A Novel Strategy to Enhance Mesenchymal Stem Cell Migration Capacity and Promote Tissue Repair in an Injury Specific Fashion

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The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Cerebellar Volume and Disease Staging in Parkinson's Disease: An ENIGMA-PD Study.

peer reviewed[en] BACKGROUND: Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated. OBJECTIVE: To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group. METHODS: Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated. RESULTS: Overall, people with PD had a regionally smaller posterior lobe (dmax  = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (dmax  = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (dmax  = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17). CONCLUSIONS: We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions

Criteri di valutazione dell'affidabilità dei materiali murari

La quota di vulnerabilità sismica di un edificio in muratura dovuta al fenomeno di delaminazione cui sono soggetti i materiaii componenti ad opera dell'ambiente, dipende in gran parte dalla diminuzione di sezione delle strutture murarie conseguente alla perdita di materiale delaminato. A misurare il livello di danno del mrrteriale nel tempo si introduce un indice di delaminazione i.d. direttamente dipendente dalla quantità di materiale perso. La valutazione dell'affidabilità del materiale viene effetuata sulla base di dati di laboratio; in particolare si fa riferimento ai risultati di una prova di cristallizzazione salina recentemente messa a punto da G. Baronio e L. Binda per i materiali murari. Il processo di alterazione del materiale viene definito nell'ambito di modelii probabilistici non stazionari e l'analisi della sua affdabilità viene effetuata nei confronti di due situazioni di danno ritenute critiche: l'inizio danno e il danno totale

Deep learning for the prediction of treatment response in depression

Background: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity in the medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigate treatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and to integrate them with clinical, molecular or -omics biomarkers. Methods: In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographic search on PUBMED, Google Scholar and Web of Science using the terms “psychiatry”, “mood disorder”, “depression”, “treatment”, “deep learning”, “neural networks”. Only studies considering patients’ datasets are considered. Results: Eight studies met the inclusion criteria. Accuracies in prediction of response to therapy were considerably high in all studies, but results may be not easy to interpret. Limitations: The major limitation for the current studies is the small sample size, which constitutes an issue for machine learning methods. Conclusions: Deep learning shows promising results in terms of prediction of treatment response, often outperforming regression methods and reaching accuracies of around 80%. This could be of great help towards personalized medicine. However, more efforts are needed in terms of increasing datasets size and improved interpretability of result

Un avviamento consapevole alla lettura e comprensione del mondo attraverso l'approccio regionale

Il contributo evidenzia che un approccio e una formazione regionale ispirati al costruttivismo e alla prospettiva geografica umanistica permettono di promuovere un\u2019idea forte di spazialit\ue0, di senso del luogo e di cittadinanza, realizzando in tal modo un vero insegnamento per competenze nella scuola dell'infanzia e primaria

Gathering of robots in a ring with mobile faults

This paper studies the well-known problem of gathering multiple mobile agents moving in a graph, but unlike previous results, we consider the problem in the presence of an adversarial mobile entity which we call the malicious agent. The malicious entity can occupy any empty node and prevent honest mobile agents from entering this node. This new adversarial model is interesting as it models transient mobile faults that can appear anywhere in a network. Moreover, our model lies between the less powerful delay-fault model, where the adversary can block an agent for only a finite time, and the more powerful but static fault model of black holes that can even destroy the agents. We study the problem for ring networks and we provide a complete characterization of the solvability of gathering, depending on the size n of the ring and the number of agents k. We consider both oriented or unoriented rings with either synchronous or asynchronous agents. We prove that in an unoriented ring network with asynchronous agents the problem is not solvable when k is even, while for synchronous agents the problem is unsolvable when both n is odd and k is even. We then present algorithms that solve gathering for all the remaining cases, thus completely solving the problem. Finally, we provide a proof-of-concept implementation of the synchronous algorithms using programmable Lego Mindstorms EV3 robots. © 2018 Elsevier B.V

Key factors in psychotherapy training: an analysis of trainers', trainees' and psychotherapists' points of view

The literature on clinical training lacks identifications of the factors that are most relevant in training programs; accordingly, the main aim of this work is to fill this research gap by assessing which factors that trainers, trainees and psychotherapists consider most relevant in psychotherapy training programs. A secondary aim is to identify whether these factors differ among trainers, trainees and psychotherapists. An ad hoc questionnaire was created and administered at 24 psychotherapy schools from 14 institutions; the sample included 641 trainees, 172 trainers and 218 psychotherapists of various theoretical orientations. The questionnaire included 63 items and used a 5-point Likert scale. An exploratory factor analysis was completed to identify the latent structure. The reliability of the dimensions was then checked. Finally, an analysis of variance and a multivariate analysis of variance were completed to achieve the study's aims. Four factors emerged from the study's results: trainers' relational characteristics, supervision, transmission of clinical know-how, and theoretical background and technical support. All these factors displayed acceptable reliability and internal consistency. Moreover, their relative rankings varied based on the participants' roles and theoretical backgrounds. This study's results indicate that the new instrument's psychometric qualities are acceptable. It thus could be used to develop a new approach to psychotherapy training, as this study's results regarding trainees' needs underline the differences between trainees' perceptions of those needs, as compared to trainers' and psychotherapists' perceptions

Twin MRI studies on genetic and environmental determinants of brain morphology and function in the early lifespan

Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability

Twin MRI studies on genetic and environmental determinants of brain morphology and function in the early lifespan

Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability