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The control of non-coding repeated DNA by DNA methylation plays an important role in genomic stability, contributing to health and healthy aging. Mind-body practices can elicit psychophysical wellbeing via epigenetic mechanisms, including DNA methylation. However, in this context the effects of movement meditations have rarely been examined. Consequently, the current study investigates the effects of a specifically structured movement meditation, called the Quadrato Motor Training (QMT) on psychophysical wellbeing and on the methylation level of repeated sequences. An 8-week daily QMT program was administered to healthy women aged 40–60 years and compared with a passive control group matched for gender and age. Psychological well-being was assessed within both groups by using self-reporting scales, including the Meaning in Life Questionnaire [MLQ] and Psychological Wellbeing Scale [PWB]). DNA methylation profiles of repeated sequences (ribosomal DNA, LINE-1 and Alu) were determined in saliva samples by deep-sequencing. In contrast to controls, the QMT group exhibited increased Search for Meaning, decreased Presence of Meaning and increased Positive Relations, suggesting that QMT may lessen the automatic patterns of thinking. In the QMT group, we also found site-specific significant methylation variations in ribosomal DNA and LINE-1 repeats, consistent with increased genome stability. Finally, the correlations found between changes in methylation and psychometric indices (MLQ and PWB) suggest that the observed epigenetic and psychological changes are interrelated. Collectively, the current results indicate that QMT may improve psychophysical health trajectories by influencing the DNA methylation of specific repetitive sequences.

Liquid chromatography tandem mass spectrometry (LC-MS/MS) is routinely used in many clinical applications, including toxicology, drug monitoring, endocrinology, microbiology, and proteomics, thanks to its versatility and effectiveness in the determination of small molecules. The standard approach for analyzing spectral data is the spectral matching that is based on creating a library of annotated spectra against which individual spectrum can be searched for. Building an own spectral library is time-consuming and dependent on the LC-MS/MS instrumentation used, whereas freely available libraries only cover a limited number of molecules. In many cases and especially with newly synthetized substances, the spectrum could not be found in any spectral databases. In-silico fingerprints are binary vectors that, through a hashing algorithm, encode features of molecules. Due to their easy computation and instrument-independence, in-silico fingerprint databases are bigger and more updated than spectral ones. Prediction of molecular fingerprints starting from the LC-MS/MS spectra would consequently assist the match of any target compound under investigation, which would benefit from the increased dimension of fingerprint databases. In this study, we developed a multi-task neural network able to predict molecular fingerprints starting from the LC-MS/MS spectra. We initially collected and pruned around 70’000 MS spectra from available sources (MassBank of North America). For each compound, fingerprints were calculated (MACCS167 keys and Dragon ECFP of 512 bits) and then multi-task feedforward neural networks were trained to predict the binary bits of molecular fingerprints. Models were validated through specific validation protocols and demonstrated to have suitable performances in terms of predictive accuracy (>85% of bits correctly predicted). Thus, the proposed networks can represent a potential approach for the development of a reliable method to enhance matching of MS spec

In this paper, we address the binary classification problem, in which one is given a set of observations, characterized by a number of (binary and non-binary) attributes and wants to determine which class each observation belongs to. The proposed classification algorithm is based on the Logical Analysis of Data (LAD) technique and belongs to the class of supervised learning algorithms. We introduce a novel metaheuristic-based approach for pattern generation within LAD. The key idea relies on the generation of a pool of patterns for each given observation of the training set. Such a pool is built with one or more criteria in mind (e.g., diversity, homogeneity, coverage, etc.), and is paramount in the achievement of high classification accuracy, as shown by the computational results we obtained. In addition, we address one of the major concerns of many data mining algorithms, i.e., the fine-tuning and calibration of parameters. We employ here a novel technique, called biased Random-Key Genetic Algorithm that allows the calibration of all the parameters of the algorithm in an automatic fashion, hence reducing the fine-tuning effort required and enhancing the performance of the algorithm itself. We tested the proposed approach on 10 benchmark instances from the UCI repository and we proved that the algorithm is competitive, both in terms of classification accuracy and running time.

The recent 2013 Italian elections are over and the situation that President Napolitano will have to settle soon for the formation of the new government is not the simplest one. After twenty years of bipolarism (more or less effective), where we were accustomed to a tight battle between two great political coalitions, the center-right and center-left, now, in the new Parliament, we have four political formations. But is it really this result, as it would seem to suggest our common sense, the prelude to an inevitable phase of ungovernability? Can a Parliament with changing majorities in Senate to be as efficient as a Parliament with a large majority in both the Houses? In this short note we will try to answer these questions going beyond common sense and analyzing the current political situation by means of a scientific, original and innovative instrument, i.e. an "agent-based simulation". We show that the situation is not so dramatic as it sounds, but it contains within itself potential positive aspects, as long as one makes the most appropriate choices., Comment: 6 pages, 5 figures

We study a prototypical model of a Parliament with two Parties or two Political Coalitions and we show how the introduction of a variable percentage of randomly selected independent legislators can increase the global efficiency of a Legislature, in terms of both the number of laws passed and the average social welfare obtained. We also analytically find an "efficiency golden rule" which allows to fix the optimal number of legislators to be selected at random after that regular elections have established the relative proportion of the two Parties or Coalitions. These results are in line with both the ancient Greek democratic system and the recent discovery that the adoption of random strategies can improve the efficiency of hierarchical organizations., Comment: 19 pages, 7 figures, new improved and longer version

Cytosine methylation has energetic and structural influences on left-handed Z-DNA formation in supercoiled plasmids. The restriction and modification enzymes from Haemophilus haemolyticus (HhaI and M.HhaI) provide a system to locate and analyze small segments of Z-DNA in large supercoiled plasmids. An approach is outlined that uses M.HhaI as an in vivo conformational probe for the detection of unusual DNA structures in a living cell. Also, characteristic features of the M.HhaI gene and protein are discussed.