Strong Lefschetz elements of the coinvariant rings of finite Coxeter groups

For the coinvariant rings of finite Coxeter groups of types other than H$_4$, we show that a homogeneous element of degree one is a strong Lefschetz element if and only if it is not fixed by any reflections. We also give the necessary and sufficient condition for strong Lefschetz elements in the invariant subrings of the coinvariant rings of Weyl groups.Comment: 18 page

Computation of significance scores of unweighted Gene Set Enrichment Analyses

<p>Abstract</p> <p>Background</p> <p>Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for interpreting microarray gene expression data, but it can be applied to any sorted list of genes. Given the gene list and an arbitrary biological category, GSEA evaluates whether the genes of the considered category are randomly distributed or accumulated on top or bottom of the list. Usually, significance scores (p-values) of GSEA are computed by nonparametric permutation tests, a time consuming procedure that yields only estimates of the p-values.</p> <p>Results</p> <p>We present a novel dynamic programming algorithm for calculating exact significance values of unweighted Gene Set Enrichment Analyses. Our algorithm avoids typical problems of nonparametric permutation tests, as varying findings in different runs caused by the random sampling procedure. Another advantage of the presented dynamic programming algorithm is its runtime and memory efficiency. To test our algorithm, we applied it not only to simulated data sets, but additionally evaluated expression profiles of squamous cell lung cancer tissue and autologous unaffected tissue.</p

Excitation test results on a single inner vertical coil for the Large Helical Device

Excitation experiments on a single inner vertical coil for the Large Helical Device (LHD) were carried out to confirm its performance. The coil is one of the LHD\u27s poloidal field coils and consists of a forced-flow Nb-Ti cable-in-conduit conductor (CICC). After cooldown for 250 hours, the superconducting transition of the whole coil was confirmed. Pressure drops were measured during the cooldown to determine the coil\u27s hydraulic characteristics. Then, the coil was successfully energized up to the specified current, 20.8 kA. In the experiments, heat generation of joints, radial displacement and acoustic emission (AE) were measured

Network centrality: an introduction

Centrality is a key property of complex networks that influences the behavior of dynamical processes, like synchronization and epidemic spreading, and can bring important information about the organization of complex systems, like our brain and society. There are many metrics to quantify the node centrality in networks. Here, we review the main centrality measures and discuss their main features and limitations. The influence of network centrality on epidemic spreading and synchronization is also pointed out in this chapter. Moreover, we present the application of centrality measures to understand the function of complex systems, including biological and cortical networks. Finally, we discuss some perspectives and challenges to generalize centrality measures for multilayer and temporal networks.Comment: Book Chapter in "From nonlinear dynamics to complex systems: A Mathematical modeling approach" by Springe

Structural and functional characterization of the LldR from Corynebacterium glutamicum: a transcriptional repressor involved in l-lactate and sugar utilization

LldR (CGL2915) from Corynebacterium glutamicum is a transcription factor belonging to the GntR family, which is typically involved in the regulation of oxidized substrates associated with amino acid metabolism. In the present study, the crystal structure of LldR was determined at 2.05-Å resolution. The structure consists of N- and C-domains similar to those of FadR, but with distinct domain orientations. LldR and FadR dimers achieve similar structures by domain swapping, which was first observed in dimeric assembly of transcription factors. A structural feature of Zn2+ binding in the regulatory domain was also observed, as a difference from the FadR subfamily. DNA microarray and DNase I footprint analyses suggested that LldR acts as a repressor regulating cgl2917-lldD and cgl1934-fruK-ptsF operons, which are indispensable for l-lactate and fructose/sucrose utilization, respectively. Furthermore, the stoichiometries and affinities of LldR and DNAs were determined by isothermal titration calorimetry measurements. The transcriptional start site and repression of LldR on the cgl2917-lldD operon were analysed by primer extension assay. Mutation experiments showed that residues Lys4, Arg32, Arg42 and Gly63 are crucial for DNA binding. The location of the putative ligand binding cavity and the regulatory mechanism of LldR on its affinity for DNA were proposed

An expression meta-analysis of predicted microRNA targets identifies a diagnostic signature for lung cancer

<p>Abstract</p> <p>Background</p> <p>Patients diagnosed with lung adenocarcinoma (AD) and squamous cell carcinoma (SCC), two major histologic subtypes of lung cancer, currently receive similar standard treatments, but resistance to adjuvant chemotherapy is prevalent. Identification of differentially expressed genes marking AD and SCC may prove to be of diagnostic value and help unravel molecular basis of their histogenesis and biologies, and deliver more effective and specific systemic therapy.</p> <p>Methods</p> <p>MiRNA target genes were predicted by union of miRanda, TargetScan, and PicTar, followed by screening for matched gene symbols in NCBI human sequences and Gene Ontology (GO) terms using the PANTHER database that was also used for analyzing the significance of biological processes and pathways within each ontology term. Microarray data were extracted from Gene Expression Omnibus repository, and tumor subtype prediction by gene expression used Prediction Analysis of Microarrays.</p> <p>Results</p> <p>Computationally predicted target genes of three microRNAs, miR-34b/34c/449, that were detected in human lung, testis, and fallopian tubes but not in other normal tissues, were filtered by representation of GO terms and their ability to classify lung cancer subtypes, followed by a meta-analysis of microarray data to classify AD and SCC. Expression of a minimal set of 17 predicted miR-34b/34c/449 target genes derived from the developmental process GO category was identified from a training set to classify 41 AD and 17 SCC, and correctly predicted in average 87% of 354 AD and 82% of 282 SCC specimens from total 9 independent published datasets. The accuracy of prediction still remains comparable when classifying 103 AD and 79 SCC samples from another 4 published datasets that have only 14 to 16 of the 17 genes available for prediction (84% and 85% for AD and SCC, respectively). Expression of this signature in two published datasets of epithelial cells obtained at bronchoscopy from cigarette smokers, if combined with cytopathology of the cells, yielded 89–90% sensitivity of lung cancer detection and 87–90% negative predictive value to non-cancer patients.</p> <p>Conclusion</p> <p>This study focuses on predicted targets of three lung-enriched miRNAs, compares their expression patterns in lung cancer by their GO terms, and identifies a minimal set of genes differentially expressed in AD and SCC, followed by validating this gene signature in multiple published datasets. Expression of this gene signature in bronchial epithelial cells of cigarette smokers also has a great sensitivity to predict the patients having lung cancer if combined with cytopathology of the cells.</p

A New Species of Saphonecrus (Hymenoptera, Cynipoidea) Associated With Plant Galls on Castanopsis (Fagaceae) in China

A new cynipid species, Saphonecrus hupingshanensis Liu, Yang, et Zhu, sp. nov. (Hymenoptera: Cynipidae: Synergini), is described from China. This is the first species of the inquilinous tribe Synergini ever known to have an association with chinquapins (Fagaceae: Castanopsis). The biology and implication to species diversity of Cynipidae in eastern and southeast Asia are discussed

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin