Emotions and Gambling: Towards a Computational Model of Gambling Experience

Gambling has been on the rise over the past years and understanding different patterns of the human behavior while gambling involves the identification of the emotions experienced while gambling, as well as how these change during a gambling activity. This work attempts to address these components towards the creation of a computational model of gambling experience. Specifically, we created a gambling game (roulette) and evaluated the interaction of participants with the game by assessing their emotional responses using the video modality. This work provides the basis for developing a multimodal interface that can help capturing the gambling experience. Within our research we attempt to answer the following research questions: (a) which are the emotions experienced by someone gambling and (b) how do the emotions detected change before and after an event

A conceptual architecture for empowering responsible online gambling with predictive, real- time, persuasive and interactive intervention

Online gambling, unlike other mediums of addiction and problematic behaviour, such as tobacco and alcohol, offers unprecedented opportunities for monitoring and understanding an addict's behaviour in real-time and adapting persuasive messages and interactions that would fit their usage and personal context. Online gambling sites usually provide Application Programming Interfaces (APIs) mainly to enable third party applications to enhance the gambling experience. In this work, we propose that gamblers' online data, such as navigation path and available offers, can be used to enable a more intelligent and proactive responsible gambling care in a real-time persuasive style. To this end, we propose a conceptual architecture of persuasive responsible online gambling technology. The novelty in our approach is indeed reliant on the real time and interactivity aspects as the intervention and the persuasion can happen in the same time as the gamblers’ behaviour is taking place

Empowering Responsible Online Gambling by Real-time Persuasive Information Systems

Online gambling, unlike other mediums of problem- atic and addictive behaviours, such as tobacco and alcohol, offers unprecedented opportunities for building information systems that are able to monitor and understand a user’s behaviour in real-time and adapt persuasive messages and interactions that would fit their personal profile and usage context. Online gambling industry usually provides Application Programming Interfaces (APIs) meant mainly to enable third-party applications to network with their gambling services and enhance a user’s gambling experience. In this industrial practice and experience paper, we advocate that such API’s can also be used to retrieve gamblers’ online data, such as browsing and betting history, promotions and available offers and use it to build more intel- ligent and proactive responsible gambling information systems. We report on our industrial experience in this field and make the argument that data available for persuasive marketing and usability should, under specific usage conditions, also be made available for responsible gambling information systems. This principle would provide equal opportunities for both directions. We discuss the psychological foundations of our proposed solution and the risks and challenges typically found when building such a software-assisted intervention, persuasion and emotion regulation technology. We also shed light on its potential implications from the perspectives of social corporate responsibility and data protection. We finally propose a conceptual architecture to demonstrate our vision and explain how it can be implemented. In the wider context, the paper is meant to provide insights on building behavioural awareness and regulation information systems in relation to problematic digital media usage

The LPL/ADAM29 expression ratio is a novel prognosis indicator in chronic lymphocytic leukemia

Although the zeta-associated protein of 70 kDa (ZAP-70) is overexpressed in patients with chronic lymphocytic leukemia (CLL) displaying unmutated IGVH genes and poor prognosis, a previous microarray study from our group identified overexpression of LPL and ADAM29 genes among unmutated and mutated CLL, respectively. To assess the prognostic value of these genes, we quantified their expression by real-time quantitative polymerase chain reaction (PCR) in a cohort of 127 patients with CLL and correlated this with clinical outcome, IGVH mutational status, and ZAP-70 protein expression. IGVH mutational status, ZAP-70, and the LPL and ADAM29 mRNA ratios (L/A ratio) were predictive of event-free survival for the whole cohort and for patients with stage A disease. in patients in stage B and C, the L/A ratio was an independent prognostic factor, whereas ZAP-70 did not predict survival. Simultaneous usage of the L/A ratio and ZAP-70 expression allowed an almost perfect (99%) assessment of the IGVH status in the 80% of patients with concordant results (L/A(+), ZAP-70(+) or L/A(-), ZAP-70(-)). LPL and ADAM29 gene expression could also be determined by a simple competitive multiplex reverse transcription PCR assay. Overall, quantification of LPL and ADAM29 gene expression is a strong prognostic indicator in CLL, providing better prognostic assessment than ZAP-70 in advanced stages of the disease.Hop La Pitie Salpetriere, Serv Hematol Biol, F-75013 Paris, FranceInst Pasteur, Unite Immunohematol & Immunopathol, F-75724 Paris, FranceUniversidade Federal de São Paulo, Disciplina Hematol & Hemoterapia, São Paulo, BrazilInst Pasteur, Dept Ecosyst & Epidemiol Malad Infect, Paris, FranceHop La Pitie Salpetriere, Serv Immunol, Paris, FranceInst Pasteur, Ctr Rech Vaccinale & Biomed, Paris, FranceUniversidade Federal de São Paulo, Disciplina Hematol & Hemoterapia, São Paulo, BrazilWeb of Scienc

