Minimally invasive procedure for removal of infected ventriculoatrial shunts

Background: Ventriculoatrial shunts were one of the most common treatments of hydrocephalus in pediatric and adult patients up to about 40 years ago. Thereafter, due to the widespread recognition of the severe cardiac and renal complications associated with ventriculoatrial shunts, they are almost exclusively implanted when other techniques fail. However, late infection or atrial thrombi of previously implanted shunts require removal of the atrial catheter several decades after implantation. Techniques derived from management of central venous access catheters can avoid cardiothoracic surgery in such instances. Methods: We retrospectively investigated all the patients requiring removal of a VA shunt for complications treated in the last 5 years in our institution. Results: We identified two patients that were implanted 28 and 40 years earlier. Both developed endocarditis with a large atrial thrombus and were successfully treated endovascularly. The successful percutaneous removal was achieved by applying, for the first time in this setting, the endoluminal dilation technique as proposed by Hong. After ventriculoatrial shunt removal and its substitution with an external drainage, both patients where successfully weaned from the need for a shunt and their infection resolved. Conclusion: Patients carrying a ventriculoatrial shunt are now rarely seen and awareness of long-term ventriculoatrial shunt complications is decreasing. However, these complications must be recognized and treated by shunt removal. Endovascular techniques are appropriate even in the presence of overt endocarditis, atrial thrombi, and tight adherence to the endocardial wall. Moreover, weaning from shunt dependence is possible even decades after shunting

Towards Empathetic Social Robots: Investigating the Interplay between Facial Expressions and Brain Activity

The pursuit of creating empathetic social robots that can understand and respond to human emotions is a critical challenge in Robotics and Artificial Intelligence. Social robots, designed to interact with humans in various settings, from healthcare to customer service, require a sophisticated understanding of human emotional states to resonate and effectively assist truly. Our research contributes to this ambitious goal by exploring the relationship between natural facial expressions and brain activity in these human-robot interactions, as captured by electroencephalogram (EEG) signals. This paper presents our initial steps towards this attempt. We want to find which areas in the participant user’s brain are most activated and how these activations correlate with facial expressions. Understanding these correlations is essential for developing social robots that recognize and empathize with various human emotions. Our approach combines neuroscience and computer science, offering a novel perspective in the quest to enhance the emotional intelligence of social robots. We share some preliminary results on a new multimodal dataset that we are developing, providing valuable insights into the potential of our work to improve the personalization and emotional depth of social robot interactions

Trauma coagulopathy and its outcomes

Background and Objectives: Trauma coagulopathy begins at the moment of trauma. This study investigated whether coagulopathy upon arrival in the emergency room (ER) is correlated with increased hemotransfusion requirement, more hemodynamic instability, more severe anatomical damage, a greater need for hospitalization, and hospitalization in the intensive care unit (ICU). We also analyzed whether trauma coagulopathy is correlated with unfavorable indices, such as acidemia, lactate increase, and base excess (BE) increase. Material and Methods: We conducted a prospective, monocentric, observational study of all patients (n = 503) referred to the Department of Emergency and Acceptance, IRCCS Fondazione Policlinico San Matteo, Pavia, for major trauma from 1 January 2018 to 30 January 2019. Results: Of the 503 patients, 204 had trauma coagulopathy (group 1), whereas 299 patients (group 2) did not. Group 1 had a higher hemotransfusion rate than group 2. In group 1, 15% of patients showed hemodynamic instability compared with only 8% of group 2. The shock index (SI) distribution was worse in group 1 than in group 2. Group 1 was more often hypotensive, tachycardic, and with low oxygen saturation, and had a more severe injury severity score than group 2. In addition, 47% of group 1 had three or more body districts involved compared with 23% of group 2. The hospitalization rate was higher in group 1 than in group 2 (76% vs. 58%). The length of hospitalization was >10 days for 45% of group 1 compared with 28% of group 2. The hospitalization rate in the ICU was higher in group 1 than in group 2 (22% vs. 14.8%). The average duration of ICU hospitalization was longer in group 1 than in group 2 (12.5 vs. 9.78 days). Mortality was higher in group 1 than in group 2 (3.92% vs. 0.98%). Group 1 more often had acidemia and high lactates than group 2. Group 1 also more often had BE <−6. Conclusions: Trauma coagulopathy patients, upon arrival in the ER, have greater hemotransfusion (p = 0.016) requirements and need hospitalization (p = 0.032) more frequently than patients without trauma coagulopathy. Trauma coagulopathy seems to be more present in patients with a higher injury severity score (ISS) (p = 0.000) and a greater number of anatomical districts involved (p = 0.000). Head trauma (p = 0.000) and abdominal trauma (p = 0.057) seem related to the development of trauma coagulopathy. Males seem more exposed than females in developing trauma coagulopathy (p = 0.018). Upon arrival in the ER, the presence of tachycardia or alteration of SI and its derivatives can allow early detection of patients with trauma coagulopathy

Assessing the representation of species included within the Canadian Living Planet Index

