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4. Prospective prenatal cell-free DNA screening for genetic conditions of heterogenous etiologies

Author

Zhang, Jinglan, Wu, Yanting, Chen, Songchang, Luo, Qiong, Xi, Hui, Li, Jianli, Qin, Xiaomei, Peng, Ying, Ma, Na, Yang, Bingxin, Qiu, Xiang, Lu, Weiliang, Chen, Yuan, Jiang, Ying, Chen, Panpan, Liu, Yifeng, Zhang, Chen, Zhang, Zhiwei, Xiong, Yu, Shen, Jie, Liang, Huan, Ren, Yunyun, Ying, Chunmei, Dong, Minyue, Li, Xiaotian, Xu, Congjian, Wang, Hua, Zhang, Dan, Xu, Chenming, and Huang, Hefeng

Published
2024
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5. A Cognitive Study of Spatial Metaphors in English 'Deep' and Chinese 'Shen'

Author

Wu, Yanting

Abstract

This paper presents an analysis and comparison of the projection of Chinese "shen" and English "deep" from the spatial domain to other domains of time, sense, emotion, behavior, and society from a cognitive perspective by using the comparative and contrastive method, explaining the universality and differences of SHEN and DEEP in the metaphorical domain concerning their collocation, usage, and frequency under Chinese and English cultural contexts, which serves as an insight for foreign language teaching, translation practice and cross-cultural communication. The results show that since humans share a similar physical structure and living environment, the spatial adjectives "deep" and "shen" show a lot of similarities. However, the usage, collocation, and frequency of use of "deep" and "shen" still reveal some variations, which might have great implications for second language learning and teaching.

Published
2022

6. What Do You Get from Turning on Your Video? Effects of Videoconferencing Affordances on Remote Class Experience During COVID-19

Author

Wu, Yanting, Sun, Yuan, and Sundar, S. Shyam

Subjects
Computer Science - Human-Computer Interaction
Abstract

The outbreak of COVID-19 forced schools to swiftly transition from in-person classes to online or remote offerings, making educators and learners alike rely on online videoconferencing platforms. Platforms like Zoom offer audio-visual channels of communication and include features that are designed to approximate the classroom experience. However, it is not clear how students' learning experiences are affected by affordances of the videoconferencing platforms or what underlying factors explain the differential effects of these affordances on class experiences of engagement, interaction, and satisfaction. In order to find out, we conducted two online survey studies: Study 1 (N = 176) investigated the effects of three types of videoconferencing affordances (i.e., modality, interactivity, and agency affordances) on class experience during the first two months after the transition to online learning. Results showed that usage of the three kinds of affordances was positively correlated with students' class engagement, interaction, and satisfaction. Perceived anonymity, nonverbal cues, and comfort level were found to be the key mediators. In addition, students' usage of video cameras in class was influenced by their classmates. Study 2 (N = 256) tested the proposed relationships at a later stage of the pandemic and found similar results, thus serving as a constructive replication. This paper focuses on reporting the results of Study 1 since it captures the timely reactions from students when they first went online, and the second study plays a supplementary role in verifying Study 1 and thereby extending its external validity. Together, the two studies provide insights for instructors on how to leverage different videoconferencing affordances to enhance the virtual learning experience. Design implications for digital tools in online education are also discussed., Comment: The paper has been published in Proceedings of the ACM on Human-Computer Interaction (https://dl.acm.org/doi/abs/10.1145/3555773)

Published
2022
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14. Longitudinal Deep Kernel Gaussian Process Regression

Author

Liang, Junjie, Wu, Yanting, Xu, Dongkuan, and Honavar, Vasant

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, i.e., irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kernels, and (ii) They fail to handle multilevel correlation structure in the data. We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR), which to the best of our knowledge, is the only method to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudinal data. Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods. L-DKGPR effectively learns the multilevel correlation with a novel addictive kernel that simultaneously accommodates both time-varying and the time-invariant effects. We derive an efficient algorithm to train L-DKGPR using latent space inducing points and variational inference. Results of extensive experiments on several benchmark data sets demonstrate that L-DKGPR significantly outperforms the state-of-the-art longitudinal data analysis (LDA) methods., Comment: Paper accepted by 35th AAAI Conference on Artificial Intelligence

Published
2020

21. Maternal inheritance of glucose intolerance via oocyte TET3 insufficiency

Author

Chen, Bin, Du, Ya-Rui, Zhu, Hong, Sun, Mei-Ling, Wang, Chao, Cheng, Yi, Pang, Haiyan, Ding, Guolian, Gao, Juan, Tan, Yajing, Tong, Xiaomei, Lv, Pingping, Zhou, Feng, Zhan, Qitao, Xu, Zhi-Mei, Wang, Li, Luo, Donghao, Ye, Yinghui, Jin, Li, Zhang, Songying, Zhu, Yimin, Lin, Xiaona, Wu, Yanting, Jin, Luyang, Zhou, Yin, Yan, Caochong, Sheng, Jianzhong, Flatt, Peter R., Xu, Guo-Liang, and Huang, Hefeng

Published
2022
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28. Genetic deconvolution of fetal and maternal cell-free DNA in maternal plasma enables next-generation non-invasive prenatal screening

Author

Xu, Chenming, Li, Jianli, Chen, Songchang, Cai, Xiaoqiang, Jing, Ruilin, Qin, Xiaomei, Pan, Dong, Zhao, Xin, Ma, Dongyang, Xu, Xiufeng, Liu, Xiaojun, Wang, Can, Yang, Bingxin, Zhang, Lanlan, Li, Shuyuan, Chen, Yiyao, Pan, Nina, Tang, Ping, Song, Jieping, Liu, Nian, Zhang, Chen, Zhang, Zhiwei, Qiu, Xiang, Lu, Weiliang, Ying, Chunmei, Li, Xiaotian, Xu, Congjian, Wang, Yanlin, Wu, Yanting, Huang, He-Feng, and Zhang, Jinglan

Published
2022
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29. Could Interaction with Social Robots Facilitate Joint Attention of Children with Autism Spectrum Disorder?

Author

Cao, Wei, Song, Wenxu, Li, Xinge, Zheng, Sixiao, Zhang, Ge, Wu, Yanting, He, Sailing, Zhu, Huilin, and Chen, Jiajia

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Human-Computer Interaction
Abstract

This research addressed whether interactions with social robots could facilitate joint attention of the autism spectrum disorder (ASD). Two conditions of initiators, namely 'Human' vs. 'Robot' were measured with 15 children with ASD and 15 age-matched typically developing (TD) children. Apart from fixation and gaze transition, a new longest common subsequence (LCS) approach was proposed to analyze eye-movement traces. Results revealed that children with ASD showed deficits of joint attention. Compared to the human agent, robot facilitate less fixations towards the targets, but it attracted more attention and allowed the children to show gaze transition and to follow joint attention logic. This results highlight both potential application of LCS analysis on eye-tracking studies and of social robot to intervention., Comment: First author: Wei Cao and Wenxu Song; Corresponding author: Huilin Zhu(huilin.zhu@m.scnu.edu.cn)ans Jiajia Chen(jiajiac@kth.se)

Published
2018

47. A Sustainable Reinforcement Method for Recycled Road Subgrade Demolition Waste as Road Bases Using Waterborne Polyurethane and Fiber.

Author

Wang, Wei, Lv, Beifeng, Wu, Yanting, Pu, Shaoyun, Jiang, Ping, and Li, Na

Subjects
CONSTRUCTION & demolition debris, GEOSYNTHETICS, WASTE treatment, SCANNING electron microscopes, COMPRESSIVE strength, POLYURETHANES
Abstract

The discard of recycled road demolition waste (RDW) leads to natural resource waste and environmental problems. The RDW is reused as road base, which is a sustainable application and promising construction technology with good environmental benefits and economic value. The performance of road base with RDW depends on the compressive strength, splitting strength, and ductility. However, owing to the poor mechanical properties, the RDW can only be reused after being treated. The traditional treatment method of waste as road base is to enhance mechanical performance using cement. However, when cement is used as a stabilizer, the road base cracks very easily, thereby damaging the pavement. Therefore, in this article, a new reutilization method of RDW as road base is proposed. The waterborne polyurethane (PU) was proposed to improve the strength of RDW, and basalt fiber (BF), polypropylene fiber (PF), and carbon fiber (CF) were used to improve the ductility of PU-stabilized RDW. The related mechanical behavior was investigated by unconfined compressive strength and splitting strength tests. Meanwhile, the mechanism of RDW enhanced by PU and PU + CF/PF/BF was revealed using scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier transform infrared reflection (FTIR) tests. The results showed that the PU could improve the strength of RDW. When BF, PF, and CF were added, the compressive strength, residual strength, splitting strength, failure strain, and ductility were further enhanced. When the fiber content was 6%, a maximum compressive strength was obtained, and the PF had the best improvement effect on the mechanical properties of PU-stabilized RDW. When the PF content was 0.6% and 0.8%, the 7-days compressive strength could meet the class II standard (3.0 MPa) of road subbase for heavy traffic expressways. The failure strain increased with the compressive strength increasing. The mechanical performance improvement effect of PU and PU + CF/PF/BF on RDW came from the bonding effect of PU, and the bridging effect and reinforcement effect of fiber. This study provides a favorable way for the recycling of RDW, which is conducive to the sustainable development of environment and the conservation and utilization of energy. [ABSTRACT FROM AUTHOR]

Published
2024
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48. The Role of MaWRKY70 in Regulating Lipoxygenase Gene Transcription during Chilling Injury Development in Banana Fruit.

Author

Lin, Han, Bai, Lijuan, Wei, Wei, Su, Wenbing, Wu, Yanting, Wu, Rong, Wang, Hui, Chen, Jianye, Lin, Hetong, and Fan, Zhongqi

Subjects
BANANAS, COLD storage, MEMBRANE permeability (Biology), MEMBRANE lipids, CELL permeability, TRANSGENIC organisms
Abstract

Banana is a typical cold-sensitive fruit; it is prone to chilling injury (CI), resulting in a quality deterioration and commodity reduction. However, the molecular mechanism underlying CI development is unclear. In this study, cold storage (7 °C for 5 days) was used to induce CI symptoms in bananas. As compared with the control storage (22 °C for 5 days), cold storage increased the CI index and cell membrane permeability. Moreover, we found that the expression levels of the WRKY transcription factor MaWRKY70 were increased consistently with the progression of CI development. A subcellular localization assay revealed that MaWRKY70 was localized in the nucleus. Transcriptional activation analyses showed that MaWRKY70 processed a transactivation ability. Further, an electrophoretic mobility shift assay (EMSA) and dual-luciferase reporter (DLR) assays showed that MaWRKY70 was directly bound to the W-box motifs in the promoters of four lipoxygenase (LOX) genes associated with membrane lipid degradation and activated their transcription. Collectively, these findings demonstrate that MaWRKY70 activates the transcription of MaLOXs, thereby acting as a possible positive modulator of postharvest CI development in banana fruit. [ABSTRACT FROM AUTHOR]

Published
2024
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