Search

Your search keyword '"Yung-Hsuan Yang"' showing total 10 results
Start Over Author "Yung-Hsuan Yang"
10 results on '"Yung-Hsuan Yang"'

1. Computed tomography–based gastric volumetry for morbid obesity to assess weight loss and fatty liver change

Author

Hsin-Yu Chen, Po-Jen Yang, Po-Chu Lee, Po-Han Chuang, Yung-Hsuan Yang, Wan-Ting Chiu, and Chih-Horng Wu

Subjects
Computed tomography, Volumetry, Morbid obesity, Laparoscopic sleeve gastrectomy, Fatty liver, Medicine (General), R5-920
Abstract

Background/purpose: Laparoscopic sleeve gastrectomy (LSG) is an effective treatment for patients with morbid obesity, but the optimal gastric volume (GV) for resection remains unclear. Accordingly, we aimed to determine the optimal percentage of excised stomach that could engender significant weight loss and improve fatty liver. Methods: This prospective study included 63 patients. Computed tomography (CT) scans were performed before and 1 year after LSG to evaluate the gastric lumen (GL) and GV. Specifically, the stomach was distended with effervescent powder, following water–contrast mixture (20:1) and assessed by three-dimensional reconstruction. The correlations of reduced gastric lumen/volume (RGL/RGV) with total body weight (BW) loss and liver–spleen density ratio (LSDR) changes were analyzed, and optimal RGL/RGV associated with significant BW and fatty liver changes were determined. Results: We noted a positive correlation between the percentage of RGV/RGL (%RGV/%RGL) and percentage of total weight loss (%TWL; r = 0.359, p = 0.004 and r = 0.271, p = 0.032). Furthermore, a %RGL value of >78.2% and %RGV value of >75.3% were associated with more significant BW loss than did limited excision (both p 59.1% and >56.4% (both p 75.3% and resulted in fatty liver disease improvement when %RGV was >56.4%.

Published
2024
Full Text
View/download PDF

2. Expression-Based Cell Lineage Analysis in Drosophila Through a Course-Based Research Experience for Early Undergraduates

Author

John M. Olson, Cory J. Evans, Kathy T. Ngo, Hee Jong Kim, Joseph Duy Nguyen, Kayla G. H. Gurley, Truc Ta, Vijay Patel, Lisa Han, Khoa T. Truong-N, Letty Liang, Maggie K. Chu, Hiu Lam, Hannah G. Ahn, Abhik Kumar Banerjee, In Young Choi, Ross G. Kelley, Naseem Moridzadeh, Awais M. Khan, Omair Khan, Szuyao Lee, Elizabeth B. Johnson, Annie Tigranyan, Jay Wang, Anand D. Gandhi, Manish M. Padhiar, Joseph Hargan Calvopina, Kirandeep Sumra, Kristy Ou, Jessie C. Wu, Joseph N. Dickan, Sabrena M. Ahmadi, Donald N. Allen, Van Thanh Mai, Saif Ansari, George Yeh, Earl Yoon, Kimberly Gon, John Y. Yu, Johnny He, Jesse M. Zaretsky, Noemi E. Lee, Edward Kuoy, Alexander N. Patananan, Daniel Sitz, PhuongThao Tran, Minh-Tu Do, Samira J. Akhave, Silverio D. Alvarez, Bobby Asem, Neda Asem, Nicole A. Azarian, Arezou Babaesfahani, Ahmad Bahrami, Manjeet Bhamra, Ragini Bhargava, Rakesh Bhatia, Subir Bhatia, Nicholas Bumacod, Jonathan J. Caine, Thomas A. Caldwell, Nicole A. Calica, Elise M. Calonico, Carman Chan, Helen H.-L. Chan, Albert Chang, Chiaen Chang, Daniel Chang, Jennifer S. Chang, Nauman Charania, Jasmine Y. Chen, Kevin Chen, Lu Chen, Yuyu Chen, Derek J. Cheung, Jesse J. Cheung, Jessica J. Chew, Nicole B. Chew, Cheng-An Tony Chien, Alana M. Chin, Chee Jia Chin, Youngho Cho, Man Ting Chou, Ke-Huan K. Chow, Carolyn Chu, Derrick M. Chu, Virginia Chu, Katherine Chuang, Arunit Singh Chugh, Mark R. Cubberly, Michael Guillermo Daniel, Sangita Datta, Raj Dhaliwal, Jenny Dinh, Dhaval Dixit, Emmylou Dowling, Melinda Feng, Christopher M. From, Daisuke Furukawa, Himaja Gaddipati, Lilit Gevorgyan, Zunera Ghaznavi, Tulika Ghosh, Jaskaran Gill, David J. Groves, Kalkidan K. Gurara, Ali R. Haghighi, Alexandra L. Havard, Nasser Heyrani, Tanya Hioe, Kirim Hong, Justin J. Houman, Molly Howland, Elaine L. Hsia, Justin Hsueh, Stacy Hu, Andrew J. Huang, Jasmine C. Huynh, Jenny Huynh, Chris Iwuchukwu, Michael J. Jang, An An Jiang, Simran Kahlon, Pei-Yun Kao, Manpreet Kaur, Matthew G. Keehn, Elizabeth J. Kim, Hannah Kim, Michelle J. Kim, Shawn J. Kim, Aleksandar Kitich, Ross A. Kornberg, Nicholas G. Kouzelos, Jane Kuon, Bryan Lau, Roger K. Lau, Rona Law, Huy D. Le, Rachael Le, Carrou Lee, Christina Lee, Grace E. Lee, Kenny Lee, Michelle J. Lee, Regina V. Lee, Sean H. K. Lee, Sung Kyu Lee, Sung-Ling D. Lee, Yong Jun Lee, Megan J. Leong, David M. Li, Hao Li, Xingfu Liang, Eric Lin, Michelle M. Lin, Peter Lin, Tiffany Lin, Stacey Lu, Serena S. Luong, Jessica S. Ma, Li Ma, Justin N. Maghen, Sravya Mallam, Shivtaj Mann, Jason H. Melehani, Ryan C. Miller, Nitish Mittal, Carmel M. Moazez, Susie Moon, Rameen Moridzadeh, Kaley Ngo, Hanh H. Nguyen, Kambria Nguyen, Thien H. Nguyen, Angela W. Nieh, Isabella Niu, Seo-Kyung Oh, Jessica R. Ong, Randi K. Oyama, Joseph Park, Yaelim A. Park, Kimberly A. Passmore, Ami Patel, Amy A. Patel, Dhruv Patel, Tirth Patel, Katherine E. Peterson, An Huynh Pham, Steven V. Pham, Melissa E. Phuphanich, Neil D. Poria, Alexandra Pourzia, Victoria Ragland, Riki D. Ranat, Cameron M. Rice, David Roh, Solomon Rojhani, Lili Sadri, Agafe Saguros, Zainab Saifee, Manjot Sandhu, Brooke Scruggs, Lisa M. Scully, Vanessa Shih, Brian A. Shin, Tamir Sholklapper, Harnek Singh, Sumedha Singh, Sondra L. Snyder, Katelyn F. Sobotka, Sae Ho Song, Siddharth Sukumar, Halley C. Sullivan, Mark Sy, Hande Tan, Sara K. Taylor, Shivani K. Thaker, Tulsi Thakore, Gregory E. Tong, Jacinda N. Tran, Jonathan Tran, Tuan D. Tran, Vivi Tran, Cindy L. Trang, Hung G. Trinh, Peter Trinh, Han-Ching H. Tseng, Ted T. Uotani, Akram V. Uraizee, Kent K. T. Vu, Kevin K. T. Vu, Komal Wadhwani, Paluk K. Walia, Rebecca S. Wang, Shuo Wang, Stephanie J. Wang, Danica D. Wiredja, Andrew L. Wong, Daniel Wu, Xi Xue, Griselda Yanez, Yung-Hsuan Yang, Zhong Ye, Victor W. Yee, Cynthia Yeh, Yue Zhao, Xin Zheng, Anke Ziegenbalg, Jon Alkali, Ida Azizkhanian, Akash Bhakta, Luke Berry, Ryen Castillo, Sonja Darwish, Holly Dickinson, Ritika Dutta, Rahul Kumar Ghosh, Riley Guerin, Jonathan Hofman, Garrick Iwamoto, Sarah Kang, Andrew Kim, Brian Kim, Hanwool Kim, Kristine Kim, Suji Kim, Julie Ko, Michael Koenig, Alejandro LaRiviere, Clifton Lee, Jiwon Lee, Brandon Lung, Max Mittelman, Mark Murata, Yujin Park, Daniel Rothberg, Ben Sprung-Keyser, Kunal Thaker, Vivian Yip, Paul Picard, Francie Diep, Nikki Villarasa, Volker Hartenstein, Casey Shapiro, Marc Levis-Fitzgerald, Leslie Jaworski, David Loppato, Ira E. Clark, and Utpal Banerjee

