Nyelv, nyelvtörvény, nyelvtudomány

<p>4a, Forest plot on the associations between DM and bile leakage after hepatectomy. 4b, Forest plot on the associations between DM and ascites after hepatectomy. 4c, Forest plot on the associations between DM and liver decompensation after hepatectomy. DM, diabetes mellitus. The boxes and lines indicate the relative ratios (RRs) and their confidence intervals (CIs) on a log scale for each study. The pooled RR is represented by a diamond. The size of the black squares indicates the relative weight of each estimate.</p

A new model based on adiabatic flame temperature for evaluation of the upper flammable limit of alkane-air-CO2 mixtures

© 2017 Elsevier B.V. For security issue of alkane used in Organic Rankine Cycle, a new model to evaluate the upper flammability limits for mixtures of alkanes, carbon dioxide and air has been proposed in present study. The linear relationship was found at upper flammability limits between molar fraction of diluent in alkane-CO 2 mixture and calculated adiabatic flame temperature. The prediction ability of the variable calculated adiabatic flame temperature model that incorporated the linear relationship above is greatly better than the models that adopted the fixed calculated adiabatic flame temperature at upper flammability limit. The average relative differences between results predicted by the new model and observed values are less than 3.51% for upper flammability limit evaluation. In order to enhance persuasion of the new model, the observed values of n-butane-CO 2 and isopentane-CO 2 mixtures measured in this study were used to confirm the validity of the new model. The predicted results indicated that the new model possesses the capacity of practical application and can adequately provide safe non-flammable ranges for alkanes diluted with carbon dioxide

内臟に發生せる稀有なる多發性神經纎維腫症の一剖檢例

Information on the 74 candidate blast resistant genes in LAFBRs. (XLSX 90 kb

Bayesian Multimodal Local False Discovery Rate in Neuroimaging Studies

For exploratory neuroimaging studies comparing disease group and healthy control (HC) group, one of the primary objectives is to identify any differential connectivities that may be potentially associated with the pathophysiology of neurological and psychiatric disorders. As such, thousands of hypotheses are tested simultaneously that is known as the large-scale simultaneous hypothesis testing problem. Most of the hypotheses tested are null, that is, nothing but noise, while a very small number of them may contain true signals. The false discovery rate (FDR) and local FDR (Lfdr) methods have been developed to address this problem. Typical neuroimaging studies usually have small sample size due to the high economic cost, leading to low statistical power and high probability of falsely significant findings. The existing methodologies do not provide a satisfied control of FDR especially for small sample size studies, neither capabilities that allow integration of multimodal neuroimaging data into statistical modeling. The information provided by multimodal imaging techniques can be complementary to each other and thus integrated multimodal analysis enables us to borrow strength from different modalities. A covariate-modulated Lfdr method has been used in genome-wide association studies and proved to be efficient by increasing power. We extend this method to multimodal neuroimaging data, aimed to improve FDR control and sensitivity to detect differential functional connectivity (FC) links between disease group and HC group in a cross-sectional, comparative, multimodal neuroimaging study with small sample size. We implement a Bayesian multimodal Lfdr approach, which utilizes a Bayesian mixture model to leverage structural connectivity (SC) statistics and enhance modeling of the density of FC statistics. The utility of Bayesian multimodal approach is illustrated with extensive simulation study and a neuroimaging study in late-life depression (LLD) in which both FC (using resting-state functional magnetic resonance imaging) and SC (using diffusion tensor imaging) data were measured on each participant. We demonstrate in simulation study, that Bayesian multimodal Lfdr method performs numerically better in terms of FDR control by comparison with the traditional Lfdr method that solely considers FC especially when the sample size is small. In addition, we employed a Bayesian multiple comparison method via a non-parametric Bayesian Dirichlet process mixture model directly on FC data in the LLD neuroimaging study, as a comparison with the results from Bayesian multimodal Lfdr method

Fungal adenylyl cyclases contain multiple domains acting as sensors for a diverse range of signals.

