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    ์ •ํ™•ํ•œ ์„œ์—ด์ •๋ ฌ๊ธฐ๋ฒ•๊ณผ ์ธ๋ฉ”๋ชจ๋ฆฌ ํ•ต์‹ฌ ์œ ์ „์ž ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ธฐ๋ฐ˜์˜ ํ–ฅ์ƒ๋œ ๋ฉ”ํƒ€์œ ์ „์ฒด ๋ถ„๋ฅ˜๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ƒ๋ฌผ์ •๋ณดํ•™์ „๊ณต, 2020. 8. ์ฒœ์ข…์‹.์ƒท๊ฑด ๋ฉ”ํƒ€์ง€๋…ธ๋ฏน์Šค๋Š” ๋ฏธ์ƒ๋ฌผ๊ณผ ์ˆ™์ฃผ ๋˜๋Š” ํ™˜๊ฒฝ์‚ฌ์ด์˜ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ๋งค์šฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๊ณผ ๋”๋ถˆ์–ด ๋ฉ”ํƒ€์ง€๋…ธ๋ฏน์Šค๋ฅผ ํ†ตํ•œ ์˜ฌ๋ฐ”๋ฅธ ๋ฏธ์ƒ๋ฌผ ์ข…์˜ ๋™์ •๊ณผ ๊ฐ ์ข…๋“ค์˜ ๋ถ„ํฌ๋Š” ๋งˆ์ดํฌ๋กœ๋ฐ”์ด์˜ด ์—ฐ๊ตฌ์˜ ํ•ต์‹ฌ ๊ตฌ์„ฑ์š”์†Œ๊ฐ€ ๋˜์—ˆ์œผ๋ฉฐ, ์ง€๋‚œ 10๋…„๊ฐ„ ์ƒท๊ฑด ๋ฉ”ํƒ€์ง€๋…ธ๋ฏน์Šค ๋ถ„์„์„ ์œ„ํ•œ ์—ฌ๋Ÿฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋“ค์ด ๊ฐœ๋ฐœ๋˜์–ด์ ธ ์™”๋‹ค. ํ•˜์ง€๋งŒ ์„œ๋กœ ๋‹ค๋ฅธ ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ ํ˜น์€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•œ ๋ฐฉ๋ฒ•๋“ค์€ ์„œ๋กœ ๋‹ค๋ฅธ ๋ถ„๋ฅ˜ ์ •๋ณด์™€ ๋ถ„์„ ํŒŒ์ดํ”„๋ผ์ธ์œผ๋กœ ์ธํ•˜์—ฌ ํŽธํ–ฅ๋œ ๊ฒฐ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด๊ธฐ๋„ ํ•˜์˜€๋Š”๋ฐ, ์ด๋ฅผ ๋ณด์™„ํ•˜๊ณ  ๋ณด๋‹ค ์ •ํ™•ํ•œ ๋ถ„๋ฅ˜ ๋™์ •์„ ์œ„ํ•ด ๋ฐฐ์–‘์ด ์–ด๋ ค์šด ํ‘œ์ค€ ๊ท ์ฃผ์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๊ท ์ฃผ์˜ ์œ ์ „์ฒด ๋ฐ์ดํ„ฐ๋ฅผ ํฌํ•จํ•˜๋Š” ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ์ค‘์š”์„ฑ์ด ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ์ƒท๊ฑด ๋ฉ”ํƒ€์ง€๋…ธ๋ฏน์Šค ๋ถ„์„์—์„œ ๋˜ ๋‹ค๋ฅธ ์ค‘์š”ํ•œ ์š”์†Œ๋Š” ๋ถ„์„์— ์†Œ์š”๋˜๋Š” ์‹œ๊ฐ„์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ ๋Œ€๋ถ€๋ถ„์˜ ์ƒ๋ฌผ์ •๋ณดํ•™์  ํ”„๋กœ๊ทธ๋žจ๋“ค์€ ๊ณ„์‚ฐ์„ ์ˆ˜ํ–‰ํ•จ์— ์žˆ์–ด ๋ฉ”๋ชจ๋ฆฌ์™€ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ตœ์ ํ™”๊ฐ€ ๋˜์–ด์žˆ์ง€ ์•Š์•„ ๋ถ„์„์— ์ƒ๋‹นํ•œ ์‹œ๊ฐ„์ด ์†Œ์š”๋˜๋Š” ๋ฌธ์ œ์ ์ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” exact match k-mer classification๊ณผ ๊ฐ™์€ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„์„ ์†๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผฐ์œผ๋ฉฐ Up-to-date Bacterial Core Gene (UBCG)๋ฅผ ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋กœ ์‚ฌ์šฉํ•˜์—ฌ ๋ณด๋‹ค ์ •ํ™•ํ•œ ์ƒท๊ฑด ๋ฉ”ํƒ€์ง€๋…ธ๋ฏน ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์˜€๋‹ค. ๋ถ„์„์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ๋‘๊ฐœ์˜ ๊ธฐ์ค€ UBCG ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๊ฐ€ ๋งŒ๋“ค์–ด ์กŒ์œผ๋ฉฐ ํ•œ ๊ฐœ๋Š” ๋ฐ•ํ…Œ๋ฆฌ์•„์˜ ๋ถ„๋ฅ˜์ฒด๊ณ„์—์„œ ์œ ํšจํ•œ ์ข…๋ช… (Valid names)๋งŒ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€ ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” ์œ ํšจํ•œ ์ข…๋ช…๊ณผ ํ•จ๊ป˜ EzBioCloud์— ์žˆ๋Š” genomospecies๋ฅผ ๊ฐ€์ง€๊ณ  ์ƒ์„ฑํ•˜์˜€๋‹ค. ๊ฒ€์ฆ์„ ์œ„ํ•ด Streptococcus ์ข…์„ ํฌํ•จํ•˜๋Š” (i) ํ•ฉ์„ฑ๋œ ๋ฉ”ํƒ€์ง€๋†ˆ ์ƒ˜ํ”Œ๊ณผ (ii) ๋งŒ์„ฑ ํ์‡„์„ฑ ํ์งˆํ™˜(COPD) ํ™˜์ž์˜ ์ž„์ƒ ๊ฒ€์ฒด (iii) ํ˜ˆ๋ฅ˜ ๊ฐ์—ผ ํ™˜์ž์˜ ์ž„์ƒ ๊ฒ€์ฒด๋กœ ์ด๋ฃจ์–ด์ง„ ์„ธ๊ฐœ์˜ ๋ฐ์ดํ„ฐ ์…‹์„ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ ๊ธฐ์กด์— ๋„๋ฆฌ ์•Œ๋ ค์ง„ ์ƒท๊ฒƒ ํŒŒ์ดํ”„๋ผ์ธ์ธ MetaPhlan2๊ณผ ๋ณธ ์—ฐ๊ตฌ์˜ ํŒŒ์ดํ”„๋ผ์ธ์„ ๋น„๊ต ๋ถ„์„ํ•˜์˜€๋‹ค. ์œ„ ๊ฒ€์ฆ ๋ถ„์„์—์„œ UBCG๋ฅผ ๊ธฐ์ค€ ์„œ์—ด๋กœ ์‚ฌ์šฉํ•˜๊ธฐ์— ์ถฉ๋ถ„ํ•จ์„ ๊ฒ€์ฆํ•˜์˜€์œผ๋ฉฐ, ๋น ๋ฅด๊ณ  ์ •ํ™•ํ•˜๊ฒŒ ๊ธฐ์ค€ ์œ ์ „์ฒด์—์„œ UBCG ์„œ์—ด์„ ๋ฝ‘์•„ ์ƒท๊ฑด ๋ถ„์„์— ์šฉ์ดํ•จ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ genomospecies๋ฅผ ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์— ์ถ”๊ฐ€ํ•จ์œผ๋กœ์จ, ๋ณด๋‹ค ๊ฐœ์„ ๋œ ๋ถ„๋ฅ˜ ์ •ํ™•๋„๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋น„๋ก ์—ฌ๋Ÿฌ ํŒŒ์ดํ”„๋ผ์ธ๊ณผ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋“ค์ด ์กด์žฌํ•˜์ง€๋งŒ ๋ณด๋‹ค ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ๋ถ„๋ฅ˜๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด์„  ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ์ง€์†์ ์ธ ์—…๋ฐ์ดํŠธ์™€ ๋ถ„๋ฅ˜ ์ฒด๊ณ„์˜ ๊ฒ€์ฆ์˜ ์ค‘์š”ํ•จ์„ ๊ฐ•์กฐํ•˜์˜€๋‹ค. ์ดํ›„ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœ๋œ ํŒŒ์ดํ”„๋ผ์ธ์„ ์ด์šฉํ•˜์—ฌ 4,000๊ฐœ์˜ ์ƒท๊ฑด ๋ฉ”ํƒ€์ง€๋†ˆ ์ƒ˜ํ”Œ์—์„œ ์‚ฌ๋žŒ์— ์žฅ๋‚ด์— ๊ฐ€์žฅ ๋งŽ์ด ๋ฐœ๊ฒฌ๋˜๋Š” Bacteroides ์ข…์— ๋Œ€ํ•œ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋งŽ์€ ์–‘์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„ํ•˜์—ฌ์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐ์กด์— ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” MetaPhlAn2 ๊ณผ ๊ฐ™์€ ๋ฐฉ๋ฒ•์€ ์‚ฌ์šฉํ•  ์ˆ˜ ์—†์—ˆ์œผ๋ฉฐ ๋ถ„์„ ๊ฒฐ๊ณผ Bacteroides๋Š” ๋„์‹œํ™”๋œ ์‚ฌ๋žŒ์—๊ฒŒ ๋งŽ์ด ๋ถ„ํฌํ•˜๋Š” ๋ฐ˜๋ฉด ์•„ํ”„๋ฆฌ์นด ํ˜น์€ ๋‚จ๋ฏธ์ง€์—ญ์—์„œ ์›์‹œ์  ๋ถ€์กฑ์˜ ์‚ถ์„ ์‚ฌ๋Š” ์‚ฌ๋žŒ์—๊ฒŒ์„œ๋Š” ์ƒ๋Œ€์ ์œผ๋กœ ์ ๊ฒŒ ๋ถ„ํฌํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๊ฐ ๋‚˜๋ผ๋ณ„ ์ธ๊ตฌ์—์„œ๋Š” ์šฐ์ ๋˜๋Š” Bacteroides ์ข…์ด ๋‹ค๋ฆ„์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋Š”๋ฐ ์ด๋Š” ๊ฐ ์—ฐ๊ตฌ์˜ ์ƒ˜ํ”Œ๋ง ๋ฐฉ๋ฒ• ํ˜น์€ ์œ„์น˜์— ๋”ฐ๋ผ ์„ค๋ช…๋˜์–ด ์งˆ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์‹คํ—˜์šฉ ์ฅ์˜ ๊ฒฐ๊ณผ์—์„œ๋Š” ๊ฐ€์žฅ ๋‹ค์–‘ํ•œ Bacteroides๋ฅผ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ ์ด๋Š” ๋งŽ์€ ์ˆ˜์˜ ๊ธฐ์ค€ ์œ ์ „์ฒด๊ฐ€ ์ƒ์ฅ์—๊ฒŒ์„œ ๋‚˜์™”๊ธฐ ๋•Œ๋ฌธ์ธ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. ๋˜ํ•œ ๊ณ ์–‘์ด๋‚˜ ๊ฐ•์•„์ง€ ๊ฐ™์€ ๋ฐ˜๋ ค๋™๋ฌผ์˜ ์ƒ˜ํ”Œ์—์„œ๋„ ๋†’์€ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์žˆ์—ˆ๋Š”๋ฐ ๊ฐ ๋™๋ฌผ๋“ค์˜ ์ƒํ™œ์–‘์‹๊ณผ ๋จน์ด์— ๋”ฐ๋ฅธ ๊ฒฐ๊ณผ์ธ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋ณด๋‹ค ๋งŽ์€ ๋ฉ”ํƒ€์ง€๋†ˆ ๋ฐ์ดํ„ฐ ๋ถ„์„์˜ ํ•„์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํ•ต์‹ฌ ์œ ์ „์ž๋“ค์„ ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ๋กœ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์˜ ์‹คํšจ์„ฑ๊ณผ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ํ•ต์‹ฌ ์œ ์ „์ž ๊ธฐ๋ฐ˜์˜ ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋Š” ๋ณด๋‹ค ์ •ํ™•ํ•˜๊ณ  ์ „์ฒด ๋ฏธ์ƒ๋ฌผ์˜ ํ’๋ถ€๋„๋ฅผ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๊ณ  k-mer ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๊ธฐ์กด์— ์กด์žฌํ•˜๋˜ ๋‹ค๋ฅธ ํŒŒ์ดํ”„๋ผ์ธ ๋ณด๋‹ค ๋”์šฑ ๋น ๋ฅธ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋น ๋ฅด๊ฒŒ ๊ธฐ์ค€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ํ•ญ์ƒ ์ตœ์‹ ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๊ฐ€์ง€๊ณ  ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด๋Š” ๊ถ๊ทน์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์˜ ํŒŒ์ดํ”„๋ผ์ธ์„ ์‹ค์งˆ์ ์œผ๋กœ ์—ฐ๊ตฌ๋‚˜ ์ง„๋‹จ ๋ชฉ์ ์œผ๋กœ ์ด์šฉํ•˜๋Š” ์—ฐ๊ตฌ์ž๋“ค์—๊ฒŒ ํฐ ๋„์›€์ด ๋  ๊ฒƒ์ด๋‹ค.Shotgun metagenomics is of great importance to understand the microbial community composition of a sample and the impact it has on its host. The proper identification and quantification of bacterial species is a key component of any microbiome research that is based on metagenomic samples. In the last decade, several algorithms and databases have been developed, however the differences between references and the type of algorithm used for the classification makes the comparisons among themselves unfair and bias. The contents of the reference database, including genome sequences of type strains or reference genomes of uncultured species, have a great impact on the performance of the classification results of metagenomic samples. Another significant factor on shotgun metagenomics is the classification speed as most current bioinformatic tools lack computational and memory optimization. Here, I propose several enhancements to a well-known method, exact match k-mer classification in order to increase the overall speed of a metagenomic classification. This method was further improved by the use of Up-to-date Bacterial Core Gene (UBCG) sequences to provide better method for a faster and accurate shotgun metagenomic profiling classification. In order to prove the efficiency of our method, I built two UBCG-based reference databases: one containing UBCG sequences of valid named species, and the second one containing UBCG sequences of all valid named species and genomospecies in the EzBioCloud database. Three datasets containing Streptococcus species were used to evaluate the improved method against the MetaPhlan2 tool which is the most widely used open-source shotgun metagenomic classifier: (i) synthetic metagenomic samples, (ii) clinical sputum samples from patients with chronic obstructive pulmonary disease (COPD), and (iii) clinical samples of a blood stream infection. In this analysis, I demonstrated that UBCG sequences can be used as references for metagenomic classification, showing that they are easy to extract from genome sequences and accurate when predicting relative abundance. I also showed that the inclusion of genomospecies in the reference databases, significantly improves the classification accuracy of bacterial species within a metagenomic sample. Finally, I showed that while publicly available pipelines and databases are easily accessible, for accurate and reliable taxonomic classification, an updated database with proper taxonomic and genomic curation must be used. The method devised in this work is then applied to profile the Bacteroides species in over 4,000 shotgun metagenomic samples, which is one of most abundant members of the human gut microbiome. This task cannot be accomplished using conventional tools such as MetaPhlAn2 due to the high processing time they require. The results in this study showed that Bacteroides is high abundant in human samples from urban areas while being low abundant in humans from rural areas, particularly African and South American tribes. Countries showed dominance for a specific Bacteroides species, but this could also be explained by the type of study were the samples came from. Mice samples showed the most diversity of Bacteroides, this can be attributed by the number of bacterial references isolated from this organism. House cat and dog samples showed correlation between each other, this may be attributed to the similarities of their lifestyle and diet. This study shows the importance of having a great number of samples for any given metagenomic analysis, and even though, we have profiled thousands of samples, more might be needed in the future. The method proposed in this thesis demonstrates that core genes are reliable reference sequences for shotgun metagenomics. Their implementation as reference sequences in metagenomic databases improves the accuracy of the abundance prediction of any given sample. Additionally, with the use of a k-mer approach, this methods running time outperforms the most popular shotgun metagenomic tools. The work presented in this thesis aims to help microbial research by providing faster and accurate metagenomic taxonomic predictions. Finally, with the ability of updating a metagenomic database with ease, will help researchers to obtain the most up-to-date results to find potential diagnosis or treatments for diseases associated to human microbial communities.Chapter 1. General Introduction 1 1.1. Introduction to metagenomics 2 1.2. 