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2. Novel application potential of cinaciguat in the treatment of mixed hyperlipidemia through targeting PTL/NPC1L1 and alleviating intestinal microbiota dysbiosis and metabolic disorders

Author

Ang Jia, Hongfei Jiang, Wenjing Liu, Pengwei Chen, Qi Xu, Renshuai Zhang, and Jufeng Sun

Subjects
Dual inhibitors, Pancreatic triglyceride lipase, Niemann–Pick C1-like 1, Cinaciguat, Gut microbiota, Therapeutics. Pharmacology, RM1-950
Abstract

Mixed hyperlipidemia, characterized by high levels of triglycerides and cholesterol, is a key risk factor leading to atherosclerosis and other cardiovascular diseases. Existing clinical drugs usually only work on a single indicator, decreasing either triglyceride or cholesterol levels. Developing dual-acting agents that reduce both triglycerides and cholesterol remains a great challenge. Pancreatic triglyceride lipase (PTL) and Niemann–Pick C1-like 1 (NPC1L1) have been identified as crucial proteins in the transport of triglycerides and cholesterol. Here, cinaciguat, a known agent used in the treatment of acute decompensated heart failure, was identified as a potent dual inhibitor targeting PTL and NPC1L1. We presented in vitro evidence from surface plasmon resonance analysis that cinaciguat interacted with PTL and NPC1L1. Furthermore, cinaciguat exhibited potent PTL-inhibition activity. Fluorescence-labeled cholesterol uptake analysis and confocal imaging showed that cinaciguat effectively inhibited cholesterol uptake. In vivo evaluation showed that cinaciguat significantly reduced the plasma levels of triglycerides and cholesterol, and effectively alleviated high-fat diet–induced intestinal microbiota dysbiosis and metabolic disorders. These results collectively suggest that cinaciguat has the potential to be further developed for the therapy of mixed hyperlipidemia.

Published
2023
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3. Inhibiting autophagy enhances sulforaphane-induced apoptosis via targeting NRF2 in esophageal squamous cell carcinoma

Author

Zhaoming Lu, Yandan Ren, Li Yang, Ang Jia, Yi Hu, Yu Zhao, Wuduo Zhao, Bin Yu, Wen Zhao, Jianying Zhang, and Guiqin Hou

Subjects
Sulforaphane, Autophagy, Chloroquine, NRF2, Esophageal squamous cell carcinoma, Therapeutics. Pharmacology, RM1-950
Abstract

Sulforaphane (SFN), a natural anti-tumor compound from cruciferous vegetables, has been reported to induce protective autophagy to cancer cells, which might impair the anti-tumor efficiency of SFN. However, the accurate function and mechanism of SFN inducing autophagy in cancers are still obscure, especially in esophageal squamous cell carcinoma (ESCC), one of malignancies with high incidence in North China. Here, we mainly explored the potential function of autophagy upon SFN treatment in ESCC and molecular mechanism. We demonstrated that SFN could inhibit cell proliferation and induce apoptosis by activating caspase pathway. Moreover, we found activation of NRF2 pathway by SFN was responsible for the induction of autophagy and also a disadvantage element to the anti-tumor effects of SFN on ESCC, indicating that SFN might induce protective autophagy in ESCC. We, therefore, investigated effects of autophagy inhibition on sensitivity of ESCC cells to SFN and found that chloroquine (CQ) could neutralize the activation of SFN on NRF2 and enhance the activation of SFN on caspase pathway, thus improved the anti-tumor efficiency of SFN on ESCC in vitro and in vivo. Our study provides a preclinical rationale for development of SFN and its analogs to the future treatment of ESCC.

Published
2021
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4. Demystifying Faulty Code with LLM: Step-by-Step Reasoning for Explainable Fault Localization

Author

Widyasari, Ratnadira, Ang, Jia Wei, Nguyen, Truong Giang, Sharma, Neil, and Lo, David

Subjects
Computer Science - Software Engineering
Abstract

Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault is laborious and time-consuming. To overcome this challenge, various fault localization tools have been developed. These tools typically generate a ranked list of suspicious program elements. However, this information alone is insufficient. A prior study emphasized that automated fault localization should offer a rationale. In this study, we investigate the step-by-step reasoning for explainable fault localization. We explore the potential of Large Language Models (LLM) in assisting developers in reasoning about code. We proposed FuseFL that utilizes several combinations of information to enhance the LLM results which are spectrum-based fault localization results, test case execution outcomes, and code description (i.e., explanation of what the given code is intended to do). We conducted our investigation using faulty code from Refactory dataset. First, we evaluate the performance of the automated fault localization. Our results demonstrate a more than 30% increase in the number of successfully localized faults at Top-1 compared to the baseline. To evaluate the explanations generated by FuseFL, we create a dataset of human explanations that provide step-by-step reasoning as to why specific lines of code are considered faulty. This dataset consists of 324 faulty code files, along with explanations for 600 faulty lines. Furthermore, we also conducted human studies to evaluate the explanations. We found that for 22 out of the 30 randomly sampled cases, FuseFL generated correct explanations., Comment: To be appeared at 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

Published
2024

7. A Hybrid Transfer Learning and Segmentation Approach for the Detection of Acute Lymphoblastic Leukemia

Author

Hau, Ang Jia, Hameed, Nazia, Walker, Adam, Hasan, Md. Mahmudul, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahmud, Mufti, editor, Mendoza-Barrera, Claudia, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, and Lugo, Eduardo, editor

Published
2023
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12. Outcomes of a Phase II Study of Intraperitoneal Paclitaxel plus Systemic Capecitabine and Oxaliplatin (XELOX) for Gastric Cancer with Peritoneal Metastases

Author

Chia, Daryl K. A., Sundar, Raghav, Kim, Guowei, Ang, Jia Jun, Lum, Jeffrey H. Y., Nga, Min En, Goh, Giap Hean, Seet, Ju Ee, Chee, Cheng Ean, Tan, Hon Lyn, Ho, Jingshan, Ngoi, Natalie Y. L., Lee, Matilda X. W., Muthu, Vaishnavi, Chan, Gloria H. J., Pang, Angela S. L., Ang, Yvonne L. E., Choo, Joan R. E., Lim, Joline S. J., Teh, Jun Liang, Lwin, Aung, Soon, Yuen, Shabbir, Asim, So, Jimmy B. Y., and Yong, Wei Peng

Published
2022
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15. Visual Feature Attribution using Wasserstein GANs

Author

Baumgartner, Christian F., Koch, Lisa M., Tezcan, Kerem Can, Ang, Jia Xi, and Konukoglu, Ender

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects., Comment: Accepted to CVPR 2018

Published
2017

22. ASO Visual Abstract: Outcomes of a Phase II Study of Intraperitoneal Paclitaxel Plus Systemic Capecitabine and Oxaliplatin (XELOX) for Gastric Cancer with Peritoneal Metastases

Author

Chia, Daryl K. A., Sundar, Raghav, Kim, Guowei, Ang, Jia Jun, Lum, Jeffrey H. Y., Nga, Min En, Goh, Giap Hean, Seet, Je Ee, Chee, Cheng Ean, Tan, Hon Lyn, Ho, Jingshan, Ngoi, Natalie Y. L., Lee, Matilda X. W., Muthu, Vaishnavi, Chan, Gloria H. J., Pang, Angela S. L., Ang, Yvonne L. E., Choo, Joan R. E., Lim, Joline S. J., Teh, Jun Liang, Lwin, Aung, Soon, Yuen, Shabbir, Asim, So, Jimmy B. Y., and Yong, Wei Peng

Published
2022
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39. Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning.

