On combining Big Data and machine learning to support eco-driving behaviours

A conscious use of the battery is one of the key elements to consider while driving an electric vehicle. Hence, supporting the drivers, with information about it, can be strategic in letting them drive in a better way, with the purpose of optimizing the energy consumption. In the context of electric vehicles, equipped with regenerative brakes, the driver\u2019s braking style can make a significant difference. In this paper, we propose an approach which is based on the combination of big data and machine learning techniques, with the aim of enhancing the driver\u2019s braking style through visual elements (displayed in the vehicle dashboard, as a Human\u2013Machine Interface), actuating eco-driving behaviours. We have designed and developed a system prototype, by exploiting big data coming from an electric vehicle and a machine learning algorithm. Then, we have conducted a set of tests, with simulated and real data, and here we discuss the results we have obtained that can open interesting discussions about the use of big data, together with machine learning, so as to improve drivers\u2019 awareness of eco-behaviours

Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform

Introduction Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values -obtained after computational manipulation- are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability. Material and methods To systematically investigate the effect of pre-processing on meta-analysis, we analysed four different collections of DNA methylation samples (datasets), each composed of two subsets, for which raw data from controls (i.e. healthy subjects) and cases (i.e. patients) are available. We pre-processed the data from each dataset with nine among the most common pipelines found in literature. Moreover, we evaluated the performance of regRCPqn, a modification of the RCP algorithm that aims to improve data consistency. For each combination of pre-processing (9 7 9), we first evaluated the between-sample variability among control subjects and, then, we identified genomic positions that are differentially methylated between cases and controls (differential analysis). Results and conclusion The pre-processing of DNA methylation data affects both the between-sample variability and the loci identified as differentially methylated, and the effects of pre-processing are strongly dataset-dependent. By contrast, application of our renormalization algorithm regRCPqn: (i) reduces variability and (ii) increases agreement between meta-analysed datasets, both critical components of data harmonization

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biological relevant knowledge from the LSTM networks

Altered speech-related cortical network in frontotemporal dementia

Background: In healthy subjects (HS), transcranial magnetic stimulation (TMS) demonstrated an increase in motor-evoked potential (MEP) amplitudes during specific linguistic tasks. This finding indicates functional connections between speech-related cortical areas and the dominant primary motor cortex (M1). Objective: To investigate M1 function with TMS and the speech-related cortical network with neuroimaging measures in frontotemporal dementia (FTD), including the non-fluent variant of primary progressive aphasia (nfv-PPA) and the behavioral variant of FTD (bv-FTD). Methods: M1 excitability changes during specific linguistc tasks were examined using TMS in 24 patients (15 with nfv-PPA and 9 with bv-FTD) and in 18 age-matched HS. In the same patients neuroimaging was used to assess changes in specific white matter (WM) bundles and grey matter (GM) regions involved in language processing, with diffusion tensor imaging (DTI) and voxel-based morphometry (VBM). Results: During the linguistic task, M1 excitability increased in HS, whereas in FTD patients it did not. M1 excitability changes were comparable in nfv-PPA and bv-FTD. DTI revealed decreased fractional anisotropy in the superior and inferior longitudinal and uncinate fasciculi. Moreover, VBM disclosed GM volume loss in the left frontal operculum though not in the parietal operculum or precentral gyrus. Furthermore, WM and GM changes were comparable in nfv-PPA and bv-FTD. There was no correlation between neurophysiological and neuroimaging changes in FTD. Atrophy in the left frontal operculum correlated with linguistic dysfunction, assessed by semantic and phonemic fluency tests. Conclusion: We provide converging neurophysiological and neuroimaging evidence of abnormal speech-related cortical network activation in FTD

Algorithm engineering for optimal alignment of protein structure distance matrices

Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular Dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model for computing optimal structural alignments based on dense inter-residue distance matrices. We therefore reformulate the problem as a special graph problem and give a tight integer linear programming model. We then present algorithm engineering techniques to handle the huge integer linear programs of real-life distance matrix alignment problems. Applying these techniques, we can compute provably optimal Dali alignments for the very first time

Search for dark Higgsstrahlung in e+ e- -> mu+ mu- and missing energy events with the KLOE experiment

We searched for evidence of a Higgsstrahlung process in a secluded sector, leading to a final state with a dark photon U and a dark Higgs boson h', with the KLOE detector at DAFNE. We investigated the case of h' lighter than U, with U decaying into a muon pair and h' producing a missing energy signature. We found no evidence of the process and set upper limits to its parameters in the range 2m_mu<m_U<1000 MeV, m_h'<m_U.Comment: 16 pages, 7 figures, submitted to Physics Letters

Free-amino acid metabolic profiling of visceral adipose tissue from obese subjects

Interest in adipose tissue pathophysiology and biochemistry have expanded considerably in the past two decades due to the ever increasing and alarming rates of global obesity and its critical outcome defined as metabolic syndrome (MS). This obesity-linked systemic dysfunction generates high risk factors of developing perilous diseases like type 2 diabetes, cardiovascular disease or cancer. Amino acids could play a crucial role in the pathophysiology of the MS onset. Focus of this study was to fully characterize amino acids metabolome modulations in visceral adipose tissues (VAT) from three adult cohorts: (i) obese patients (BMI 43-48) with metabolic syndrome (PO), (ii) obese subjects metabolically well (O), and (iii) non obese individuals (H). 128 metabolites identified as 20 protein amino acids, 85 related compounds and 13 dipeptides were measured by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) and gas chromatography-/mass spectrometry GC/MS, in visceral fat samples from a total of 53 patients. Our analysis indicates a probable enhanced BCAA (leucine, isoleucine, valine) degradation in both VAT from O and PO subjects, while levels of their oxidation products are increased. Also PO and O VAT samples were characterized by: elevated levels of kynurenine, a catabolic product of tryptophan and precursor of diabetogenic substances, a significant increase of cysteine sulfinic acid levels, a decrease of 1-methylhistidine, and an up regulating trend of 3-methylhistidine levels. We hope this profiling can aid in novel clinical strategies development against the progression from obesity to metabolic syndrome

Free-amino acid metabolic profiling of visceral adipose tissue from obese subjects

Interest in adipose tissue pathophysiology and biochemistry have expanded considerably in the past two decades due to the ever increasing and alarming rates of global obesity and its critical outcome defined as metabolic syndrome (MS). This obesity-linked systemic dysfunction generates high risk factors of developing perilous diseases like type 2 diabetes, cardiovascular disease or cancer. Amino acids could play a crucial role in the pathophysiology of the MS onset. Focus of this study was to fully characterize amino acids metabolome modulations in visceral adipose tissues (VAT) from three adult cohorts: (i) obese patients (BMI 43-48) with metabolic syndrome (PO), (ii) obese subjects metabolically well (O), and (iii) non obese individuals (H). 128 metabolites identified as 20 protein amino acids, 85 related compounds and 13 dipeptides were measured by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) and gas chromatography-/mass spectrometry GC/MS, in visceral fat samples from a total of 53 patients. Our analysis indicates a probable enhanced BCAA (leucine, isoleucine, valine) degradation in both VAT from O and PO subjects, while levels of their oxidation products are increased. Also PO and O VAT samples were characterized by: elevated levels of kynurenine, a catabolic product of tryptophan and precursor of diabetogenic substances, a significant increase of cysteine sulfinic acid levels, a decrease of 1-methylhistidine, and an up regulating trend of 3-methylhistidine levels. We hope this profiling can aid in novel clinical strategies development against the progression from obesity to metabolic syndrome

Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

This work was supported by National Institute of Biomedical Imaging and Bioengineering Grant No. U54EB020403 (to the ENIGMA consortium)