Towards an Italian Energy Data Space

The efficient use and the sustainable production of energy are some of the main challenges to face the ever increasing request for energy and the need to limit the damages to the Earth. Smart energy grids, pervasive computing and communication technologies have enabled the stakeholders in the energy industry to collect large amounts of useful and highly granular energy data. They are generated in large volumes and in a variety of different formats, depending on their originating systems and prospected purposes. Moreover, the data type can be structured and unstructured, in open or proprietary formats. This work focuses on harnessing the power of Big Data Management to propose a first model of an Italian Energy Data Lake: the goal is to create a repository of national energy data that respects the FAIRness' key principles [1], aimed at providing a decision support system and the availability of FAIR data for open science. Starting from data of two thematic areas that are part of the nine common European Data Spaces identified in the European Data Strategy[2], namely the Green Deal data space and the Energy data space, an open and extensible platform to enable secure, resilient acquisition and sharing of information will be presented, for enabling the Green Deal priority actions on issues such as climate change, circular economy, pollution, biodiversity, and deforestation

Stability and individual variability of social attachment in imprinting

Filial imprinting has become a model for understanding memory, learning and social behaviour in neonate animals. This mechanism allows the youngs of precocial bird species to learn the characteristics of conspicuous visual stimuli and display affiliative response to them. Although longer exposures to an object produce stronger preferences for it afterwards, this relation is not linear. Sometimes, chicks even prefer to approach novel rather than familiar objects. To date, little is known about how filial preferences develop across time. This study aimed to investigate filial preferences for familiar and novel imprinting objects over time. After hatching, chicks were individually placed in an arena where stimuli were displayed on two opposite screens. Using an automated setup, the duration of exposure and the type of stimuli were manipulated while the time spent at the imprinting stimulus was monitored across 6 days. We showed that prolonged exposure (3 days vs 1 day) to a stimulus produced robust filial imprinting preferences. Interestingly, with a shorter exposure (1 day), animals re-evaluated their filial preferences in functions of their spontaneous preferences and past experiences. Our study suggests that predispositions influence learning when the imprinting memories are not fully consolidated, driving animal preferences toward more predisposed stimuli

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

The structural connectome constrains fast brain dynamics

Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics

Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well-being

The menstrual cycle (MC) is a sex hormone-related phenomenon that repeats itself cyclically during the woman's reproductive life. In this explorative study, we hypothesized that coordinated variations of multiple sex hormones may affect the large-scale organization of the brain functional network and that, in turn, such changes might have psychological correlates, even in the absence of overt clinical signs of anxiety and/or depression. To test our hypothesis, we investigated longitudinally, across the MC, the relationship between the sex hormones and both brain network and psychological changes. We enrolled 24 naturally cycling women and, at the early-follicular, peri-ovulatory, and mid-luteal phases of the MC, we performed: (a) sex hormone dosage, (b) magnetoencephalography recording to study the brain network topology, and (c) psychological questionnaires to quantify anxiety, depression, self-esteem, and well-being. We showed that during the peri-ovulatory phase, in the alpha band, the leaf fraction and the tree hierarchy of the brain network were reduced, while the betweenness centrality (BC) of the right posterior cingulate gyrus (rPCG) was increased. Furthermore, the increase in BC was predicted by estradiol levels. Moreover, during the luteal phase, the variation of estradiol correlated positively with the variations of both the topological change and environmental mastery dimension of the well-being test, which, in turn, was related to the increase in the BC of rPCG. Our results highlight the effects of sex hormones on the large-scale brain network organization as well as on their possible relationship with the psychological state across the MC. Moreover, the fact that physiological changes in the brain topology occur throughout the MC has widespread implications for neuroimaging studies

Clinical connectome fingerprints of cognitive decline

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that “clinical fingerprints” can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks

Mutations in the SPAST gene causing hereditary spastic paraplegia arerelated to global topological alterations in brain functional networks

Aim: Our aim was to describe the rearrangements of the brain activity related to genetic mutations in the SPAST gene. Methods: Ten SPG4 patients and ten controls underwent a 5 min resting state magnetoencephalography recording and neurological examination. A beamformer algorithm reconstructed the activity of 90 brain areas. The phase lag index was used to estimate synchrony between brain areas. The minimum spanning tree was used to estimate topological metrics such as the leaf fraction (a measure of network integration) and the degree divergence (a measure of the resilience of the network against pathological events). The betweenness centrality (a measure to estimate the centrality of the brain areas) was used to estimate the centrality of each brain area. Results: Our results showed topological rearrangements in the beta band. Specifically, the degree divergence was lower in patients as compared to controls and this parameter related to clinical disability. No differences appeared in leaf fraction nor in betweenness centrality. Conclusion: Mutations in the SPAST gene are related to a reorganization of the brain topology

A night of sleep deprivation alters brain connectivity and affects specific executive functions

Sleep is a fundamental physiological process necessary for efficient cognitive functioning especially in relation to memory consolidation and executive functions, such as attentional and switching abilities. The lack of sleep strongly alters the connectivity of some resting-state networks, such as the default mode network and attentional network. In this study, by means of magnetoencephalography (MEG) and specifc cognitive tasks, we investigated how brain topology and cognitive functioning are affected by 24 h of sleep deprivation (SD). Thirty-two young men underwent resting-state MEG recording and evaluated in letter cancellation task (LCT) and task switching (TS) before and after SD. Results showed a worsening in the accuracy and speed of execution in the LCT and a reduction of reaction times in the TS, evidencing thus a worsening of attentional but not of switching abilities. Moreover, we observed that 24 h of SD induced large-scale rearrangements in the functional network. These findings evidence that 24 h of SD is able to alter brain connectivity and selectively affects cognitive domains which are under the control of different brain network

The clinical features of the piriformis syndrome: a systematic review

Piriformis syndrome, sciatica caused by compression of the sciatic nerve by the piriformis muscle, has been described for over 70 years; yet, it remains controversial. The literature consists mainly of case series and narrative reviews. The objectives of the study were: first, to make the best use of existing evidence to estimate the frequencies of clinical features in patients reported to have PS; second, to identify future research questions. A systematic review was conducted of any study type that reported extractable data relevant to diagnosis. The search included all studies up to 1 March 2008 in four databases: AMED, CINAHL, Embase and Medline. Screening, data extraction and analysis were all performed independently by two reviewers. A total of 55 studies were included: 51 individual and 3 aggregated data studies, and 1 combined study. The most common features found were: buttock pain, external tenderness over the greater sciatic notch, aggravation of the pain through sitting and augmentation of the pain with manoeuvres that increase piriformis muscle tension. Future research could start with comparing the frequencies of these features in sciatica patients with and without disc herniation or spinal stenosis

Neural hypernetwork approach for pulmonary embolism diagnosis

Background Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. A pulmonary embolism is a blockage of the main artery of the lung or one of its branches, frequently fatal. Results Our study uses data on 28 diagnostic features of 1427 people considered to be at risk of pulmonary embolism enrolled in the Department of Internal and Subintensive Medicine of an Italian National Hospital “Ospedali Riuniti di Ancona”. Patients arrived in the department after a first screening executed by the emergency room. The resulting neural hypernetwork correctly recognized 94 % of those developing pulmonary embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space). Conclusion In this work we successfully derived a new integrative approach for the analysis of partial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The novelty of this method is that it does not use clinical parameters extracted by imaging analysis