The Longest Filled Common Subsequence Problem

Inspired by a recent approach for genome reconstruction from incomplete data, we consider a variant of the longest common subsequence problem for the comparison of two sequences, one of which is incomplete, i.e. it has some missing elements. The new combinatorial problem, called Longest Filled Common Subsequence, given two sequences A and B, and a multiset M of symbols missing in B, asks for a sequence B* obtained by inserting the symbols of M into B so that B* induces a common subsequence with A of maximum length. First, we investigate the computational and approximation complexity of the problem and we show that it is NP-hard and APX-hard when A contains at most two occurrences of each symbol. Then, we give a 3/5 approximation algorithm for the problem. Finally, we present a fixed-parameter algorithm, when the problem is parameterized by the number of symbols inserted in B that "match" symbols of A

10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017

Agapito, G., Cannataro, M., Castelli, M., Dondi, R., & Zoppis, I. (2017). 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017. Procedia Computer Science, 108, 1113-1114. https://doi.org/10.1016/j.procs.2017.05.279We present the 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017, held in Zurich, 12 - 14 June 2017.publishersversionpublishe

Computational methods for resting-state EEG of patients with disorders of consciousness

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application

Tumor size, stage and grade alterations of urinary peptidome in RCC

Background: Several promising biomarkers have been found for RCC, but none of them has been used in clinical practice for predicting tumour progression. The most widely used features for predicting tumour aggressiveness still remain the cancer stage, size and grade. Therefore, the aim of our study is to investigate the urinary peptidome to search and identify peptides whose concentrations in urine are linked to tumour growth measure and clinical data. Methods: A proteomic approach applied to ccRCC urinary peptidome (n = 117) based on prefractionation with activated magnetic beads followed by MALDI-TOF profiling was used. A systematic correlation study was performed on urinary peptide profiles obtained from MS analysis. Peptide identity was obtained by LC-ESI-MS/MS. Results: Fifteen, twenty-six and five peptides showed a statistically significant alteration of their urinary concentration according to tumour size, pT and grade, respectively. Furthermore, 15 and 9 signals were observed to have urinary levels statistically modified in patients at different pT or grade values, even at very early stages. Among them, C1RL, A1AGx, ZAG2G, PGBM, MMP23, GP162, ADA19, G3P, RSPH3, DREB, NOTC2 SAFB2 and CC168 were identified. Conclusions: We identified several peptides whose urinary abundance varied according to tumour size, stage and grade. Among them, several play a possible role in tumorigenesis, progression and aggressiveness. These results could be a useful starting point for future studies aimed at verifying their possible use in the managements of RCC patients

Managing chronic pathologies with a stepped mHealth-based approach in clinical psychology and medicine

Chronic diseases and conditions typically require long-term monitoring and treatment protocols both in traditional settings and in out-patient frameworks. The economic burden of chronic conditions is a key challenge and new and mobile technologies could offer good solutions. mHealth could be considered an evolution of eHealth and could be defined as the practice of medicine and public health supported by mobile communication devices. mHealth approach could overcome limitations linked with the traditional, restricted, and highly expensive in-patient treatment of many chronic pathologies. Possible applications include stepped mHealth approach, where patients can be monitored and treated in their everyday contexts. Unfortunately, many barriers for the spread of mHealth are still present. Due the significant impact of psychosocial factors on disease evolution, psychotherapies have to be included into the chronic disease protocols. Existing psychological theories of health behavior change have to be adapted to the new technological contexts and requirements. In conclusion, clinical psychology and medicine have to face the "chronic care management" challenge in both traditional and mHealth settings