225 research outputs found
sj-pdf-2-jcb-10.1177_0271678X231159958 - Supplemental material for Harmonization of sensorimotor deficit assessment in a registered multicentre pre-clinical randomized controlled trial using two models of ischemic stroke
No full textSupplemental material, sj-pdf-2-jcb-10.1177_0271678X231159958 for Harmonization of sensorimotor deficit assessment in a registered multicentre pre-clinical randomized controlled trial using two models of ischemic stroke by Alessia Valente, Jacopo Mariani, Serena Seminara, Mauro Tettamanti, Giuseppe Pignataro, Carlo Perego, Luigi Sironi, Felicita Pedata, Diana Amantea, Marco Bacigaluppi, Antonio Vinciguerra, Susanna Diamanti, Martina Viganò, Francesco Santangelo, Chiara Paola Zoia, Virginia Rodriguez-Menendez, Laura Castiglioni, Joanna Rzemieniec, Ilaria Dettori, Irene Bulli, Elisabetta Coppi, Chiara Di Santo, Ornella Cuomo, Giorgia Serena Gullotta, Erica Butti, Giacinto Bagetta, Gianvito Martino, Maria-Grazia De Simoni, Carlo Ferrarese, Stefano Fumagalli, Simone Beretta and for the TRICS study group in Journal of Cerebral Blood Flow & Metabolism</p
sj-pdf-1-jcb-10.1177_0271678X231159958 - Supplemental material for Harmonization of sensorimotor deficit assessment in a registered multicentre pre-clinical randomized controlled trial using two models of ischemic stroke
No full textSupplemental material, sj-pdf-1-jcb-10.1177_0271678X231159958 for Harmonization of sensorimotor deficit assessment in a registered multicentre pre-clinical randomized controlled trial using two models of ischemic stroke by Alessia Valente, Jacopo Mariani, Serena Seminara, Mauro Tettamanti, Giuseppe Pignataro, Carlo Perego, Luigi Sironi, Felicita Pedata, Diana Amantea, Marco Bacigaluppi, Antonio Vinciguerra, Susanna Diamanti, Martina Viganò, Francesco Santangelo, Chiara Paola Zoia, Virginia Rodriguez-Menendez, Laura Castiglioni, Joanna Rzemieniec, Ilaria Dettori, Irene Bulli, Elisabetta Coppi, Chiara Di Santo, Ornella Cuomo, Giorgia Serena Gullotta, Erica Butti, Giacinto Bagetta, Gianvito Martino, Maria-Grazia De Simoni, Carlo Ferrarese, Stefano Fumagalli, Simone Beretta and for the TRICS study group in Journal of Cerebral Blood Flow & Metabolism</p
Analysing Trajectories of Mobile Users: From Data Warehouses to Recommender Systems
No full textThis chapter discusses a general framework for the analysis of trajectories of moving objects, designed around a Trajectory Data Warehouse (TDW). We argue that data warehouse technologies, combined with geographic visual analytics tools, can play an important role in granting very fast, accurate and understandable analysis of mobility data. We describe how in the last decade the TDW models have changed in order to provide the user with a more suitable model of the reality of interest and we also cope with the challenge of semantic trajectories. As a use case we illustrate how the framework can be instantiated for realizing a recommender system for tourists
Leveraging feature selection to detect potential tax fraudsters
No full textTax evasion is any act that knowingly or unknowingly, legally or unlawfully, leads to non-payment or underpayment of tax due. Enforcing the correct payment of taxes by taxpayers is fundamental in maintaining investments that are necessary and benefits a society as a whole. Indeed, without taxes it is not possible to guarantee basic services such as health-care, education, sanitation, transportation, infrastructure, among other services essential to the population. This issue is especially relevant in developing countries such as Brazil. In this work we consider a real-world case study involving the Treasury Office of the State of Ceará (SEFAZ-CE, Brazil), the agency in charge of supervising more than 300,000 active taxpayers companies. SEFAZ-CE maintains a very large database containing vast amounts of information concerning such companies. Its enforcement team struggles to perform thorough inspections on taxpayers accounts as the underlying traditional human-based inspection processes involve the evaluation of countless fraud indicators (i.e., binary features), thus requiring burdensome amounts of time and being potentially prone to human errors. On the other hand, the vast amount of taxpayer information collected by fiscal agencies opens up the possibility of devising novel techniques able to tackle fiscal evasion much more effectively than traditional approaches. In this work we address the problem of using feature selection to select the most relevant binary features to improve the classification of potential tax fraudsters. Finding out possible fraudsters from taxpayer data with binary features presents several challenges. First, taxpayer data typically have features with low linear correlation between themselves. Also, tax frauds may originate from intricate illicit tactics, which in turn requires to uncover non-linear relationships between multiple features. Finally, few features may be correlated with the targeted class. In this work we propose ALICIA, a new feature selection method based on association rules and propositional logic with a carefully crafted graph centrality measure that attempts to tackle the above challenges while, at the same time, being agnostic to specific classification techniques. ALICIA is structured in three phases: first, it generates a set of relevant association rules from a set of fraud indicators (features). Subsequently, from such association rules ALICIA builds a graph, which structure is then used to determine the most relevant features. To achieve this ALICIA applies a novel centrality measure we call the Feature Topological Importance. We perform an extensive experimental evaluation to assess the validity of our proposal on four different real-world datasets, where we compare our solution with eight other feature selection methods. The results show that ALICIA achieves F-measure scores up to 76.88%, and consistently outperforms its competitors
Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis
No full textWe propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees
Perception of social phenomena through the multidimensional analysis of online social networks
