A web-oriented software for the optimization of pooled experiments in NGS for detection of rare mutations.

Background: The cost per patient of next generation sequencing for detection of rare mutations may be significantly reduced using pooled experiments. Recently, some techniques have been proposed for the planning of pooled experiments and for the optimal allocation of patients into pools. However, the lack of a user friendly resource for planning the design of pooled experiments forces the scientists to do frequent, complex and long computations.

Lamin A/C sustains PcG protein architecture, maintaining transcriptional repression at target genes.

Beyond its role in providing structure to the nuclear envelope, lamin A/C is involved in transcriptional regulation. However, its cross talk with epigenetic factors--and how this cross talk influences physiological processes--is still unexplored. Key epigenetic regulators of development and differentiation are the Polycomb group (PcG) of proteins, organized in the nucleus as microscopically visible foci.

De novo assembly and sex-specific transcriptome profiling in the sand fly Phlebotomus perniciosus (Diptera, Phlebotominae), a major Old World vector of Leishmania infantum.

Background: The phlebotomine sand fly Phlebotomus perniciosus (Diptera: Psychodidae, Phlebotominae) is a major Old World vector of the protozoan Leishmania infantum, the etiological agent of visceral and cutaneous leishmaniases in humans and dogs, a worldwide re-emerging diseases of great public health concern, affecting 101 countries. Despite the growing interest in the study of this sand fly species in the last years, the development of genomic resources has been limited so far. To increase the available sequence data for P.

Divide et impera: subgoaling reduces the complexity of probabilistic inference and problem solving.

It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving.

Likelihood-fuzzy analysis of parotid gland shrinkage in radiotherapy patients.

In head-and-neck radiotherapy, an early detection of patients who will undergo parotid glands shrinkage during the treatment is of primary importance, since this condition has been found to be associated with acute toxicity. In this work, a recently proposed approach, here named Likelihood-Fuzzy Analysis, based on both statistical learning and Fuzzy Logic, is proposed to support the identification of early predictors of parotid shrinkage from Computed Tomography images acquired during radiotherapy. For this purpose, a set of textural image parameters was extracted and considered as candidate of parotid shrinkage prediction; for all these parameters and combinations of maximum three of them, a fuzzy rule base was extracted, gaining very good results in terms of accuracy, sensitivity and specificity.

Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods.

Motivation: Protein-protein interaction (PPI) networks are powerful models to represent the pairwise protein interactions of the organisms. Clustering PPI networks can be useful for isolating groups of interacting proteins that participate in the same biological processes or that perform together specific biological functions. Evolutionary orthologies can be inferred this way, as well as functions and properties of yet uncharacterized proteins.

An evolutionary approach for image segmentation.

The paper explores the use of evolutionary techniques in dealing with the image segmentation problem. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A genetic algorithm that uses a fitness function based on an extension of the normalized cut criterion is proposed.

A coclustering approach for mining large protein-protein interaction networks.

Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks.

An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease.

Assisted Living provides a long-term care option that combines supportive systems and services for monitoring and assessing the health status with activities of daily living and health care. Daily monitoring of the health status in subjects characterized by chronic and/or degenerative conditions is not possible in all those cases where the disease progression has to be evaluated only by a direct interaction between the patients and the healthcare structures on a regular basis, over time and for life. In this respect, this work proposes an evolutionary-fuzzy decision support system (DSS) for assessing the health status of subjects affected by multiple sclerosis (MS) during the disease progression over time.

A self-organizing approach to background subtraction for visual surveillance applications.

Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science.