α-synuclein (SNCA) is an established susceptibility gene for Parkinson's disease (PD), one of the most common human neurodegenerative disorders. Increased SNCA is considered to lead to PD and dementia with Lewy bodies. Four single-nucleotide polymorphisms (SNPs) in SNCA 3' region were prominently associated with PD among different ethnic groups. To examine how these SNPs influence disease susceptibility, we analyzed their potential effects on SNCA gene expression. We found that rs356219 showed allele-specific features. Gel shift assay using nuclear extracts from SH-SY5Y cells showed binding of one or more proteins to the protective allele, rs356219-A. We purified the rs356219-A-protein complex with DNA affinity beads and identified a bound protein using mass spectrometry. This protein, YY1 (Yin Yang 1), is an ubiquitous transcription factor with multiple functions. We next investigated SNCA expression change in SH-SY5Y cells by YY1 transfection. We also analyzed the expression of antisense noncoding RNA (ncRNA) RP11-115D19.1 in SNCA 3'-flanking region, because rs356219 is located in intron of RP11-115D19.1. Little change was observed in SNCA expression levels; however, RP11-115D19.1 expression was prominently stimulated by YY1. In autopsied cortices, positive correlation was observed among RP11-115D19.1, SNCA and YY1 expression levels, suggesting their functional interactions in vivo. Knockdown of RP11-115D19.1 increased SNCA expression significantly in SH-SY5Y cells, suggesting its repressive effect on SNCA expression. Our findings of the protective allele-specific YY1 and antisense ncRNA raised a novel possible mechanism to regulate SNCA expression.

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In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets in competitions. Furthermore, many researchers have attempted to apply these deep learning-based techniques to 3D point clouds observed by aerial laser scanners (ALSs). However, most of these studies were developed for 3D point clouds without radiometric information. In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of geometric information and radiometric waveform data. Thus, we propose a data-driven semantic segmentation model called the full-waveform network (FWNet), which handles the waveform of full-waveform lidar data without any conversion process, such as projection onto a 2D grid or calculating handcrafted features. Our FWNet is based on a PointNet-based architecture, which can extract the local and global features of each input waveform data, along with its corresponding geographical coordinates. Subsequently, the classifier consists of 1D convolutional operational layers, which predict the class vector corresponding to the input waveform from the extracted local and global features. Our trained FWNet achieved higher scores in its recall, precision, and F1 score for unseen test data—higher scores than those of previously proposed methods in full-waveform lidar data analysis domain. Specifically, our FWNet achieved a mean recall of 0.73, a mean precision of 0.81, and a mean F1 score of 0.76. We further performed an ablation study, that is assessing the effectiveness of our proposed method, of the above-mentioned metric. Moreover, we investigated the effectiveness of our PointNet based local and global feature extraction method using the visualization of the feature vector. In this way, we have shown that our network for local and global feature extraction allows training with semantic segmentation without requiring expert knowledge on full-waveform lidar data or translation into 2D images or voxels.

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Powder neutron diffraction experiments with Rietveld analyses were carried out on pure and Ta-substituted Zn 2 TiO 4 at room temperature to investigate the defect structure together with the oxide ion conduction mechanism. The refined structure of pure-Zn 2 TiO 4 agreed well with the previously reported inverse spinel-type structure with cubic symmetry. For Zn 2 x2 Ti 1 x Ta x O 4 solid solutions of cubic phase, the tantalum substitution as well as the vacancy formation was made at the octahedral cation site, while the tetrahedral cation site was occupied by zinc ions without any significant deficiency. Essentially the similar results were attained in the tetragonal phase obtained by long-term annealing. By evaluating the occupation factors of tentatively introduced oxide ion interstitials at various positions, oxide ion conduction was supposed to be made with the interstitialcy diffusion mechanism, where displacement of a regular oxide ion toward the cation vacancy enabled the migration of other oxide ions through the oxide ion vacancy at which the displaced oxide ion had been initially.

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We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds. Our idea is motivated by the analysis that the standard self-attention (SA) that operates globally tends to produce almost the same attention maps for different query positions, revealing difficulties for learning query-independent and query-dependent information jointly. Therefore, we reformulate the SA and propose query-independent (Unary) and query-dependent (Pairwise) components to facilitate the learning of both terms. In contrast to the SA, the UPA ensures query dependence via operating locally. Extensive experiments show that the UPA outperforms the SA consistently on various point cloud understanding tasks including shape classification, part segmentation, and scene segmentation. Moreover, simply equipping the popular PointNet++ method with the UPA even outperforms or is on par with the stateof-the-art attention-based approaches. In addition, the UPA systematically boosts the performance of both standard and modern networks when it is integrated into them as a compositional module.