PyFml - a Textual Language For Feature Modeling

The Feature model is a typical approach to capture variability in a software product line design and implementation. For that, most works automate feature model using a limited graphical notation represented by propositional logic and implemented by Prolog or Java programming languages. These works do not properly combine the extensions of classical feature models and do not provide scalability to implement large size problem issues. In this work, we propose a textual feature modeling language based on Python programming language (PyFML), that generalizes the classical feature models with instance feature cardinalities and attributes which be extended with highlight of replication and complex logical and mathematical cross-tree constraints. textX Meta-language is used for building PyFML to describe and organize feature model dependencies, and PyConstraint Problem Solver is used to implement feature model variability and its constraints validation. The work provides a textual human-readable language to represent feature model and maps the feature model descriptions directly into the object-oriented representation to be used by Constraint Problem Solver for computation. Furthermore, the proposed PyFML makes the notation of feature modeling more expressive to deal with complex software product line representations and using PyConstraint Problem SolverComment: 13 pages, 13 figures, 29 refrence

Evidence for symmetric chromosomal inversions around the replication origin in bacteria.

BackgroundWhole-genome comparisons can provide great insight into many aspects of biology. Until recently, however, comparisons were mainly possible only between distantly related species. Complete genome sequences are now becoming available from multiple sets of closely related strains or species.ResultsBy comparing the recently completed genome sequences of Vibrio cholerae, Streptococcus pneumoniae and Mycobacterium tuberculosis to those of closely related species - Escherichia coli, Streptococcus pyogenes and Mycobacterium leprae, respectively - we have identified an unusual and previously unobserved feature of bacterial genome structure. Scatterplots of the conserved sequences (both DNA and protein) between each pair of species produce a distinct X-shaped pattern, which we call an X-alignment. The key feature of these alignments is that they have symmetry around the replication origin and terminus; that is, the distance of a particular conserved feature (DNA or protein) from the replication origin (or terminus) is conserved between closely related pairs of species. Statistically significant X-alignments are also found within some genomes, indicating that there is symmetry about the replication origin for paralogous features as well.ConclusionsThe most likely mechanism of generation of X-alignments involves large chromosomal inversions that reverse the genomic sequence symmetrically around the origin of replication. The finding of these X-alignments between many pairs of species suggests that chromosomal inversions around the origin are a common feature of bacterial genome evolution

Does replication groups scoring reduce false positive rate in SNP interaction discovery?

BACKGROUNG. Computational methods that infer single nucleotide polymorphism (SNP) interactions from phenotype data may uncover new biological mechanisms in non-Mendelian diseases. However, practical aspects of such analysis face many problems. Present experimental studies typically use SNP arrays with hundreds of thousands of SNPs but record only hundreds of samples. Candidate SNP pairs inferred by interaction analysis may include a high proportion of false positives. Recently, Gayan et al. (2008) proposed to reduce the number of false positives by combining results of interaction analysis performed on subsets of data (replication groups), rather than analyzing the entire data set directly. If performing as hypothesized, replication groups scoring could improve interaction analysis and also any type of feature ranking and selection procedure in systems biology. Because Gayan et al. do not compare their approach to the standard interaction analysis techniques, we here investigate if replication groups indeed reduce the number of reported false positive interactions. RESULTS. A set of simulated and false interaction-imputed experimental SNP data sets were used to compare the inference of SNP-SNP interactions by means of replication groups to the standard approach where the entire data set was directly used to score all candidate SNP pairs. In all our experiments, the inference of interactions from the entire data set (e.g. without using the replication groups) reported fewer false positives. CONCLUSIONS. With respect to the direct scoring approach the utility of replication groups does not reduce false positive rates, and may, depending on the data set, often perform worse

Rad62 protein functionally and physically associates with the Smc5/Smc6 protein complex and is required for chromosome integrity and recombination repair in fission yeast