Author

Tang, Jia Yong, Chung, Boaz Yi Heng, Ang, Jia Chun, Chong, Jia Wen, Tan, Raymond R., Aviso, Kathleen B., Chemmangattuvalappil, Nishanth G., and Thangalazhy-Gopakumar, Suchithra

Subjects
BIOCHAR, ROUGH sets, MACHINE learning, POTENTIAL energy, BIOMASS, PREDICTION models
Abstract

Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11–14 wt%, volatile matter of 60–62 wt% and carbon content of 42–45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C–475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield. [ABSTRACT FROM AUTHOR]

Published
2024
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44. A Study of Customers’ Satisfaction at Burger King in Asian Countries

Author

Teoh, Kok Ban, primary, Lew, Tek Yew, additional, Gupta, Shikhar, additional, Ang, Jia Wei, additional, Ang, Hui hui, additional, Bong, Allychia Ling Li, additional, Nazira, Aina, additional, Zhafira, Adzra, additional, Chauhan, Shalini, additional, Pandey, Shivangi, additional, Liem, Gai Sin, additional, and Mohan, Nitin, additional

Published
2023
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46. Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise

Author

McManus, I. C., Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Smith, Daniel T., Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Akeredolu, Alimatu-Sadia, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Aojula, Sonam, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Chatterjee, Shaunak, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, De Waal, Hanelie, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Hussain, Ifrah, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Sopian, Aninditya Salma, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Van Den Tooren, Harriet, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and Devine, Oliver Patrick

Published
2020
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47. The Analysis of Teaching of Medical Schools (AToMS) survey: an analysis of 47,258 timetabled teaching events in 25 UK medical schools relating to timing, duration, teaching formats, teaching content, and problem-based learning

Author

Devine, Oliver Patrick, Harborne, Andrew Christopher, Horsfall, Hugo Layard, Joseph, Tobin, Marshall-Andon, Tess, Samuels, Ryan, Kearsley, Joshua William, Abbas, Nadine, Baig, Hassan, Beecham, Joseph, Benons, Natasha, Caird, Charlie, Clark, Ryan, Cope, Thomas, Coultas, James, Debenham, Luke, Douglas, Sarah, Eldridge, Jack, Hughes-Gooding, Thomas, Jakubowska, Agnieszka, Jones, Oliver, Lancaster, Eve, MacMillan, Calum, McAllister, Ross, Merzougui, Wassim, Phillips, Ben, Phillips, Simon, Risk, Omar, Sage, Adam, Sooltangos, Aisha, Spencer, Robert, Tajbakhsh, Roxanne, Adesalu, Oluseyi, Aganin, Ivan, Ahmed, Ammar, Aiken, Katherine, Akeredolu, Alimatu-Sadia, Alam, Ibrahim, Ali, Aamna, Anderson, Richard, Ang, Jia Jun, Anis, Fady Sameh, Aojula, Sonam, Arthur, Catherine, Ashby, Alena, Ashraf, Ahmed, Aspinall, Emma, Awad, Mark, Yahaya, Abdul-Muiz Azri, Badhrinarayanan, Shreya, Bandyopadhyay, Soham, Barnes, Sam, Bassey-Duke, Daisy, Boreham, Charlotte, Braine, Rebecca, Brandreth, Joseph, Carrington, Zoe, Cashin, Zoe, Chatterjee, Shaunak, Chawla, Mehar, Chean, Chung Shen, Clements, Chris, Clough, Richard, Coulthurst, Jessica, Curry, Liam, Daniels, Vinnie Christine, Davies, Simon, Davis, Rebecca, De Waal, Hanelie, Desai, Nasreen, Douglas, Hannah, Druce, James, Ejamike, Lady-Namera, Esere, Meron, Eyre, Alex, Fazmin, Ibrahim Talal, Fitzgerald-Smith, Sophia, Ford, Verity, Freeston, Sarah, Garnett, Katherine, General, Whitney, Gilbert, Helen, Gowie, Zein, Grafton-Clarke, Ciaran, Gudka, Keshni, Gumber, Leher, Gupta, Rishi, Harlow, Chris, Harrington, Amy, Heaney, Adele, Ho, Wing Hang Serene, Holloway, Lucy, Hood, Christina, Houghton, Eleanor, Houshangi, Saba, Howard, Emma, Human, Benjamin, Hunter, Harriet, Hussain, Ifrah, Hussain, Sami, Jackson-Taylor, Richard Thomas, Jacob-Ramsdale, Bronwen, Janjuha, Ryan, Jawad, Saleh, Jelani, Muzzamil, Johnston, David, Jones, Mike, Kalidindi, Sadhana, Kalsi, Savraj, Kalyanasundaram, Asanish, Kane, Anna, Kaur, Sahaj, Al-Othman, Othman Khaled, Khan, Qaisar, Khullar, Sajan, Kirkland, Priscilla, Lawrence-Smith, Hannah, Leeson, Charlotte, Lenaerts, Julius Elisabeth Richard, Long, Kerry, Lubbock, Simon, Burrell, Jamie Mac Donald, Maguire, Rachel, Mahendran, Praveen, Majeed, Saad, Malhotra, Prabhjot Singh, Mandagere, Vinay, Mantelakis, Angelos, McGovern, Sophie, Mosuro, Anjola, Moxley, Adam, Mustoe, Sophie, Myers, Sam, Nadeem, Kiran, Nasseri, Reza, Newman, Tom, Nzewi, Richard, Ogborne, Rosalie, Omatseye, Joyce, Paddock, Sophie, Parkin, James, Patel, Mohit, Pawar, Sohini, Pearce, Stuart, Penrice, Samuel, Purdy, Julian, Ramjan, Raisa, Randhawa, Ratan, Rasul, Usman, Raymond-Taggert, Elliot, Razey, Rebecca, Razzaghi, Carmel, Reel, Eimear, Revell, Elliot John, Rigbye, Joanna, Rotimi, Oloruntobi, Said, Abdelrahman, Sanders, Emma, Sangal, Pranoy, Grandal, Nora Sangvik, Shah, Aadam, Shah, Rahul Atul, Shotton, Oliver, Sims, Daniel, Smart, Katie, Smith, Martha Amy, Smith, Nick, Sopian, Aninditya Salma, South, Matthew, Speller, Jessica, Syer, Tom J., Ta, Ngan Hong, Tadross, Daniel, Thompson, Benjamin, Trevett, Jess, Tyler, Matthew, Ullah, Roshan, Utukuri, Mrudula, Vadera, Shree, Van Den Tooren, Harriet, Venturini, Sara, Vijayakumar, Aradhya, Vine, Melanie, Wellbelove, Zoe, Wittner, Liora, Yong, Geoffrey Hong Kiat, Ziyada, Farris, and McManus, I. C.

Published
2020
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48. A Review on Sensor Technologies and Control Methods for Mobile Robot with Obstacle Detection System.

Author

Ang Jia He and Min Thu Soe

Subjects
ROBOTS, ARTIFICIAL intelligence, DIGITAL technology, MACHINE learning, DEEP learning
Abstract

Obstacle detection system is a system that reacts to the object in the path and perform action such as stopping robot movement and collision prevention according to the design of algorithm which enhance the safety level of robot. This paper examines the overview of sensor technology that associates with obstacle detection system and car-like robot. This review summarizes the effectiveness and weakness of common type of sensors such as lidar, radar, ultrasonic sensor, infrared sensor, computer vision, sensor fusion and sensor array. This paper will also discuss on control methods for car-like robot that includes hand gestures, voice control, infrared remote control, Android based Bluetooth mobile control, and Wi-Fi based mobile control, outlining the effectiveness and limitation of each control method. [ABSTRACT FROM AUTHOR]

Published
2024
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