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    Genetic variants of PNPLA3, TM6SF2, SAMM50 and metabolomic profiles

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021.8. ์ด๊ฒฝ์žฌ.๋ฐฐ๊ฒฝ ์ผ๋ถ€ ์œ ์ „์ž ๋ณ€์ด์™€ ๋‹ค๋ฅธ ๋Œ€์‚ฌ์ฒด ์–‘์ƒ์ด ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์งˆํ™˜๊ณผ ๊ด€๋ จ๋˜์–ด ์žˆ๋‹ค๊ณ  ๋ณด๊ณ ๋˜๊ณ  ์žˆ์ง€๋งŒ ์œ ์ „์ž ๋ณ€์ด์™€ ๋Œ€์‚ฌ์ฒด ๋ถ„์„์„ ๋™์‹œ์— ์‹œํ–‰ํ•œ ์—ฐ๊ตฌ๋Š” ๊ฑฐ์˜ ์—†์œผ๋ฉฐ ์†Œ์•„์ฒญ์†Œ๋…„ ์ง‘๋‹จ์—์„œ๋Š” ์‹œํ–‰๋œ ์—ฐ๊ตฌ๊ฐ€ ์—†๋‹ค. ๋ชฉ์  ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์†Œ์•„ ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„ ์งˆํ™˜์— ๋ฏธ์น˜๋Š” ์œ ์ „์  ๋ณ€์ด์˜ ์˜ํ–ฅ์„ ํŒŒ์•…ํ•˜๊ณ , ๋”๋ถˆ์–ด ํ™˜์ž์™€ ๋Œ€์กฐ๊ตฐ์˜ ๋Œ€์‚ฌ์ฒด ์ฐจ์ด์™€ ์œ ์ „์ž๋ณ€์ด์™€ ์—ฐ๊ด€์„ฑ์„ ๋ถ„์„ํ•˜์—ฌ ์งˆ๋ณ‘์˜ ๋ณ‘์ธ์„ ๋ณด๋‹ค ๋ช…ํ™•ํžˆ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•จ์ด๋‹ค. ๋ฐฉ๋ฒ• ์œ ์ „์ž ๋ถ„์„์„ ์œ„ํ•˜์—ฌ 228๋ช…์˜ ์†Œ์•„ ํ™˜์ž์™€ 225๋ช…์˜ ๋Œ€์กฐ๊ตฐ (๊ณผ์ฒด์ค‘ 69, ์ •์ƒ์ฒด์ค‘ 156๋ช…)์ด ๋“ฑ๋ก๋˜์—ˆ๊ณ  ๊ทธ์ค‘ 105๋ช…์˜ ํ™˜์ž์™€ 61๋ช…์˜ ๋Œ€์กฐ๊ตฐ (๊ณผ์ฒด์ค‘ 118๋ช…, ์ •์ƒ์ฒด์ค‘ 48๋ช…)์„ ๋Œ€์‚ฌ์ฒด ๋ถ„์„์„ ์œ„ํ•ด ํฌํ•จํ•˜์˜€๋‹ค. ๋‹ค์Œ๊ณผ ๊ฐ™์€ PNPLA3 (rs738409), TM6SF2 (rs58542926) and SAMM50 (rs2073080, rs3761472) 4๊ฐœ์˜ ์œ ์ „์ž ๋ณ€์ด๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ํ˜ˆ์žฅ ๋Œ€์‚ฌ์ฒด๋Š” Biocrates AbsoluteIDQ p400 kit์™€ Thermo Q Exactive Plus orbitrap mass spectrometer๋ฅผ ํ†ตํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ชจ๋“  ์ฐธ๊ฐ€์ž๋Š” ์‹ ์ฒด๊ณ„์ธก์„ ์‹œํ–‰ํ•˜์˜€๊ณ  ์ผ๋ฐ˜ ํ˜ˆ์•ก๊ฒ€์‚ฌ์™€, ๊ฐ„๊ธฐ๋Šฅ๊ฒ€์‚ฌ๋ฅผ ๋ฐ›์•˜์œผ๋ฉฐ, ๊ณผ์ฒด์ค‘์ง‘๋‹จ์—์„œ๋Š” ๋ณต๋ถ€๋‘˜๋ ˆ์™€ ํ˜ˆ์••, ๊ณต๋ณต ํ˜ˆ๋‹น, ์ธ์Š๋ฆฐ, ์ง€๋ฐฉ๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๊ฐ„ ์ดˆ์ŒํŒŒ๋ฅผ ํ†ตํ•ด ์ง€๋ฐฉ๊ฐ„ ์œ ๋ฌด์™€ ์ง€๋ฐฉ๊ฐ„ ๋‹จ๊ณ„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€์œผ๋ฉฐ ๋น„์นจ์Šต์  ์„ฌ์œ ํ™” ์ ์ˆ˜๋ฅผ ๋”ฐ๋ผ ์„ฌ์œ ํ™” ์ •๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์งˆํ™˜์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์œ ์ „์ž ๋ณ€์ด์™€ ๋Œ€์‚ฌ์ฒด ์ฐจ์ด๋Š” ๋ชจ๋‘ ์ผ๋ฐ˜์†Œ์•„ ์ง‘๋‹จ๊ณผ ๊ณผ์ฒด์ค‘์ง‘๋‹จ์—์„œ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ PNPPLA3 (rs738409), TM6SF2 (rs58542926), SAMM50 (rs2073080, rs3761472) ๋ณ€์ด๋Š” ์ผ๋ฐ˜ ์†Œ์•„์ง‘๋‹จ (์Šน์‚ฐ๋น„: 