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    Structural Prediction of Proteinโ€“Protein Interactions by Docking: Application to Biomedical Problems

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    A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of proteinโ€“protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in proteinโ€“protein interactions, or providing modeled structural data for drug discovery targeting proteinโ€“protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

    FY10 Engineering Innovations, Research and Technology Report

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    A systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality.

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ธ€๋กœ๋ฒŒํ–‰์ •์ „๊ณต, 2023. 2. ์ตœํƒœํ˜„.The society is anticipated to gain a lot from Autonomous Vehicles (AV), such as improved traffic flow and a decrease in accidents. They heavily rely on improvements in various Artificial Intelligence (AI) processes and strategies. Though some researchers in this field believe AV is the key to enhancing safety, others believe AV creates new challenges when it comes to ensuring the security of these new technology/systems and applications. The article conducts a systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality. According to inclusion and exclusion criteria, articles from EBSCO, ProQuest, IEEE Explorer, Web of Science were chosen, and they were then sorted. The findings reveal that the most of these publications have been published in advanced transport-related journals. Future improvements in the automobile industry and the development of intelligent transportation systems could help reduce the number of fatal traffic accidents. Technologies for autonomous cars provide effective ways to enhance the driving experience and reduce the number of traffic accidents. A multitude of driving-related problems, such as crashes, traffic, energy usage, and environmental pollution, will be helped by autonomous driving technology. More research is needed for the significant majority of the studies that were assessed. They need to be expanded so that they can be tested in real-world or computer-simulated scenarios, in better and more realistic scenarios, with better and more data, and in experimental designs where the results of the proposed strategy are compared to those of industry standards and competing strategies. Therefore, additional study with improved methods is needed. Another major area that requires additional research is the moral and ethical choices made by AVs. Government, policy makers, manufacturers, and designers all need to do many actions in order to deploy autonomous vehicles on the road effectively. The government should develop laws, rules, and an action plan in particular. It is important to create more effective programs that might encourage the adoption of emerging technology in transportation systems, such as driverless vehicles. In this regard, user perception becomes essential since it may inform designers about current issues and observations made by people. The perceptions of autonomous car users in developing countries like Azerbaijan haven't been thoroughly studied up to this point. The manufacturer has to fix the system flaw and needs a good data set for efficient operation. In the not-too-distant future, the widespread use