Physiological responses of maize (Zea mays L.) seedlings to the B chromosome - Nai-Yuan Cheng; Yi-Ting Hsu; Tzu-Che Lin; Ya-Ming Cheng (The Nucleus, 2026)

Targeting TGFβ receptor-mediated snail and twist: WSG, a polysaccharide from Ganoderma lucidum, and it-based dissolvable microneedle patch suppress melanoma cells - Hung-Chih Lo; Tzu-En Lin; Che-Yu Lin; Wei-Hao Wang; Yu-Chen Chen; Pei-Hsien Tsai; Jung-Chen Su; Mei-Kuang Lu; Wei-Hung Hsu; Tung-Yi Lin (Carbohydrate Polymers, 2024)

Clay nanosheets simultaneously intercalated and stabilized by PEGylated chitosan as drug delivery vehicles for cancer chemotherapy - Hsuan-Jung Huang; Shih-Yu Huang; Tzu-Hao Wang; Tzu-Yun Lin; Nan-Ching Huang; Orion Shih; U-Ser Jeng; Che-Yi Chu; Wen-Hsuan Chiang (Carbohydrate Polymers, 2023)

The r-X1 deletion induces terminal deficiencies in the maize B chromosome - Yen-Hua Huang; Tzu-Che Lin; Wan-Yi Chiou; Ya-Ming Cheng (Chromosome Research, 2021)

Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks - Tzu-Hsuan Huang; Wei-Kai Lee; Chih-Chun Wu; Cheng-Chia Lee; Chia-Feng Lu; Huai-Che Yang; Chun-Yi Lin; Wen-Yuh Chung; Po-Shan Wang; Yen-Ling Chen; Hsiu-Mei Wu; Wan-You Guo; et al. (Journal of Medical and Biological Engineering, 2021)
Purpose The first step in typical treatment of vestibular schwannoma (VS) is to localize the tumor region, which is time-consuming and subjective because it relies on repeatedly reviewing different parametric magnetic resonance (MR) images. A reliable, automatic VS detection method can streamline the process. Methods A convolutional neural network architecture, namely YOLO-v2 with a residual network as a backbone, was used to detect VS tumors from MR images. To heighten performance, T1-weighted–contrast-enhanced, T2-weighted, and T1-weighted images were combined into triple-channel images for feature learning. The triple-channel images were cropped into three sizes to serve as input images of YOLO-v2. The VS detection effectiveness levels were evaluated for two backbone residual networks that downsampled the inputs by 16 and 32. Results The results demonstrated the VS detection capability of YOLO-v2 with a residual network as a backbone model. The average precision was 0.7953 for a model with 416 × 416-pixel input images and 16 instances of downsampling, when both the thresholds of confidence score and intersection-over-union were set to 0.5. In addition, under an appropriate threshold of confidence score, a high average precision, namely 0.8171, was attained by using a model with 448 × 448-pixel input images and 16 instances of downsampling. Conclusion We demonstrated successful VS tumor detection by using a YOLO-v2 with a residual network as a backbone model on resized triple-parametric MR images. The results indicated the influence of image size, downsampling strategy, and confidence score threshold on VS tumor detection.

Suppressive effect of microRNA319 expression on rice plant height - Wei-Ting Liu; Peng-Wen Chen; Li-Chi Chen; Chia-Chun Yang; Shu-Yun Chen; GuanFu Huang; Tzu Che Lin; Hsin-Mei Ku; Jeremy J. W. Chen (Theoretical and Applied Genetics, 2017)

Characterization of mitochondrial genome of Formosan sambar (Rusa unicolor swinhoei) - Chia-Hsuan Chen; Hsiu-Lin Huang; Mu-Tzu Chang; Li-Chia Chiang; Shih-Lin Cheng; Bing-Tsan Liu; Chih-Hua Wang; Ming-Che Wu; Mu-Chiou Huang (Biologia, 2011)

Passenger thermal perceptions, thermal comfort requirements, and adaptations in short- and long-haul vehicles - Tzu-Ping Lin; Ruey-Lung Hwang; Kuo-Tsang Huang; Chen-Yi Sun; Ying-Che Huang (International Journal of Biometeorology, 2010)

Denbinobin induces apoptosis by apoptosis-inducing factor releasing and DNA damage in human colorectal cancer HCT-116 cells - Tzu-Hsuan Chen; Shiow-Lin Pan; Jih-Hwa Guh; Chien-Chih Chen; Yao-Ting Huang; Hui-Chen Pai; Che-Ming Teng (Naunyn-Schmiedeberg's Archives of Pharmacology, 2008)