Search

Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure-Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it.

Tumours are nowadays the second world-leading cause of death after cardiovascular diseases. During the last decades of cancer research, lifestyle and random/genetic factors have been blamed for cancer mortality, with obesity, seden-tary habits, alcoholism, and smoking contributing as supposed major causes. However, there is an emerging consensus that environmental pollution should be considered one of the main triggers. Unfortunately, all this preliminary scien-tific evidence has not always been followed by governments and institutions, which still fail to pursue research on can-cer's environmental connections. In this unprecedented national-scale detailed study, we analyzed the links between cancer mortality, socio-economic factors, and sources of environmental pollution in Italy, both at wider regional and finer provincial scales, with an artificial intelligence approach. Overall, we found that cancer mortality does not have a random or spatial distribution and exceeds the national average mainly when environmental pollution is also higher, despite healthier lifestyle habits. Our machine learning analysis of 35 environmental sources of pollution showed that air quality ranks first for importance concerning the average cancer mortality rate, followed by sites to be reclaimed, urban areas, and motor vehicle density. Moreover, other environmental sources of pollution proved to be relevant for the mortality of some specific cancer types. Given these alarming results, we call for a rearrangement of the priority of cancer research and care that sees the reduction and prevention of environmental contamination as a priority action to put in place in the tough struggle against cancer.

The impact of knowledge transfer induced by universities on economy, society, and culture is widely acknowledged; nevertheless, this aspect is often neglected by university rankings. Here, we considered three of the most popular global university rankings and specific knowledge transfer indicators by U-multirank, a European ranking system launched by the European Commission, in order to answer to the following research question: how do the world top universities, evaluated according to global university rankings, perform from a knowledge transfer point of view? To this aim, the top universities have been compared with the others through the calculation of a Global Performance Indicator in Knowledge Transfer (GPI KT), a hierarchical clustering, and an outlier analysis. The results show that the universities best rated by global rankings do not always perform as well from knowledge transfer point of view. By combining the obtained results, it is possible to state that only 5 universities (Berkeley, Stanford, MIT, Harvard, CALTEC), among the top in the world, exhibit a high-level performance in knowledge transfer activities. For a better understanding of the success factors and best practices in knowledge transfer, a brief description of the 5 cited universities, in terms of organization of technology transfer service, relationship with business, entrepreneurship programs, and, more generally, third mission activities, is provided. A joint reading of the results suggests that the most popular global university rankings probably fail to effectively photograph third mission activities because they can manifest in a variety of forms, due to the intrinsic and intangible nature of third mission variables, which are difficult to quantify with simple and few indicators.

The COVID-19 pandemic has now spread worldwide, becoming a real global health emergency. The main goal of this work is to present a framework for studying the impact of COVID-19 on Italian territory during the first year of the pandemic. Our study was based on different kinds of health features and lifestyle risk factors and exploited the capabilities of machine learning techniques. Furthermore, we verified through our model how these factors influenced the severity of the pandemics. Using publicly available datasets provided by the Italian Civil Protection, Italian Ministry of Health and Italian National Statistical Institute, we cross-validated the regression performance of a Random Forest model over 21 Italian regions. The robustness of the predictions was assessed by comparison with two other state-of-the-art regression tools. Our results showed that the proposed models reached a good agreement with data. We found that the features strongly associated with the severity of COVID-19 in Italy are the people aged over 65 flu vaccinated (24.6%) together with individual lifestyle behaviors. These findings could shed more light on the clinical and physiological aspects of the disease.

Graphical abstract, High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous “omic” data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

We propose a characterization of complex networks, based on the potential of an associated Schrödinger equation. The potential is designed so that the energy spectrum of the Schrödinger equation coincides with the graph spectrum of the normalized Laplacian. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure. The median potential over several random network realizations, which we call ensemble potential, is fitted via a Landau-like function, and its length scale is found to diverge as the critical connection probability is approached from above. The ruggedness of the ensemble potential profile is quantified by using the Higuchi fractal dimension, which displays a maximum at the critical connection probability. This demonstrates that this technique can be successfully employed in the study of random networks, as an alternative indicator of the percolation phase transition. We apply the proposed approach to the investigation of real-world networks describing infrastructures (US power grid). Curiously, although no notion of phase transition can be given for such networks, the fractality of the ensemble potential displays signatures of criticality. We also show that standard techniques (such as the scaling features of the largest connected component) do not detect any signature or remnant of criticality.