1.99~ 3.26, P <0.05)๊ณผ ๊ณผ์ฒด์ค‘์†Œ์•„์ง‘๋‹จ (์Šน์‚ฐ๋น„: 2.22~ 22.94, P <0.05์—์„œ ๋…๋ฆฝ์ ์œผ๋กœ NAFLD์œ„ํ—˜์„ ๋†’์˜€๋‹ค. ๋‹ค๋ฅธ ๋…๋ฆฝ์ ์ธ ์œ„ํ—˜์ธ์ž๋Š” ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜-Z ์ ์ˆ˜์™€, ๋‚จ์„ฑ์ด์—ˆ์œผ๋ฉฐ, ๊ณผ์ฒด์ค‘์ง‘๋‹จ์—์„œ๋Š” ๊ณต๋ณต ์ธ์Š๋ฆฐ์ด ์ถ”๊ฐ€์ ์ธ ์œ„ํ—˜ ์ธ์ž์˜€๋‹ค. ์ด ์œ ์ „์ž๋ณ€์ด๋“ค์€ ๋‚˜์ด, ์„ฑ๋ณ„, ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜-Z์ ์ˆ˜์™€ ๊ด€๋ จ์—†์ด ์•Œ๋ผ๋‹Œ ์•„๋ฏธ๋…ธ ์ „์ดํšจ์†Œ์™€, ์ดˆ์ŒํŒŒ์ƒ์˜ ์ง€๋ฐฉ๊ฐ„ ์ •๋„, ์„ฌ์œ ํ™” ์ˆ˜์น˜๋ฅผ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ์ด๋Ÿฌํ•œ ์œ ์ „์ ์˜ํ–ฅ์€ ์ „์ฒด ์†Œ์•„ ์ง‘๋‹จ๋ณด๋‹ค ๊ณผ์ฒด์ค‘์ง‘๋‹จ์—์„œ ๋”์šฑ ์ปธ๋‹ค. ๊ณผ์ฒด์ค‘์ง‘๋‹จ๊ณผ, ์ •์ƒ์ฒด์ค‘ ์ง‘๋‹จ ๋ชจ๋‘์—์„œ ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์งˆํ™˜ ํ™˜์ž๋Š” ๋Œ€์กฐ๊ตฐ์— ๋น„ํ•ด ๋” ๋†’์€ ํ˜ˆ์žฅ ๋ฅ˜์‹ , ์•„์ด์†Œ๋ฅ˜์‹ , ๋ฐœ๋ฆฐ ๊ฐ™์€ branched chain ์•„๋ฏธ๋…ธ์‚ฐ๊ณผ, ๊ธ€๋ฃจํƒ€๋ฉ”์ดํŠธ, ํƒ€์ด๋กœ์‹ , ํฌ์ŠคํŒŒํ‹ฐ๋”œ์ฝœ๋ฆฐ, ์Šคํ•‘๊ณ ๋งˆ์ด์—˜๋ฆฐ, ๋‹ค์ด๊ธ€๋ฆฌ์„ธ๋ผ์ด๋“œ, ์ค‘์„ฑ์ง€๋ฐฉ ๋†๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ ์ด๊ฒƒ๋“ค์ค‘ branched chain ์•„๋ฏธ๋…ธ์‚ฐ, ๊ธ€๋ฃจํƒ€๋ฉ”์ดํŠธ ๋“ฑ์€ HOMA-IR๊ณผ ์–‘์„ฑ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. ๊ธ€๋ผ์ด์‹ , ๊ธ€๋ฃจํƒ€๋ฏผ, ์„ธ๋ฆฐ ๋†๋„๋Š” ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„ํ™˜์ž์—์„œ ๋‚ฎ์•˜์œผ๋ฉฐ ๊ธ€๋ผ์ด์‹ , ๊ธ€๋ฃจํƒ€๋ฏผ์€ HOMA-IR๊ณผ ์Œ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์—ฌ ํ™˜์ž์™€ ๋Œ€์กฐ๊ตฐ๊ฐ„์˜ ๋Œ€์‚ฌ์ฒด ์ฐจ์ด๋Š” ์ธ์Š๋ฆฐ์ €ํ•ญ์„ฑ๊ณผ ์—ฐ๊ด€์ด ์žˆ์Œ์„ ์‹œ์‚ฌํ•˜์˜€๋‹ค. TM6SF2 ๋ณ€์ด๊ฐ€ ์žˆ๋Š” ํ™˜์ž์—์„œ๋Š” ๋ณ€์ด๊ฐ€ ์—†๋Š” ํ™˜์ž๋ณด๋‹ค ๋” ๋‚ฎ์€ ํ˜ˆ์žฅ ํฌ์ŠคํŒŒํ‹ฐ๋”œ์ฝœ๋ฆฐ, ์Šคํ•‘๊ณ ๋งˆ์ด์—˜๋ฆฐ, ์ค‘์„ฑ์ง€๋ฐฉ ๋†๋„๋ฅผ ๋‚˜ํƒ€๋ƒˆ์œผ๋ฉฐ ์ด๊ฒƒ์€ ๋น„์•Œ์ฝœ์„ฑ์ง€๋ฐฉ๊ฐ„ ์œ ๋ฌด์™€ ๋ฐ˜๋Œ€๋˜๋Š” ๋ถ„ํฌ์˜€๋‹ค. ๊ฒฐ๋ก  ๋ณธ ์—ฐ๊ตฌ๋Š” ์†Œ์•„์ฒญ์†Œ๋…„ ์—ฐ๋ น์—์„œ ์ตœ์ดˆ๋กœ ๋‹ค์–‘ํ•œ ์œ ์ „์ž ๋ณ€์ด์™€ ํ˜ˆ์žฅ ๋Œ€์‚ฌ์ฒด๋ฅผ ๋™์‹œ์— ๋ถ„์„ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. PNPLA3, TM6SF2, SAMM50 ์€ ์†Œ์•„์ฒญ์†Œ๋…„ ์ผ๋ฐ˜์ง‘๋‹จ๊ณผ ๊ณผ์ฒด์ค‘์ง‘๋‹จ ๋ชจ๋‘์—์„œ ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์งˆํ™˜์˜ ๋ฐœ์ƒ๊ณผ ์ค‘์ฆ๋„์— ๋…๋ฆฝ์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์ณค์œผ๋ฉฐ ๊ทธ ์˜ํ–ฅ์€ ๊ณผ์ฒด์ค‘์ง‘๋‹จ์—์„œ ์ปธ๋‹ค. 101๊ฐœ์˜ ๋Œ€์‚ฌ์ฒด๊ฐ€ ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„์งˆํ™˜๊ณผ ๋Œ€์กฐ๊ตฐ ์‚ฌ์ด์— ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ ๊ทธ์ค‘ 49๊ฐœ๋Š” ์ธ์Š๋ฆฐ ์ €ํ•ญ์„ฑ๊ณผ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค. TM6SF2 ๋ณ€์ด๋ฅผ ๊ฐ€์ง„ ์‚ฌ๋žŒ์€ ํ˜ˆ์žฅ ์ง€๋ฐฉ๋†๋„๊ฐ€ ๋” ๋‚ฎ์•˜์œผ๋ฉฐ, ์ด๊ฒƒ์€ ์ผ๋ฐ˜์ ์ธ NAFLDํ™˜์ž์™€๋Š” ๋ฐ˜๋Œ€๋˜๋Š” ์–‘์ƒ์ด์—ˆ์œผ๋ฉฐ ๋‹ค๋ฅธ ์œ ์ „์ž ๋ณ€์ด์— ๋”ฐ๋ฅธ ๋Œ€์‚ฌ์ฒด ๋ณ€ํ™”๋Š” ํ›„์† ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๊ฒ ๋‹ค.Background Some genetic variants and different metabolomic profiles have been reported to be associated with nonalcoholic fatty liver disease (NAFLD). Few studies have reported various genetic variants and associated metabolites simultaneously, and no study has been conducted in pediatric populations. Objective The aim of this study was to investigate the effects of genetic variants on pediatric NAFLD and analyze metabolic differences between NAFLD patients and controls in a pediatric population. In addition, other risk factors for pediatric NAFLD were aimed to investigate. Methods NAFLD was defined if hepatic steatosis was shown on ultrasound. A total of 228 NAFLD patients (body mass index-Z [BMI-Z] = 2.51 ยฑ 1.01) and 225 controls (BMI-Z = 0.22 ยฑ 1.48) were included. All participants underwent examination by anthropometry and blood cell count and liver function analysis. Four variants of PNPLA3 (rs738409), TM6SF2 (rs58542926) and SAMM50 (rs2073080, rs3761472) were genotyped by TaqMan allelic discrimination assay. Metabolic profiles were checked in children with overweight. The pediatric NAFLD fibrosis score (PNFS), the AST/platelet ratio index (APRI) and fibrosis-4 (FIB-4) were used to evaluate the degree of hepatic fibrosis. Genetic risk factors for NAFLD in all participants were analyzed by adjusting for age, sex and BMI-Z. Subgroup analysis was conducted in children with overweight with more metabolic adjustments. The genetic risk score was calculated to evaluate the synergetic effects of 4 genetic variants. Among them, 166 (105 NAFLD and 61 control) children were enrolled for metabolomic analysis. The plasma metabolome was quantified using a Biocrates AbsoluteIDQ p400 kit and Thermo Q Exactive Plus Orbitrap mass spectrometer. Results The four genetic variants (rs738409, rs58542926, rs2073080 and rs3761472), male sex and BMI-Z independently increased susceptibility to NAFLD. These variants remained significant risk factors with higher odds ratio in children with overweight in addition to fasting insulin and triglyceride. These variants increased the alanine aminotransferase level, PNFS, APRI, and FIB-4 independently. As the genetic risk score increased, aspartate transaminase, alanine aminotransferase, PNFS, APRI, and FIB-4 increased independently suggesting synergetic effects of these 4 variants. NAFLD patients showed a higher plasma levels of branched chain amino acids (BCAAs, leucine, isoleucine, valine), tyrosine, phosphatidylcholines (PCs), sphingomyelins (SMs), diglyceride, triglycerides (TGs) than control. Some of these metabolites including BAAA, glutamate, PCs, SMs had positive association with homeostasis model assessment-estimated insulin resistance (HOMA-IR). Plasma levels of glutamine, glycine and serin were lower in NAFLD patients than control. Glutamine and glycine showed negative correlation with HOMA-IR. The carries of TM6SF2 variants significantly showed lower plasma PCs, SMs and TG compared to wild type and the distribution of metabolites was reversed to the NAFLD. Conclusion The effects of genetic variants and metabolomic profiles in children with NAFLD was first demonstrated in this study. Genetic variants of PNPLA3, TM6SF2 and SAMM50 are associated with the development and severity of pediatric NAFLD and their effects are greater in children with overweight than normal weight. These variants have synergetic effects on severity of pediatric NAFLD. A total of 49 metabolites showed significant differences between subjects with NAFLD and control, are associated with insulin resistance. While variants of TM6SF2 results in lower plasma lipids, other variants did not show significant differences in metabolome.Chapter 1. Introduction 1 Chapter 2. Method 4 Chapter 3. Results 15 Chapter 4. Discussion 46 Chapter 5. Conclusion 61 Chapter 6. Abbreviations 63 Chapter 7. Bibliography 65 Chapter 8. Supplementary tables 79 Chapter 9. Abstract in Korean 84 Chapter 10. Aknowledgements 87๋ฐ•

    Growing Educational Differentials in the Retreat from Marriage among Korean Men

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    Applying discrete-time hazard models to person-year data constructed from 1% microdata sample of 2010 Korean Census, we explore how menโ€™s education affects their transition to first marriage, and how the relationship between education and marriage has changed across three 10-year birth cohorts of Korean men born from 1946 to 1975. Currently, there is only limited knowledge on how education is related to marriage formation and how the effect is contingent upon macro contexts of education, economy, and family among East Asian men. We find that the high educated delay marriage until later ages but catch up to the extent to which they are eventually more likely to marry than the low educated. There is a continued trend across cohorts toward the delay and avoidance of marriage at all educational levels. However, the trend of retreat from marriage has been more substantial for men with high school or less education compared to men with a university degree, leading to growing educational gaps over time in marriage. We discuss the findings in the contexts of deteriorating economic prospects of Korean men with lower education and also the declining pool of potential spouses for the low educated

    Technical Efficiency in the Iron and Steel Industry: A Stochastic Frontier Approach

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    In this paper we examine the technical efficiency of firms in the iron and steel industry and try to identify the factors contributing to the industry's efficiency growth, using a time-varying stochastic frontier model. Based on our findings, which pertain to 52 iron and steel firms over the period of 1978-1997, POSCO and Nippon Steel were the most efficient firms, with their production, on average, exceeding 95 percent of their potential output. Our findings also shed light on possible sources of efficiency growth in the industry. If a firm is government-owned, its privatization is likely to improve its technical efficiency to a great extent. A firm's technical efficiency also tends to be positively related to its production level as measured by a share of the total world production of crude steel. Another important source of efficiency growth identified by our empirical findings is adoption of new technologies and equipment. Our findings clearly indicate that continued efforts to update technologies and equipment are critical in pursuit of efficiency in the iron and steel industry.

    Reactive oxygen species regulate urokinase plasminogen activator expression and cell invasion via mitogen-activated protein kinase pathways after treatment with hepatocyte growth factor in stomach cancer cells

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    Abstract Background Reactive oxygen species (ROS) are closely associated with the intracellular signal cascade, thus strongly implicating involvement in tumor progression. However, the mechanism by which ROS are generated and how ROS target downstream molecules to trigger tumor metastasis is unclear. In this study, we investigated the underlying signal pathways in ROS-induced urokinase plasminogen activator (uPA) expression in the human gastric cancer cells, NUGC-3 and MKN-28. Methods and Results Intracellular ROS, as determined using the fluorescent probe, 2'-7' dichlorofluorescein diacetate, decreased after treatment with hepatocyte growth factor (HGF). We confirmed that Rac-1 regulated ROS production after activation of the AKT pathway with HGF. Exogenously added H2O2 promoted the expression of HGF, but not in a dose-dependent manner and also showed negative expression of HGF after co-treatment with H2O2 and HGF. Treatment with NAC, an intracellular free radical scavenger, decreased the enhancement of uPA production and tumor invasion in both cells. We clarified the downstream pathways regulated by ROS after treatment with H2O2, which showed negative control between FRK and p38 kinase activities for uPA regulation. Conclusion HGF regulates Rac-1-induced ROS production through the Akt pathway and ROS regulates uPA production and invasion via MAP kinase, which provides novel insight into the mechanisms underlying the progression of gastric cancer.</p

    Rigorous Simulation Model of Kerogen Pyrolysis for the In-situ Upgrading of Oil Shales

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    Oil shale is a vast, yet untapped energy source, and the pyrolysis of kerogen in the oil shales releases recoverable hydrocarbons. In this dissertation, we investigate how to increase process efficiency and decrease the costs of in-situ upgrading process for kerogen pyrolysis, which is applicable to the majority of the oil shales. In-situ upgrading processes include (a) Shell In-situ Conversion Process (ICP), (b) ExxonMobil Electrofrac, and (c) Texas A&M (TAMU) Steamfrac. We evaluate these three processes in realistic scenarios using our newly developed multi-phase, multi-component, nonisothermal simulator. Kerogen pyrolysis is represented by 6 kinetic reactions resulting in 10 components and 4 phases. Expanding TAMU Flow and Transport Simulator (FTSim), we develop a fully functional capability that describes the kerogen pyrolysis and the accompanying system changes. The simulator describes the coupled process of mass transport and heat flow through porous and fractured media, and accurately accounts for phase equilibria and transitions. It provides a powerful tool to evaluate the efficiency and the productivity of the in-situ upgrading processes. We validate our simulator by reproducing the field production data of the Shell ICP implemented in Green River Formation. We conduct the sensitivity analyses of the presence and absence of pre-existing fracture system, oil shale grade, permeability of the fracture network, and thermal conductivity of the formation. Validated model has the oil shale grade of 25 gal/ton, fracture domain permeability of 150 md, and formation thermal conductivity of 2.0 W/m-K. In the application cases, we analyze the significant factors affecting each process. In the Shell ICP, the ExxonMobil Electrofrac, and the TAMU Steamfrac, we study the effects of heater temperature, electrical conductivities of injection material, and steam injection strategy, respectively. We find that the best case of the Shell ICP showed the highest energy efficiency of 144 %. The best cases of the ExxonMobil Electrofrac and the TAMU Steamfrac show the energy efficiency of 74.1 %, and 54.1 %, respectively. We obtain positive Net Present Value (NPV) in the TAMU Steamfrac by much less number of wells than the Shell ICP and the ExxonMobil Electrofrac, though it has the lowest energy efficiency
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