16S rRNA sequencing 3 1.3. Shotgun metagenomic sequencing 5 1.3.1. History 5 1.3.2. Sample extraction 7 1.3.3. Library preparation 8 1.3.4. Sequencing 8 1.4. Shotgun metagenomic classification 9 1.4.1. Homology-based approaches 9 1.4.2. Exact match K-mer approaches 11 Chapter 2. An exact match k-mer algorithm 13 2.1. An exact match k-mer classification approach 14 2.1.1. Definition of the problem 14 2.1.2. Building a k-mer reference database 14 2.1.2.1. K-mer counting 14 2.1.2.2. K-mer mapping 16 2.1.3. Classification of a metagenomic read 16 2.1.3.1. K-mer search 19 2.1.3.2. Scoring a metagenomic read 20 2.1.4. Calculating the metagenome profile 20 2.1.4.1. Normalization for LCA-assigned reads 21 2.1.4.2. Normalization for cell count relative abundance 22 2.2. RAM memory usage 22 2.3. Quality Control 23 2.3.1. Read Trimming 23 2.3.2. Host read removal 24 Chapter 3. Revealing unrecognized species in the genus Streptococcus 28 3.1. A brief history of streptococcus in clinical metagenomics 29 3.2. Results and Discussion 32 3.2.1. Building a core gene reference database 32 3.2.2. Evaluation of Pipelines using Synthetic Metagenomes 36 3.2.3. Chronic obstructive pulmonary disease samples 44 3.2.3. Evaluating the value of genomospecies references in a metagenomic database 56 3.2.4. Identifying accurately a Streptococcal infection using clinical data 63 3.2.5. Effects of different ANI thresholds on the classification of genomospecies 69 3.3. Materials and Methods 76 3.3.1. Selecting the reference genomes 76 3.3.2. Average nucleotide identity and hierarchical clustering 76 3.3.3. Synthetic and Real metagenomic samples 77 3.3.4. Extracting the core genes 77 3.3.5. Taxonomic profiling 83 3.3.6. Biomarker discovery 84 3.4. Conclusions 85 Chapter 4. A large-scale shotgun metagenomic analysis on Bacteroides 86 4.1. Introduction 87 4.2. Bacteroides on the human gut 89 4.2.1. Collecting the samples 89 4.2.2. Methods 89 4.2.2.1. Reference Genomes 89 4.2.2.2. Metagenome profiling 90 4.2.3. Results 103 4.3. Bacteroides on Animal Species 128 4.3.1. Methods 128 4.3.2. Results 128 4.4. Discussion and conclusions 133 General Conclusion 135 References 139 Appendix I. A list of genomes from the genus Streptococcus used on Chapters 3 analysis. 146 ๊ตญ๋ฌธ์ดˆ๋ก 155Docto

    Advanced Control Strategies for Mobile Hydraulic Applications

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    Mobile hydraulic machines are affected by numerous undesired dynamics, mainly discontinuous motion and vibrations. Over the years, many methods have been developed to limit the extent of those undesired dynamics and improve controllability and safety of operation of the machine. However, in most of the cases, today\u27s methods do not significantly differ from those developed in a time when electronic controllers were slower and less reliable than they are today. This dissertation addresses this aspect and presents a unique control method designed to be applicable to all mobile hydraulic machines controlled by proportional directional valves. In particular, the proposed control method is targeted to hydraulic machines such as those used in the field including construction (wheel loaders, excavators, and backhoes, etc.), load handling (cranes, reach-stackers, and aerial lift, etc.), agriculture (harvesters, etc.), forestry, and aerospace. For these applications the proposed control method is designed to achieve the following goals: A. Improvement of the machine dynamics by reducing mechanical vibrations of mechanical arms, load, as well as operator seat; B. Reduction of the energy dissipation introduced by current vibration damping methods; C. Reduction of system slowdowns introduced by current vibration damping methods. Goal A is generally intended for all machines; goal B refers to those applications in which the damping is introduced by means of energy losses on the main hydraulic transmission line; goal C is related to those applications in which the vibration attenuation is introduced by slowing down the main transmission line dynamics. Two case studies are discussed in this work: 1. Hydraulic crane: the focus is on the vibrations of the mechanical arms and load (goals A and B). 2. Wheel loader: the focus is on the vibrations of the driver\u27s seat and bucket (goals A and C). The controller structure is basically unvaried for different machines. However, what differs in each application are the controller parameters, whose adaptation and tuning method represent the main innovations of this work. The proposed controller structure is organized so that the control parameters are adapted with respect to the instantaneous operating point which is identified by means of feedback sensors. The Gain Scheduling technique is used to implement the controller whose set of parameters are function of the specific identified operating point. The optimal set of control parameters for each operating point is determined through the non-model-based controller tuning. The technique determines the optimal set of controller parameters through the optimization of the experimental machine dynamics. The optimization is based on an innovative application of the Extremum Seeking algorithm. The optimal controller parameters are then indexed into the Gain Scheduler. The proposed method does not require the modification of the standard valve controlled machine layout since it only needs for the addition of feedback sensors. The feedback signals are used by the control unit to modify the electric currents to the proportional directional valves and cancel the undesired dynamics of the machine by controlling the actuator motion. In order for the proposed method to be effective, the proportional valve bandwidth must be significantly higher than the frequency of the undesired dynamics. This condition, which is typically true for heavy machineries, is further investigated in the research. The research mostly focuses on the use of pressure feedback. In fact, although the use of position, velocity, or acceleration sensors on the vibrating bodies of the machine would provide a more straightforward measurement of the vibration, they are extremely rare on mobile hydraulic machines where mechanical and environmental stress harm their integrity. A comparison between pressure feedback and acceleration