ProsocialLearn: D2.5 evaluation strategy and protocols

This document describes the evaluation strategy for the assessment of game effectiveness, market value impact and ethics procedure to drive detailed planning of technical validation, short and longitudinal studies and market viability tests

Enabling Responsible Online Gambling by Real-time Persuasive Technologies

Online gambling, unlike other offline addiction forms, provides unprecedented opportunities for monitoring users’ behaviour in real-time, along with the ability to adapt persuasive interactions and messages that would match the gamblers usage and personal context. Online gambling industry usually offers Application Programming Interfaces (APIs) that are mainly intended to allow third-party applications to interact with their services and enhance user’s experience. In this paper, we claim that such API’s can also be utilised to retrieve gamblers’ online data, such as browsing and betting history and other available offers, and use it to build more proactive and intelligent responsible gambling systems. We report on our experience in this field and make the argument that the available data for persuasive marketing and usability should, under certain usage conditions, also be made available for responsible online gambling services. We discuss the psychological foundations of our proposed approach and the risks and challenges typically resulted when building such a software-assisted intervention, persuasion and emotion regulation technology. We also explain the potential impact of corporate social responsibility and data protection prospects. Furthermore, we explore the required principles that should be followed by the gambling industry for enabling responsible online gambling. We finally propose a conceptual architecture to show our vision and explain how it can be implemented. In the broader context, the paper is intended to provide insights on building behavioural awareness and regulation information systems related to problematic digital media usage. Keywords: Persuasive technologies, responsible online gambling, gambling data availability, corporate social responsibility

Characteristics and Outcomes of Patients With Cerebral Venous Sinus Thrombosis in SARS-CoV-2 Vaccine–Induced Immune Thrombotic Thrombocytopenia

Importance: Thrombosis with thrombocytopenia syndrome (TTS) has been reported after vaccination with the SARS-CoV-2 vaccines ChAdOx1 nCov-19 (Oxford-AstraZeneca) and Ad26.COV2.S (Janssen/Johnson & Johnson). Objective: To describe the clinical characteristics and outcome of patients with cerebral venous sinus thrombosis (CVST) after SARS-CoV-2 vaccination with and without TTS. Design, setting, and participants: This cohort study used data from an international registry of consecutive patients with CVST within 28 days of SARS-CoV-2 vaccination included between March 29 and June 18, 2021, from 81 hospitals in 19 countries. For reference, data from patients with CVST between 2015 and 2018 were derived from an existing international registry. Clinical characteristics and mortality rate were described for adults with (1) CVST in the setting of SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia, (2) CVST after SARS-CoV-2 vaccination not fulling criteria for TTS, and (3) CVST unrelated to SARS-CoV-2 vaccination. Exposures: Patients were classified as having TTS if they had new-onset thrombocytopenia without recent exposure to heparin, in accordance with the Brighton Collaboration interim criteria. Main outcomes and measures: Clinical characteristics and mortality rate. Results: Of 116 patients with postvaccination CVST, 78 (67.2%) had TTS, of whom 76 had been vaccinated with ChAdOx1 nCov-19; 38 (32.8%) had no indication of TTS. The control group included 207 patients with CVST before the COVID-19 pandemic. A total of 63 of 78 (81%), 30 of 38 (79%), and 145 of 207 (70.0%) patients, respectively, were female, and the mean (SD) age was 45 (14), 55 (20), and 42 (16) years, respectively. Concomitant thromboembolism occurred in 25 of 70 patients (36%) in the TTS group, 2 of 35 (6%) in the no TTS group, and 10 of 206 (4.9%) in the control group, and in-hospital mortality rates were 47% (36 of 76; 95% CI, 37-58), 5% (2 of 37; 95% CI, 1-18), and 3.9% (8 of 207; 95% CI, 2.0-7.4), respectively. The mortality rate was 61% (14 of 23) among patients in the TTS group diagnosed before the condition garnered attention in the scientific community and 42% (22 of 53) among patients diagnosed later. Conclusions and relevance: In this cohort study of patients with CVST, a distinct clinical profile and high mortality rate was observed in patients meeting criteria for TTS after SARS-CoV-2 vaccination.info:eu-repo/semantics/publishedVersio

Accurate molecular classification of cancer using simple rules

<p>Abstract</p> <p>Background</p> <p>One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible.</p> <p>Methods</p> <p>We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV) of training sets and classification of independent test sets.</p> <p>Results</p> <p>We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML]), lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML). Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods.</p> <p>Conclusion</p> <p>In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction.</p