Label Preference Schemes in GMPLS Controlled Networks

The GMPLS assumption that all available labels are equal is reasonable in electronic networks but not always true in WDM optical networks where labels correspond to physical wavelengths. In this paper we present two schemes for collecting the preference for specific labels during GMPLS signaling. For this purpose a new use of the Suggested Label object is proposed, and a novel object called Suggested Vector is introduced. The approach is validated through simulations showing significant wavelength converter usage reduction in aWDM optical network

Green-Aware Routing in GMPLS Networks

The increasing amount of traffic in the Internet has been accommodated by the exponential growth of bandwidth provided by the optical networks technologies. However, such a growth has been also accompanied by an increase in the energy consumption and the concomitant green house gases (GHG) emissions. Despite the efforts for improving energy efficiency in silicon technologies and network designs, the large energy consumption still poses challenges for the future development of Internet. In this paper, we propose an extension of the Open Shortest Path First — Traffic Engineering (OSPF-TE) protocol and a green-aware routing and wavelength assignment (RWA) algorithm for minimizing the GHG emissions by routing connection requests through green network elements (NE). The network behavior and the performance of the algorithm are analyzed through simulations under different scenarios, and results show that it is possible to reduce GHGs emissions at the expense of an increase in the path length, and, in some cases, in the blocking probability. The trade-off between emissions and performance is studied. To the authors knowledge, this is the first work that provides a detailed study of a green-aware OSPF protocol

The 68 kDa subunit of mammalian cleavage factor I interacts with the U7 small nuclear ribonucleoprotein and participates in 3′-end processing of animal histone mRNAs

Metazoan replication-dependent histone pre-mRNAs undergo a unique 3′-cleavage reaction which does not result in mRNA polyadenylation. Although the cleavage site is defined by histone-specific factors (hairpin binding protein, a 100-kDa zinc-finger protein and the U7 snRNP), a large complex consisting of cleavage/polyadenylation specificity factor, two subunits of cleavage stimulation factor and symplekin acts as the effector of RNA cleavage. Here, we report that yet another protein involved in cleavage/polyadenylation, mammalian cleavage factor I 68-kDa subunit (CF Im68), participates in histone RNA 3′-end processing. CF Im68 was found in a highly purified U7 snRNP preparation. Its interaction with the U7 snRNP depends on the N-terminus of the U7 snRNP protein Lsm11, known to be important for histone RNA processing. In vivo, both depletion and overexpression of CF Im68 cause significant decreases in processing efficiency. In vitro 3′-end processing is slightly stimulated by the addition of low amounts of CF Im68, but inhibited by high amounts or by anti-CF Im68 antibody. Finally, immunoprecipitation of CF Im68 results in a strong enrichment of histone pre-mRNAs. In contrast, the small CF Im subunit, CF Im25, does not appear to be involved in histone RNA processin

The binding orientation of epibatidine at α7 nACh receptors

Epibatidine is an alkaloid toxin that binds with high affinity to nicotinic and muscarinic acetylcholine receptors, and has been extensively used as a research tool. To examine binding interactions at the nicotinic receptor, it has been co-crystallised with the structural homologue acetylcholine binding protein (AChBP; PDB ID 2BYQ), and with an AChBP chimaera (3SQ6) that shares 64% sequence identity with the α7 nACh receptor. However, the binding orientations revealed by AChBP co-crystal structures may not precisely represent their receptor homologues and experimental evidence is needed to verify the ligand poses. Here we identify potential binding site interactions between epibatidine and AChBP residues, and substitute equivalent positions in the α7 nACh receptor. The effects of these are probed by [$^{3}$H]epibatidine binding following the expression α7 nACh receptor cysteine mutants in HEK 293 cells. Of the sixteen mutants created, the affinity of epibatidine was unaffected by the substitutions Q55C, L106C, L116C, T146C, D160C and S162C, reduced by C186A and C187A, increased by Q114C and S144C, and abolished by W53C, Y91C, N104C, W145C, Y184C and Y191C. These results are consistent with the predicted orientations in AChBP and suggest that epibatidine is likely to occupy a similar location at α7 nACh receptors. We speculate that steric constraints placed upon the C-5 position of the pyridine ring in 3SQ6 may account for the relatively poor affinities of epibatidine derivatives that are substituted at this position.This work was funded by the British Heart Foundation [PG/13/39/3029 to AJT], HOLCIM Stiftung zur Förderung der wissenschaftlichen Fortbildung [to M-DR], the NOMIS Foundation [to M-DR], and the Swiss National Science Foundation [SNSF professorship PP00P2_146321/1 to ML]