feedback alternatives of the proposed method is investigated with the aim to outline the conditions making one alternative preferable over the other one (for those applications were both alternatives are technically viable in terms of sensors and wiring reliability). A mid-sized hydraulic crane (case study 1) was instrumented at Maha Fluid Power Research Center to study the effectiveness of the proposed control method, its stability and its experimental validation. Up to 30% vibration damping and 40% energy savings were observed for a specific cycle over the standard vibration damping method for this application. The proposed control method was also applied to a wheel loader (case study 2), and up to 70% vibrations attenuation on the bucket and 30% on the driver\u27s cab were found in simulations. These results also served to demonstrate the applicability of the control method to different hydraulic machines. Improved system response and a straightforward controller parameters tuning methodology are the features which give to the proposed method the potential to become a widespread technology for fluid power machines. The proposed method also has potential for improving several current vibration damping methods in terms of energy efficiency as well as simplification of both the hydraulic system layout and tuning process

    SPS phase control system performance via analytical simulation

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    A solar power satellite transmission system which incorporates automatic beam forming, steering, and phase control is discussed. The phase control concept centers around the notation of an active retrodirective phased array as a means of pointing the beam to the appropriate spot on Earth. The transmitting antenna (spacetenna) directs the high power beam so that it focuses on the ground-based receiving antenna (rectenna). A combination of analysis and computerized simulation was conducted to determine the far field performance of the reference distribution system, and the beam forming and microwave power generating systems

    Skills Development for Inclusive and Sustainable Growth in Developing Asia-Pacific

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    Focusing on the Asia-Pacific region, which in recent years has been the engine of global economic growth , this volume surveys trends and prospects in technical and vocational education and training (TVET) with particular reference to achieving inclusive growth and the greening of economies. Underlying the increasing pressure for new models of TVET provision is the rapid pace of technological change, demand for a work force which is highly responsive to evolving needs and a transforming market place that calls for higher order skills and lifelong learning. The book proposes a re-engineered, modernized TVET system that fosters an innovative approach which enhances the employability of workers as well as the sustainability of their livelihoods. The book includes contributions from leading policy makers, researchers, and practitioners, including those in the private sector in analyzing and forecasting the most urgent priorities in skills development. The book argues for creative approaches to TVET design and delivery particularly with a view to improve job prospects , and meeting the goals of inclusion, sustainable development and social cohesion. Addressing