A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes - as is the case in biological networks - due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications

Advanced methods for wind turbine performance analysis based on SCADA data and CFD simulations

Deep comprehension of wind farm performance is a complicated task due to the multivariate dependence of wind turbine power on environmental variables and working parameters and to the intrinsic limitations in the quality of SCADA-collected measurements. Given this, the objective of this study is to propose an integrated approach based on SCADA data and Computational Fluid Dynamics simulations, which is aimed at wind farm performance analysis. The selected test case is a wind farm situated in southern Italy, where two wind turbines had an apparent underperformance. The concept of a space–time comparison at the wind farm level is leveraged by analyzing the operation curves of the wind turbines and by comparing the simulated average wind field against the measured one, where each wind turbine is treated like a virtual meteorological mast. The employed formulation for the CFD simulations is Reynolds-Average Navier–Stokes (RANS). In this work, it is shown that, based on the above approach, it has been possible to identify an anemometer bias at a wind turbine, which has subsequently been fixed. The results of this work affirm that a deep comprehension of wind farm performance requires a non-trivial space–time comparison, of which CFD simulations can be a fundamental part

Using social media for sub-event detection during disasters

AbstractSocial media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred after a disaster (e.g., collapsed buildings, broken gas pipes, floods). SEDOM-DD has been evaluated with datasets of different sizes that contain real posts from social media related to different natural disasters (e.g., earthquakes, floods and hurricanes). Starting from such data, we generated synthetic datasets with different features, such as different percentages of relevant posts and/or geotagged posts. Experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy. For example, with a percentage of relevant posts of 80% and geotagged posts of 15%, our method detects the sub-events and their areas with an accuracy of 85%, revealing the high accuracy and effectiveness of the proposed approach

Electrophysiological actions of zonisamide on striatal neurons: Selective neuroprotection against complex I mitochondrial dysfunction

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Ancient oral microbiomes support gradual Neolithic dietary shifts towards agriculture

The human microbiome has recently become a valuable source of information about host life and health. To date little is known about how it may have evolved during key phases along our history, such as the Neolithic transition towards agriculture. Here, we shed light on the evolution experienced by the oral microbiome during this transition, comparing Palaeolithic hunter-gatherers with Neolithic and Copper Age farmers that populated a same restricted area in Italy. We integrate the analysis of 76 dental calculus oral microbiomes with the dietary information derived from the identification of embedded plant remains. We detect a stronger deviation from the hunter-gatherer microbiome composition in the last part of the Neolithic, while to a lesser extent in the early phases of the transition. Our findings demonstrate that the introduction of agriculture affected host microbiome, supporting the hypothesis of a gradual transition within the investigated populations

Brain tumor location influences the onset of acute psychiatric adverse events of levetiracetam therapy: an observational study.

To explore possible correlations among brain lesion location, development of psychiatric symptoms and the use of antiepileptic drugs (AEDs) in a population of patients with brain tumor and epilepsy. The medical records of 283 patients with various types of brain tumor (161 M/122 F, mean age 64.9 years) were analysed retrospectively. Patients with grade III and IV glioma, previous history of epileptic seizures and/or psychiatric disorders were excluded. Psychiatric symptoms occurring after initiation of AED therapy were considered as treatment emergent psychiatric adverse events (TE-PAEs) if they fulfilled the following conditions: (1) onset within 4 weeks after the beginning of AED therapy; (2) disappearance on drug discontinuation; (3) absence of any other identified possible concurrent cause. The possible influence of the following variables were analysed: (a) AED drug and dose; (b) location and neuroradiologic features of the tumor, (c) location and type of EEG epileptic abnormalities, (d) tumor excision already or not yet performed; (e) initiation or not of radiotherapy. TE-PAEs occurred in 27 of the 175 AED-treated patients (15.4%). Multivariate analysis showed a significant association of TE-PAEs occurrence with location of the tumor in the frontal lobe (Odds ratio: 5.56; 95% confidence interval 1.95-15.82; p value: 0.005) and treatment with levetiracetam (Odds ratio: 3.61; 95% confidence interval 1.48-8.2; p value: 0.001). Drug-unrelated acute psychiatric symptoms were observed in 4 of the 108 AED-untreated patients (3.7%) and in 7 of the 175 AED-treated patients (4%). The results of the present study suggest that an AED alternative to levetiracetam should be chosen to treat epileptic seizures in patients with a brain tumor located in the frontal lobe to minimize the possible onset of TE-PAEs

From domestic to regional: The civil war conundrum and the cases of Syria and Algeria

This paper seeks to answer a simple question: When do regional powers get involved in civil wars? Some civil wars see a significant involvement of regional actors, while others show a remarkable level of isolation. What explains this difference? This research answers this question by looking at two case studies: the Algerian civil war (1991–2002) and the Syrian civil war (2011–up to date). The paper identifies and develops five factors of regional involvement. These are: capabilities, regional dynamics, country’s relevance, regional security issues/containment and domestic–external links. civil wars are today one of the most prominent and deadly forms of conflict, and this paper contributes to understanding the important but understudied issue of regional involvement.N/