<p>Evidence from many studies of the past decade or so supports a model in which fungal adenylyl cyclases function as a hub of signal sensing and integration. This figure illustrates all the conserved domains in fungal adenylyl cyclases and the external and internal signals each of the domains senses in <i>C. albicans</i> adenylyl cyclase Cyr1. For abbreviations and protein names, please refer to the text.</p

TgrC1 Has Distinct Functions in <i>Dictyostelium</i> Development and Allorecognition

<div><p>The cell adhesion glycoproteins, TgrB1 and TgrC1, are essential for <i>Dictyostelium</i> development and allorecognition, but it has been impossible to determine whether their pleiotropic roles are due to one common function or to distinct functions in separate pathways. Mutations in the respective genes, <i>tgrB1</i> and <i>tgrC1</i>, abrogate both development and allorecognition and the defects cannot be suppressed by activation of the cyclic AMP dependent protein kinase PKA, a central regulator of <i>Dictyostelium</i> development. Here we report that mutations in genes outside the known PKA pathway partially suppress the <i>tgrC1</i>-null developmental defect. We separated the pleiotropic roles of <i>tgrC1</i> by testing the effects of a suppression mutation, <i>stc^insA</i> under different conditions. <i>stcA^ins</i> modified only the developmental defect of <i>tgrC1^–</i> but not the allorecognition defect, suggesting that the two functions are separable. The suppressor mutant phenotype also revealed that <i>tgrC1</i> regulates stalk differentiation in a cell-autonomous manner and spore differentiation in a non-cell-autonomous manner. Moreover, <i>stcA^ins</i> did not modify the developmental defect of <i>tgrB1^–</i>, but the less robust phenotype of <i>tgrB1^–</i> obscures the possible role of <i>stcA</i> relative to <i>tgrB1</i>.</p></div

<i>tgrC1⁻</i> development is partially restored by suppressor mutations.

<p>A. We developed the suppressor strains (<i>tgrC1⁻stcA^ins</i>, <i>tgrC1⁻stcB^ins</i> and <i>tgrC1⁻stcC^ins</i>), the laboratory wild-type strain AX4 and the parental <i>tgrC1⁻</i> strain on non-nutrient agar. We photographed the cells with light microscopy from above at 16 hour, 24 hour and 72 hour as indicated (strain genotypes are indicated on the left). Scale bar, 1 mm. B. We determined the sporulation efficiency of the same strains at 72 hour of development on black filters. The results are shown as the average (n = 3) and s.d. of the fraction of cells that made spores normalized to the wild type AX4 (%, y-axis). Strain genotypes are indicated on the bottom. C. An illustration of the <i>stcA</i> gene and the insertional mutation. The thick red line represents the two exons and the thin angled lines represent the single intron. The triangle represents the pBSR1 plasmid, which was inserted at the 3’-end of the gene. The inserted plasmid is not drawn to scale. Numbers (bp) below the lines indicate the beginning and the end of the gene model, the predicted splicing borders and the insertion site.</p

<i>stcA^ins</i> does not modify kin recognition.

<p>We labeled cells with GFP (green text) or RFP (red text), grew them independently, mixed two strains at 1:1 ratios and allowed them to develop together on non-nutrient agar. Photographs were taken at 10 hour of development. Cells with the genotypes indicated on the left side of each row were mixed with cells of different genotypes as indicated above each panel. Scale bar, 0.2 mm.</p

<i>stcA^ins</i> does not modify the <i>tgrB1⁻</i> phenotype.

<p>A. We developed <i>tgrB1⁻</i> and <i>tgrB1⁻stcA^ins</i> on non-nutrient agar and photographed the structures from above with light microscopy at 16 hour, 24 hour and 72 hour as indicated on the top. Strain genotypes are indicated on the left. Scale bar, 0.2 mm. B. We measured the sporulation efficiency of <i>tgrB1⁻</i> and <i>tgrB1⁻stcA^ins</i> at 72 hour. The results are shown as the average and s.d. of the fraction of cells (%, y-axis; n = 3) that made spores scaled to the wild type AX4. Strain genotypes are indicated on the bottom.</p

Division of blocks.

Division of blocks.</p