A Context-Aware Recommendation System with a Crowding Forecaster

Recommendation systems (RSs) are increasing their popularity in recent years. Many big IT companies like Google, Amazon and Netflix, have a RS at the core of their business. In this paper, we propose a modular platform for enhancing a RS for the tourism domain with a crowding forecaster, which is able to produce an estimation about the current and future occupation of different Points of Interest (PoIs) by taking into consideration also contextual aspects. The main advantage of the proposed system is its modularity and the ability to be easily tailored to different application domains. Moreover, the use of standard and pluggable components allows the system to be integrated in different application scenarios

MACE: connecting architectural content repositories to enable new educational experiences inside a collective external memory

In the practice and learning of Architecture and Civil Engineering, it is fundamental to access a big amount of learning material. A considerable part of the knowledge which once was written in books is now being moved to digital media. Today, most of the contents are produced and presented in digital format only. Spread around the world, digital content repositories containing a big amount of notions exist, but are oftentimes unknown and disjointed. As a consequence, they are not very efficient resources for learning at the moment. The European research project MACE (Metadata for Architectural Contents in Europe) aims at connecting digital architectural repositories by harvesting their metadata and enriching it through the integration of content and domain, context, competence and process, and usage and social metadata. The network created will allow for federated access and search over all connected repositories, allowing a new way of exploring notions and knowledge in the architectural domain, using the web as a "collective external memory

Cross-species models of attention-deficit/hyperactivity disorder and autism spectrum disorder : lessons from CNTNAP2, ADGRL3, and PARK2

Animal and cellular models are essential tools for all areas of biological research including neuroscience. Model systems can also be used to investigate the pathophysiology of psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In this review, we provide a summary of animal and cellular models for three genes linked to ADHD and ASD in human patients - CNTNAP2, ADGRL3, and PARK2. We also highlight the strengths and weaknesses of each model system. By bringing together behavioral and neurobiological data, we demonstrate how a cross-species approach can provide integrated insights into gene function and the pathogenesis of ADHD and ASD. The knowledge gained from transgenic models will be essential to discover and validate new treatment targets for these disorders

COVID-19 teleassistance and teleconsultation: a matched case-control study (MIRATO project, Lombardy, Italy)

BackgroundDuring the COVID-19 pandemic, telemedicine has been recognised as a powerful modality to shorten the length of hospital stay and to free up beds for the sicker patients. Lombardy, and in particular the areas of Bergamo, Brescia, and Milan, was one of the regions in Europe most hit by the COVID-19 pandemic. The primary aim of the MIRATO project was to compare the incidence of severe events (hospital readmissions and mortality) in the first three months after discharge between COVID-19 patients followed by a Home-Based Teleassistance and Teleconsultation (HBTT group) program and those discharged home without Telemedicine support (non-HBTT group).MethodsThe study was designed as a matched case-control study. The non-HBTT patients were matched with the HBTT patients for sex, age, presence of COVID-19 pneumonia and number of comorbidities. After discharge, the HBTT group underwent a telecare nursing and specialist teleconsultation program at home for three months, including monitoring of vital signs and symptoms. Further, in this group we analysed clinical data, patients' satisfaction with the program, and quality of life.ResultsFour hundred twenty-two patients per group were identified for comparison. The median age in both groups was 70 ± 11 years (62% males). One or more comorbidities were present in 86% of the HBTT patients and 89% in the non-HBTT group (p = ns). The total number of severe events was 17 (14 hospitalizations and 3 deaths) in the HBTT group and 40 (26 hospitalizations and 16 deaths) in the non-HBTT group (p = 0.0007). The risk of hospital readmission or death after hospital discharge was significantly lower in HBTT patients (Log-rank Test p = 0.0002). In the HBTT group, during the 3-month follow-up, 5,355 teleassistance contacts (13 ± 4 per patient) were performed. The number of patients with one or more symptoms declined significantly: from 338 (78%) to 183 (45%) (p < 0.00001). Both the physical (ΔPCS12: 5.9 ± 11.4) component and the mental (ΔMCS12: 4.4 ± 12.7) component of SF-12 improved significantly (p < 0.0001). Patient satisfaction with the program was very high in all participants.ConclusionsCompared to usual care, an HBTT program can reduce severe events (hospital admissions/mortality) at 3-months from discharge and improve symptoms and quality of life.Clinical trial registrationwww.ClinicalTrials.gov, NCT04898179

