個人化情報活用のためのパーソナルデータと挙動解析による統合モデリング手法

早大学位記番号:新6782早稲田大

Ambient air pollutants relate to hospital admissions for chronic obstructive pulmonary disease in Ganzhou, China

OBJECTIVE To evaluate the relationship between ambient air pollutants and chronic obstructive pulmonary disease in relatively low-polluted areas in China. METHODS Atmospheric pollutants levels and meteorological data were obtained from January 2016 to December 2020. The medical database including daily hospital admissions for chronic obstructive pulmonary disease (ICD10: J44) was derived from the First Affiliated Hospital of Gannan Medical University. The generalized additive model was used to analyze the percentage change with 95% confidence interval in daily hospital admissions for chronic obstructive pulmonary disease associated with a 10 µg/m3 increase in atmospheric pollutants levels. RESULTS In total, occurred 4,980 chronic obstructive pulmonary disease hospital admissions (not including emergency department visits) during 2016–2020. The mean concentrations of daily PM2.5, PM10, SO2, NO2, O3, and CO were 37.5 μg/m3, 60.1 μg/m3, 18.7 μg/m3, 23.5 μg/m3, 70.0 μg/m3, and 1.2 mg/m3 in Ganzhou. Each 10 µg/m3 increment of PM2.5, PM10, NO2, and O3 were significantly associated with 2.8% (95%CI: 1.0–4.7), 1.3% (95%CI: 0.3–2.4), 2.8% (95%CI: 0.4–5.4), and 1.5% (95%CI: 0.2–2.7) elevation in daily chronic obstructive pulmonary disease hospital admissions. The estimates of delayed effects of PM2.5, PM10, NO2, and O3 were observed at lag6, lag6, lag8, lag1, respectively. The health effects of particulate pollutants (PM2.5 and PM10) may be independent of other pollutants. The adverse effects of air pollutants were more evident in the warm season (May–Oct) than in the cold season (Nov–Apr). CONCLUSION Our study demonstrated that elevated concentrations of atmospheric pollutant (PM2.5, PM10, NO2, and O3), especially particulate pollutants, can be associated with increased daily count of hospital admissions for chronic obstructive pulmonary disease , which may promote further understanding of the potential hazards of relatively low levels of air pollution on chronic obstructive pulmonary disease and other respiratory disorders

Cryo-EM structure of the mature dengue virus at 3.5-Å resolution.

Regulated by pH, membrane-anchored proteins E and M function during dengue virus maturation and membrane fusion. Our atomic model of the whole virion from cryo-electron microscopy at 3.5-Å resolution reveals that in the mature virus at neutral extracellular pH, the N-terminal 20-amino-acid segment of M (involving three pH-sensing histidines) latches and thereby prevents spring-loaded E fusion protein from prematurely exposing its fusion peptide. This M latch is fastened at an earlier stage, during maturation at acidic pH in the trans-Golgi network. At a later stage, to initiate infection in response to acidic pH in the late endosome, M releases the latch and exposes the fusion peptide. Thus, M serves as a multistep chaperone of E to control the conformational changes accompanying maturation and infection. These pH-sensitive interactions could serve as targets for drug discovery

A convolutional neural network based Chinese text detection algorithm via text structure modeling

Text detection in natural scene environment plays an important role in many computer vision applications. While existing text detection methods are focused on English characters, there is strong application demands on text detection in other languages, such as Chinese. As Chinese characters are much more complex than English characters, innovative and more efficient text detection techniques are required for Chinese texts. In this paper, we present a novel text detection algorithm for Chinese characters based on a specific designed convolutional neural network (CNN). The CNN model contains a text structure component detector layer, a spatial pyramid layer and a multi-input-layer deep belief network (DBN). The CNN is pretrained via a convolutional sparse auto-encoder (CSAE) in an unsupervised way, which is specifically designed for extracting complex features from Chinese characters. In particular, the text structure component detectors enhance the accuracy and uniqueness of feature descriptors by extracting multiple text structure components in various ways. The spatial pyramid layer is then introduced to enhance the scale invariability of the CNN model for detecting texts in multiple scales. Finally, the multi-input-layer DBN is used as the fully connected layers in the CNN model to ensure that features from multiple scales are comparable. A multilingual text detection dataset, in which texts in Chinese, English and digits are labeled separately, is set up to evaluate the proposed text detection algorithm. The proposed algorithm shows a significant 10% performance improvement over the baseline CNN algorithms. In addition the proposed algorithm is evaluated over a public multilingual image benchmark and achieves state-of-the-art results for text detection under multiple languages. Furthermore a simplified version of the proposed algorithm with only general components is compared to existing general text detection algorithms on the ICDAR 2011 and 2013 datasets, showing comparable detection performance to the existing algorithms

Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing

The hippocampus has been known as one of the most important structures referred to as Alzheimer’s disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists

Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.Comment: ICCV 2023 camera-ready, Project Page: https://me.kiui.moe/nerf2mes

Coordinated System Reliability Assessment And Production Cost Simulation In Transmission Planning Of Eastern Interconnection

This paper presents a transmission need analysis for the Eastern Interconnection (EI) using a coordinated technical approach consisting of system reliability assessment (SRA) and production cost simulation (PCS). As North American Transmission Systems are being evolved with increasing levels of renewable energy resources such as wind, solar, storage, biomass, hydro, etc., maintaining grid reliability and managing transmission congestion cost are becoming increasingly challenging. It also poses complexity and challenges in technical and economic planning of the transmission grid. The coordinated SRA and PCS were conducted to assess transmission reliability and congestion for the interconnected grids of the EI in a 10-year planning horizon. The paper discusses new automation tools and models developed for such assessment including case studies showing the applicability of the coordinated methodology and developed models