Convergent findings indicate that cannabis use and variation in the cannabinoid CB1 receptor coding gene (CNR1) modulate prefrontal function during working memory (WM). Other results also suggest that cannabis modifies the physiological relationship between genetically induced expression of CNR1 and prefrontal WM processing. However, it is possible that cannabis exerts its modifying effect on prefrontal physiology by interacting with complex molecular ensembles co-regulated with CB1. Since co-regulated genes are likely co-expressed, we investigated how genetically predicted co-expression of a molecular network including CNR1 interacts with cannabis use in modulating WM processing in humans. Using post-mortem human prefrontal data, we first computed a polygenic score (CNR1-PCI), combining the effects of single nucleotide polymorphisms (SNPs) on co-expression of a cohesive gene set including CNR1, and positively correlated with such co-expression. Then, in an in vivo study, we computed CNR1-PCI in 88 cannabis users and 147 non-users and investigated its interaction with cannabis use on brain activity during WM. Results revealed an interaction between cannabis use and CNR1-PCI in the dorsolateral prefrontal cortex (DLPFC), with a positive relationship between CNR1-PCI and DLPFC activity in cannabis users and a negative relationship in non-users. Furthermore, DLPFC activity in cannabis users was positively correlated with the frequency of cannabis use. Taken together, our results suggest that co-expression of a CNR1-related network predicts WM-related prefrontal activation as a function of cannabis use. Furthermore, they offer novel insights into the biological mechanisms associated with the use of cannabis.

University psychological counseling (UPC) is receiving growing attention as a means to promote mental health and academic success among young adults and prevent irregular attendance and dropout. However, thus far, little effort has been directed towards the implementation of services attuned to students’ expectations and needs. This work intends to contribute to the existing literature on this topic, by exploring the perceptions of UPC among a population of 39,277 students attending one of the largest universities in the South of Italy. Almost half of the total population correctly identified the UPC target population as university students, and about one third correctly expected personal distress to be the main need that UPC should target. However, a large percentage did not have a clear idea about UPC target needs, activities, and population. When two specific student subsamples were analyzed using a person-centered analysis, namely (i) those who expressed their intention to use the counseling service but had not yet done so and (ii) those who had already used it, the first subsample clustered into two groups, characterized by an “emotional” and a “psychopathological” focus, respectively, while the second subsample clustered into three groups with a “clinical”, “socioemotional”, and “learning” focus, respectively. This result shows a somewhat more “superficial” and “common” representation of UPC in the first subsample and a more “articulated” and “flexible” vision in the second subsample. Taken together, these findings suggest that UPC services could adopt “student-centered” strategies to both identify and reach wider audiences and specific student subgroups. Recommended strategies include robust communication campaigns to help students develop a differentiated perception of the available and diverse academic services, and the involvement of active students to remove the barriers of embarrassment and shame often linked to the stigma of using mental health services.

In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines, our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients.

We present a study of the solar-cycle variations of >80 MeV proton flux intensities in the lower edge of the inner radiation belt, based on the measurements of the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) mission. The analyzed data sample covers an ~8 year interval from 2006 July to 2014 September, thus spanning from the decaying phase of the 23rd solar cycle to the maximum of the 24th cycle. We explored the intensity temporal variations as a function of drift shell and proton energy, also providing an explicit investigation of the solar-modulation effects at different equatorial pitch angles. PAMELA observations offer new important constraints for the modeling of low-altitude particle radiation environment at the highest trapping energies., accepted for publication in the Astrophysical Journal Letters

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker. (c) Copyright (c) 2021 Lombardi, Diacono, Amoroso, Monaco, Tavares, Bellotti and Tangaro.

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation.

The increased incidence and the significant health burden associated with Parkinson’s disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.

The Coronavirus disease 2019 (COVID-19) pandemic has officially spread all over the world since the beginning of 2020. Although huge efforts are addressed by scientists to shed light over the several questions raised by the novel SARS-CoV-2 virus, many aspects need to be clarified, yet. In particular, several studies have pointed out significant variations between countries in per-capita mortality. In this work, we investigated the association between COVID-19 mortality with climate variables and air pollution throughout European countries using the satellite remote sensing images provided by the Sentinel-5p mission. We analyzed data collected for two years of observations and extracted the concentrations of several pollutants; we used these measurements to feed a Random Forest regression. We performed a cross-validation analysis to assess the robustness of the model and compared several regression strategies. Our findings reveal a significant statistical association between air pollution (NO2) and COVID-19 mortality and a significant role played by the socio-demographic features, like the number of nurses or the hospital beds and the gross domestic product per capita.