of highly automated vehicles (AVs) may open up intriguing new possibilities for resolving persistent issues in current safety-related research. Further research is required to better understand and quantify the significant policy implications of Avs, taking into consideration factors like penetration rate, public adoption, technological advancements, traffic patterns, and business models. It only needs to take into account peer-reviewed, full-text journal papers for the investigation, but it's clear that a larger database and more documents would provide more results and a more thorough analysis.์ž์œจ์ฃผํ–‰์ฐจ(AV)๋ฅผ ํ†ตํ•ด ๊ตํ†ต ํ๋ฆ„์ด ๊ฐœ์„ ๋˜๊ณ  ์‚ฌ๊ณ ๊ฐ€ ์ค„์–ด๋“œ๋Š” ๋“ฑ ์‚ฌํšŒ๊ฐ€ ์–ป๋Š” ๊ฒƒ์ด ๋งŽ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ๊ทธ๋“ค์€ ๋‹ค์–‘ํ•œ ์ธ๊ณต์ง€๋Šฅ(AI) ํ”„๋กœ์„ธ์Šค์™€ ์ „๋žต์˜ ๊ฐœ์„ ์— ํฌ๊ฒŒ ์˜์กดํ•œ๋‹ค. ์ด ๋ถ„์•ผ์˜ ์ผ๋ถ€ ์—ฐ๊ตฌ์ž๋“ค์€ AV๊ฐ€ ์•ˆ์ „์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์—ด์‡ ๋ผ๊ณ  ๋ฏฟ์ง€๋งŒ, ๋‹ค๋ฅธ ์—ฐ๊ตฌ์ž๋“ค์€ AV๊ฐ€ ์ด๋Ÿฌํ•œ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ /์‹œ์Šคํ…œ ๋ฐ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ๋ณด์•ˆ์„ ๋ณด์žฅํ•˜๋Š” ๊ฒƒ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค๊ณ  ๋ฏฟ๋Š”๋‹ค. ์ด ๋…ผ๋ฌธ์€ ์ž์œจ์ฃผํ–‰์ฐจ ๊ธฐ์ˆ ๊ณผ ๊ตํ†ต ๊ด€๋ จ ์‚ฌ๋ง๋ฅ  ์‚ฌ์ด์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ๋ฌธํ—Œ ๊ฒ€ํ† ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ํฌํ•จ ๋ฐ ์ œ์™ธ ๊ธฐ์ค€์— ๋”ฐ๋ผ EBSCO, ProQuest, IEEE Explorer ๋ฐ Web of Science์˜ ๊ธฐ์‚ฌ๋ฅผ ์„ ํƒํ•˜๊ณ  ๋ถ„๋ฅ˜ํ–ˆ๋‹ค.์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ด๋Ÿฌํ•œ ์ถœํŒ๋ฌผ์˜ ๋Œ€๋ถ€๋ถ„์ด ๊ณ ๊ธ‰ ์šด์†ก ๊ด€๋ จ ์ €๋„์— ๊ฒŒ์žฌ๋˜์—ˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋ฏธ๋ž˜์˜ ์ž๋™์ฐจ ์‚ฐ์—…์˜ ๊ฐœ์„ ๊ณผ ์ง€๋Šฅํ˜• ๊ตํ†ต ์‹œ์Šคํ…œ์˜ ๊ฐœ๋ฐœ์€ ์น˜๋ช…์ ์ธ ๊ตํ†ต ์‚ฌ๊ณ ์˜ ์ˆ˜๋ฅผ ์ค„์ด๋Š” ๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ ๊ธฐ์ˆ ์€ ์šด์ „ ๊ฒฝํ—˜์„ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ๊ตํ†ต ์‚ฌ๊ณ ์˜ ์ˆ˜๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•์„ ์ œ๊ณตํ•œ๋‹ค. ์ถฉ๋Œ, ๊ตํ†ต, ์—๋„ˆ์ง€ ์‚ฌ์šฉ, ํ™˜๊ฒฝ ์˜ค์—ผ๊ณผ ๊ฐ™์€ ์ˆ˜๋งŽ์€ ์šด์ „ ๊ด€๋ จ ๋ฌธ์ œ๋“ค์€ ์ž์œจ ์ฃผํ–‰ ๊ธฐ์ˆ ์— ์˜ํ•ด ๋„์›€์„ ๋ฐ›์„ ๊ฒƒ์ด๋‹ค. ํ‰๊ฐ€๋œ ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ์— ๋Œ€ํ•ด ๋” ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์‹ค์ œ ๋˜๋Š” ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๋‚˜๋ฆฌ์˜ค, ๋” ์ข‹๊ณ  ํ˜„์‹ค์ ์ธ ์‹œ๋‚˜๋ฆฌ์˜ค, ๋” ์ข‹๊ณ  ๋” ๋งŽ์€ ๋ฐ์ดํ„ฐ, ๊ทธ๋ฆฌ๊ณ  ์ œ์•ˆ๋œ ์ „๋žต ๊ฒฐ๊ณผ๊ฐ€ ์‚ฐ์—… ํ‘œ์ค€ ๋ฐ ๊ฒฝ์Ÿ ์ „๋žต์˜ ๊ฒฐ๊ณผ์™€ ๋น„๊ต๋˜๋Š” ์‹คํ—˜ ์„ค๊ณ„์—์„œ ํ…Œ์ŠคํŠธ๋  ์ˆ˜ ์žˆ๋„๋ก ํ™•์žฅ๋˜์–ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐœ์„ ๋œ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•œ ๋˜ ๋‹ค๋ฅธ ์ฃผ์š” ๋ถ„์•ผ๋Š” AV์˜ ๋„๋•์ , ์œค๋ฆฌ์  ์„ ํƒ์ด๋‹ค. ์ •๋ถ€, ์ •์ฑ… ์ž…์•ˆ์ž, ์ œ์กฐ์—…์ฒด ๋ฐ ์„ค๊ณ„์ž๋Š” ๋ชจ๋‘ ์ž์œจ ์ฃผํ–‰ ์ฐจ๋Ÿ‰์„ ํšจ๊ณผ์ ์œผ๋กœ ๋„๋กœ์— ๋ฐฐ์น˜ํ•˜๊ธฐ ์œ„ํ•ด ๋งŽ์€ ์กฐ์น˜๋ฅผ ์ทจํ•ด์•ผ ํ•œ๋‹ค. ์ •๋ถ€๋Š” ํŠนํžˆ ๋ฒ•, ๊ทœ์น™, ์‹คํ–‰ ๊ณ„ํš์„ ๊ฐœ๋ฐœํ•ด์•ผ ํ•œ๋‹ค. ์šด์ „์ž ์—†๋Š” ์ฐจ๋Ÿ‰๊ณผ ๊ฐ™์€ ์šด์†ก ์‹œ์Šคํ…œ์—์„œ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์˜ ์ฑ„ํƒ์„ ์žฅ๋ คํ•  ์ˆ˜ ์žˆ๋Š” ๋ณด๋‹ค ํšจ๊ณผ์ ์ธ ํ”„๋กœ๊ทธ๋žจ์„ ๋งŒ๋“œ๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์ด์™€ ๊ด€๋ จํ•˜์—ฌ, ์„ค๊ณ„์ž์—๊ฒŒ ํ˜„์žฌ ์ด์Šˆ์™€ ์‚ฌ๋žŒ์— ์˜ํ•œ ๊ด€์ฐฐ์„ ์•Œ๋ ค์ค„ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ์šฉ์ž ์ธ์‹์ด ํ•„์ˆ˜์ ์ด ๋œ๋‹ค.