Induction of A9 dopaminergic neurons from neural stem cells improves motor function in an animal model of Parkinson's disease

Neural stem cells (NSCs) are widely endorsed as a cell source for replacement strategies in neurodegenerative disease. However, their usefulness is currently limited by the inability to induce specific neurotransmitter phenotypes in these cells. In order to direct dopaminergic neuronal fate, we overexpressed Pitx3 in NSCs that were then exposed to E11 developing ventral mesencephalon (VM) in explant culture. This resulted in a significant potentiation of dopaminergic differentiation of the cells. When transplanted into the 6-hydroxydopamine lesioned Parkinsonian rats, these cografts of VM and Pitx3 overexpressing NSCs resulted in a significant restitution of motor function. In addition, there were greater numbers of Girk2 positive A9 neurons in the periphery of the transplants that were NSC derived. This demonstrates that given the correct signals, NSCs can be induced to become dopaminergic neurons that can differentiate into the correct nigrastriatal phenotype required for the treatment of Parkinson's diseas

Induction of A9 dopaminergic neurons from neural stem cells improves motor function in an animal model of Parkinsons disease

Neural stem cells (NSCs) are widely endorsed as a cell source for replacement strategies in neurodegenerative disease. However, their usefulness is currently limited by the inability to induce specific neurotransmitter phenotypes in these cells. In order to direct dopaminergic neuronal fate, we overexpressed Pitx3 in NSCs that were then exposed to EII developing ventral mesencephalon (VM) in explant culture. This resulted in a significant potentiation of dopaminergic differentiation of the cells. When transplanted into the 6-hydroxydopamine lesioned Parkinsonian rats, these cografts of VM and Pitx3 overexpressing NSCs resulted in a significant restitution of motor function. In addition, there were greater numbers of Girk2 positive A9 neurons in the periphery of the transplants that were NSC derived. This demonstrates that given the correct signals, NSCs can be induced to become dopaminergic neurons that can differentiate into the correct nigrastriatal phenotype required for the treatment of Parkinsons disease

Watch your Watch: Inferring Personality Traits from Wearable Activity Trackers

International audienceWearable devices, such as wearable activity trackers (WATs), are increasing in popularity. Although they can help to improve one’s quality of life, they also raise serious privacy issues. One particularly sensitive type of information has recently attracted substantial attention, namely personality; as personality provides a means to influence individuals (e.g., voters in the Cambridge Analytica scandal). This paper presents the first empirical study to show a significant correlation between WAT data and personality traits (Big Five). We conduct an experiment with 200+ participants. The ground truth was established by using the NEO-PI-3 questionnaire. The participants’ step count, heart rate, battery level, activities, sleep time, etc. were collected for four months. By following a principled machine-learning approach, the participants’ personality privacy was quantified. Our results demonstrate that WATs data brings valuable information to infer the openness, extraversion, and neuroticism personality traits. We further study the importance of the different features (i.e., data types) and found that step counts play a key role in the inference of extraversion and neuroticism, while openness is more related to heart rate

Watch your Watch: Inferring Personality Traits from Wearable Activity Trackers

Wearable devices, such as wearable activity trackers (WATs), are increasing in popularity. Although they can help to improve one’s quality of life, they also raise serious privacy issues. One particularly sensitive type of information has recently attracted substantial attention, namely personality; as personality provides a means to influence individuals (e.g., voters in the Cambridge Analytica scandal). This paper presents the first empirical study to show a significant correlation between WAT data and personality traits (Big Five). We conduct an experiment with 200+ participants. The ground truth was established by using the NEO-PI-3 questionnaire. The participants’ step count, heart rate, battery level, activities, sleep time, etc. were collected for four months. By following a principled machine-learning approach, the participants’ personality privacy was quantified. Our results demonstrate that WATs data brings valuable information to infer the openness, extraversion, and neuroticism personality traits. We further study the importance of the different features (i.e., data types) and found that step counts play a key role in the inference of extraversion and neuroticism, while openness is more related to heart rate