Parkinson’s disease (PD) is a complex neurodegenerative disease with etiology rooted in genetic vulnerability and environmental factors. Here we combine quantitative epidemiologic study of pesticide exposures and PD with toxicity screening in dopaminergic neurons derived from PD patient induced pluripotent stem cells (iPSCs) to identify Parkinson’s-relevant pesticides. Agricultural records enable investigation of 288 specific pesticides and PD risk in a comprehensive, pesticide-wide association study. We associate long-term exposure to 53 pesticides with PD and identify co-exposure profiles. We then employ a live-cell imaging screening paradigm exposing dopaminergic neurons to 39 PD-associated pesticides. We find that 10 pesticides are directly toxic to these neurons. Further, we analyze pesticides typically used in combinations in cotton farming, demonstrating that co-exposures result in greater toxicity than any single pesticide. We find trifluralin is a driver of toxicity to dopaminergic neurons and leads to mitochondrial dysfunction. Our paradigm may prove useful to mechanistically dissect pesticide exposures implicated in PD risk and guide agricultural policy.

The paper introduces a personalized intelligent recommender system. The system characters include: (1) integrating Agent technology to improve the recommender system reactivity, proactivity and autonomy; (2) giving a mixed recommendation method with content-based recommendation; (3) proposing the personalized user model, which can describe long-term interests and short-term interests, effectively deal with the user interests drift problem.

We present an ontology-based device description model which abstracts the general information of device in smart home. And we use XML to describe the device model which could be understood by machine. The device model is not restricted by software platform and easy to be reused.