Nowadays, world rankings are promoted and used by international agencies, governments and corporations to evaluate country performances in a specific domain, often providing a guideline for decision makers. Although rankings allow a direct and quantitative comparison of countries, sometimes they provide a rather oversimplified representation, in which relevant aspects related to socio-economic development are either not properly considered or still analyzed in silos. In an increasingly data-driven society, a new generation of cutting-edge technologies is breaking data silos, enabling new use of public indicators to generate value for multiple stakeholders. We propose a complex network framework based on publicly available indicators to extract important insight underlying global rankings, thus adding value and significance to knowledge provided by these rankings. This approach enables the unsupervised identification of communities of countries, establishing a more targeted, fair and meaningful criterion to detect similarities. Hence, the performance of states in global rankings can be assessed based on their development level. We believe that these evaluations can be crucial in the interpretation of global rankings, making comparison between countries more significant and useful for citizens and governments and creating ecosystems for new opportunities for development.

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer’s dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.

A crucial need for policy design is to guide the planning and the allocation of funds in a targeted way, especially in developing countries. This requirement is particularly relevant in the expanding field of E-government. In this paper, we present a preliminary analysis of a public database of Digital Governance projects, based on the combination of Complex Network models and Natural Language Processing techniques. We embed projects into a complex network, in which the strength of a connection binding two projects is determined by the semantic affinity between their titles. We use an unsupervised community detection algorithm to partition the network in sets of similar projects, and interpret the results to unveil hidden information in the database. Project ratings provided by an independent evaluation group are inspected in order to investigate the strategical value of the topics underlying the communities, laying the foundation for novel quantitative methods of policy design.

Objective: To determine the aetiology of forearm pain. In particular to determine the relative contribution of (a) psychological factors, features of somatisation, and health anxiety and behaviour, (b) work related mechanical factors, and (c) work related psychosocial factors in the onset of forearm pain. Design: 2 year prospective population based cohort study, with retrospective assessment of exposures at work. Setting: Altrincham, Greater Manchester. Participants: 1953 individuals aged 18-65 years. Outcome measures: Forearm pain of new onset. Results: At follow up, 105 (8.3%) participants reported forearm pain of new onset lasting at least one day in the past month. Among these, 67% also reported shoulder pain, 65% back pain, and 45% chronic widespread pain. Increased risks of onset were associated with high levels of psychological distress (relative risk 2.4, 95% confidence interval 1.5 to 3.8), reporting at least two other somatic symptoms (1.7, 0.95 to 3.0), and high scores on the illness behaviour subscale of the illness attitude scales. The two work related mechanical exposures associated with the highest risk of forearm pain in the future were repetitive movements of the arm (4.1, 1.7 to 10) or wrists (3.4, 1.3 to 8.7), whereas the strongest work related psychosocial risk was dissatisfaction with support from colleagues or supervisors (4.7, 2.2 to 10). Conclusions: Psychological distress, aspects of illness behaviour, and other somatic symptoms are important predictors of onset of forearm pain in addition to work related psychosocial and mechanical factors. Misleading terms such as “cumulative trauma disorder” or “repetitive strain injury,” implying a single uniform aetiology, should be avoided.

Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. Initiatives to share open access morphological data contributed significantly to the advance in brain structure characterization. Indeed, such initiatives allow large brain morphology multi-site datasets to be shared, which increases the statistical sensitivity of the outcomes. However, using neuroimaging data from multi-site studies requires harmonizing data across the site to avoid bias. In this work we evaluated three different harmonization techniques on the Autism Brain Imaging Data Exchange (ABIDE) dataset for age prediction analysis in two groups of subjects (i.e., controls and autism spectrum disorder). We extracted the morphological features from T1-weighted images of a mixed cohort of 654 subjects acquired from 17 sites to predict the biological age of the subjects using three machine learning regression models. A machine learning framework was developed to quantify the effects of the different harmonization strategies on the final performance of the models and on the set of morphological features that are relevant to the age prediction problem in both the presence and absence of pathology. The results show that, even if two harmonization strategies exhibit similar accuracy of predictive models, a greater mismatch occurs between the sets of most age-related predictive regions for the Autism Spectrum Disorder (ASD) subjects. Thus, we propose to use a stability index to extract meaningful features for a robust clinical validation of the outcomes of multiple harmonization strategies.

