], confirming that extremes of both low and high levels of hemoglobin were associated with increased mortality.</p> <p>Conclusions</p> <p>WHO-defined anemia was common in both men and women among patients with acute stroke and predicted poor outcome. Moreover, the association between admission hemoglobin and mortality was not linear; risk for death increased at both extremes of hemoglobin.</p

Validation of differential gene expression algorithms: Application comparing fold-change estimation to hypothesis testing

<p>Abstract</p> <p>Background</p> <p>Sustained research on the problem of determining which genes are differentially expressed on the basis of microarray data has yielded a plethora of statistical algorithms, each justified by theory, simulation, or ad hoc validation and yet differing in practical results from equally justified algorithms. Recently, a concordance method that measures agreement among gene lists have been introduced to assess various aspects of differential gene expression detection. This method has the advantage of basing its assessment solely on the results of real data analyses, but as it requires examining gene lists of given sizes, it may be unstable.</p> <p>Results</p> <p>Two methodologies for assessing predictive error are described: a cross-validation method and a posterior predictive method. As a nonparametric method of estimating prediction error from observed expression levels, cross validation provides an empirical approach to assessing algorithms for detecting differential gene expression that is fully justified for large numbers of biological replicates. Because it leverages the knowledge that only a small portion of genes are differentially expressed, the posterior predictive method is expected to provide more reliable estimates of algorithm performance, allaying concerns about limited biological replication. In practice, the posterior predictive method can assess when its approximations are valid and when they are inaccurate. Under conditions in which its approximations are valid, it corroborates the results of cross validation. Both comparison methodologies are applicable to both single-channel and dual-channel microarrays. For the data sets considered, estimating prediction error by cross validation demonstrates that empirical Bayes methods based on hierarchical models tend to outperform algorithms based on selecting genes by their fold changes or by non-hierarchical model-selection criteria. (The latter two approaches have comparable performance.) The posterior predictive assessment corroborates these findings.</p> <p>Conclusions</p> <p>Algorithms for detecting differential gene expression may be compared by estimating each algorithm's error in predicting expression ratios, whether such ratios are defined across microarray channels or between two independent groups.</p> <p>According to two distinct estimators of prediction error, algorithms using hierarchical models outperform the other algorithms of the study. The fact that fold-change shrinkage performed as well as conventional model selection criteria calls for investigating algorithms that combine the strengths of significance testing and fold-change estimation.</p

The Biological Basis of a Universal Constraint on Color Naming: Cone Contrasts and the Two-Way Categorization of Colors

Many studies have provided evidence for the existence of universal constraints on color categorization or naming in various languages, but the biological basis of these constraints is unknown. A recent study of the pattern of color categorization across numerous languages has suggested that these patterns tend to avoid straddling a region in color space at or near the border between the English composite categories of “warm” and “cool”. This fault line in color space represents a fundamental constraint on color naming. Here we report that the two-way categorization along the fault line is correlated with the sign of the L- versus M-cone contrast of a stimulus color. Moreover, we found that the sign of the L-M cone contrast also accounted for the two-way clustering of the spatially distributed neural responses in small regions of the macaque primary visual cortex, visualized with optical imaging. These small regions correspond to the hue maps, where our previous study found a spatially organized representation of stimulus hue. Altogether, these results establish a direct link between a universal constraint on color naming and the cone-specific information that is represented in the primate early visual system

Effect of Anthropogenic Landscape Features on Population Genetic Differentiation of Przewalski's Gazelle: Main Role of Human Settlement

Anthropogenic landscapes influence evolutionary processes such as population genetic differentiation, however, not every type of landscape features exert the same effect on a species, hence it is necessary to estimate their relative effect for species management and conservation. Przewalski's gazelle (Procapra przewalskii), which inhabits a human-altered area on Qinghai-Tibet Plateau, is one of the most endangered antelope species in the world. Here, we report a landscape genetic study on Przewalski's gazelle. We used skin and fecal samples of 169 wild gazelles collected from nine populations and thirteen microsatellite markers to assess the genetic effect of anthropogenic landscape features on this species. For comparison, the genetic effect of geographical distance and topography were also evaluated. We found significant genetic differentiation, six genetic groups and restricted dispersal pattern in Przewalski's gazelle. Topography, human settlement and road appear to be responsible for observed genetic differentiation as they were significantly correlated with both genetic distance measures [FST/(1−FST) and F′ST/(1−F′ST)] in Mantel tests. IBD (isolation by distance) was also inferred as a significant factor in Mantel tests when genetic distance was measured as FST/(1−FST). However, using partial Mantel tests, AICc calculations, causal modeling and AMOVA analysis, we found that human settlement was the main factor shaping current genetic differentiation among those tested. Altogether, our results reveal the relative influence of geographical distance, topography and three anthropogenic landscape-type on population genetic differentiation of Przewalski's gazelle and provide useful information for conservation measures on this endangered species

Complex body size trends in the evolution of sloths (Xenarthra: Pilosa)

Background Extant sloths present an evolutionary conundrum in that the two living genera are superficially similar (small-bodied, folivorous, arboreal) but diverged from one another approximately 30 million years ago and are phylogenetically separated by a radiation of medium to massive, mainly ground-dwelling, taxa. Indeed, the species in the two living genera are among the smallest, and perhaps most unusual, of the 50+ known sloth species, and must have independently and convergently evolved small size and arboreality. In order to accurately reconstruct sloth evolution, it is critical to incorporate their extinct diversity in analyses. Here, we used a dataset of 57 species of living and fossil sloths to examine changes in body mass mean and variance through their evolution, employing a general time-variable model that allows for analysis of evolutionary trends in continuous characters within clades lacking fully-resolved phylogenies, such as sloths. Results Our analyses supported eight models, all of which partition sloths into multiple subgroups, suggesting distinct modes of body size evolution among the major sloth lineages. Model-averaged parameter values supported trended walks in most clades, with estimated rates of body mass change ranging as high as 126 kg/million years for the giant ground sloth clades Megatheriidae and Nothrotheriidae. Inclusion of living sloth species in the analyses weakened reconstructed rates for their respective groups, with estimated rates for Megalonychidae (large to giant ground sloths and the extant two-toed sloth) were four times higher when the extant genus Choloepus was excluded. Conclusions Analyses based on extant taxa alone have the potential to oversimplify or misidentify macroevolutionary patterns. This study demonstrates the impact that integration of data from the fossil record can have on reconstructions of character evolution and establishes that body size evolution in sloths was complex, but dominated by trended walks towards the enormous sizes exhibited in some recently extinct forms

Semi-Parametric Graphical Estimation Techniques for Long-Memory Data

This paper reviews several periodogram-based methods for estimating the long-memory parameter H in time series and suggests a way to robustify them. The high frequencies tend to bias the estimates. Using only low frequencies eliminates the bias but increases the variance. We hence suggest plotting the estimates of H as a function of a parameter which balances bias versus variance and, if the plot flattens in a central region, to use the flat part for estimating H. We apply this technique to the periodogram regression method, the Whittle approximation to maximum likelihood and to the local Whittle method. We investigate its effectiveness on several simulated fractional ARIMA series and also apply it to estimate the long-memory parameter H in computer network traffic. 1 Introduction Time series with long memory have been considered in many fields including hydrology, biology and computer networks. Unfortunately, estimating the long memory (long-range dependence) parameter H in a given d..