Understanding constraint expressions in large conceptual schemas by automatic filtering

Human understanding of constraint expressions (also called schema rules) in large conceptual schemas is very di cult. This is due to the fact that the elements (entity types, attributes, relationship types) involved in an expression are de ned in di fferent places in the schema, which may be very distant from each other and embedded in an intricate web of irrelevant elements. The problem is insignifi cant when the conceptual schema is small, but very signi cant when it is large. In this paper we describe a novel method that, given a set of constraint expressions and a large conceptual schema, automatically filters the conceptual schema, obtaining a smaller one that contains the elements of interest for the understanding of the expressions. We also show the application of the method to the important case of understanding the specication of event types, whose constraint expressions consists of a set of pre and postconditions. We have evaluated the method by means of its application to a set of large conceptual schemas. The results show that the method is eff ective and e cient. We deal with conceptual schemas in UML/OCL, but the method can be adapted to other languages.Peer ReviewedPreprin

e-Tourism: a tourist recommendation and planning application

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to realize the recommended activities. Having the list of recommended activities organized as an agenda (i.e. an executable plan), is a relevant characteristic that most recommender systems lack.This work has been partially funded by Consolider Ingenio 2010 CSD2007-00022 project, by the Spanish Government MICINN TIN2008-6701-C03-01 project and by the Valencian Government GVPRE/2008/384 project. We thank J. Benton for having provided us with the system Sapa to execute our experiments.Sebastiá Tarín, L.; García García, I.; Onaindia De La Rivaherrera, E.; Gúzman Álvarez, CA. (2009). e-Tourism: a tourist recommendation and planning application. International Journal on Artificial Intelligence Tools. 18(5):717-738. https://doi.org/10.1142/S0218213009000378S71773818

Enteric Neurospheres Are Not Specific to Neural Crest Cultures: Implications for Neural Stem Cell Therapies

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited

Social navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details on various approaches in implementing social navigation support in the information space as we also connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of example applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to various other social information access approaches we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state

PEDIA: prioritization of exome data by image analysis

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

IL-1β Stimulates COX-2 Dependent PGE2 Synthesis and CGRP Release in Rat Trigeminal Ganglia Cells

OBJECTIVE: Pro-inflammatory cytokines like Interleukin-1 beta (IL-1β) have been implicated in the pathophysiology of migraine and inflammatory pain. The trigeminal ganglion and calcitonin gene-related peptide (CGRP) are crucial components in the pathophysiology of primary headaches. 5-HT1B/D receptor agonists, which reduce CGRP release, and cyclooxygenase (COX) inhibitors can abort trigeminally mediated pain. However, the cellular source of COX and the interplay between COX and CGRP within the trigeminal ganglion have not been clearly identified. METHODS AND RESULTS: 1. We used primary cultured rat trigeminal ganglia cells to assess whether IL-1β can induce the expression of COX-2 and which cells express COX-2. Stimulation with IL-1β caused a dose and time dependent induction of COX-2 but not COX-1 mRNA. Immunohistochemistry revealed expression of COX-2 protein in neuronal and glial cells. 2. Functional significance was demonstrated by prostaglandin E2 (PGE(2)) release 4 hours after stimulation with IL-1β, which could be aborted by a selective COX-2 (parecoxib) and a non-selective COX-inhibitor (indomethacin). 3. Induction of CGRP release, indicating functional neuronal activation, was seen 1 hour after PGE(2) and 24 hours after IL-1β stimulation. Immunohistochemistry showed trigeminal neurons as the source of CGRP. IL-1β induced CGRP release was blocked by parecoxib and indomethacin, but the 5-HT1B/D receptor agonist sumatriptan had no effect. CONCLUSION: We identified a COX-2 dependent pathway of cytokine induced CGRP release in trigeminal ganglia neurons that is not affected by 5-HT1B/D receptor activation. Activation of neuronal and glial cells in the trigeminal ganglion by IL-β leads to an elevated expression of COX-2 in these cells. Newly synthesized PGE(2) (by COX-2) in turn activates trigeminal neurons to release CGRP. These findings support a glia-neuron interaction in the trigeminal ganglion and demonstrate a sequential link between COX-2 and CGRP. The results could help to explain the mechanism of action of COX-2 inhibitors in migraine