Smc5 and Smc6 proteins form a heterodimeric SMC (structural maintenance of chromosome) protein complex like SMC1-SMC3 cohesin and SMC2-SMC4 condensin, and they associate with non-SMC proteins Nse1 and Nse2 stably and Rad60 transiently. This multiprotein complex plays an essential role in maintaining chromosome integrity and repairing DNA double strand breaks (DSBs). This study characterizes a Schizosaccharomyces pombe mutant rad62-1, which is hypersensitive to methyl methanesulfonate (MMS) and synthetically lethal with rad2 (a feature of recombination mutants). rad62-1 is hypersensitive to UV and gamma rays, epistatic with rhp51, and defective in repair of DSBs. rad62 is essential for viability and genetically interacts with rad60, smc6, and brc1. Rad62 protein physically associates with the Smc5-6 complex. rad62-1 is synthetically lethal with mutations in the genes promoting recovery from stalled replication, such as rqh1, srs2, and mus81, and those involved in nucleotide excision repair like rad13 and rad16. These results suggest that Rad62, like Rad60, in conjunction with the Smc5-6 complex, plays an essential role in maintaining chromosome integrity and recovery from stalled replication by recombination

A novel mechanism underlying the innate immune response induction upon viral-dependent replication of host cell mRNA: A mistake of +sRNA viruses' replicases

Viruses are lifeless particles designed for setting virus-host interactome assuring a new generation of virions for dissemination. This interactome generates a pressure on host organisms evolving mechanisms to neutralize viral infection, which places the pressure back onto virus, a process known as virus-host cell co-evolution. Positive-single stranded RNA (+sRNA) viruses are an important group of viral agents illustrating this interesting phenomenon. During replication, their genomic +sRNA is employed as template for translation of viral proteins; among them the RNA-dependent RNA polymerase (RdRp) is responsible of viral genome replication originating double-strand RNA molecules (dsRNA) as intermediates, which accumulate representing a potent threat for cellular dsRNA receptors to initiate an antiviral response. A common feature shared by these viruses is their ability to rearrange cellular membranes to serve as platforms for genome replication and assembly of new virions, supporting replication efficiency increase by concentrating critical factors and protecting the viral genome from host anti-viral systems. This review summarizes current knowledge regarding cellular dsRNA receptors and describes prototype viruses developing replication niches inside rearranged membranes. However, for several viral agents it's been observed both, a complex rearrangement of cellular membranes and a strong innate immune antiviral response induction. So, we have included recent data explaining the mechanism by, even though viruses have evolved elegant hideouts, host cells are still able to develop dsRNA receptors-dependent antiviral response.Fil: Delgui, Laura Ruth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Colombo, Maria Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; Argentin

Role for RNA: DNA hybrids in origin-independent replication priming in a eukaryotic system

DNA replication initiates at defined replication origins along eukaryotic chromosomes, ensuring complete genome duplication within a single S-phase. A key feature of replication origins is their ability to control the onset of DNA synthesis mediated by DNA polymerase-α and its intrinsic RNA primase activity. Here, we describe a novel origin-independent replication process that is mediated by transcription. RNA polymerase I transcription constraints lead to persistent RNA:DNA hybrids (R-loops) that prime replication in the ribosomal DNA locus. Our results suggest that eukaryotic genomes have developed tools to prevent R-loop–mediated replication events that potentially contribute to copy number variation, particularly relevant to carcinogenesis

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Attention please: evaluative priming effects in a valent/non-valent categorization task (Reply to Werner and Rothermund, 2013)

It has previously been argued (a) that automatic evaluative stimulus processing is dependent upon feature-specific attention allocation (FSAA) and (b) that evaluative priming effects can arise in the absence of dimensional overlap between the prime set and the response set. In opposition to these claims, Werner and Rothermund (2013) recently reported that they were unable to replicate the evaluative priming effect in a valent/non-valent categorisation task. In this manuscript, I report the results of a conceptual replication of the studies by Werner and Rothermund (2013). A clear-cut evaluative priming effect was found, thus supporting the initial claims about FSAA and dimensional overlap. An explanation for these divergent findings is discussed

Creating fair lineups for suspects with distinctive features

In their descriptions, eyewitnesses often refer to a culprit's distinctive facial features. However, in a police lineup, selecting the only member with the described distinctive feature is unfair to the suspect and provides the police with little further information. For fair and informative lineups, the distinctive feature should be either replicated across foils or concealed on the target. In the present experiments, replication produced more correct identifications in target-present lineups—without increasing the incorrect identification of foils in target-absent lineups—than did concealment. This pattern, and only this pattern, is predicted by the hybrid-similarity model of recognition