issues such as the chronic mismatches between skills acquired and actual skills required in the work place, the volume proposes diversified approaches towards workforce development and partnerships with the private sector to improve the quality and relevance of skills development . The new imperatives created by โ€˜greeningโ€™ economies and responses required in skills development and training are addressed. Developing TVET is a high priority for governments in the Asia Pacific region as they seek to achieve long-term sustainable growth since the .continued success of their economic destinies depend on it. The volume also includes an emerging framework for skills development for inclusive and sustainable growth in the Asia and Pacific region

    Network-on-Chip

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    Addresses the Challenges Associated with System-on-Chip Integration Network-on-Chip: The Next Generation of System-on-Chip Integration examines the current issues restricting chip-on-chip communication efficiency, and explores Network-on-chip (NoC), a promising alternative that equips designers with the capability to produce a scalable, reusable, and high-performance communication backbone by allowing for the integration of a large number of cores on a single system-on-chip (SoC). This book provides a basic overview of topics associated with NoC-based design: communication infrastructure design, communication methodology, evaluation framework, and mapping of applications onto NoC. It details the design and evaluation of different proposed NoC structures, low-power techniques, signal integrity and reliability issues, application mapping, testing, and future trends. Utilizing examples of chips that have been implemented in industry and academia, this text presents the full architectural design of components verified through implementation in industrial CAD tools. It describes NoC research and developments, incorporates theoretical proofs strengthening the analysis procedures, and includes algorithms used in NoC design and synthesis. In addition, it considers other upcoming NoC issues, such as low-power NoC design, signal integrity issues, NoC testing, reconfiguration, synthesis, and 3-D NoC design. This text comprises 12 chapters and covers: The evolution of NoC from SoCโ€”its research and developmental challenges NoC protocols, elaborating flow control, available network topologies, routing mechanisms, fault tolerance, quality-of-service support, and the design of network interfaces The router design strategies followed in NoCs The evaluation mechanism of NoC architectures The application mapping strategies followed in NoCs Low-power design techniques specifically followed in NoCs The signal integrity and reliability issues of NoC The details of NoC testing strategies reported so far The problem of synthesizing application-specific NoCs Reconfigurable NoC design issues Direction of future research and development in the field of NoC Network-on-Chip: The Next Generation of System-on-Chip Integration covers the basic topics, technology, and future trends relevant to NoC-based design, and can be used by engineers, students, and researchers and other industry professionals interested in computer architecture, embedded systems, and parallel/distributed systems

    The skin microbiopsy

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