About 30-50% of the world human population are infected with the protozoan parasite Toxoplasma gondii (Nicolle et Manceaux, 1908). Latent toxoplasmosis has many specific behavioural and physiological effects on the human body and influences the course of pregnancy, including secondary sex ratio of children of infected mothers. It was suggested that an increased concentration of glucose could be the proximate cause of increased sex ratio. There are some indirect indications of possible association between toxoplasmosis and certain forms of diabetes. Here we searched for a possible link between latent toxoplasmosis and the level of glucose in the blood. In a cross-sectional study, we found that pregnant women with latent toxoplasmosis had significantly higher blood glucose levels during the oral glucose tolerance test (n = 191, p = 0.010; the level of fasting plasma glucose: mean = 5.04 mmol/l vs mean = 4.88 mmol/l; blood glucose level at 1 hour mean = 7.73 mmol/l vs mean = 6.89 mmol/l and blood glucose level at two hours mean = 6.43 mmol/l vs mean = 5.74 mmol/l) and higher prevalence (19.5 %) of gestational diabetes mellitus (n = 532, p = 0.033, odds ratio = 1.78) in the 24-28th gestational weeks than T. gondii-free women (12.0 %). Increased level of glucose and increased incidence of gestational diabetes mellitus could have considerable clinical impact as contributors to the development of the metabolic syndrome and type 2 diabetes in T. gondii-infected women. Our results also brought the first empirical support for the hypothesis that the glucose concentration may play a role in T. gondii-associated offspring sex ratio shifts., Šárka Kaňková, Jaroslav Flegr, Pavel Calda., and Obsahuje bibliografii
The main purpose of this paper is to prove that the elliptic curve $E\colon y^2=x^3+27x-62$ has only the integral points $(x, y)=(2, 0)$ and $(28844402, \pm 154914585540)$, using elementary number theory methods and some known results on quadratic and quartic Diophantine equations.
We consider the weighted space $W_1^{(2)}(\mathbb R,q)$ of Sobolev type $$ W_1^{(2)}(\mathbb R,q)=\left \{y\in A_{\rm loc}^{(1)}(\mathbb R)\colon \|y''\|_{L_1(\mathbb R)}+\|qy\|_{L_1(\mathbb R)}<\infty \right \} $$ and the equation $$ - y''(x)+q(x)y(x)=f(x),\quad x\in \mathbb R. \leqno (1) $$ Here $f\in L_1(\mathbb R)$ and $0\le q\in L_1^{\rm loc}(\mathbb R).$ \endgraf We prove the following: \item {1)} The problems of embedding $W_1^{(2)}(\mathbb R,q)\hookrightarrow L_1(\mathbb R)$ and of correct solvability of (1) in $L_1(\mathbb R) $ are equivalent; \item {2)} an embedding $W_1^{(2)}(\mathbb R,q)\hookrightarrow L_1(\mathbb R) $ exists if and only if $$\exists a>0\colon \inf _{x\in \mathbb R}\int _{x-a}^{x+a} q(t) {\rm d} t>0.$$.
Recently, the the support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first step is feature extraction. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. The PCA linearly transforms the original inputs into new uncorrelated features. The KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the sunspot data, Santa Fe data set A and five real futures contracts, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, among the three methods, there is the best performance in the KPCA feature extraction, followed by the ICA feature extraction.
Recently, a support vector machine (SVM) has been receiving increasing attention in the field of regression estimation due to its remarkable characteristics such as good generalization performance, the absence of local minima and sparse representation of the solution. However, within the SVMs framework, there are very few established approaches for identifying important features. Selecting significant features from all candidate features is the first step in regression estimation, and this procedure can improve the network performance, reduce the network complexity, and speed up the training of the network.
This paper investigates the use of saliency analysis (SA) and genetic algorithm (GA) in SVMs for selecting important features in the context of regression estimation. The SA measures the importance of features by evaluating the sensitivity of the network output with respect to the feature input. The derivation of the sensitivity of the network output to the feature input in terms of the partial derivative in SVMs is presented, and a systematic approach to remove irrelevant features based on the sensitivity is developed. GA is an efficient search method based on the mechanics of natural selection and population genetics. A simple GA is used where all features are mapped into binary chromosomes with a bit "1" representing the inclusion of the feature and a bit of "0" representing the absence of the feature. The performances of SA and GA are tested using two simulated non-linear time series and five real financial time series. The experiments show that with the simulated data, GA and SA detect the same true feature set from the redundant feature set, and the method of SA is also insensitive to the kernel function selection. With the real financial data, GA and SA select different subsets of the features. Both selected feature sets achieve higher generation performance in SVMs than that of the full feature set. In addition, the generation performance between the selected feature sets of GA and SA is similar. All the results demonstrate that that both SA and GA are effective in the SVMs for identifying important features.
This paper proposes an endogenous human resources selection process by using linguistic information from a competency management perspective. We consider different sets of appraisers taking part in the evaluation process, having a different knowledge about the candidates that are being evaluated. Then, appraisers can express their assessments in different linguistic domains according to their knowledge. The proposed method converts each linguistic label into a fuzzy set on a common domain. Candidates are ranked by using different aggregation operators in order to allow the management team to make a final decision.
5-hydroxytryptamine (5-HT) is involved in the stress-induced alteration of colonic functions, specifically motility and secretion, but its precise mechanisms of regulation remain unclear. In the present study, we have investigated the effects of 5-HT on rat colonic mucosal secretion after acute water immersion restraint stress, as well as the underlying mechanism of this phenomenon, using short circuit current recording (ISC), real-time polymerase chain reaction, Western blot analysis, and enzyme-linked immunosorbance assays. After 2 h of water immersion restraint stress, the baseline ISC and 5-HT-induced ISC responses of the colonic mucosa were significantly increased. Pretreatment with selective 5-HT4 receptor antagonist, SB204070, inhibited the 5-HT-induced colonic ISC response by 96 % in normal rats and 91.2 % in acute-stress rats. However, pretreatment with the selective antagonist of 5-HT3 receptor, MDL72222 or Y-25130, had no obvious effect on 5-HT-induced ISC responses under either set of conditions. Total protein expression of both the mucosal 5-HT3 receptors and the 5-HT4 receptors underwent no significant changes following acute stress. Both colonic basal cAMP levels and foskolin-induced ISC responses were significantly enhanced in acute stress rats. 5-HT significantly enhanced the intracellular cAMP level via 5-HT4 receptors in the colonic mucosa from both control and stressed animals, and 5-HT-induced cAMP increase in stressed rats was not more than that in control rats. Taken together, the present results indicate that acute water immersion restraint stress enhances colonic secretory responses to 5-HT in rats, a process in which increased cellular cAMP accumulation is involved., Y. Li, L. S. Li, X. L. Zhang, Y. Zhang, J. D. Xu, J. X. Zhu., and Obsahuje bibliografii