Institut für Regionale Innovation und Sozialforschung

Abstract Background Tissue transglutaminase antibodies (tTG-IgA) are the primary tool for celiac disease (CeD) screening, can be incorporated into a formal diagnosis of CeD without the need for biopsy-confirmation, and are recommended for subsequent monitoring of disease status. However, it remains understudied how frequently children are further tested after an initial positive result. Methods Administrative data from Alberta, Canada were utilized to identify children with their first positive tTG-IgA result between 2016 and 2023. Children were stratified according to baseline result: low (1.0-2.9× the upper limit of normal [ULN]), medium (3.0-9.9× the ULN), and high (≥10× the ULN). Mean cumulative functions were estimated to determine the average number of repeated tTG-IgA tests across time. Further differences were evaluated by sex, age, urban-rural status, and socioeconomic status. Results Among 4405 children with incident CeD autoimmunity, almost half (49.1%) had high baseline values at index positivity. These children also had the most overall and positive follow-up tests, on average. For example, within 5 years, those with baseline tTG-IgA ≥10× the ULN had, on average, 3.7 overall (95% CI, 3.6-3.9) and 2.5 positive (95% CI, 2.4-2.6) repeat tests, respectively, compared to 2.1 overall (95% CI, 2.0-2.2) and 0.8 positive (95% CI, 0.7-0.8) among those with 1.0-2.9× the ULN. Significant differences were also present across the urban-rural spectrum and the socioeconomic gradient (eg, highest in metropolitan centres). Conclusion These findings showcase notable variation in follow-up tTG-IgA testing among children with incident CeD autoimmunity in Alberta, Canada.

This Note from the Field is from the Iris Music Project, a non-profit organization dedicated to reimagining senior living communities as places of creative exchanges. Using examples from Iris Piano Trio’s Ensemble-in-Residence model, this article argues that creating community bonds needs to be the central goal of programming in senior living communities in order to mitigate loneliness among older adults. The article also suggests that the core tenets of the Ensemble-in-Residence model could be applied to other artistic disciplines, creating a wider workforce of individuals poised to help solve the persistent problem of loneliness in these settings.

Jason Hoelscher-Obermaier, Edward Stevinson, Valentin Stauber, Ivaylo Zhelev, Viktor Botev, Ronin Wu, Jeremy Minton. Proceedings of the first Workshop on Information Extraction from Scientific Publications. 2022.

ZusammenfassungTeilnehmer und Nicht-Teilnehmer eines Mathematikvorkurses (N = 394) an einer Fachhochschule wurden auf Unterschiede in leistungsassoziierten Persönlichkeitsmerkmalen und schulischen Vorleistungen untersucht. Eine logistische Regressionsanalyse weist auf nur marginale Unterschiede in den erfassten Merkmalen zwischen den beiden Gruppen hin. Während Nicht-Teilnehmer zwar über einen etwas besseren Schulnotendurchschnitt verfügten, berichteten Teilnehmer eine leicht höhere mathematikbezogene Selbstwirksamkeitserwartung und allgemeine Misserfolgsängstlichkeit. Persönlichkeit und Leistungspotential scheinen bei der Teilnahmeentscheidung von nachrangiger Bedeutung zu sein. Als relevant erweisen sich aber lebensweltliche Umstände wie etwa die noch fehlende Unterkunft am Studienort und Zeitmangel. Die Ergebnisse legen nahe, dass Vorkurse weder vorrangig von Studienanfängern mit schlechteren Voraussetzungen besucht werden, noch von solchen mit besonders günstigen. Die intendierte kompensatorische Wirkung können sie damit nur teilweise entfalten.

Guerraoui proposed an indulgent solution for the binary consensus problem. Namely, he showed that an arbitrary behavior of the failure detector never violates safety requirements even if it compromises liveness. Consensus implementations are often used in a repeated manner. Dutta and Guerraoui proposed a zero-degrading solution, i.e., during system runs in which the failure detector behaves perfectly, a node failure during one consensus instance has no impact on the performance of future instances. Our study, which focuses on indulgent zero-degrading binary consensus, aims at the design of an even more robust communication abstraction. We do so through the lenses of self-stabilization - a very strong notion of fault-tolerance. In addition to node and communication failures, self-stabilizing algorithms can recover after the occurrence of arbitrary transient faults; these faults represent any violation of the assumptions according to which the system was designed to operate (as long as the algorithm code stays intact). This work proposes the first, to the best of our knowledge, self-stabilizing algorithm for indulgent zero-degrading binary consensus for time-free message-passing systems prone to detectable process failures. The proposed algorithm has an stabilization time (in terms of asynchronous cycles) from arbitrary transient faults. Since the proposed solution uses an ω failure detector, we also present the first, to the best of our knowledge, self-stabilizing asynchronous ω failure detector, which is a variation on the one by Mostéfaoui, Mourgaya, and Raynal.

We find new homogeneous r matrices containing supercharges, and give examples of backgrounds associated with their corresponding Yang-Baxter deformed superstrings. We obtain these r matrices and backgrounds as special scaling limits of recently found unimodular inhomogeneous r matrices and associated deformations of AdS 2 S 2 T 6 and AdS 5 S 5 . The new r matrices this gives are of jordanian type, but also unimodular by construction, so that their backgrounds satisfy the regular supergravity equations of motion. In general, our deformations are equivalent to doing non-abelian T duality with respect to superalgebras specified by the r matrices. Curiously, one of our backgrounds is also equivalent to one produced by TsT transformations and an S duality transformation, manifesting integrability there.

Background and Purpose: e-ASPECTS derived automated acute ischemic volume (AAIV) measurements on non-contrast computed tomography (NCCT) were correlated with clinical variables and diffusion weighted lesion volumes (DWI) in acute ischemic stroke patients. Methods: Data from three studies were reanalyzed with e-ASPECTS Version 7. The National Institute of Health Stroke Scale (NIHSS) determined stroke severity at baseline and clinical outcome was measured with the modified Rankin Scale (mRS) between 45 to 120 days. Spearman ranked correlation coefficients (R) of AAIV and e-ASPECTS scores with DWI lesion volumes, NIHSS and mRS were calculated. Multivariate regression analysis (odds ratio, OR with 95% confidence intervals, CI) and receiver operating characteristic (ROC) curve analysis (area under the curve, AUC with 95% CI) with regard to good outcome (mRS 0-2) were performed. Results: In total 388 patients were included. Median AAIV was 33.5+/-24.9 ml, (interquartile range) e-ASPECTS was 9 (8-10) and mean time to scan was 155+/-119 minutes. AAIV and e-ASPECTS correlated strongly (R=-0.78, p<0.001), and AAIV, respectively e-ASPECTS correlated with NIHSS (R=0.33, p<0.001; R=-0.35, p<0.001) and mRS (R=0.31, p<0.001; R=-0.31, p<0.001). AAIV correlated strongly with DWI lesion volume (n=37, R=0.69, p<0.001). AAIV (OR 0.98 per ml, 95% CI 0.97-0.99) was an independent predictor of good outcome, and 29.8 ml (AUC: 0.67, 0.61-0.71) was the optimal cut-off for good outcome. Conclusion: AAIV on NCCT is a new feature of the e-ASPECTS software, which significantly correlates with clinical severity and clinical outcome as well as DWI lesion volumes.

Augmented Reality (AR) scenarios aim to provide realistic blending between real world and virtual objects. A key factor for realistic AR is thus a correct illumination simulation. This consists in estimating the characteristics of real light sources and use them to model virtual lighting. In this paper, we briefly introduce a novel method for recovering both 3D position and intensity of multiple light sources using detected cast shadows. Our algorithm has been successfully tested on a set of real scenes where virtual objects have visually coherent shadows.

Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the " word of mouth " effect. In this paper, we propose two evidential influence maximization models for Twitter social network. The proposed approach uses the theory of belief functions to estimate users influence. Furthermore, the proposed influence estimation measure fuses many influence aspects in Twitter, like the importance of the user in the network structure and the popularity of user's tweets (messages). In our experiments, we compare the proposed solutions to existing ones and we show the performance of our models.

In this paper, we propose a new data based model for influence maximization in online social networks. We use the theory of belief functions to overcome the data imperfection problem. Besides, the proposed model searches to detect influencer users that adopt a positive opinion about the product, the idea, etc, to be propagated. Moreover, we present some experiments to show the performance of our model.