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204. Policy Transfer with Strategy Optimization

Author

Yu, Wenhao, Liu, C. Karen, and Turk, Greg

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

Computer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation usually does not transfer directly to the real hardware due to the differences between the two environments. Transfer learning using domain randomization is a promising approach, but it usually assumes that the target environment is close to the distribution of the training environments, thus relying heavily on accurate system identification. In this paper, we present a different approach that leverages domain randomization for transferring control policies to unknown environments. The key idea that, instead of learning a single policy in the simulation, we simultaneously learn a family of policies that exhibit different behaviors. When tested in the target environment, we directly search for the best policy in the family based on the task performance, without the need to identify the dynamic parameters. We evaluate our method on five simulated robotic control problems with different discrepancies in the training and testing environment and demonstrate that our method can overcome larger modeling errors compared to training a robust policy or an adaptive policy.

Published
2018

205. Learning Symmetric and Low-energy Locomotion

Author

Yu, Wenhao, Turk, Greg, and Liu, C. Karen

Subjects
Computer Science - Learning, Computer Science - Graphics, Computer Science - Robotics
Abstract

Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement learning (DRL) is a promising approach for the automatic creation of locomotion control. Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function. Although several different DRL algorithms can successfully create a running controller, the resulting motions usually look nothing like a real runner. This paper takes a minimalist learning approach to the locomotion problem, without the use of motion examples, finite state machines, or morphology-specific knowledge. We introduce two modifications to the DRL approach that, when used together, produce locomotion behaviors that are symmetric, low-energy, and much closer to that of a real person. First, we introduce a new term to the loss function (not the reward function) that encourages symmetric actions. Second, we introduce a new curriculum learning method that provides modulated physical assistance to help the character with left/right balance and forward movement. The algorithm automatically computes appropriate assistance to the character and gradually relaxes this assistance, so that eventually the character learns to move entirely without help. Because our method does not make use of motion capture data, it can be applied to a variety of character morphologies. We demonstrate locomotion controllers for the lower half of a biped, a full humanoid, a quadruped, and a hexapod. Our results show that learned policies are able to produce symmetric, low-energy gaits. In addition, speed-appropriate gait patterns emerge without any guidance from motion examples or contact planning., Comment: Accepted to SIGGRAPH 2018. Supplementary video: https://www.youtube.com/watch?v=zkH90rU-uew&feature=youtu.be

Published
2018
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213. Multi-task Learning with Gradient Guided Policy Specialization

Author

Yu, Wenhao, Liu, C. Karen, and Turk, Greg

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Learning
Abstract

We present a method for efficient learning of control policies for multiple related robotic motor skills. Our approach consists of two stages, joint training and specialization training. During the joint training stage, a neural network policy is trained with minimal information to disambiguate the motor skills. This forces the policy to learn a common representation of the different tasks. Then, during the specialization training stage we selectively split the weights of the policy based on a per-weight metric that measures the disagreement among the multiple tasks. By splitting part of the control policy, it can be further trained to specialize to each task. To update the control policy during learning, we use Trust Region Policy Optimization with Generalized Advantage Function (TRPOGAE). We propose a modification to the gradient update stage of TRPO to better accommodate multi-task learning scenarios. We evaluate our approach on three continuous motor skill learning problems in simulation: 1) a locomotion task where three single legged robots with considerable difference in shape and size are trained to hop forward, 2) a manipulation task where three robot manipulators with different sizes and joint types are trained to reach different locations in 3D space, and 3) locomotion of a two-legged robot, whose range of motion of one leg is constrained in different ways. We compare our training method to three baselines. The first baseline uses only joint training for the policy, the second trains independent policies for each task, and the last randomly selects weights to split. We show that our approach learns more efficiently than each of the baseline methods.