To effectively combat the biodiversity crisis, we need ambitious targets and reliable indicators to accurately track trends and measure conservation impact. In Canada, the Living Planet Index (LPI) has been adapted to produce a national indicator by both World Wildlife Fund-Canada (Canadian Living Planet Index; C-LPI) and Environment and Climate Change Canada (Canadian Species Index) to provide insight into the status of Canadian wildlife, by evaluating temporal trends in vertebrate population abundance. The indicator includes data for just over 50% of Canadian vertebrate species. To assess whether the current dataset is representative of the distribution of life history characteristics of Canadian wildlife, we analyzed the representation of species-specific biotic variables (i.e., body size, trophic level, lifespan) for vertebrates within the C-LPI compared to native vertebrates lacking LPI data. Generally, there was considerable overlap in the distribution of biotic variables for species in the C-LPI compared to native Canadian vertebrate species lacking LPI data. Nevertheless, some differences among distributions were found, driven in large part by discrepancy in the representation of fishes—where the C-LPI included larger-bodied and longer-lived species. We provide recommendations for targeted data collection and additional analyses to further strengthen the applicability, accuracy, and representativity of biodiversity indicators

Hardness and approximation for the geodetic set problem in some graph classes

In this paper, we study the computational complexity of finding the \emph{geodetic number} of graphs. A set of vertices $S$ of a graph $G$ is a \emph{geodetic set} if any vertex of $G$ lies in some shortest path between some pair of vertices from $S$. The \textsc{Minimum Geodetic Set (MGS)} problem is to find a geodetic set with minimum cardinality. In this paper, we prove that solving the \textsc{MGS} problem is NP-hard on planar graphs with a maximum degree six and line graphs. We also show that unless $P=NP$, there is no polynomial time algorithm to solve the \textsc{MGS} problem with sublogarithmic approximation factor (in terms of the number of vertices) even on graphs with diameter $2$. On the positive side, we give an $O\left(\sqrt[3]{n}\log n\right)$-approximation algorithm for the \textsc{MGS} problem on general graphs of order $n$. We also give a $3$-approximation algorithm for the \textsc{MGS} problem on the family of solid grid graphs which is a subclass of planar graphs

Deep learning approach for predicting university dropout: A case study at roma tre university

Based on current trends in graduation rates, 39% of today young adults on average across OECD countries are expected to complete tertiary-type A (university level) education during their lifetime. In 2017, an average of 10.6% of young people (aged 1824) in the EU-28 were early leavers from education and training. Therefore the level of dropout in the scenery of European education is one of the major issue to be faced in a near future. The main aim of the research is to predict, as early as possible, which student will dropout in the Higher Education (HE) context. The accurate knowledge of this information would allow one to effectively carry out targeted actions in order to limit the incidence of the phenomenon. The recent breakthrough on Neural Networks with the use of Convolutional Neural Networks (CNN) architectures has become disruptive in AI. By stacking together tens or hundreds of convolutional neural layers, a “deep” network structure is obtained, which has been proved very effective in producing high accuracy models. In this research the administrative data of about 6000 students enrolled from 2009 in the Department of Education at Roma Tre University had been used to train a Convolutional Neural Network based. Then, the trained network provides a predictive model that predicts whether the student will dropout. Furthermore, we compared the results obtained using deep learning models to the ones using Bayesian networks. The accuracy of the obtained deep learning models ranged from 67.1% for the first-year students up to 94.3% for the third-year students

University dropout prediction through educational data mining techniques: A systematic review

The dropout rates in the European countries is one of the major issues to be faced in a near future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training according to Eurostat’s statistics. The main aim of this review is to identify studies which uses educational data mining techniques to predict university dropout in traditional courses. In Scopus and Web of Science (WoS) catalogues, we identified 241 studies related to this topic from which we selected 73, focusing on what data mining techniques are used for predicting university dropout. We identified 6 data mining classification techniques, 53 data mining algorithms and 14 data mining tools

Exploiting Micro Facial Expressions for More Inclusive User Interfaces

Current image/video acquisition and analysis techniques allow for not only the identification and classification of objects in a scene but also more sophisticated processing. For example, there are video cameras today able to capture micro facial expressions, namely, facial expressions that occur in a fraction of a second. Such micro expressions can provide useful information to define a person's emotional state. In this article, we propose to use these features to collect useful information for designing and implementing increasingly effective interactive technologies. In particular, facial micro expressions could be used to develop interfaces capable of fostering the social and cultural inclusion of users belonging to different realities and categories. The preliminary experimental results obtained by recording the reactions of individuals while observing artworks demonstrate the existence of correlations between the action units (i.e., single components of the muscular movement in which it is possible to break down facial expressions) and the emotional reactions of a sample of users, as well as correlations within some homogeneous groups of testers

A Deep Learning-based Approach to Model Museum Visitors

Although ubiquitous and fast access to the Internet allows us to admire objects and artworks exhibited worldwide from the comfort of our home, visiting a museum or an exhibition remains an essential experience today. Current technologies can help make that experience even more satisfying. For instance, they can assist the user during the visit, personalizing her experience by suggesting the artworks of her higher interest and providing her with related textual and multimedia content. To this aim, it is necessary to automatically acquire information relating to the active user. In this paper, we show how a deep neural network-based approach can allow us to obtain accurate information for understanding the behavior of the visitor alone or in a group. This information can also be used to identify users similar to the active one to suggest not only personalized itineraries but also possible visiting companions for promoting the museum as a vehicle for social and cultural inclusion

The META4RS Proposal: Museum Emotion and Tracking Analysis For Recommender Systems

In this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems