2,764 research outputs found
Structural Prediction of ProteinโProtein Interactions by Docking: Application to Biomedical Problems
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 proteinโprotein 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 proteinโprotein interactions, or providing modeled structural data for drug discovery targeting proteinโprotein 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
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Microarray image processing: A novel neural network framework
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods.
Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri
A systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality.
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ํ์ ๋ํ์ ๊ธ๋ก๋ฒํ์ ์ ๊ณต, 2023. 2. ์ตํํ.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.์์จ์ฃผํ์ฐจ(AV)๋ฅผ ํตํด ๊ตํต ํ๋ฆ์ด ๊ฐ์ ๋๊ณ ์ฌ๊ณ ๊ฐ ์ค์ด๋๋ ๋ฑ ์ฌํ๊ฐ ์ป๋ ๊ฒ์ด ๋ง์ ๊ฒ์ผ๋ก ์์๋๋ค. ๊ทธ๋ค์ ๋ค์ํ ์ธ๊ณต์ง๋ฅ(AI) ํ๋ก์ธ์ค์ ์ ๋ต์ ๊ฐ์ ์ ํฌ๊ฒ ์์กดํ๋ค. ์ด ๋ถ์ผ์ ์ผ๋ถ ์ฐ๊ตฌ์๋ค์ AV๊ฐ ์์ ์ฑ์ ํฅ์์ํค๋ ์ด์ ๋ผ๊ณ ๋ฏฟ์ง๋ง, ๋ค๋ฅธ ์ฐ๊ตฌ์๋ค์ AV๊ฐ ์ด๋ฌํ ์๋ก์ด ๊ธฐ์ /์์คํ
๋ฐ ์ ํ๋ฆฌ์ผ์ด์
์ ๋ณด์์ ๋ณด์ฅํ๋ ๊ฒ๊ณผ ๊ด๋ จํ์ฌ ์๋ก์ด ๋ฌธ์ ๋ฅผ ์ผ๊ธฐํ๋ค๊ณ ๋ฏฟ๋๋ค. ์ด ๋
ผ๋ฌธ์ ์์จ์ฃผํ์ฐจ ๊ธฐ์ ๊ณผ ๊ตํต ๊ด๋ จ ์ฌ๋ง๋ฅ ์ฌ์ด์ ๊ด๊ณ์ ๋ํ ์ฒด๊ณ์ ์ธ ๋ฌธํ ๊ฒํ ๋ฅผ ์ํํ๋ค. ํฌํจ ๋ฐ ์ ์ธ ๊ธฐ์ค์ ๋ฐ๋ผ EBSCO, ProQuest, IEEE Explorer ๋ฐ Web of Science์ ๊ธฐ์ฌ๋ฅผ ์ ํํ๊ณ ๋ถ๋ฅํ๋ค.์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ ์ด๋ฌํ ์ถํ๋ฌผ์ ๋๋ถ๋ถ์ด ๊ณ ๊ธ ์ด์ก ๊ด๋ จ ์ ๋์ ๊ฒ์ฌ๋์์์ ๋ณด์ฌ์ค๋ค. ๋ฏธ๋์ ์๋์ฐจ ์ฐ์
์ ๊ฐ์ ๊ณผ ์ง๋ฅํ ๊ตํต ์์คํ
์ ๊ฐ๋ฐ์ ์น๋ช