Published
2017

214. Learning Human Behaviors for Robot-Assisted Dressing

Author

Clegg, Alexander, Yu, Wenhao, Tan, Jie, Kemp, Charlie C., Turk, Greg, and Liu, C. Karen

Subjects
Computer Science - Robotics
Abstract

We investigate robotic assistants for dressing that can anticipate the motion of the person who is being helped. To this end, we use reinforcement learning to create models of human behavior during assistance with dressing. To explore this kind of interaction, we assume that the robot presents an open sleeve of a hospital gown to a person, and that the person moves their arm into the sleeve. The controller that models the person's behavior is given the position of the end of the sleeve and information about contact between the person's hand and the fabric of the gown. We simulate this system with a human torso model that has realistic joint ranges, a simple robot gripper, and a physics-based cloth model for the gown. Through reinforcement learning (specifically the TRPO algorithm) the system creates a model of human behavior that is capable of placing the arm into the sleeve. We aim to model what humans are capable of doing, rather than what they typically do. We demonstrate successfully trained human behaviors for three robot-assisted dressing strategies: 1) the robot gripper holds the sleeve motionless, 2) the gripper moves the sleeve linearly towards the person from the front, and 3) the gripper moves the sleeve linearly from the side., Comment: 8 pages, 9 figures

Published
2017

215. Learning to Navigate Cloth using Haptics

Author

Clegg, Alexander, Yu, Wenhao, Erickson, Zackory, Tan, Jie, Liu, C. Karen, and Turk, Greg

Subjects
Computer Science - Robotics
Abstract

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination., Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.html

Published
2017

216. Preparing for the Unknown: Learning a Universal Policy with Online System Identification

Author

Yu, Wenhao, Tan, Jie, Liu, C. Karen, and Turk, Greg

Subjects
Computer Science - Learning, Computer Science - Robotics, Computer Science - Systems and Control
Abstract

We present a new method of learning control policies that successfully operate under unknown dynamic models. We create such policies by leveraging a large number of training examples that are generated using a physical simulator. Our system is made of two components: a Universal Policy (UP) and a function for Online System Identification (OSI). We describe our control policy as universal because it is trained over a wide array of dynamic models. These variations in the dynamic model may include differences in mass and inertia of the robots' components, variable friction coefficients, or unknown mass of an object to be manipulated. By training the Universal Policy with this variation, the control policy is prepared for a wider array of possible conditions when executed in an unknown environment. The second part of our system uses the recent state and action history of the system to predict the dynamics model parameters mu. The value of mu from the Online System Identification is then provided as input to the control policy (along with the system state). Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment. We have evaluated the performance of this system on a variety of tasks, including the problem of cart-pole swing-up, the double inverted pendulum, locomotion of a hopper, and block-throwing of a manipulator. UP-OSI is effective at these tasks across a wide range of dynamic models. Moreover, when tested with dynamic models outside of the training range, UP-OSI outperforms the Universal Policy alone, even when UP is given the actual value of the model dynamics. In addition to the benefits of creating more robust controllers, UP-OSI also holds out promise of narrowing the Reality Gap between simulated and real physical systems., Comment: Accepted as a conference paper at RSS 2017

Published
2017

217. Interventions for gender equality in STEM education: A meta‐analysis.

Author

Yu, Wenhao, He, Jiaqi, Luo, Julan, and Shu, Xiaoyan

Subjects
*EFFECT sizes (Statistics), *GENDER role, *EMPATHY, *MATHEMATICS, *SCIENCE, *ENGINEERING, *EDUCATIONAL outcomes, *SAMPLE size (Statistics), *META-analysis, *DESCRIPTIVE statistics, *EDUCATIONAL tests & measurements, *GENDER inequality, *SYSTEMATIC reviews, *MOTIVATION (Psychology), *TECHNOLOGY, *MEDICAL coding, *COLLEGE students, *INTERPERSONAL relations, *EDUCATIONAL attainment, *PUBLICATION bias
Abstract

Background: Although gender inequality in education is gaining increasing attention, female underrepresentation remains pervasive in STEM fields. Many studies have applied various interventions to narrow the gender gap in STEM education. Objectives: In this study, we conducted a systematic meta‐analysis to examine the effectiveness of various interventions for gender equality in STEM education and tested the influence of moderator variables. Methods: Ten databases—ERIC, IEEE, JSTOR, ProQuest‐Education, SAGE Journals, Scopus, Springer Link, Taylor & Francis, Web of Science, and Wiley Online Library—and seven high‐quality journals in related fields were selected as literature resources. The Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) process was used to identify eligible articles for the random‐effects meta‐analysis. Results and Conclusions: The overall effect size (Hedges' g) was 0.434, indicating that the interventions promoting gender equality in STEM education had a near‐medium positive effect. There were significant differences by the type of intervention, but not by the type of intervention outcome. The results of the moderator analysis showed a significant difference in moderator variables, including country, educational level, form of experiment, intervention level, and different measurement methods, whereas there was no significant difference in the moderator variables, including economic development level and sample size. Implications: This study examined the effectiveness of gender equality interventions and the impact on it of different types of intervention, intervention outcomes, and moderator variables. These results have implications for the design of gender equality interventions in STEM education. Lay Description: What is already known about this topic: Gender equality in STEM education is valuable in reducing barriers and underrepresentation of female students.Different interventions have been applied to promote gender equality, but their effectiveness varies. What this paper adds: In STEM education, there were significant differences among the different types of interventions, but not among the different types of intervention outcomes.This study tested the significance of the moderating effect of the moderator variables in the gender equality intervention on the effectiveness of the intervention, including economic development level (developed or developing country), country, educational level, sample size, form of experiment, intervention level, and measurement method. Implications of study findings: Among the different types of interventions, those by different teachers and interpersonal interactions were the most effective.The results of the moderator analysis at the intervention level showed that the effectiveness of the interventions at the environmental level was significantly greater than that at the individual level.The study holds specific implications for the design of interventions for gender equality in STEM education. [ABSTRACT FROM AUTHOR]

Published
2024
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218. MapGPT: an autonomous framework for mapping by integrating large language model and cartographic tools.

Author

Zhang, Yifan, He, Zhengting, Li, Jingxuan, Lin, Jianfeng, Guan, Qingfeng, and Yu, Wenhao

Subjects
LANGUAGE models, ARTIFICIAL intelligence, RESEARCH personnel, CARTOGRAPHY, NATURAL languages
Abstract

The mapping process generally involves intricate operations, such as symbol design, layout design, and text annotation, demanding a high level of professional expertise. The high requirement for map producers hinders the promotion and widespread adoption of mapping. Consequently, researchers are concentrating on techniques to automate and enhance the intelligence of the mapping process. For example, some studies attempt to train deep learning models for mapping, including methods like map style transfer. However, these approaches typically treat the entire map as a global input and generate a new map as output, lacking the flexibility to consider and control detailed elements within a map. Therefore, in this paper, we propose a large language model-based intelligent mapping framework, termed MapGPT, which can be used for mapping by considering the map as an integration of various map elements. Specifically, multiple professional mapping tools are designed in MapGPT, and each tool is designed to control a corresponding map element. With these tools, a large language model is used to first understand the demand of users based on mere natural language descriptions, and subsequently automatically invoke appropriate tools in sequence to generate a map. Furthermore, by utilizing a memory component to store interaction information, users can interact with MapGPT through conversation to adjust map elements such as color and position. In conclusion, MapGPT offers user-friendly mapping experience, showing potential to be a mapping assistant for professional map producers. A comprehensive demonstration of this framework is provided in a visual case study video, accessible at . [ABSTRACT FROM AUTHOR]

Published
2024
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219. Numerical Analysis of Hydrogen Behavior Inside Hydrogen Storage Cylinders under Rapid Refueling Conditions Based on Different Shapes of Hydrogen Inlet Ports.

Author

Zhang, Enhui, Zhao, Yangchun, Zhang, Jiahui, Wang, Wenchao, and Yu, Wenhao

Subjects
COMPUTATIONAL fluid dynamics, HYDROGEN analysis, HYDROGEN storage, NUMERICAL analysis, BEHAVIORAL assessment
Abstract

In order to investigate the effects of different shapes of hydrogen inlet ports on the behavioral characteristics of hydrogen in Type IV hydrogen storage cylinders under rapid refueling conditions, a mathematical model of hydrogen temperature rise and a three-dimensional numerical analysis model were developed. The rectangular, hexagonal, triangular, Reuleaux triangular, circular, elliptical and conical inlet ports were researched by using computational fluid dynamics methods. The results showed that, for the same refueling flow rate and cross-sectional area, the hydrogen temperature inside a cylinder with a rectangular inlet port is higher and the jet tilt angle is larger than for a hexagonal port, while the thermal stratification phenomenon is not obvious. The hydrogen temperature inside a cylinder with a triangular inlet port is lower than that with a Reuleaux triangle port and the jet tilt angle is larger, and neither has significant thermal stratification. The hydrogen temperature inside a cylinder with a circular inlet port is higher than that with an ellipse port, the jets are not tilted on either one, and the phenomenon of thermal stratification is prominent. Further analysis indicated that enlarging the cross-sectional area and increasing the refueling flow rate results in a higher hydrogen temperature and intensified thermal stratification and an upward-angled jet can effectively reduce or eliminate thermal stratification. [ABSTRACT FROM AUTHOR]

Published
2024
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220. Next track point prediction using a flexible strategy of subgraph learning on road networks.

Author

Zhang, Yifan, Yu, Wenhao, and Zhu, Di

Subjects
*INTELLIGENT transportation systems, *TRAFFIC monitoring, *TRAFFIC engineering, *TRAFFIC congestion, *CITIES & towns
Abstract

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Published
2024
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221. Research and Analysis of Liquid Cooling Heat Dissipation Equipment for Insulated-Gate Bipolar Transistor Modules of Wind Power Converters Based on the Finite Element Analysis Method.

Author

Zhu, Xinyu, Hu, Xue, Zhang, Lixin, Li, Wenchun, Gong, Lijiao, Yu, Wenhao, Wang, Haonan, Chen, Feng, and Zhang, Xinwang

Subjects
INSULATED gate bipolar transistors, HEAT sinks, FINITE element method, PRESSURE drop (Fluid dynamics), WIND power
Abstract

The primary objective of this study is to develop a simulation model for a liquid cooling plate (LCP) for insulated-gate bipolar transistor (IGBT) modules, with the aim of reducing the operating temperature of wind power converters (WPCs). The initial impetus for this study was the observation that the energy conversion efficiency of a WPC declines when the operating temperature of the IGBT module exceeds a critical threshold. Three LCPs, with and without heat sinks, were modelled under extreme conditions using the finite element simulation method. The effect of the number and height of the fins on the cooling efficacy was evaluated through the simulation and analysis of the LCP model with heat sinks. The results demonstrate that the optimal configuration, comprising five 10 mm fins and 13 10 mm struts, can achieve the following reductions: maximum temperature by 11.4 K, heat dissipation efficiency by 3.33%, pressure drop by 10.6 KPa, and pump power by 31.00%. Moreover, the findings suggest that the number of fins has a significant impact on temperature fluctuations, whereas the height of the fins exerts a markedly significant influence on pressure drop. [ABSTRACT FROM AUTHOR]

Published
2024
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238. Research on Foundation Model for Spatial Data Intelligence: China's 2024 White Paper on Strategic Development of Spatial Data Intelligence

Author

Wang, Shaohua, Xie, Xing, Li, Yong, Guo, Danhuai, Cai, Zhi, Liu, Yu, Yue, Yang, Pan, Xiao, Lu, Feng, Wu, Huayi, Gui, Zhipeng, Ding, Zhiming, Zheng, Bolong, Zhang, Fuzheng, Qin, Tao, Wang, Jingyuan, Tao, Chuang, Chen, Zhengchao, Lu, Hao, Li, Jiayi, Chen, Hongyang, Yue, Peng, Yu, Wenhao, Yao, Yao, Sun, Leilei, Zhang, Yong, Chen, Longbiao, Du, Xiaoping, Li, Xiang, Zhang, Xueying, Qin, Kun, Gong, Zhaoya, Dong, Weihua, Meng, Xiaofeng, Wang, Shaohua, Xie, Xing, Li, Yong, Guo, Danhuai, Cai, Zhi, Liu, Yu, Yue, Yang, Pan, Xiao, Lu, Feng, Wu, Huayi, Gui, Zhipeng, Ding, Zhiming, Zheng, Bolong, Zhang, Fuzheng, Qin, Tao, Wang, Jingyuan, Tao, Chuang, Chen, Zhengchao, Lu, Hao, Li, Jiayi, Chen, Hongyang, Yue, Peng, Yu, Wenhao, Yao, Yao, Sun, Leilei, Zhang, Yong, Chen, Longbiao, Du, Xiaoping, Li, Xiang, Zhang, Xueying, Qin, Kun, Gong, Zhaoya, Dong, Weihua, and Meng, Xiaofeng

