Detailed instantaneous ionization rate of H2+_2^+ in intense laser field

Component instantaneous ionization rate (IIR) is introduced and the approach of its calculation is formulated. The component IIR's and the overall (time-averaged) component ionization rates are calculated for H$_2^+$ at different values of inter-nuclear distance in a linearly polarized laser field with $1.0 \times10^{14}$W cm$^{-2}$ intensity and $\lambda \sim 1064$nm wavelength by direct numerical solution of the fixed-nuclei full dimensional time-dependent Schr \"odinger equation. The exact overall component ionization rates calculated by time-averaging of the component IIR are compared with those calculated approximately via the virtual detector method (VD). Details of the time dependent behavior of the outgoing and incoming electron wavepackets of the H$_2^+$ system in intense laser field at sub-femtosecond time scale are studied based on the calculated component IIR. It is shown clearly that the positive (outgoing electron wavepacket) signals of the IIR and its z component are strong and sharp but the negative (returning electron wavepacket) signals of the IIR are smooth and weak. The structure of the $\rho$ component of the IIR has smooth structure. Relation between the R-dependent ionization rate and duration of the ramp of the laser pulse is studied and it is explicitly shown that for internuclear distance R<5.6, when the laser pulse is turned on without a ramp, the first peak of R-dependent ionization rates moves towards the peak of the lower time dependent Floquet quasi-energy state (QES).Comment: 28 pages, 7 figure

Global and Local Visual Processing: Influence of Perceptual Field Variables

The Global Precedence Effect (GPE) suggests that the processing of global properties of a visual stimulus precedes the processing of local properties. The generality of this theory was argued for four decades during different known Perceptual Field Variables. The effect size of various PFVs, regarding the findings during these four decades, were pooled in our recent meta-analysis study. Pursuing the study, in the present paper, we explore the effects of Congruency, Size, and Sparsity and their interaction on global advantage in two different experiments with different task paradigms; Matching judgment and Similarity judgment. Upon results of these experiments, Congruency and Size have significant effects and Sparsity has small effects. Also, the task paradigm and its interaction with other PFVs are shown significant effects in this study, which shows the prominence of the role of task paradigms in evaluating PFVs' effects on GPE. Also, we found that the effects of these parameters were not specific to the special condition that individuals were instructed to retinal stabilize. So, the experiments were more extendible to daily human behavior

A tale of two symmetrical tails: Structural and functional characteristics of palindromes in proteins

<p>Abstract</p> <p>Background</p> <p>It has been previously shown that palindromic sequences are frequently observed in proteins. However, our knowledge about their evolutionary origin and their possible importance is incomplete.</p> <p>Results</p> <p>In this work, we tried to revisit this relatively neglected phenomenon. Several questions are addressed in this work. (1) It is known that there is a large chance of finding a palindrome in low complexity sequences (i.e. sequences with extreme amino acid usage bias). What is the role of sequence complexity in the evolution of palindromic sequences in proteins? (2) Do palindromes coincide with conserved protein sequences? If yes, what are the functions of these conserved segments? (3) In case of conserved palindromes, is it always the case that the whole conserved pattern is also symmetrical? (4) Do palindromic protein sequences form regular secondary structures? (5) Does sequence similarity of the two "sides" of a palindrome imply structural similarity? For the first question, we showed that the complexity of palindromic peptides is significantly lower than randomly generated palindromes. Therefore, one can say that palindromes occur frequently in low complexity protein segments, without necessarily having a defined function or forming a special structure. Nevertheless, this does not rule out the possibility of finding palindromes which play some roles in protein structure and function. In fact, we found several palindromes that overlap with conserved protein Blocks of different functions. However, in many cases we failed to find any symmetry in the conserved regions of corresponding Blocks. Furthermore, to answer the last two questions, the structural characteristics of palindromes were studied. It is shown that palindromes may have a great propensity to form α-helical structures. Finally, we demonstrated that the two sides of a palindrome generally do not show significant structural similarities.</p> <p>Conclusion</p> <p>We suggest that the puzzling abundance of palindromic sequences in proteins is mainly due to their frequent concurrence with low-complexity protein regions, rather than a global role in the protein function. In addition, palindromic sequences show a relatively high tendency to form helices, which might play an important role in the evolution of proteins that contain palindromes. Moreover, reverse similarity in peptides does not necessarily imply significant structural similarity. This observation rules out the importance of palindromes for forming symmetrical structures. Although palindromes frequently overlap with conserved Blocks, we suggest that palindromes overlap with Blocks only by coincidence, rather than being involved with a certain structural fold or protein domain.</p

