Nrf2-Mediated Pathway Activated by L. (Rosaceae) Fruit Extract: Bioinformatics Analyses and Experimental Validation.

In our previous studies, fruit (PSF) ethanol extract was showed to exert antioxidant, antimicrobial, anti-inflammatory and wound healing activities. In the present study, an integrated bioinformatics analysis combined with experimental validation was carried out to investigate the biological mechanism(s) that are responsible for the reported PSF beneficial effects as an antioxidant during a pro-inflammatory TLR4 insult. Bioinformatics analysis using miRNet 2.

Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition.

The increasing diffusion of tiny wearable devices and, at the same time, the advent of machine learning techniques that can perform sophisticated inference, represent a valuable opportunity for the development of pervasive computing applications. Moreover, pushing inference on edge devices can in principle improve application responsiveness, reduce energy consumption and mitigate privacy and security issues. However, devices with small size and low-power consumption and factor form, like those dedicated to wearable platforms, pose strict computational, memory, and energy requirements which result in challenging issues to be addressed by designers.

A Study on the Influence of Speed on Road Roughness Sensing: The SmartRoadSense Case.

SmartRoadSense is a crowdsensing project aimed at monitoring the conditions of the road surface. Using the sensors of a smartphone, SmartRoadSense monitors the vertical accelerations inside a vehicle traveling the road and extracts a roughness index conveying information about the road conditions. The roughness index and the smartphone GPS data are periodically sent to a central server where they are processed, associated with the specific road, and aggregated with data measured by other smartphones.

Genome-wide computational approach for the prediction of duplications generating protein localization signals.

Investigating the possible generation of motifs accountable for aberrant protein dislocation subsequent to the rise of short tandem duplications is interesting, given the pathogenic potential of this mechanism, as demonstrated in diseases such adult myeloid leukemia (AML). In this paper we introduce a new computational method for predicting genomic points which, after hypothetical mutation events such as micro-duplications, might encode molecular patterns such as localization or export signals. The proposed framework allows to study motifs of unconstrained length defined as regular expressions at a genome-wide level, providing an in silico platform capable of analyzing the potential effect of duplications on abnormal cellular localization.

A lossy compression technique enabling duplication-aware sequence alignment.

In spite of the recognized importance of tandem duplications in genome evolution, commonly adopted sequence comparison algorithms do not take into account complex mutation events involving more than one residue at the time, since they are not compliant with the underlying assumption of statistical independence of adjacent residues. As a consequence, the presence of tandem repeats in sequences under comparison may impair the biological significance of the resulting alignment. Although solutions have been proposed, repeat-aware sequence alignment is still considered to be an open problem and new efficient and effective methods have been advocated.

Improved biological network reconstruction using graph Laplacian regularization.

Biological networks reconstruction is a crucial step towards the functional characterization and elucidation of living cells. Computational methods for inferring the structure of these networks are of paramount importance since they provide valuable information regarding organization and behavior of the cell at a system level and also enable careful design of wet-lab experiments. Despite many recent advances, according to the scientific literature, there is room for improvements from both the efficiency and the accuracy point of view in link prediction algorithms.

A monte carlo method for assessing the quality of duplication-aware alignment algorithms.

The increasing availability of high throughput sequencing technologies poses several challenges concerning the analysis of genomic data. Within this context, duplication-aware sequence alignment taking into account complex mutation events is regarded as an important problem, particularly in light of recent evolutionary bioinformatics researches that highlighted the role of tandem duplications as one of the most important mutation events. Traditional sequence comparison algorithms do not take into account these events, resulting in poor alignments in terms of biological significance, mainly because of their assumption of statistical independence among contiguous residues.

Using sequence compression to speedup probabilistic profile matching.

Motivation: Matching a biological sequence against a probabilistic pattern (or profile) is a common task in computational biology. A probabilistic profile, represented as a scoring matrix, is more suitable than a deterministic pattern to retain the peculiarities of a given segment of a family of biological sequences. Brute-force algorithms take O(NP) to match a sequence of N characters against a profile of length P << N.