The herbicide paraquat (PQ) has increasingly been reported in epidemiological studies to enhance the risk of developing Parkinson's disease (PD). Furthermore, case-control studies report that individuals with genetic variants in the dopamine transporter (DAT, SLC6A) have a higher PD risk when exposed to PQ. However, it remains a topic of debate whether PQ can enter dopamine (DA) neurons through DAT. We report here a mechanism by which PQ is transported by DAT: In its native divalent cation state, PQ 2+ is not a substrate for DAT; however, when converted to the monovalent cation PQ + by either a reducing agent or NADPH oxidase on microglia, it becomes a substrate for DAT and is accumulated in DA neurons, where it induces oxidative stress and cytotoxicity. Impaired DAT function in cultured cells and mutant mice significantly attenuated neurotoxicity induced by PQ + . In addition to DAT, PQ + is also a substrate for the organic cation transporter 3 (Oct3, Slc22a3), which is abundantly expressed in non-DA cells in the nigrostriatal regions. In mice with Oct3 deficiency, enhanced striatal damage was detected after PQ treatment. This increased sensitivity likely results from reduced buffering capacity by non-DA cells, leading to more PQ + being available for uptake by DA neurons. This study provides a mechanism by which DAT and Oct3 modulate nigrostriatal damage induced by PQ 2+ /PQ + redox cycling.neurodegeneration | extraneuronal monoamine transporter | astrocytes | in vivo microdialysis P arkinson's disease (PD) is characterized primarily by the loss of dopamine (DA) neurons in the substantia nigra pars compacta (1). Although in past decades discoveries of genetic mutations linked to PD have significantly impacted our current understanding of the pathogenesis of this devastating disorder, it is likely that the environment plays a critical role in the etiology of sporadic PD. Human epidemiological studies indicate that exposure to herbicides, pesticides, and heavy metals increase the risk of PD. One such environmental toxicant is paraquat (PQ 2+ , N,N′-dimethyl-4-4′-bipiridinium) (2, 3). This molecule exists natively as a divalent cation, but can undergo redox cycling with cellular diaphorases such as NADPH oxidase and nitric oxide synthase (4) (NOS) to yield the monovalent cation PQ + . From this redox cycle, superoxide is generated, leading to oxidative stress-related cytotoxicity. (For clarity and brevity, the abbreviations PQ 2+ and PQ + will be used to signify the respective cations, whereas PQ represents a general term when the valency is ambiguous.) On the basis of its structural similarity to 1-methyl-4-phenylpyridinium (MPP + ), an active metabolite of the parkinsonian agent 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) (5), PQ 2+ has been predicted to be a potential environmental parkinsonian toxicant (6), and with subsequent recent epidemiological studies (2, 3), there has been increasing interest in this herbicide as a potential pathogenic agent in PD.When PQ 2+ is injected into mice, it induces a ...

In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation. However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space. In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment. The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps. To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively. Experiments on 300W, 300-VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-ofthe-art models. … … … … 128 128 Eq. (1) Eq. (1) Eq. (3) Eq. (4)

No abstract

Artificial intelligence techniques aimed at more naturally simulating human comprehension fit the paradigm of multi-label classification. Generally, an enormous amount of high-quality multi-label data is needed to form a multilabel classifier. The creation of such datasets is usually expensive and timeconsuming. A lower cost way to obtain multi-label datasets for use with such comprehension-simulation techniques is to use noisy crowdsourced annotations. We propose incorporating label dependency into the label-generation process to estimate the multiple true labels for each instance given crowdsourced multi-label annotations. Three statistical quality control models based on the work of Dawid and Skene are proposed. The label-dependent DS (D-DS ) model simply incorporates dependency relationships among all labels. The label pairwise DS (P-DS ) model groups labels into pairs to prevent interference from uncorrelated labels. The Bayesian network labeldependent DS (ND-DS ) model compactly represents label dependency using conditional independence properties to overcome the data sparsity problem. Results of two experiments, "affect annotation for lines in story" and "intention annotation for tweets", show that (1) the ND-DS model most effectively handles the multi-label estimation problem with annotations provided by only about five workers per instance and that (2) the P-DS model is best if there are pairwise comparison relationships among the labels. To sum up, flexibly using label dependency to obtain multi-label datasets is a promising way to reduce the cost of data collection for future applications with minimal degradation in the quality of the results.