Novel zebrafish models for autism spectrum disorder

Autism spectrum disorder (ASD) refers to a group of complex neurodevelopment disorders influenced by multiple genes and environmental factors. ASD is characterized by repetitive and stereotyped behaviour together with deficits in social communication and interaction. The lack of knowledge regarding the underlying genetics and neurobiology of this disorder has hindered the discovery of novel drugs. The aim of this project is to investigate the function of two ASD candidate genes, reelin (reln) and ywhaz, to gain insights into the neurological basis of ASD and improve drug treatments. To this end, we have used reln and ywhaz mutant lines to investigate the function of ASD-candidate genes in zebrafish. The first results chapter focuses on reln, the archetypal ASD candidate gene. reln-/- exhibits an impaired behavioural phenotype which resembles specific symptoms of ASD and it provides significant insights into the relationship between Reln activity and the role of the serotoninergic system in ASD. The second chapter describe the generation of a stable zebrafish ywhaz-/- mutant line using the Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated protein-9 nuclease (CRISPR/Cas9) technique. The third chapter focuses on the characterisation of ywhaz-/-, highlighting the importance of functional ywhaz signalling in the adult brain, and shedding light on the role of both the serotoninergic and dopaminergic system in ASD. ywhaz-/- reveal an impaired behaviour which resembles some of the defects occurring in ASD and it can be rescued by fluoxetine and quinpirole treatment. In summary, both the mutant lines analysed in this project can be considered suitable models to analyse some defects which occur in ASD, in particularly cerebellar defects. This study supports the idea that damage of certain cerebellar areas can result in the core symptoms of ASD

E-learning in didactic workshops. The virtual atelier system ‘T-Labs’: storage, conceptual metatagging and sharing of the architectural design knowledge

In the Faculty of Architecture of the University IUAV of Venice, the e-learning experimentation in architectural design teaching started in 1999. Since then, our research group developed a virtual atelier system based on prototypes called “TDraw” (Spigai et al. 2003) and “WITarch” (Spigai et al. 2004): informatics tools for the on-line revision and storage of students’ design tasks. The didactic experimentation provided important information about the mechanisms of learning process and knowledge transmission in the didactic atelier: this kind of teaching methodology produce a large amount of notions, a rich information flux that can be factored in single learning objects and raised to a collective knowledge. At present, our research group is improving and combining the previous instruments in a single tool: the new “T-Labs” system (Spigai et al. 2006); through this system, starting from the interrogatives that arise during the teacher-student relationship, each student can found possible design solutions, which would be exemplified by the students’ design tasks annotated and commented by the teacher and stored in the system. The experience matured in these years acts an important role in the new MACE project; in fact, the analysis and the observation of the atelier activity brought about the definition and the validation of a series of conceptual categories through which is possible to map the architectural design generative process and the whole architectural design subject

Semiotic based facetted classification to support browsing architectural contents in MACE

The research project MACE1 presented in this paper aims at organizing already existing architectural and engineering digital archives stored in European repositories. The new settled interconnections will be driven both by usage and by structured-knowledge; moreover, contents and interconnections will be accessible in an intuitive and interactive way. The goal is to create an ensemble of findable, accessible and reusable information, hence forming the basis of a meaningful, extensible network of architectural information

ICARE: An Intuitive Context-Aware Recommender with Explanations

The chapter presents a framework, called Intuitive Context-Aware Recommender with Explanations (ICARE), that can provide contextual recommendations, together with their explanations, useful to achieve a specific and predefined goal. We apply ICARE in the healthcare scenario to infer personalized recommendations related to the activities (fitness and rest periods) a specific user should follow or avoid in order to obtain a high value for the sleep quality score, also on the base of their current context and the physical activities performed during the past days. We leverage data mining techniques to extract frequent and context-aware sequential rules that can be used both to provide positive and negative recommendations and to explain them

The Synergies of Context and Data Aging in Recommendations

In this paper, we investigate the synergies of data aging and contextual information in data mining techniques used to infer frequent, up-to-date, and contextual user behaviours that enable making recommendations on actions to take or avoid in order to fulfill a specific positive goal. We conduct experiments in two different domains: wearable devices and smart TVs