Global haplotype partitioning for maximal associated SNP pairs

<p>Abstract</p> <p>Background</p> <p>Global partitioning based on pairwise associations of SNPs has not previously been used to define haplotype blocks within genomes. Here, we define an association index based on LD between SNP pairs. We use the Fisher's exact test to assess the statistical significance of the LD estimator. By this test, each SNP pair is characterized as associated, independent, or not-statistically-significant. We set limits on the maximum acceptable proportion of independent pairs within all blocks and search for the partitioning with maximal proportion of associated SNP pairs. Essentially, this model is reduced to a constrained optimization problem, the solution of which is obtained by iterating a dynamic programming algorithm.</p> <p>Results</p> <p>In comparison with other methods, our algorithm reports blocks of larger average size. Nevertheless, the haplotype diversity within the blocks is captured by a small number of tagSNPs. Resampling HapMap haplotypes under a block-based model of recombination showed that our algorithm is robust in reproducing the same partitioning for recombinant samples. Our algorithm performed better than previously reported models in a case-control association study aimed at mapping a single locus trait, based on simulation results that were evaluated by a block-based statistical test. Compared to methods of haplotype block partitioning, we performed best on detection of recombination hotspots.</p> <p>Conclusion</p> <p>Our proposed method divides chromosomes into the regions within which allelic associations of SNP pairs are maximized. This approach presents a native design for dimension reduction in genome-wide association studies. Our results show that the pairwise allelic association of SNPs can describe various features of genomic variation, in particular recombination hotspots.</p

An Analysis of Gene Expression Variations in Lymphoma, Using a Fuzzy Classification Model

Introduction: Cancer is a major cause of mortality in the modern world, and one of the most important health problems in societies. During recent years, research on cancer as a system biology disease is focused on molecular differences between cancer cells and healthy cells. Most of the proposed methods for classifying cancer using gene expression data act as black boxes and lack biological interpretability. The goal of this study is to design an interpretable fuzzy model for classifying gene expression data of Lymphoma cancer. Method: In this research, the investigated microarray contained 45 samples of lymphoma. Total number of genes was 4026 samples. At first, we offer a hybrid approach to reduce the data dimension for detecting genes involved in lymphoma cancer. In lymphoma microarray, six out of 4029 genes were selected. Then, a fuzzy interpretable classifier was presented for classification of data. Fuzzy inference was performed using two rules which had the highest scores. Weka3.6.9 software was used to reduce the features and the fuzzy classifier model was implemented in MATLAB R2010a. Results of this study were assessed by two measures of accuracy and precision. Results: In pre-processing stage, in order to classify gene expression data of Lymphoma, six out of 4026 genes were identified as cancer-causing genes, and then the fuzzy classifier model was applied on the obtained data. The accuracy of the results of classification was 96 percent using 10 rules with the highest scores and that using 2 rules with the highest scores was about 98 percent. Conclusion: In the proposed approach, for the first time, a fully fuzzy method named a minimal rule fuzzy classification (MRFC) was introduced for extracting fuzzy rules with biological interpretability and meaning extraction from gene expression data. Among the most outstanding features of this method is the ability of extracting a small set of rules to interpret effective gene expression in cancer patients. Another result of this approach is successfully addressing the problem of disproportion between the number of samples and genes in microarrays with the proposed Filter-Wrapper Feature Selection method (FWFS)