CNVs in Three Psychiatric Disorders

BACKGROUND: We aimed to determine the similarities and differences in the roles of genic and regulatory copy number variations (CNVs) in bipolar disorder (BD), schizophrenia (SCZ), and autism spectrum disorder (ASD). METHODS: Based on high-resolution CNV data from 8708 Japanese samples, we performed to our knowledge the largest cross-disorder analysis of genic and regulatory CNVs in BD, SCZ, and ASD. RESULTS: In genic CNVs, we found an increased burden of smaller (500 kb) exonic CNVs in SCZ/ASD. Pathogenic CNVs linked to neurodevelopmental disorders were significantly associated with the risk for each disorder, but BD and SCZ/ASD differed in terms of the effect size (smaller in BD) and subtype distribution of CNVs linked to neurodevelopmental disorders. We identified 3 synaptic genes (DLG2, PCDH15, and ASTN2) as risk factors for BD. Whereas gene set analysis showed that BD-associated pathways were restricted to chromatin biology, SCZ and ASD involved more extensive and similar pathways. Nevertheless, a correlation analysis of gene set results indicated weak but significant pathway similarities between BD and SCZ or ASD (r = 0.25–0.31). In SCZ and ASD, but not BD, CNVs were significantly enriched in enhancers and promoters in brain tissue. CONCLUSIONS: BD and SCZ/ASD differ in terms of CNV burden, characteristics of CNVs linked to neurodevelopmental disorders, and regulatory CNVs. On the other hand, they have shared molecular mechanisms, including chromatin biology. The BD risk genes identified here could provide insight into the pathogenesis of BD

Comparing Tunnel-In-The-Sky Display on HDD and HUD from Task Occupation Point of View

A series of flight simulations was carried out to investigate the causal factors of attention capture, focusing on a traffic detection task while following a curved trajectory using a Tunnel-in-the-Sky display. The location (head-up or head-down) and size of the display were varied, and traffic detection time and path tracking performance were measured. The results show that the HUD gave the best path tracking at the expense of traffic detection performance, and supports the hypothesis that using a limited viewing volume and high display gain with a Tunnel-in-the-Sky display induces pilots to rely on precise guidance cues instead of the “tunnel” itself, consequently focusing much attention on the control task

Development of Method for CRM Skills Asessment

Crew Resource Management (CRM) is currently considered as one of the most effective methods for avoiding human errors or minimizing their effects. In training, measurement of the level of flight crews’ CRM Skills is necessary in order to evaluate objectively which Skills have been adequately learned and which are lacking. The Japan Aerospace Exploration Agency (JAXA) has developed CRM Skills Behavioral Markers and CRM Skills Measurement Methods that can identify a crew’s level of CRM Skills by which human errors and threats are managed. A series of simulated-LOFT (line oriented flight simulation training) were conducted to examine the applicability of the method

A Proposal of Printed Table Digitization Algorithm with Image Processing

Nowadays, digital transformation (DX) is the key concept to change and improve the operations in governments, companies, and schools. Therefore, any data should be digitized for processing by computers. Unfortunately, a lot of data and information are printed and handled on paper, although they may originally come from digital sources. Data on paper can be digitized using an optical character recognition (OCR) software. However, if the paper contains a table, it becomes difficult because of the separated characters by rows and columns there. It is necessary to solve the research question of "how to convert a printed table on paper into an Excel table while keeping the relationships between the cells?" In this paper, we propose a printed table digitization algorithm using image processing techniques and OCR software for it. First, the target paper is scanned into an image file. Second, each table is divided into a collection of cells where the topology information is obtained. Third, the characters in each cell are digitized by OCR software. Finally, the digitalized data are arranged in an Excel file using the topology information. We implement the algorithm on Python using OpenCV for the image processing library and Tesseract for the OCR software. For evaluations, we applied the proposal to 19 scanned and 17 screenshotted table images. The results show that for any image, the Excel file is generated with the correct structure, and some characters are misrecognized by OCR software. The improvement will be in future works