Functional connectivity analysis aims at assessing the strength of functional coupling between the signal responses in distinct brain areas. Usually, functional magnetic resonance imaging (fMRI) time series connections are estimated through zero-lag correlation metrics that quantify the statistical similarity between pairs of regions or spectral measures that assess synchronization at a frequency band of interest. Here, we explored the application of a new metric to assess the functional synchronization in phase space between fMRI time series in a resting state. We applied a complete topological analysis to the resulting connectivity matrix to uncover both the macro-scale organization of the brain and detect the most important nodes. The synchronization metric is also compared with Pearson&rsquo, s correlation coefficient and spectral coherence to highlight similarities and differences between the topologies of the three functional networks. We found that the individual topological organization of the resulting synchronization-based connectivity networks shows a finer modular organization than that identified with the other two metrics and a low overlap with the modular partitions of the other two networks suggesting that the derived topological information is not redundant and could be potentially integrated to provide a multi-scale description of functional connectivity.

Convergent findings indicate that cannabis use and variation in the cannabinoid CB1 receptor coding gene (CNR1) modulate prefrontal function during working memory (WM). Other results also suggest that cannabis modifies the physiological relationship between genetically induced expression of CNR1 and prefrontal WM processing. However, it is possible that cannabis exerts its modifying effect on prefrontal physiology by interacting with complex molecular ensembles co-regulated with CB1. Since co-regulated genes are likely co-expressed, we investigated how genetically predicted co-expression of a molecular network including CNR1 interacts with cannabis use in modulating WM processing in humans. Using post-mortem human prefrontal data, we first computed a polygenic score (CNR1-PCI), combining the effects of single nucleotide polymorphisms (SNPs) on co-expression of a cohesive gene set including CNR1, and positively correlated with such co-expression. Then, in an in vivo study, we computed CNR1-PCI in 88 cannabis users and 147 non-users and investigated its interaction with cannabis use on brain activity during WM. Results revealed an interaction between cannabis use and CNR1-PCI in the dorsolateral prefrontal cortex (DLPFC), with a positive relationship between CNR1-PCI and DLPFC activity in cannabis users and a negative relationship in non-users. Furthermore, DLPFC activity in cannabis users was positively correlated with the frequency of cannabis use. Taken together, our results suggest that co-expression of a CNR1-related network predicts WM-related prefrontal activation as a function of cannabis use. Furthermore, they offer novel insights into the biological mechanisms associated with the use of cannabis.

Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer&rsquo, s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.

Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer&rsquo, s disease (AD). Several scores such as Alzheimer&rsquo, s Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7).

Precise time-dependent measurements of the Z = 2 component in the cosmic radiation provide crucial information about the propagation of charged particles through the heliosphere. The PAMELA experiment, with its long flight duration (2006 June 15-2016 January 23) and the low energy threshold (80 MeV/n) is an ideal detector for cosmic-ray solar modulation studies. In this paper, the helium nuclei spectra measured by the PAMELA instrument from 2006 July to 2009 December over a Carrington rotation time basis are presented. A state-of-the-art three-dimensional model for cosmic-ray propagation inside the heliosphere was used to interpret the time-dependent measured fluxes. Proton-to-helium flux ratio time profiles at various rigidities are also presented in order to study any features that could result from the different masses and local interstellar spectra shapes.

The cognitive and computational neurosciences have developed neurorehabilitative tools able to treat suffering subjects from early symptoms, in order to give priority to a home environment. In this way, the curative treatment would not burden the hospital with excessive costs and the patient with psychological disorientation. Recent studies have shown the efficacy of video games on improving cognitive processes impaired by ageing’s physiological effect, neurodegenerative or other diseases, with potential beneficial effects. The PERvasive game for perSOnalized treatment of cognitive and functional deficits associated with chronic and Neurodegenerative diseases (PERSON) project proposed new tools for cognitive rehabilitation, aiming to improve the quality of life for patients with cognitive impairments, especially at early stages, by the use of sophisticated, non-invasive technology. This article is an overview of game solutions for training cognitive abilities and it presents the tools developed within the PERSON project. These tools are serious games based on virtual reality, connected to a brain-computer interface based on electroencephalography (EEG) and to haptic devices. The project was born thanks to a strategic synergy between research and public health, to implement a technology for personalized medicine that relies on the cloud infrastructure of the REte di CAlcolo per SuperB ed altre applicazioni (ReCaS)-Bari data centre. PERSON developed a completely open source and innovative framework to interface the game device with the computational resources in the cloud. We exploited the container technology and the Software as a Service (SaaS) paradigm to implement a genetic algorithm that analyses the neural responses in EEG recordings. The paper focuses on technical aspects of the designed tools. A test was conducted on a few volunteers for the purpose of tuning the overall system. The paper does not contain results of a clinical trial as this is planned in a second testing phase, when the user’s perception of the system will also be tested.