No full textWe propose an analytical framework aimed at investigating different views of the discussions regarding polarized topics which occur in Online Social Networks (OSNs). The framework supports the analysis along multiple dimensions, i.e., time, space and sentiment of the opposite views about a controversial topic emerging in an OSN.To assess its usefulness in mining insights about social phenomena, we apply it to two different Twitter case studies: the discussions about the refugee crisis and the United Kingdom European Union membership referendum. These complex and contended topics are very important issues for EU citizens and stimulated a multitude of Twitter users to take side and actively participate in the discussions. Our framework allows to monitor in a scalable way the raw stream of relevant tweets and to automatically enrich them with location information (user and mentioned locations), and sentiment polarity (positive vs. negative). The analyses we conducted show how the framework captures the differences in positive and negative user sentiment over time and space. The resulting knowledge can support the understanding of complex dynamics by identifying variations in the perception of specific events and locations
Speed prediction in large and dynamic traffic sensor networks
No full textSmart cities are nowadays equipped with pervasive networks of sensors that monitor traffic in real-time and record huge volumes of traffic data. These datasets constitute a rich source of information that can be used to extract knowledge useful for municipalities and citizens. In this paper we are interested in exploiting such data to estimate future speed in traffic sensor networks, as accurate predictions have the potential to enhance decision making capabilities of traffic management systems. Building effective speed prediction models in large cities poses important challenges that stem from the complexity of traffic patterns, the number of traffic sensors typically deployed, and the evolving nature of sensor networks. Indeed, sensors are frequently added to monitor new road segments or replaced/removed due to different reasons (e.g., maintenance). Exploiting a large number of sensors for effective speed prediction thus requires smart solutions to collect vast volumes of data and train effective prediction models. Furthermore, the dynamic nature of real-world sensor networks calls for solutions that are resilient not only to changes in traffic behavior, but also to changes in the network structure, where the cold start problem represents an important challenge. We study three different approaches in the context of large and dynamic sensor networks: local, global, and cluster-based. The local approach builds a specific prediction model for each sensor of the network. Conversely, the global approach builds a single prediction model for the whole sensor network. Finally, the cluster-based approach groups sensors into homogeneous clusters and generates a model for each cluster. We provide a large dataset, generated from 1.3 billion records collected by up to 272 sensors deployed in Fortaleza, Brazil, and use it to experimentally assess the effectiveness and resilience of prediction models built according to the three aforementioned approaches. The results show that the global and cluster-based approaches provide very accurate prediction models that prove to be robust to changes in traffic behavior and in the structure of sensor networks
Long-term power spectral analysis in angioedema: proposal of a translational approach
No full textC1 inhibitor hereditary angioedema (C1-INH-HAE) is a rare disease characterized by self-limiting edema associated with localized vasodilation due to increased levels of bradykinin. Although an involvement of the autonomic nervous system has been demonstrated by means of the analysis of the heart period (HP) variability during remission, no evaluation of the cardiac autonomic profile in the prodromal phase and during an attack has been performed. We positioned a multiday electrocardiogram recorder in a C1-INH-HAE 46-years old female patient until an attack occurrence. The HP variability indices were computed in the day of the attack and in the day before, in the 4 hours preceding (PRE) and following the attack. We found that the index related to the vagal modulation directed to the sinus node increased in the PRE period during the day of the attack. This is explainable by the expected vasodilation linked to the release of bradykinin that is the main mediator of the attack. We conclude that the HP parameters linked to the vagal cardiac modulation could be exploited and tested in future studies as early markers of the angioedema attack. The possibility to predict the attack could be of interest in the development of drugs to prevent the attack itself
Clinical Use of Paraprobiotics for Pregnant Women with Periodontitis: Randomized Clinical Trial
No full textPeriodontal disease is very common in pregnant women. Paraprobiotics are a subset of probiotics. They can be defined as inactivated microbial cells providing health benefits to the host and are considered particularly safe. The aim of this study was to compare the periodontal health of pregnant women and puerperae after 6 months of home use of paraprobiotics. A total of 30 pregnant women were enrolled and divided into two groups: the test group, who had to use a paraprobiotic-based toothpaste (Biorepair Peribioma Pro, Coswell S.p.A., Funo di Argelato, BO, Italy) and mousse (Mousse Mouthwash Biorepair Peribioma, Coswell S.p.A.) twice a day, and the control group, who had to use only the paraprobiotic-based toothpaste. The time frames of the study were: 1 month (T1), 3 months (T2) and 6 months (T3), and data were collected during pregnancy and in the period immediately following delivery. The following indices were evaluated at T0, T1, T2 and T3: clinical attachment loss (CAL), probing pocket depth (PPD), bleeding on probing (BOP), plaque control record (PCR), modified marginal gingival index (mMGI), papillary marginal gingival index (PMGI) and recessions (R). All data were subjected to statistical analysis. PCR decreased significantly from T0 to T1 in the control group and from T0 to T2 and from T0 to T3 in the test group. BOP tended to decrease in both groups, but a significant reduction was observed only in the test group. CAL, PPD, PMGI and mMGI tended to decrease gradually in both groups without significant differences between or within groups. The combination of the paraprobiotic-based toothpaste and the paraprobiotic-based mousse significantly reduced BoP and plaque control over time, although there were no significant differences with the use of the paraprobiotic-based toothpaste alone. In addition, the combination of the two products promoted a trend towards the better stabilization of recessions
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