The 68 kDa subunit of mammalian cleavage factor I interacts with the U7 small nuclear ribonucleoprotein and participates in 3′-end processing of animal histone mRNAs

Metazoan replication-dependent histone pre-mRNAs undergo a unique 3′-cleavage reaction which does not result in mRNA polyadenylation. Although the cleavage site is defined by histone-specific factors (hairpin binding protein, a 100-kDa zinc-finger protein and the U7 snRNP), a large complex consisting of cleavage/polyadenylation specificity factor, two subunits of cleavage stimulation factor and symplekin acts as the effector of RNA cleavage. Here, we report that yet another protein involved in cleavage/polyadenylation, mammalian cleavage factor I 68-kDa subunit (CF Im68), participates in histone RNA 3′-end processing. CF Im68 was found in a highly purified U7 snRNP preparation. Its interaction with the U7 snRNP depends on the N-terminus of the U7 snRNP protein Lsm11, known to be important for histone RNA processing. In vivo, both depletion and overexpression of CF Im68 cause significant decreases in processing efficiency. In vitro 3′-end processing is slightly stimulated by the addition of low amounts of CF Im68, but inhibited by high amounts or by anti-CF Im68 antibody. Finally, immunoprecipitation of CF Im68 results in a strong enrichment of histone pre-mRNAs. In contrast, the small CF Im subunit, CF Im25, does not appear to be involved in histone RNA processing

miRGator v2.0 : an integrated system for functional investigation of microRNAs

miRGator is an integrated database of microRNA (miRNA)-associated gene expression, target prediction, disease association and genomic annotation, which aims to facilitate functional investigation of miRNAs. The recent version of miRGator v2.0 contains information about (i) human miRNA expression profiles under various experimental conditions, (ii) paired expression profiles of both mRNAs and miRNAs, (iii) gene expression profiles under miRNA-perturbation (e.g. miRNA knockout and overexpression), (iv) known/predicted miRNA targets and (v) miRNA-disease associations. In total, >8000 miRNA expression profiles, ∼300 miRNA-perturbed gene expression profiles and ∼2000 mRNA expression profiles are compiled with manually curated annotations on disease, tissue type and perturbation. By integrating these data sets, a series of novel associations (miRNA–miRNA, miRNA–disease and miRNA–target) is extracted via shared features. For example, differentially expressed genes (DEGs) after miRNA knockout were systematically compared against miRNA targets. Likewise, differentially expressed miRNAs (DEmiRs) were compared with disease-associated miRNAs. Additionally, miRNA expression and disease-phenotype profiles revealed miRNA pairs whose expression was regulated in parallel in various experimental and disease conditions. Complex associations are readily accessible using an interactive network visualization interface. The miRGator v2.0 serves as a reference database to investigate miRNA expression and function (http://miRGator.kobic.re.kr)

MIPS: analysis and annotation of genome information in 2007

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de)

Evaluation of clustering algorithms for gene expression data

BACKGROUND: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist. RESULTS: In this paper, we propose two validation measures each with two parts: one measuring the statistical consistency (stability) of the clusters produced and the other representing their biological functional congruence. Smaller values of these indices indicate better performance for a clustering algorithm. We illustrate this approach using two case studies with publicly available gene expression data sets: one involving a SAGE data of breast cancer patients and the other involving a time course cDNA microarray data on yeast. Six well known clustering algorithms UPGMA, K-Means, Diana, Fanny, Model-Based and SOM were evaluated. CONCLUSION: No single clustering algorithm may be best suited for clustering genes into functional groups via expression profiles for all data sets. The validation measures introduced in this paper can aid in the selection of an optimal algorithm, for a given data set, from a collection of available clustering algorithms

Heterogeneous network embedding enabling accurate disease association predictions.

BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation

Rare coding SNP in DZIP1 gene associated with late-onset sporadic Parkinson's disease

We present the first application of the hypothesis-rich mathematical theory to genome-wide association data. The Hamza et al. late-onset sporadic Parkinson's disease genome-wide association study dataset was analyzed. We found a rare, coding, non-synonymous SNP variant in the gene DZIP1 that confers increased susceptibility to Parkinson's disease. The association of DZIP1 with Parkinson's disease is consistent with a Parkinson's disease stem-cell ageing theory.Comment: 14 page