Abstract

This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models. It provides an in-depth discussion on the definition, development history, current status, and trends of spatial data intelligent large models, as well as the challenges they face. The report systematically elucidates the key technologies of spatial data intelligent large models and their applications in urban environments, aerospace remote sensing, geography, transportation, and other scenarios. Additionally, it summarizes the latest application cases of spatial data intelligent large models in themes such as urban development, multimodal systems, remote sensing, smart transportation, and resource environments. Finally, the report concludes with an overview and outlook on the development prospects of spatial data intelligent large models., Comment: in Chinese language

Published
2024

239. Gameplay Filters: Safe Robot Walking through Adversarial Imagination

Author

Nguyen, Duy P., Hsu, Kai-Chieh, Yu, Wenhao, Tan, Jie, Fisac, Jaime F., Nguyen, Duy P., Hsu, Kai-Chieh, Yu, Wenhao, Tan, Jie, and Fisac, Jaime F.

Abstract

Ensuring the safe operation of legged robots in uncertain, novel environments is crucial to their widespread adoption. Despite recent advances in safety filters that can keep arbitrary task-driven policies from incurring safety failures, existing solutions for legged robot locomotion still rely on simplified dynamics and may fail when the robot is perturbed away from predefined stable gaits. This paper presents a general approach that leverages offline game-theoretic reinforcement learning to synthesize a highly robust safety filter for high-order nonlinear dynamics. This gameplay filter then maintains runtime safety by continually simulating adversarial futures and precluding task-driven actions that would cause it to lose future games (and thereby violate safety). Validated on a 36-dimensional quadruped robot locomotion task, the gameplay safety filter exhibits inherent robustness to the sim-to-real gap without manual tuning or heuristic designs. Physical experiments demonstrate the effectiveness of the gameplay safety filter under perturbations, such as tugging and unmodeled irregular terrains, while simulation studies shed light on how to trade off computation and conservativeness without compromising safety.

Published
2024

240. Crested Porcupine Optimizer-Optimized CNN-BiLSTM-Attention Model for Predicting Main Girder Temperature in Bridges.

Author

Gao, Yan, Wang, Jianxun, Yu, Wenhao, Yi, Lu, and Guo, Fengqi

Subjects
STANDARD deviations, CONVOLUTIONAL neural networks, DEEP learning, FEATURE extraction, LONG-span bridges
Abstract

Stage-built long-span bridges deform with temperature, affecting alignment to design needs. In this paper, a model for predicting temperature time series is proposed, which can predict temperatures in engineering practice and utilize the predicted results to adjust the elevation of stage construction. The model employs convolutional neural networks (CNNs) for initial feature extraction, followed by bidirectional long short-term memory (BiLSTM) layers to capture temporal dependencies. An attention mechanism is applied to the LSTM output, enhancing the model's ability to focus on the most relevant parts of the sequence. The Crested Porcupine Optimizer (CPO) is used to fine-tune parameters like the number of LSTM units, dropout rate, and learning rate. The experiments on the measured temperature data of an under-construction cable-stayed bridge are conducted to validate our model. The results indicate that our model outperforms the other five models in comparison, with all the R2 values exceeding 0.97. The average of the mean absolute error (MAE) on the 30 measure points is 0.19095, and the average of the root mean square error (RMSE) is 0.28283. Furthermore, the model's low sensitivity to data makes it adaptable and effective for predicting temperatures and adjusting the elevation in large-span bridge construction. [ABSTRACT FROM AUTHOR]

Published
2024
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241. Mechanism of LINC01410 promoting proliferation and migration in esophageal squamous cell carcinoma.

Author

CAO Fei, YU Wenhao, TANG Xiaonan, MA Zidong, CHANG Tingmin, GONG Yabin, LIAO Mingjuan, and KANG Xiaohong

Subjects
*PRIMARY cell culture, *GENE expression, *LINCRNA, *WESTERN immunoblotting, *GENE expression profiling, *COLONY-forming units assay
Abstract

Background and purpose: Long non-coding RNA (lncRNA) LINC01410, with a length of 647 bp, participates in a variety of tumor biological processes. However, the role and mechanism of LINC01410 involved in esophageal squamous cell carcinoma (ESCC) remain unclear. This study aimed to explore the potential mechanism of LINC01410 promoting ESCC proliferation and invasion, to provide a potential prognostic indicator and therapeutic target for individuals with ESCC. Methods: Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases were used to analyze the expression of LINC01410 and overall survival in esophageal squamous cell carcinoma data set in the Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed to identify the underlying signaling pathways involved in the biological effects of LINC01410 in ESCC. A total of 62 pairs of ESCC tissues and paracancerous tissues from ESCC patients who underwent radical surgery in the Department of Thoracic Surgery at the First Affiliated Hospital of Xinxiang Medical College and the First People's Hospital of Pingdingshan City from January 2020 to December 2021 were collected. This project has been approved by the Hospital Ethics Committee (First Affiliated Hospital of Xinxiang Medical College, No. 2018036; First People's Hospital of Pingdingshan City, No. 2019-018). The expression of LINC01410 in ESCC tissues was detected by real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR). We transfected EC109 cells with LV-NC or LV-over/LINC01410 and EC9706 cells with shRNA-NC or shRNA-LINC01410. Stable transfected cells (EC109/NC, EC109/OE, EC9706/NC and EC9706/KD) were selected in primary cell culture medium containing puromycin. The expression of LINC01410 was detected by RTFQ-PCR. The impact of LINC01410 on ESCC cell proliferation was determined by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and colony formation assays. The effect of LINC01410 on ESCC cell invasion was detected by transwell migration assay. T cell factor/lymphoid enhancer factor 1 (TCF/LEF1) luciferase reporter assay was performed to validate the effect of LINC01410 on the activity of canonical Wnt/β-catenin signaling pathway. The expressions of Wnt/β-catenin and epithelial-mesenchymal transition (EMT) signal pathway related proteins in ESCC cells were detected by Western blot. Results: By analyzing the LINC01410 expression from ESCC samples in TCGA by GEPIA2, we found LINC01410 was consistently increased in ESCC tumors compared with normal tissues (P<0.05), and high LINC01410 expression was associated with poorer overall survival (OS). RTFQ-PCR assay showed that expressions of LINC01410 were higher in esophageal cancer tissues and esophageal cancer cells (EC109 and EC9706) than in precancerous tissues and HEEC cells (P<0.05). The expression level of LINC01410 was significantly correlated with invasion range, lymph node metastasis and TNM stage in ESCC patients (P<0.01). LINC01410 expression was also upregulated in EC109/OE, however the expression of LINC01410 in EC9706/KD was decreased (P<0.01). MTT assay showed overexpression of LINC01410 increased the viability of EC109 cells, while knockdown of LINC01410 decreased the viability of EC9706 cells (P<0.01). Colony formation assay indicated that overexpression of LINC01410 enhanced the clonogenic ability of ESCC cells, while knockdown of LINC01410 reduced colony formation (P <0.01). Transwell migration assay showed that LINC01410 overexpression drastically increased the number of migratory cells, while silencing of LINC01410 suppressed the migration in EC9706 cells (P<0.01). GSEA revealed that Wnt/β-catenin and EMT pathways were significantly enriched in ESCC samples with a high level of LINC01410. TCF/LEF1 luciferase reporter assay showed higher levels of Wnt-dependent activities were observed in EC109/OE cells, whereas silenced LINC01410 in EC9706 cells led to contrary results (P<0.01). Western blot analysis showed that overexpression of LINC01410 in EC109 cell significantly increased the expression levels of N-cadherin, β-catenin, cyclin D1, c-Myc and decreased E-cadherin expression, while knockdown LINC01410 resulted in opposite results. Conclusion: LINC01410 promotes proliferation and metastasis of ESCC, which might be caused by activation of Wnt/β-catenin and EMT signaling pathways. [ABSTRACT FROM AUTHOR]

Published
2024
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