Gene therapy in the Netherlands

- Extensive research is ongoing worldwide on the clinical utility of gene therapy, particularly for the treatment of cancer and genetic disorders. - Two gene therapy products have already been approved recently in China. - Clinical experience with gene therapy has also been accumulating in the Netherlands: over 200 Dutch patients have now been treated in clinical trials. - Published results indicate that gene therapy is generally safe. - Gene therapy appears to be effective for some genetic disorders, such as severe combined immune deficiency and haemophilia B. - The efficacy of gene therapy, particularly in the treatment of cancer, appears to be limited up till now.</p

Gene therapy in the Netherlands

- Extensive research is ongoing worldwide on the clinical utility of gene therapy, particularly for the treatment of cancer and genetic disorders. - Two gene therapy products have already been approved recently in China. - Clinical experience with gene therapy has also been accumulating in the Netherlands: over 200 Dutch patients have now been treated in clinical trials. - Published results indicate that gene therapy is generally safe. - Gene therapy appears to be effective for some genetic disorders, such as severe combined immune deficiency and haemophilia B. - The efficacy of gene therapy, particularly in the treatment of cancer, appears to be limited up till now.</p

Tune in to your emotions: a robust personalized affective music player

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application

Estimating Value Preferences in a Hybrid Participatory System

We propose methods for an AI agent to estimate the value preferences of individuals in a hybrid participatory system, considering a setting where participants make choices and provide textual motivations for those choices. We focus on situations where there is a conflict between participants' choices and motivations, and operationalize the philosophical stance that 'valuing is deliberatively consequential.' That is, if a user's choice is based on a deliberation of value preferences, the value preferences can be observed in the motivation the user provides for the choice. Thus, we prioritize the value preferences estimated from motivations over the value preferences estimated from choices alone. We evaluate the proposed methods on a dataset of a large-scale survey on energy transition. The results show that explicitly addressing inconsistencies between choices and motivations improves the estimation of an individual's value preferences. The proposed methods can be integrated in a hybrid participatory system, where artificial agents ought to estimate humans' value preferences to pursue value alignment. Interactive IntelligenceTechnology, Policy and ManagementEthics & Philosophy of Technolog

Estimating Value Preferences in a Hybrid Participatory System

We propose methods for an AI agent to estimate the value preferences of individuals in a hybrid participatory system, considering a setting where participants make choices and provide textual motivations for those choices. We focus on situations where there is a conflict between participants' choices and motivations, and operationalize the philosophical stance that 'valuing is deliberatively consequential.' That is, if a user's choice is based on a deliberation of value preferences, the value preferences can be observed in the motivation the user provides for the choice. Thus, we prioritize the value preferences estimated from motivations over the value preferences estimated from choices alone. We evaluate the proposed methods on a dataset of a large-scale survey on energy transition. The results show that explicitly addressing inconsistencies between choices and motivations improves the estimation of an individual's value preferences. The proposed methods can be integrated in a hybrid participatory system, where artificial agents ought to estimate humans' value preferences to pursue value alignment. </p