Parkinson's disease (PD) is the most common neurological disorder, after Alzheimer's disease, and is characterized by a long prodromal stage lasting up to 20 years. As age is a prominent factor risk for the disease, next years will see a continuous increment of PD patients, making urgent the development of efficient strategies for early diagnosis and treatments. We propose here a novel approach based on complex networks for accurate early diagnoses using magnetic resonance imaging (MRI) data; our approach also allows us to investigate which are the brain regions mostly affected by the disease. First of all, we define a network model of brain regions and associate to each region proper connectivity measures. Thus, each brain is represented through a feature vector encoding the local relationships brain regions interweave. Then, Random Forests are used for feature selection and learning a compact representation. Finally, we use a Support Vector Machine to combine complex network features with clinical scores typical of PD prodromal phase and provide a diagnostic index. We evaluated the classification performance on the Parkinson's Progression Markers Initiative (PPMI) database, including a mixed cohort of 169 normal controls (NC) and 374 PD patients. Our model compares favorably with existing state-of-the-art MRI approaches. Besides, as a difference with previous approaches, our methodology ranks the brain regions according to disease effects without any a priori assumption.

Background Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challenge has been promoted, on the international Kaggle platform, to assess the performance of different classification methods for prediction of MCI and its conversion to AD. New method This work presents a classification strategy based on Random Forest feature selection and Deep Neural Network classification using a mixed cohort including the four classes of classification problem, that is HC, AD, MCI and cMCI, to train the model. Moreover, we compare this approach with a novel classification strategy based on fuzzy logic learned on a mixed cohort including only HC and AD. Experiments A training set of 240 subjects and a test set including mixed cohort of 500 real and simulated subjects were used. The data included AD patients, MCI subjects converting to AD (cMCI), MCI subjects and healthy controls (HC). This work ranked third for overall accuracy (38.8%) over 19 participating teams. Comparison with existing method(s) The “International challenge for automated prediction of MCI from MRI data” hosted by the Kaggle platform has been promoted to validate different methodologies with a common set of data and evaluation procedures. Conclusion DNNs reach a classification accuracy significantly higher than other machine learning strategies; on the other hand, fuzzy logic is particularly accurate with cMCI, suggesting a combination of these approaches could lead to interesting future perspectives.

Network connectivity has been thoroughly investigated in several domains, including physics, neuroscience, and social sciences. This work tackles the possibility of characterizing the topological properties of real-world networks from a quantum-inspired perspective. Starting from the normalized Laplacian of a network, we use a well-defined procedure, based on the dressing transformations, to derive a 1-dimensional Schrödinger-like equation characterized by the same eigenvalues. We investigate the shape and properties of the potential appearing in this equation in simulated small-world and scale-free network ensembles, using measures of fractality. Besides, we employ the proposed framework to compare real-world networks with the Erdős-Rényi, Watts-Strogatz and Barabási-Albert benchmark models. Reconstructed potentials allow to assess to which extent real-world networks approach these models, providing further insight on their formation mechanisms and connectivity properties.

The first results from measuring the spectra of solar neutrons with energies of ~10-1000 MeV in the solar flares of 2006-2015 observed by the PAMELA international space experiment are presented. The PAMELA neutron detector with 3He counters and a moderator with an area of 0.18 m2 allows us to estimate the flux of solar neutrons during solar flares. Solar neutrons with energies of ~10-1000 MeV likely occurred in 21 out of the 24 analyzed flares of 2006-2015. © 2017, Allerton Press, Inc.

In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer&rsquo, s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer&rsquo, s Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 &times, 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity.

Alzheimer’s disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer’s disease.

The PAMELA magnetic spectrometer was launched onboard the Resurs-DK1 satellite into a near-polar Earth orbit with an altitude of 350-600 km, in order to study fluxes of cosmic ray particles and antiparticles in the wide energy range of ~80 MeV to hundreds of GeV. The results from observations of electron and positron fluxes in 2006-2016 are presented.

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.

In the last decade, a large amount of neuroimaging datasets became publicly available on different archives, so there is an increasing need to manage heterogeneous data, aggregate and process them by means of large-scale computational resources. ReCaS datacenter offers the most important features to manage big datasets, process them, store results in efficient manner and make all the pipeline steps available for reproducible data analysis. Here, we present a scientific computing environment in ReCaS datacenter to deal with common problems of large-scale neuroimaging processing. We show the general architecture of the datacenter and the main steps to perform multidimensional neuroimaging processing.