์ ์ธ ๊ตํต ์ฌ๊ณ ์ ์๋ฅผ ์ค์ด๋ ๋ฐ ๋์์ด ๋ ์ ์๋ค. ์์จ์ฃผํ ์๋์ฐจ ๊ธฐ์ ์ ์ด์ ๊ฒฝํ์ ํฅ์์ํค๊ณ ๊ตํต ์ฌ๊ณ ์ ์๋ฅผ ์ค์ผ ์ ์๋ ํจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ์ ์ ๊ณตํ๋ค. ์ถฉ๋, ๊ตํต, ์๋์ง ์ฌ์ฉ, ํ๊ฒฝ ์ค์ผ๊ณผ ๊ฐ์ ์๋ง์ ์ด์ ๊ด๋ จ ๋ฌธ์ ๋ค์ ์์จ ์ฃผํ ๊ธฐ์ ์ ์ํด ๋์์ ๋ฐ์ ๊ฒ์ด๋ค. ํ๊ฐ๋ ๋๋ถ๋ถ์ ์ฐ๊ตฌ์ ๋ํด ๋ ๋ง์ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค. ์ค์ ๋๋ ์ปดํจํฐ ์๋ฎฌ๋ ์ด์
์๋๋ฆฌ์ค, ๋ ์ข๊ณ ํ์ค์ ์ธ ์๋๋ฆฌ์ค, ๋ ์ข๊ณ ๋ ๋ง์ ๋ฐ์ดํฐ, ๊ทธ๋ฆฌ๊ณ ์ ์๋ ์ ๋ต ๊ฒฐ๊ณผ๊ฐ ์ฐ์
ํ์ค ๋ฐ ๊ฒฝ์ ์ ๋ต์ ๊ฒฐ๊ณผ์ ๋น๊ต๋๋ ์คํ ์ค๊ณ์์ ํ
์คํธ๋ ์ ์๋๋ก ํ์ฅ๋์ด์ผ ํ๋ค. ๋ฐ๋ผ์ ๊ฐ์ ๋ ๋ฐฉ๋ฒ์ ๋ํ ์ถ๊ฐ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค. ์ถ๊ฐ ์ฐ๊ตฌ๊ฐ ํ์ํ ๋ ๋ค๋ฅธ ์ฃผ์ ๋ถ์ผ๋ AV์ ๋๋์ , ์ค๋ฆฌ์ ์ ํ์ด๋ค. ์ ๋ถ, ์ ์ฑ
์
์์, ์ ์กฐ์
์ฒด ๋ฐ ์ค๊ณ์๋ ๋ชจ๋ ์์จ ์ฃผํ ์ฐจ๋์ ํจ๊ณผ์ ์ผ๋ก ๋๋ก์ ๋ฐฐ์นํ๊ธฐ ์ํด ๋ง์ ์กฐ์น๋ฅผ ์ทจํด์ผ ํ๋ค. ์ ๋ถ๋ ํนํ ๋ฒ, ๊ท์น, ์คํ ๊ณํ์ ๊ฐ๋ฐํด์ผ ํ๋ค. ์ด์ ์ ์๋ ์ฐจ๋๊ณผ ๊ฐ์ ์ด์ก ์์คํ
์์ ์๋ก์ด ๊ธฐ์ ์ ์ฑํ์ ์ฅ๋ คํ ์ ์๋ ๋ณด๋ค ํจ๊ณผ์ ์ธ ํ๋ก๊ทธ๋จ์ ๋ง๋๋ ๊ฒ์ด ์ค์ํ๋ค. ์ด์ ๊ด๋ จํ์ฌ, ์ค๊ณ์์๊ฒ ํ์ฌ ์ด์์ ์ฌ๋์ ์ํ ๊ด์ฐฐ์ ์๋ ค์ค ์ ์๊ธฐ ๋๋ฌธ์ ์ฌ์ฉ์ ์ธ์์ด ํ์์ ์ด ๋๋ค.์ ์กฐ์
์ฒด๋ ์์คํ
๊ฒฐํจ์ ์์ ํด์ผ ํ๋ฉฐ ํจ์จ์ ์ธ ์๋์ ์ํด ์ข์ ๋ฐ์ดํฐ ์ธํธ๊ฐ ํ์ํ๋ค. ๋ฉ์ง ์์ ๋ฏธ๋์, ๊ณ ๋๋ก ์๋ํ๋ ์ฐจ๋(AV)์ ๊ด๋ฒ์ํ ์ฌ์ฉ์ ํ์ฌ์ ์์ ๊ด๋ จ ์ฐ๊ตฌ์์ ์ง์์ ์ธ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํ ํฅ๋ฏธ๋ก์ด ์๋ก์ด ๊ฐ๋ฅ์ฑ์ ์ด์ด์ค ์ ์๋ค. ๋ณด๊ธ๋ฅ , ๊ณต๊ณต ์ฑํ, ๊ธฐ์ ๋ฐ์ , ๊ตํต ํจํด ๋ฐ ๋น์ฆ๋์ค ๋ชจ๋ธ๊ณผ ๊ฐ์ ์์๋ฅผ ๊ณ ๋ คํ์ฌ Avs์ ์ค์ํ ์ ์ฑ
์ํฅ์ ๋ ์ ์ดํดํ๊ณ ์ ๋ํํ๊ธฐ ์ํ ์ถ๊ฐ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค. ์กฐ์ฌ๋ฅผ ์ํด ๋๋ฃ ๊ฒํ ๋ฅผ ๊ฑฐ์น ์ ๋ฌธ ์ ๋ ๋
ผ๋ฌธ๋ง ๊ณ ๋ คํ๋ฉด ๋์ง๋ง, ๋ฐ์ดํฐ๋ฒ ์ด์ค๊ฐ ์ปค์ง๊ณ ๋ฌธ์๊ฐ ๋ง์์ง๋ฉด ๋ ๋ง์ ๊ฒฐ๊ณผ์ ๋ ์ฒ ์ ํ ๋ถ์์ด ์ ๊ณต๋ ๊ฒ์ด ๋ถ๋ช
ํ๋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
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
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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