Subjects
g-trace, gene expression, education, stem, cure, Genetics, QH426-470
Abstract

A variety of genetic techniques have been devised to determine cell lineage relationships during tissue development. Some of these systems monitor cell lineages spatially and/or temporally without regard to gene expression by the cells, whereas others correlate gene expression with the lineage under study. The GAL4 Technique for Real-time and Clonal Expression (G-TRACE) system allows for rapid, fluorescent protein-based visualization of both current and past GAL4 expression patterns and is therefore amenable to genome-wide expression-based lineage screens. Here we describe the results from such a screen, performed by undergraduate students of the University of California, Los Angeles (UCLA) Undergraduate Research Consortium for Functional Genomics (URCFG) and high school summer scholars as part of a discovery-based education program. The results of the screen, which reveal novel expression-based lineage patterns within the brain, the imaginal disc epithelia, and the hematopoietic lymph gland, have been compiled into the G-TRACE Expression Database (GED), an online resource for use by the Drosophila research community. The impact of this discovery-based research experience on student learning gains was assessed independently and shown to be greater than that of similar programs conducted elsewhere. Furthermore, students participating in the URCFG showed considerably higher STEM retention rates than UCLA STEM students that did not participate in the URCFG, as well as STEM students nationwide.

Published
2019
Full Text
View/download PDF

3. Man with trauma after a fall

Author

Chia‐Yun Chang, Chi‐Ju Lu, Yung‐Hsuan Yang, and Kao‐Lang Liu

Subjects
Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Published
2020
Full Text
View/download PDF

5. Expression-Based Cell Lineage Analysis inDrosophilaThrough a Course-Based Research Experience for Early Undergraduates

Author

Daniel Chang, Earl Yoon, David Loppato, Justin N. Maghen, Silverio D. Alvarez, Kristy Ou, Katherine E. Peterson, Manjeet Bhamra, Helen H.-L. Chan, Lilit Gevorgyan, Clifton Lee, Peter Trinh, David M. Li, Elizabeth Kim, Garrick Iwamoto, Yue Zhao, Victor W. Yee, Kimberly Gon, Lisa M. Scully, Akram V. Uraizee, Sangita Datta, Sae Ho Song, Lisa Han, Andrew C. J. Huang, PhuongThao Tran, George K. Yeh, John M. Olson, Shawn J. Kim, Lili Sadri, Hannah G. Ahn, Grace E. Lee, Cindy L. Trang, Arezou Babaesfahani, Stephanie Wang, Saif Ansari, Hanwool Kim, Rakesh Bhatia, Brandon Lung, Victoria Ragland, Noemi E. Lee, Casey Shapiro, Van Thanh Mai, Sean H. K. Lee, Holly Dickinson, Joseph Park, Rona Law, Carrou Lee, Cameron M. Rice, Kirandeep Sumra, Alexandra L. Pourzia, Raj Dhaliwal, Vijay A. Patel, Michael Guillermo Daniel, Elaine L. Hsia, Hiu Wai Lam, Yujin Park, Jason H. Melehani, Annie Tigranyan, Xin Zheng, Nasser Heyrani, Jennifer Chang, Neda Asem, Joseph Duy Nguyen, Hanh H. Nguyen, Ted T. Uotani, Harnek Singh, Riley Guerin, Han-Ching H. Tseng, Kevin K. T. Vu, Max Mittelman, Gregory E. Tong, Cynthia Yeh, Jonathan Tran, Steven V. Pham, Bryan Lau, Kenny Lee, Tulika Ghosh, Roger K. Lau, An Huynh Pham, Christopher M. From, Elizabeth B. Johnson, Bobby Asem, Abhik Banerjee, Utpal Banerjee, Derek J. Cheung, Kambria Nguyen, Zainab Saifee, Carmel M. Moazez, Hung G. Trinh, Hannah Kim, Sonja Darwish, Lu Chen, Sondra L. Snyder, Pei-Yun Kao, Kevin Chen, Youngho Cho, Joseph Hargan Calvopina, Neil D. Poria, Vanessa Shih, Danica D. Wiredja, Shuo Wang, Zunera Ghaznavi, Jenny Dinh, Andrew J. Kim, Simran Kahlon, Awais M. Khan, Yuyu Chen, Julie Ko, Elise M. Calonico, Joseph N. Dickan, Huy D. Le, Leslie Jaworski, Xingfu Liang, Brooke Scruggs, Marc Levis-Fitzgerald, Susie Moon, Sara K. Taylor, An An Jiang, Nicholas Bumacod, Siddharth Sukumar, Matthew G. Keehn, Aleksandar Kitich, Cheng-An Tony Chien, Alexander N. Patananan, Jesse J. Cheung, Ragini Bhargava, Michael J. Jang, Alana M. Chin, Nikki Villarasa, Carman Chan, Ami Patel, Stacy Hu, Chiaen Chang, Solomon Rojhani, Michelle J. Kim, Shivani K. Thaker, Daisuke Furukawa, Arunit Singh Chugh, Volker Hartenstein, Hao Li, Akash Bhakta, Khoa T. Truong-N, Mark Sy, Daniel Rothberg, Melinda Feng, Kristine Kim, Nicole A. Azarian, David J. Groves, Sravya Mallam, Man Ting Chou, Katelyn F. Sobotka, Kathy T. Ngo, Justin Hsueh, Jasmine Y. Chen, Jessica Chew, Ira E. Clark, Zhong Ye, Mark R. Cubberly, Komal Wadhwani, Sumedha Singh, Jonathan J. Caine, Tamir Sholklapper, Jane Kuon, Nicholas G. Kouzelos, Emmylou Dowling, Francie Diep, Carolyn Chu, Kalkidan K. Gurara, Isabella Niu, Yaelim A. Park, Yong Jun Lee, Ali R. Haghighi, Nauman Charania, Nitish Mittal, Chee Jia Chin, Ben Sprung-Keyser, Naseem Moridzadeh, Donald N. Allen, Virginia Chu, Cory J. Evans, Luke Berry, Ahmad Bahrami, Justin J. Houman, Jesse M. Zaretsky, Kimberly A. Passmore, Chris Iwuchukwu, Rebecca S. Wang, Christina Lee, David Roh, Hee Jong Kim, Szuyao Lee, Rameen S. Moridzadeh, Randi K. Oyama, Amy A. Patel, Regina Lee, Serena S. Luong, Manpreet Kaur, Dhruv Patel, Xi Xue, Johnny He, Seo-Kyung Oh, Sabrena M. Ahmadi, Angela W. Nieh, Anke Ziegenbalg, Thien Nguyen, Jacinda N. Tran, John Y. Yu, Brian S. Kim, Eric Lin, Omair Khan, Ross G. Kelley, Sung Kyu Lee, Jessica S. Ma, Tanya Hioe, Nicole B. Chew, Kayla G. H. Gurley, Mark R Murata, Jiwon Lee, Hande Tan, Andrew L. Wong, Michael Koenig, Sung-Ling D. Lee, In Young Choi, Daniel Sitz, Paluk K. Walia, Maggie K. Chu, Ke-Huan K. Chow, Jessica R. Ong, Michelle J. Lee, Jenny Huynh, Rachael Le, Riki D. Ranat, Griselda Yanez, Yung-Hsuan Yang, Molly Howland, Ross A. Kornberg, Agafe Saguros, Peter Lin, Jessie C. Wu, Rahul Kumar Ghosh, Anand D. Gandhi, Jaskaran Gill, Edward Kuoy, Tulsi Thakore, Ritika Dutta, Himaja Gaddipati, Truc Ta, Melissa E. Phuphanich, Stacey Lu, Suji Kim, Letty Liang, Megan J. Leong, Derrick M. Chu, Michelle M. Lin, Alejandro LaRiviere, Alexandra L. Havard, Subir Bhatia, Minh-Tu Do, Sarah Kang, Jon Alkali, Kent K. T. Vu, Brian A. Shin, Samira J. Akhave, Kirim Hong, Thomas A. Caldwell, Jonathan Hofman, Vivi Tran, Katherine Chuang, Paul D. Picard, Jay Wang, Nicole A. Calica, Tiffany Y. Lin, Ryan C. Miller, Ida Azizkhanian, Manjot Sandhu, Dhaval Dixit, Kaley Ngo, Daniel Wu, Halley C. Sullivan, Jasmine C. Huynh, Tirth Patel, Vivian Yip, Manish M. Padhiar, Li Ma, Ryen Castillo, Shivtaj Mann, Tuan D. Tran, Albert J. Chang, and Kunal Thaker