์ œ์กฐ์—…์ฒด๋Š” ์‹œ์Šคํ…œ ๊ฒฐํ•จ์„ ์ˆ˜์ •ํ•ด์•ผ ํ•˜๋ฉฐ ํšจ์œจ์ ์ธ ์ž‘๋™์„ ์œ„ํ•ด ์ข‹์€ ๋ฐ์ดํ„ฐ ์„ธํŠธ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ฉ€์ง€ ์•Š์€ ๋ฏธ๋ž˜์—, ๊ณ ๋„๋กœ ์ž๋™ํ™”๋œ ์ฐจ๋Ÿ‰(AV)์˜ ๊ด‘๋ฒ”์œ„ํ•œ ์‚ฌ์šฉ์€ ํ˜„์žฌ์˜ ์•ˆ์ „ ๊ด€๋ จ ์—ฐ๊ตฌ์—์„œ ์ง€์†์ ์ธ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ํฅ๋ฏธ๋กœ์šด ์ƒˆ๋กœ์šด ๊ฐ€๋Šฅ์„ฑ์„ ์—ด์–ด์ค„ ์ˆ˜ ์žˆ๋‹ค. ๋ณด๊ธ‰๋ฅ , ๊ณต๊ณต ์ฑ„ํƒ, ๊ธฐ์ˆ  ๋ฐœ์ „, ๊ตํ†ต ํŒจํ„ด ๋ฐ ๋น„์ฆˆ๋‹ˆ์Šค ๋ชจ๋ธ๊ณผ ๊ฐ™์€ ์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ Avs์˜ ์ค‘์š”ํ•œ ์ •์ฑ… ์˜ํ–ฅ์„ ๋” ์ž˜ ์ดํ•ดํ•˜๊ณ  ์ •๋Ÿ‰ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์กฐ์‚ฌ๋ฅผ ์œ„ํ•ด ๋™๋ฃŒ ๊ฒ€ํ† ๋ฅผ ๊ฑฐ์นœ ์ „๋ฌธ ์ €๋„ ๋…ผ๋ฌธ๋งŒ ๊ณ ๋ คํ•˜๋ฉด ๋˜์ง€๋งŒ, ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๊ฐ€ ์ปค์ง€๊ณ  ๋ฌธ์„œ๊ฐ€ ๋งŽ์•„์ง€๋ฉด ๋” ๋งŽ์€ ๊ฒฐ๊ณผ์™€ ๋” ์ฒ ์ €ํ•œ ๋ถ„์„์ด ์ œ๊ณต๋  ๊ฒƒ์ด ๋ถ„๋ช…ํ•˜๋‹ค.Abstract 3 Table of Contents 6 List of Tables 7 List of Figures 7 List of Appendix 7 CHAPTER 1: INTRODUCTION 8 1.1. Background 8 1.2. Purpose of Research 13 CHAPTER 2: AUTONOMOUS VEHICLES 21 2.1. Intelligent Traffic Systems 21 2.2. System Architecture for Autonomous Vehicles 22 2.3. Key components in AV classification 27 CHAPTER 3: METHODOLOGY AND DATA COLLECTION PROCEDURE 35 CHAPTER 4: FINDINGS AND DISCUSSION 39 4.1. RQ1: Do autonomous vehicles reduce traffic-related deaths 40 4.2. RQ2: Are there any challenges to using autonomous vehicles 63 4.3. RQ3: As a developing country, how effective is the use of autonomous vehicles for reducing traffic mortality 72 CHAPTER 5: CONCLUSION 76 5.1. Summary 76 5.2. Implications and Recommendations 80 5.3. Limitation of the study 91 Bibliography 93 List of Tables Table 1: The 6 Levels of Autonomous Vehicles Table 2: Search strings Table 3: Inclusion and exclusion criteria List of Figures Figure 1: Traffic Death Comparison with Europe Figure 2: Research strategy and study selection process List of Appendix Appendix 1: List of selected articles์„

    High-precision grasping and placing for mobile robots

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    This work presents a manipulation system for multiple labware in life science laboratories using the H20 mobile robots. The H20 robot is equipped with the Kinect V2 sensor to identify and estimate the position of the required labware on the workbench. The local features recognition based on SURF algorithm is used. The recognition process is performed for the labware to be grasped and for the workbench holder. Different grippers and labware containers are designed to manipulate different weights of labware and to realize a safe transportation

    Dealing with perimetric variability in clinical glaucoma care

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    Glaucoma is an eye disease in which the optic nerve is affected with irreversibleresulting in visual loss. With various treatments that target thelowering intraocular pressure, it is possible to prevent the progression of visual field lossprevent or delay. To assess whether glaucoma is stable (i.e. thetreatment provides a sufficient reduction in intraocular pressure) or progressive (i.e.undertreatment) the visual field examination has an essential role. Despitethis study is the gold standard in glaucoma care, is test-retest variabilityan important shortcoming addressed in this thesis
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