The JEM-EUSO (Joint Experiment Missions for the Extreme Universe Space Observatory) program aims at developing Ultra-Violet (UV) fluorescence telescopes for efficient detections of Extensive Air Showers (EASs) induced by Ultra-High Energy Cosmic Rays (UHECRs) from satellite orbit. In order to demonstrate key technologies for JEM-EUSO, we constructed the EUSO-Balloon instrument that consists of a ∼1 m 2 refractive telescope with two Fresnel lenses and an array of multi-anode photo-multiplier tubes at the focus. Distinguishing it from the former balloon-borne experiments, EUSO-Balloon has the capabilities of single photon counting with a gate time of 2.3 µs and of imaging with a total of 2304 pixels. As a pathfinder mission, the instrument was launched for an 8 h stratospheric flight on a moonless night in August 2014 over Timmins, Canada. In this work, we analyze the count rates over ∼2.5 h intervals. The measurements are of diffuse light, e.g. of airglow emission, back-scattered from the Earth's atmosphere as well as artificial light sources. Count rates from such diffuse light are a background for EAS detections in future missions and relevant factor for the analysis of EAS events. We also obtain the geographical distribution of the count rates over a ∼780 km 2 area along the balloon trajectory. In developed areas, light sources such as the airport, mines, and factories are clearly identified. This demonstrates the correct location of signals that will be required for the EAS analysis in future missions. Although a precise determination of count rates is relevant for the existing instruments, the absolute intensity of diffuse light is deduced for the limited conditions by assuming spectra models and considering simulations of the instrument response. Based on the study of diffuse light by EUSO-Balloon, we also discuss the implications for coming pathfinders and future space-based UHECR observation missions., 0, SCOPUS: ar.j, info:eu-repo/semantics/published

In this paper, a generalized synchronization-based metric is described to assess functional connectivity in human brain. The metric is a generalized synchronization measure that considers both the amplitude and phase coupling between pairs of fMRI series. This method differs from the correlation measures used in the literature, as it is more sensitive to nonlinear coupling phenomena between time series and it is more robust against the physiological noise.

Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5-10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21-32% additional cases, whose 19-28% more positives and 4-14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution., Graphical abstract Image 1, Highlights • Air quality is linked to respiratory diseases and may facilitate microbial infections. • The prolonged exposure to air pollution can affect SARS-CoV-2 outbreak. • We found that air quality is the best predictor for SARS-CoV-2 mortality in Italy. • A prominent role is played by PM2.5 and emissions from industries and farms. • A worsening of air quality may inflate the death toll of similar future pandemics., Short summary: Artificial intelligence reveals that, in Italy, air pollution mainly due to fine particulate matter produced by industries, farms, and road traffic may severely affect the susceptibility to respiratory infections caused by SARS-CoV-like pathogens.

Fluxes of electrons and positrons with energies above ~100 MeV in the near-Earth space are measured with the PAMELA magnetic spectrometer aboard the Resurs DK-1 satellite launched on June 15, 2006, into a quasipolar orbit with an altitude of 350–600 km and an inclination of 70°. Calculating the trajectories of detected electrons and positrons in the magnetosphere of the Earth allows us to determine their origin and isolate particles produced during interaction between cosmic rays and the residual atmosphere. Spatial distributions of albedo, quasitrapped, and trapped (in the radiation belt) positrons and electrons are presented. The ratio of positron and electron fluxes suggests that the fluxes of trapped particles of the radiation belt and quasitrapped secondary particles have different mechanisms of formation.

Remote sensing images find application in several different domains, such as land cover or land usage observation, environmental monitoring, and urbanization. This latter field has recently witnessed an interesting development with the use of remote sensing for infrastructural monitoring. In this work, we present an analysis of Sentinel-1 images, which were used to monitor the Italian provinces of Bologna and Modena located at the Emilia Region Apennines foothill. The goal of this study was the development of a machine learning-based detection system to monitor the deterioration of public aqueduct infrastructures based on Persistent Scatterer Interferometry (PSI). We evaluated the land deformation over a temporal range of five years; these series feed a k-means clustering algorithm to separate the pixels of the region according to different deformation patterns. Furthermore, we defined the critical areas as those areas where different patterns collided or overlapped. The proposed approach provides an informative tool for the structural health monitoring of underground infrastructures.