Subjects
Lineage (genetic), QH426-470, Biology, 03 medical and health sciences, Gene expression, Genetics, stem, Molecular Biology, Drosophila, Genetics (clinical), 030304 developmental biology, education, 0303 health sciences, g-trace, 4. Education, 05 social sciences, 050301 education, biology.organism_classification, cure, Imaginal disc, Haematopoiesis, Undergraduate research, Expression (architecture), Evolutionary biology, gene expression, 0503 education, Functional genomics
Abstract

A variety of genetic techniques have been devised to determine cell lineage relationships during tissue development. Some of these systems monitor cell lineages spatially and/or temporally without regard to gene expression by the cells, whereas others correlate gene expression with the lineage under study. The GAL4 Technique for Real-time and Clonal Expression (G-TRACE) system allows for rapid, fluorescent protein-based visualization of both current and past GAL4 expression patterns and is therefore amenable to genome-wide expression-based lineage screens. Here we describe the results from such a screen, performed by undergraduate students of the University of California, Los Angeles (UCLA) Undergraduate Research Consortium for Functional Genomics (URCFG) and high school summer scholars as part of a discovery-based education program. The results of the screen, which reveal novel expression-based lineage patterns within the brain, the imaginal disc epithelia, and the hematopoietic lymph gland, have been compiled into the G-TRACE Expression Database (GED), an online resource for use by the Drosophila research community. The impact of this discovery-based research experience on student learning gains was assessed independently and shown to be greater than that of similar programs conducted elsewhere. Furthermore, students participating in the URCFG showed considerably higher STEM retention rates than UCLA STEM students that did not participate in the URCFG, as well as STEM students nationwide.

Published
2019
Full Text
View/download PDF

6. Improved local binary pattern for real scene optical character recognition

Author

Yung Hsuan Yang and Chu-Sing Yang

Subjects
business.industry, Local binary patterns, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, Pattern recognition, 02 engineering and technology, Optical character recognition, computer.software_genre, Luminance, Scale space, Artificial Intelligence, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Computer Vision and Pattern Recognition, Artificial intelligence, Invariant (mathematics), business, computer, Software, Mathematics
Abstract

A strong edge descriptor is an important topic in a wide range of applications. Local binary pattern (LBP) techniques have been applied to numerous fields and are invariant with respect to luminance and rotation. However, the performance of LBP for optical character recognition is not as good as expected. In this study, we propose a robust edge descriptor called improved LBP (ILBP), which is designed for optical character recognition. ILBP overcomes the noise problems observed in the original LBP by searching over scale space, which is implemented using an integral image with a reduced number of features to achieve recognition speed. In experiments, we evaluated ILBP's performance on the ICDAR03, chars74K, IIIT5K, and Bib digital databases. The results show that ILBP is more robust to blur and noise than LBP.

Published
2017
Full Text
View/download PDF

7. Man with trauma after a fall

Author

Chi‐Ju Lu, Kao-Lang Liu, Yung‐Hsuan Yang, and Chia‐Yun Chang

Subjects
business.industry, lcsh:Medical emergencies. Critical care. Intensive care. First aid, Medicine, lcsh:RC86-88.9, Images in Emergency Medicine, business
Published
2020

8. Dependence of the Noise Behavior on the Drain Current for Thin Film Transistors

Author

Chun-Yi Chang, Chung-Lun Hsieh, Huan-Ean Chen, Yung-Hsuan Yang, Wei-Kuang Chao, and Ya-Hsiang Tai

Subjects
Amorphous silicon, Materials science, Silicon, business.industry, Low-temperature polycrystalline silicon, chemistry.chemical_element, Oxide thin-film transistor, Noise (electronics), Electronic, Optical and Magnetic Materials, Amorphous solid, chemistry.chemical_compound, chemistry, Thin-film transistor, Electronic engineering, Optoelectronics, Flicker noise, Electrical and Electronic Engineering, business
Abstract

In this letter, a noise formula is newly proposed to calculate the low frequency noise for the three kinds of amorphous silicon, low temperature polycrystalline silicon, and amorphous indium-gallium-zinc oxide (a-IGZO) thin film transistors (TFTs). It is found that the noise behavior of the TFT depends on its drain current in a simple manner. Based on the analysis, the ratios of drain current to the noise level for these TFTs are compared. It reveals that a-IGZO TFT is the best candidate to be used in the active pixel sensor.

Published
2014
Full Text
View/download PDF

9. The fast stroke filter

Author

Yung-Hsuan Yang, Chen-Wei Lee, and Yi-Ching Su

Subjects
Computer science, Orientation (computer vision), business.industry, Matched filter, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, medicine.disease, ComputingMethodologies_PATTERNRECOGNITION, Line segment, Sampling (signal processing), Filter (video), ComputerApplications_MISCELLANEOUS, Line (geometry), Redundancy (engineering), medicine, Computer vision, Artificial intelligence, business, Stroke, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

A stroke is defined as a straight line or arc corresponding to a segment of character, and a character is composed of one or several strokes. A novel fast stroke filter which can localize stroke-like structures with very high speed has been proposed in this paper. Compared with related work, our fast stroke filter attacks the stroke detection problem in a more efficient way. It uses line segments to sampling the intercept of stroke body along both perpendicular axes, and then stroke orientation can simply be determined by a set of qualification rules. Much computing redundancy existing in the original stroke filter can be reduced, and the computation scale has been downsized from rectangle to line. According to our experimental results, fast stroke filter is superior to original stroke filter in both speed and quality, and the averaged speed-up is over 30 times.

Published
2010
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results