PACMAS: A Personalized, Adaptive, and Cooperative MultiAgent System Architecture

In this paper, a generic architecture, designed to support the implementation of applications aimed at managing information among different and heterogeneous sources, is presented. Information is filtered and organized according to personal interests explicitly stated by the user. User pro- files are improved and refined throughout time by suitable adaptation techniques. The overall architecture has been called PACMAS, being a support for implementing Personalized, Adaptive, and Cooperative MultiAgent Systems. PACMAS agents are autonomous and flexible, and can be made personal, adaptive and cooperative, depending on the given application. The peculiarities of the architecture are highlighted by illustrating three relevant case studies focused on giving a support to undergraduate and graduate students, on predicting protein secondary structure, and on classifying newspaper articles, respectively

Four-dimensional surface evolution of active rifting from spaceborne SAR data

We present a case study for detecting the four-dimensional (4-D) displacement of rift zones affected by large-magnitude deformation, by using space-borne synthetic aperture radar (SAR) data. Our method relies on the combination of displacement time series generated from pixel offset estimates on the amplitude information of multitemporal SAR images acquired from different orbit passes and with different looking geometries. We successfully applied the technique on advanced SAR (ASAR) Envisat data acquired from ascending and descending orbits over the Afar rift zone (Ethiopia) during the 2006-2010 time span. Our results demonstrate the effectiveness of the proposed technique to retrieve the full 4-D (i.e., north, east, up, and time) displacement field associated with active rifting affected by very large-magnitude deformation

Devising novel performance measures for assessing the behavior of multilayer perceptrons trained on regression tasks

This methodological article is mainly aimed at establishing a bridge between classification and regression tasks, in a frame shaped by performance evaluation. More specifically, a general procedure for calculating performance measures is proposed, which can be applied to both classification and regression models. To this end, a notable change in the policy used to evaluate the confusion matrix is made, with the goal of reporting information about regression performance therein. This policy, called generalized token sharing, allows to a) assess models trained on both classification and regression tasks, b) evaluate the importance of input features, and c) inspect the behavior of multilayer perceptrons by looking at their hidden layers. The occurrence of success and failure patterns at the hidden layers of multilayer perceptrons trained and tested on selected regression problems, together with the effectiveness of layer-wise training, is also discussed

Framing Apache Spark in life sciences

Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities

The First in-silico Model of Leg Movement Activity During Sleep

We developed the first model simulator of leg movements activity during sleep. We designed and calibrated a phenomenological model on control subjects not showing significant periodic leg movements (PLM). To test a single generator hypothesis behind PLM—a single pacemaker possibly resulting from two (or more) interacting spinal/supraspinal generators—we added a periodic excitatory input to the control model. We describe the onset of a movement in one leg as the firing of a neuron integrating physiological excitatory and inhibitory inputs from the central nervous system, while the duration of the movement was drawn in accordance with statistical evidence. The period and the intensity of the periodic input were calibrated on a dataset of subjects showing PLM (mainly restless legs syndrome patients). Despite its many simplifying assumptions—the strongest being the stationarity of the neural processes during night sleep—the model simulations are in remarkable agreement with the polysomnographically recorded data

Removing duplicate reads using graphics processing units

Background: During library construction polymerase chain reaction is used to enrich the DNA before sequencing. Typically, this process generates duplicate read sequences. Removal of these artifacts is mandatory, as they can affect the correct interpretation of data in several analyses. Ideally, duplicate reads should be characterized by identical nucleotide sequences. However, due to sequencing errors, duplicates may also be nearly-identical. Removing nearly-identical duplicates can result in a notable computational effort. To deal with this challenge, we recently proposed a GPU method aimed at removing identical and nearly-identical duplicates generated with an Illumina platform. The method implements an approach based on prefix-suffix comparison. Read sequences with identical prefix are considered potential duplicates. Then, their suffixes are compared to identify and remove those that are actually duplicated. Although the method can be efficiently used to remove duplicates, there are some limitations that need to be overcome. In particular, it cannot to detect potential duplicates in the event that prefixes are longer than 27 bases, and it does not provide support for paired-end read libraries. Moreover, large clusters of potential duplicates are split into smaller with the aim to guarantees a reasonable computing time. This heuristic may affect the accuracy of the analysis. Results: In this work we propose GPU-DupRemoval, a new implementation of our method able to (i) cluster reads without constraints on the maximum length of the prefixes, (ii) support both single- and paired-end read libraries, and (iii) analyze large clusters of potential duplicates. Conclusions: Due to the massive parallelization obtained by exploiting graphics cards, GPU-DupRemoval removes duplicate reads faster than other cutting-edge solutions, while outperforming most of them in terms of amount of duplicates reads

The effects of flank collapses on volcano plumbing systems

The growth of large volcanoes is commonly interrupted by episodes of flank collapse that may be accompanied by catastrophic debris avalanches, explosive eruptions, and tsunamis. El Hierro, the youngest island of the Canary Archipelago, has been repeatedly affected by such mass-wasting events in the last 1 Ma. Our field observations and petrological data suggest that the largest and most recent of these flank collapses—the El Golfo landslide—likely influenced the magma plumbing system of the island, leading to the eruption of higher proportions of denser and less evolved magmas. The results of our numerical simulations indicate that the El Golfo landslide generated pressure changes exceeding 1 MPa down to upper-mantle depths, with local amplification in the surroundings and within the modeled magma plumbing system. Stress perturbations of that order might drastically alter feeding system processes, such as degassing, transport, differentiation, and mixing of magma batches

G-CNV: A GPU-based tool for preparing data to detect CNVs with read-depth methods

Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals