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1. An Iterative Approach to Topic Modelling

Author

Wong, Albert, Cheng, Florence Wing Yau, Keung, Ashley, Hercules, Yamileth, Garcia, Mary Alexandra, Lim, Yew-Wei, and Pham, Lien

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

Topic modelling has become increasingly popular for summarizing text data, such as social media posts and articles. However, topic modelling is usually completed in one shot. Assessing the quality of resulting topics is challenging. No effective methods or measures have been developed for assessing the results or for making further enhancements to the topics. In this research, we propose we propose to use an iterative process to perform topic modelling that gives rise to a sense of completeness of the resulting topics when the process is complete. Using the BERTopic package, a popular method in topic modelling, we demonstrate how the modelling process can be applied iteratively to arrive at a set of topics that could not be further improved upon using one of the three selected measures for clustering comparison as the decision criteria. This demonstration is conducted using a subset of the COVIDSenti-A dataset. The early success leads us to believe that further research using in using this approach in conjunction with other topic modelling algorithms could be viable.

Published
2024

2. Data Extraction, Transformation, and Loading Process Automation for Algorithmic Trading Machine Learning Modelling and Performance Optimization

Author

Ebadifard, Nassi, Parihar, Ajitesh, Khmelevsky, Youry, Hains, Gaetan, Wong, Albert, and Zhang, Frank

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

A data warehouse efficiently prepares data for effective and fast data analysis and modelling using machine learning algorithms. This paper discusses existing solutions for the Data Extraction, Transformation, and Loading (ETL) process and automation for algorithmic trading algorithms. Integrating the Data Warehouses and, in the future, the Data Lakes with the Machine Learning Algorithms gives enormous opportunities in research when performance and data processing time become critical non-functional requirements.

Published
2023

3. Translating Natural Language Queries to SQL Using the T5 Model

Author

Wong, Albert, Pham, Lien, Lee, Young, Chan, Shek, Sadaya, Razel, Khmelevsky, Youry, Clement, Mathias, Cheng, Florence Wing Yau, Mahony, Joe, and Ferri, Michael

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This paper presents the development process of a natural language to SQL model using the T5 model as the basis. The models, developed in August 2022 for an online transaction processing system and a data warehouse, have a 73\% and 84\% exact match accuracy respectively. These models, in conjunction with other work completed in the research project, were implemented for several companies and used successfully on a daily basis. The approach used in the model development could be implemented in a similar fashion for other database environments and with a more powerful pre-trained language model.

Published
2023

5. Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms

Author

Wong, Albert, Whang, Steven, Sagre, Emilio, Sachin, Niha, Dutra, Gustavo, Lim, Yew-Wei, Hains, Gaetan, Khmelevsky, Youry, and Zhang, Frank

Subjects
Quantitative Finance - Trading and Market Microstructure, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning
Abstract

Creating accurate predictions in the stock market has always been a significant challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE in the short term from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. We report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, MTT, and MPE. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer (up to 10 seconds). Results from this study may improve by further tuning the individual parameters or introducing more exogenous variables.

Published
2023

6. Phase Identification of Smart Meters Using a Fourier Series Compression and a Statistical Clustering Algorithm

Author

Chiu, Jeremy J., Wong, Albert, Park, James, Mahony, Joe, Ferri, Michael, and Berson, Tim

Subjects
Statistics - Applications, Electrical Engineering and Systems Science - Systems and Control
Abstract

Accurate labeling of phase connectivity in electrical distribution systems is important for maintenance and operations but is often erroneous or missing. In this paper, we present a process to identify which smart meters must be in the same phase using a hierarchical clustering method on voltage time series data. Instead of working with the time series data directly, we apply the Fourier transform to represent the data in their frequency domain, remove $98\%$ of the Fourier coefficients, and use the remaining coefficients to cluster the meters are in the same phase. Result of this process is validated by confirming that cluster (phase) membership of meters does not change over two monthly periods. In addition, we also confirm that meters that belong to the same feeder within the distribution network are correctly classified into the same cluster, that is, assigned to the same phase., Comment: 5 pages, 6 figures, 4 tables

Published
2022
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12. Gamers Private Network Performance Forecasting. From Raw Data to the Data Warehouse with Machine Learning and Neural Nets

Author

Wong, Albert, Chiu, Chun Yin, Hains, Gaétan, Humphrey, Jack, Fuhrmann, Hans, Khmelevsky, Youry, and Mazur, Chris

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning, Computer Science - Performance
Abstract

Gamers Private Network (GPN) is a client/server technology that guarantees a connection for online video games that is more reliable and lower latency than a standard internet connection. Users of the GPN technology benefit from a stable and high-quality gaming experience for online games, which are hosted and played across the world. After transforming a massive volume of raw networking data collected by WTFast, we have structured the cleaned data into a special-purpose data warehouse and completed the extensive analysis using machine learning and neural nets technologies, and business intelligence tools. These analyses demonstrate the ability to predict and quantify changes in the network and demonstrate the benefits gained from the use of a GPN for users when connected to an online game session., Comment: 8 pages, 12 figures

Published
2021

13. Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer

Author

Orecchia, Luca, Tijerina, Adan, Simoes, Adrian, Ali, Ahmed, Nic an Riogh, Aisling, Wong, Albert, Kiciak, Alex, Ridgway, Alexander, Szostek, Alexandra, Dhanasekaran, Ananda, Cheong, Anderson, Atayi, Andrew, Ashpak, Ashna, Gutierrez Hidalgo, Beatriz, Teixeira, Bernardo, Maria Scornajenghi, Carlo, Marramaque, Carolina, Reynoldson, Charmian, Ho Chee Kong, Christopher, Crewe, Claire, Griffiths, Daniel, Amporore, Daniele, Mak, David, Sarkar, Debashis, Chung Wei Ling, Desmond, Bheenick, Divya, Hogan, Donnacha, Orakwe, Dubem, Gordon, Emma, Checcucci, Enrico, Nawaz, Faisal, Ribeiro Gonçalves, Fernando, Lozano Palacio, Fernando, Prata, Francesco, Claps, Francesco, Del Giudice, Francesco, Aggarwal, Gaurav, Marcq, Gautier, Hatzichristodoulou, Georgios, Karagiannidis, Georgios, Maria Pirola, Giacomo, Russo, Giorgio, Hytham, Hamid, Chun Khoo, Hau, Abozied, Hesham, Patel, Hiten D., Colvin, Holly, Ali, Iftikhar, Chibuzo, Ijeoma, Fakhradiyev, Ildar, Sokolakis, Ioannis, Tsikopoulos, Ioannis, Chong, James, Bondad, Jasper, Abbaraju, Jayasimha, Teoh, Jeremy, Whitburn, Jessica, Hayes, John, Luis Bauza Quetglas, Jose, Antonio Herranz Yague, Jose, Gomez Rivas, Juan, Colombo Stenstrom, Juan, de Mello, Kathryn Bandeira, Brodie, Katie, Tzelves, Lazaros, Lazaros, Lazarou, Paramore, Louise, Rico, Luis, Lilis, Lydia, Ullmann, Maha, Srour, Mahmoud, Boltri, Matteo, Mustafa, Mohamed, Eyad Takahji, Mohammad, Almusimie, Mohammed, Shakeel Inder, Mohammud, Elgamal, Mostafa, Misurati, Muftah, Ali, Muhammad-Kabir, Binnawara, Muhannud, Bhaloo, Nabeela, Vidal Crespo, Natalia, Ernesto Morales Palacios, Nelson, Santoni, Nicola, Pyrgidis, Nikolaos, Hamilton, Olivia, Abad Lopez, Pablo, Maheshkumar, Pattabiraman, Sountoulides, Petros, Piazza, Pietro, Moreno, Pilar, Sarmah, Piyush, Narayan, Prabhat, Banthia, Ravi, Matulewicz, Richard, Contieri, Roberto, David, Rotimi, Mohammad, Sabir, Abu, Sadiq, Alonso, Santiago, Ippoliti, Simona, Weber, Stephan, Abuhasanein, Suleiman, Lee, Taeweon, Klatte, Tobias, Trung Thanh, Tran, Wazir, Usman, Ulker, Volkan, Yeoh, W.S., Feuer, Zach, Elahi, Zain, Gall, Zara, Khadhouri, Sinan, Hramyka, Artsiom, Gallagher, Kevin, Light, Alexander, Edison, Marie, Alexander, Cameron, Kulkarni, Meghana, Zimmermann, Eleanor, Nathan, Arjun, Tran, Trung-Thanh, Kelsey, Tom, and Kasivisvanathan, Veeru

Published
2024
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17. Monitoring stance towards vaccination in Twitter messages

Author

Kunneman, Florian, Lambooij, Mattijs, Wong, Albert, Bosch, Antal van den, and Mollema, Liesbeth

Subjects
Computer Science - Computation and Language, Computer Science - Social and Information Networks
Abstract

We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms. We found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, outperforming a rule-based sentiment analysis baseline that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. The outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions., Comment: 16 pages, 2 figures, accepted for BMC Medical Informatics and Decision Making journal

Published
2019

18. Optics of Intraocular Lenses

Author

Wong, Albert Chak Ming, Azar, Dimitri, Section editor, Hunter, David G., Section editor, Albert, Daniel M., editor, Miller, Joan W., editor, Azar, Dimitri T., editor, and Young, Lucy H., editor

Published
2022
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24. Developing a Diagnostic Multivariable Prediction Model for Urinary Tract Cancer in Patients Referred with Haematuria: Results from the IDENTIFY Collaborative Study

Author

Chaudry, Aasem, Sharma, Abhishek, Bennett, Adam, Ahmad, Adnan, Abroaf, Ahmed, Suliman, Ahmed Musa, Lloyd, Aimee, McKay, Alastair, Wong, Albert, Silva, Alberto, Schneider, Alexandre, MacKay, Alison, Knight, Allen, Grigorakis, Alkiviadis, Bdesha, Amar, Nagle, Amy, Cebola, Ana, Dhanasekaran, Ananda Kumar, Kondža, Andraž, Barcelos, André, Galosi, Andrea Benedetto, Ebur, Andrea, Minervini, Andrea, Russell, Andrew, Webb, Andrew, de Jalón, Ángel García, Desai, Ankit, Czech, Anna Katarzyna, Mainwaring, Anna, Adimonye, Anthony, Das, Arighno, Figueiredo, Arnaldo, Villers, Arnauld, Leminski, Artur, Chippagiri, Arvinda, Lal, Asim Ahmed, Yıldırım, Asıf, Voulgaris, Athanasios Marios, Uzan, Audrey, Oo, Aye Moh Moh, Younis, Ayman, Zelhof, Bachar, Mukhtar, Bashir, Ayres, Ben, Challacombe, Ben, Sherwood, Benedict, Ristau, Benjamin, Lai, Billy, Nellensteijn, Brechtje, Schreiter, Brielle, Trombetta, Carlo, Dowling, Catherine, Hobbs, Catherine, Benitez, Cayo Augusto Estigarribia, Lebacle, Cédric, Ho, Cherrie Wing Yin, Ng, Chi-Fai, Mount, Chloe, Lam, Chon Meng, Blick, Chris, Brown, Christian, Gallegos, Christopher, Higgs, Claire, Browne, Clíodhna, McCann, Conor, Plaza Alonso, Cristina, Beder, Daniel, Cohen, Daniel, Gordon, Daniel, Wilby, Daniel, Gordon, Danny, Hrouda, David, Lau, David Hua Wu, Karsza, Dávid, Mak, David, Martin-Way, David, Suthaharan, Denula, Patel, Dhruv, Carrion, Diego M, Nyanhongo, Donald, Bass, Edward, Mains, Edward, Chau, Edwin, Canelon Castillo, Elba, Day, Elizabeth, Desouky, Elsayed, Gaines, Emily, Papworth, Emma, Yuruk, Emrah, Kilic, Enes, Dinneen, Eoin, Palagonia, Erika, Xylinas, Evanguelos, Khawaja, Faizan, Cimarra, Fernando, Bardet, Florian, Kum, Francesca, Peters, Francesca, Kovács, Gábor, Tanasescu, Geroge, Hellawell, Giles, Tasso, Giovanni, Lam, Gitte, La Montagna, Giuseppe, Pizzuto, Giuseppe, Lenart, Gordan, MacLennan, Graeme, Özgür, Günal, Bi, Hai, Lyons, Hannah, Warren, Hannah, Ahmed, Hashim, Simpson, Helen, Burden, Helena, Gresty, Helena, Rios Pita, Hernado, Clarke, Holly, Serag, Hosam, Kynaston, Howard, Crawford-Smith, Hugh, Mostafid, Hugh, Otaola-Arca, Hugo, Koo, Hui Fen, Ibrahim, Ibrahim, Ouzaid, Idir, Puche-Sanz, Ignacio, Tomašković, Igor, Tinay, Ilker, Sahibzada, Iqbal, Thangasamy, Isaac, Cadena, Iván Revelo, Irani, Jacques, Udzik, Jakub, Brittain, James, Catto, James, Green, James, Tweedle, James, Hernando, Jamie Borrego, Leask, Jamie, Kalsi, Jas, Frankel, Jason, Toniolo, Jason, Raman, Jay D., Courcier, Jean, Kumaradeevan, Jeevan, Clark, Jennifer, Jones, Jennifer, Teoh, Jeremy Yuen-Chun, Iacovou, John, Kelly, John, Selph, John P., Aning, Jonathan, Deeks, Jon, Cobley, Jonathan, Olivier, Jonathan, Maw, Jonny, Herranz-Yagüe, José Antonio, Nolazco, Jose Ignacio, Cózar-Olmo, Jose Manuel, Bagley, Joseph, Jelski, Joseph, Norris, Joseph, Testa, Joseph, Meeks, Joshua, Hernandez, Juan, Vásquez, Juan Luis, Randhawa, Karen, Dhera, Karishma, Gronostaj, Katarzyna, Houlton, Kathleen, Lehman, Kathleen, Gillams, Kathryn, Adasonla, Kelvin, Brown, Kevin, Murtagh, Kevin, Mistry, Kiki, Davenport, Kim, Kitamura, Kosuke, Derbyshire, Laura, Clarke, Laurence, Morton, Lawrie, Martinez, Levin, Goldsmith, Louise, Paramore, Louise, Cormier, Luc, Dell'Atti, Lucio, Simmons, Lucy, Martinez-Piñeiro, Luis, Rico, Luis, Chan, Luke, Forster, Luke, Ma, Lulin, Moore, Madeline, Gallego, Maria Camacho, Freire, Maria José, Emberton, Mark, Feneley, Mark, Antón-Juanilla, Marta, Rivero, Marta Viridiana Muñoz, Pirša, Matea, Tallè, Matteo, Crockett, Matthew, Liew, Matthew, Trail, Matthew, Peters, Max, Cooper, Meghan, Kulkarni, Meghana, Ager, Michael, He, Ming, Li, Mo, Omran Breish, Mohamed, Tarin, Mohamed, Aldiwani, Mohammed, Matanhelia, Mudit, Pasha, Muhammad, Akalın, Mustafa Kaan, Abdullah, Nasreen, Hale, Nathan, Gadiyar, Neha, Kocher, Neil, Bullock, Nicholas, Campain, Nicholas, Pavan, Nicola, Al-Ibraheem, Nihad, Bhatt, Nikita, Bedi, Nishant, Shrotri, Nitin, Lobo, Niyati, Balderas, Olga, Kouli, Omar, Capoun, Otakar, Oteo Manjavacas, Pablo, Gontero, Paolo, Mariappan, Paramananthan, Marchiñena, Patricio Garcia, Erotocritou, Paul, Sweeney, Paul, Planelles, Paula, Acher, Peter, Black, Peter C., Osei-Bonsu, Peter K, Østergren, Peter, Smith, Peter, Willemse, Peter-Paul Michiel, Chlosta, Piotr L., Ul Ain, Qurrat, Barratt, Rachel, Esler, Rachel, Khalid, Raihan, Hsu, Ray, Stamirowski, Remigiusz, Mangat, Reshma, Cruz, Ricardo, Ellis, Ricky, Adams, Robert, Hessell, Robert, Oomen, Robert J.A., McConkey, Robert, Ritchie, Robert, Jarimba, Roberto, Chahal, Rohit, Andres, Rosado Mario, Hawkins, Rosalyn, David, Rotimi, Manecksha, Rustom P., Agrawal, Sachin, Hamid, Syed Sami, Deem, Samuel, Goonewardene, Sanchia, Swami, Satchi Kuchibhotla, Hori, Satoshi, Khan, Shahid, Mohammud Inder, Shakeel, Sangaralingam, Shanthi, Marathe, Shekhar, Raveenthiran, Sheliyan, Horie, Shigeo, Sengupta, Shomik, Parson, Sian, Parker, Sidney, Hawlina, Simon, Williams, Simon, Mazzoli, Simone, Grzegorz Kata, Slawomir, Pinheiro Lopes, Sofia, Ramos, Sónia, Rai, Sonpreet, Rintoul-Hoad, Sophie, O'Meara, Sorcha, Morris, Steve, Turner, Stacey, Venturini, Stefano, Almpanis, Stephanos, Joniau, Steven, Jain, Sunjay, Mallett, Susan, Nikles, Sven, Shahzad, Yan, Sylvia, Lee, Taeweon, Uçar, Taha, Drake, Tamsin, Toma, Tarq, Cabañuz Plo, Teresa, Bonnin, Thierry, Muilwijk, Tim, Wollin, Tim, Chu, Timothy Shun Man, Appanna, Timson, Brophy, Tom, Ellul, Tom, Austin, Tomas, Smrkolj, Tomaž, Rowe, Tracey, Sukhu, Troy, Patel, Trushar, Garg, Tullika, Çaşkurlu, Turhan, Bele, Uros, Haroon, Usman, Crespo-Atín, Víctor, Parejo Cortes, Victor, Capapé Poves, Victoria, Gnanapragasam, Vincent, Gauhar, Vineet, During, Vinnie, Kumar, Vivek, Fiala, Vojtech, Mahmalji, Wasim, Lam, Wayne, Fung Chin, Yew, Filtekin, Yigit, Chyn Phan, Yih, Ibrahim, Youssed, Glaser, Zachary A, Abiddin, Zainal Adwin, Qin, Zijian, Zotter, Zsuzsanna, Zainuddin, Zulkifli, Khadhouri, Sinan, Gallagher, Kevin M., MacKenzie, Kenneth R., Shah, Taimur T., Gao, Chuanyu, Moore, Sacha, Zimmermann, Eleanor F., Edison, Eric, Jefferies, Matthew, Nambiar, Arjun, Anbarasan, Thineskrishna, Mannas, Miles P., Marra, Giancarlo, Gómez Rivas, Juan, Marcq, Gautier, Assmus, Mark A., Claps, Francesco, Boltri, Matteo, Burnhope, Tara, Nkwam, Nkwam, Boxall, Nicholas E., Downey, Alison P., Sukhu, Troy A., Chin, Yew-Fung, Green, James S.A., Goulao, Beatriz, Nielsen, Matthew, McGrath, John S., and Kasivisvanathan, Veeru

Published
2022
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25. The D2R-DISC1 protein complex and associated proteins are altered in schizophrenia and normalized with antipsychotic treatment

Author

Wang, Jijun, Su, Ping, Yang, Jian, Xu, Lihua, Yuan, Aihua, Li, Chunbo, Zhang, Tianhong, Dong, Fang, Zhou, Jingjing, Samsom, James, Wong, Albert H.C., and Liu, Fang

Subjects
Schizophrenia -- Diagnosis, Mass spectrometry -- Methods, Antipsychotic drugs -- Dosage and administration, Health, Psychology and mental health
Abstract

Background: For decades, the dopamine D2 receptor (D2R) has been known as the main target of antipsychotic medications, but the mechanism for antipsychotic effects beyond this pharmacological target remains unclear. Disrupted-in-schizophrenia 1 (DISCI) is a gene implicated in the etiology of schizophrenia, and we have found elevated levels of the D2R-DISC1 complex in the postmortem brain tissue of patients with schizophrenia. Methods: We used coimmunoprecipitation to measure D2R-DISC1 complex levels in peripheral blood samples from patients with schizophrenia and unaffected controls in 3 cohorts (including males and females) from different hospitals. We also used label-free mass spectrometry to conduct proteomic analysis of these samples. Results: Levels of the D2R-DISC1 complex were elevated in the peripheral blood samples of patients with schizophrenia from 3 independent cohorts, and were normalized with antipsychotic treatment. Proteomic analysis of the blood samples from patients with high D2R-DISC1 complex levels that were normalized with antipsychotic treatment revealed a number of altered proteins and pathways associated with D2R, DISC1 and the D2R-DISC1 complex. We identified additional proteins and pathways that were associated with antipsychotic treatment in schizophrenia, and that may also be novel targets for schizophrenia treatment. Limitations: Sample sizes were relatively small, but were sufficient to detect associations between D2R-DISC1 levels, schizophrenia and treatment response. The relevance of leukocyte changes to the symptoms of schizophrenia is unknown. The coimmunoprecipitation lanes included several nonspecific bands. Conclusion: Levels of the D2R-DISC1 complex were elevated in patients with schizophrenia and reduced with antipsychotic treatment. This finding reinforces the independent role of each protein in schizophrenia. Our results enhanced our understanding of the molecular pathways involved in schizophrenia and in antipsychotic medications, and identified novel potential molecular targets for treating schizophrenia., Introduction Schizophrenia is a chronic mental illness characterized by episodes of psychotic symptoms, with persistent cognitive and social impairments that emerge in early adulthood. Dopamine D2 receptors (D2Rs) are the [...]

Published
2022
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34. List of contributors

Author

Al-Chalabi, Nzaar, primary, Andreassen, Ole A., additional, Baron, David, additional, Blum, Kenneth, additional, Bowirrat, Abdalla, additional, Brady, Philip, additional, Brewer, Raymond, additional, Cadet, Jean Lud, additional, Corvin, Aiden, additional, Cote, Alanna, additional, De Luca, Vincenzo, additional, Deckert, Jürgen, additional, Diehl, Caroline K., additional, Dolžan, Vita, additional, Erhardt, Angelika, additional, Fatemi, Ali Bani, additional, Febo, Marcelo, additional, Ferrera, Alessandra G., additional, Frei, Oleksandr, additional, Gold, Mark S., additional, Goldberg, Joseph F., additional, Heron, Elizabeth A., additional, Hindley, Guy, additional, Huckins, Laura M., additional, Igblom, Amarachukwu, additional, Juki, Marin M., additional, K. Davis, Lea, additional, Kiri Dennis, Jessica, additional, Kong, Camillia, additional, Kores Plesničar, Blanka, additional, Mattheisen, Manuel, additional, Miller, Gregory A., additional, Mishrekar, Asmita, additional, Nemeroff, Charles B., additional, Nurnberger, John I., additional, Ormond, Cathal, additional, O’Connell, Kevin S., additional, Pisanu, Claudia, additional, Qian, Jessica, additional, Ramoz, Nicolas, additional, Rockstroh, Brigitte, additional, Ryan, Niamh M., additional, Samsom, James N., additional, Shadrin, Alexey, additional, Smeland, Olav B., additional, Squassina, Alessio, additional, Terzi, Tea, additional, Thanos, Panayotis K., additional, Tsermpini, Evangelia Eirini, additional, Wang, Yin, additional, Wang, Yunpeng, additional, Wong, Albert H.C., additional, Yee, Cindy M., additional, Young, Hannah, additional, and Zhou, Young, additional

Published
2022
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38. Thoracic Decompression

Author

Kerolus, Mena G., Turel, Mazda K., Wong, Albert P., Smith, Zachary A., Fessler, Richard G., Phillips, Frank M., editor, Lieberman, Isador H., editor, Polly Jr., David W., editor, and Wang, Michael Y., editor

Published
2019
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42. Small-molecule targeting AMPA-mediated excitotoxicity has therapeutic effects in mouse models for multiple sclerosis

Author

Zhai, Dongxu, primary, Yan, Shuxin, additional, Samsom, James, additional, Wang, Le, additional, Su, Ping, additional, Jiang, Anlong, additional, Zhang, Haorui, additional, Jia, Zhengping, additional, Wallach, Izhar, additional, Heifets, Abraham, additional, Zanato, Chiara, additional, Tseng, Chih-Chung, additional, Wong, Albert H.C., additional, Greig, Iain R., additional, and Liu, Fang, additional

Published
2023
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47. Meningococcal W135 Disease Vaccination Intent, the Netherlands, 2018-2019

Author

de Vries, Marion, Claassen, Liesbeth, Wierik, Margreet J.M. te, Coban, Feray, Wong, Albert, Timmermans, Danielle R.M., and Timen, Aura

Subjects
Meningitis -- Health aspects -- Surveys, Vaccination -- Health aspects -- Surveys, Health
Abstract

Since the end of 2015, invasive meningococcal disease (IMD) caused by Neisseria meningitidis strain W135 has emerged as a severe threat to public health in the Netherlands (1). Before 2015, [...]

Published
2020
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48. The glucocorticoid receptor-FKBP51 complex contributes to fear conditioning and posttraumatic stress disorder

Author

Li, Haiyin, Su, Ping, Lai, Terence K.Y., Jiang, Anlong, Liu, Jing, Zhai, Dongxu, Campbell, Charlie T.G., Lee, Frankie H.F., Yong, WeiDong, Pasricha, Suvercha, Li, Shupeng, Wong, Albert H.C., Ressler, Kerry J., and Liu, Fang

Subjects
Protein binding -- Comparative analysis, Peptides -- Comparative analysis, Glucocorticoids -- Comparative analysis, Animal experimentation -- Comparative analysis, Post-traumatic stress disorder -- Comparative analysis, Genes, Time, Major depressive disorder, Depression (Mood disorder), Health care industry, University of Toronto. Centre for Addiction and Mental Health
Abstract

Posttraumatic stress disorder (PTSD) can develop after exposure to severe psychological trauma, leaving patients with disabling anxiety, nightmares, and flashbacks. Current treatments are only partially effective, and development of better treatments is hampered by limited knowledge of molecular mechanisms underlying PTSD. We have discovered that the glucocorticoid receptor (GR) and FK506 binding protein 51 (FKBP51) form a protein complex that is elevated in PTSD patients compared with unaffected control subjects, subjects exposed to trauma without PTSD, and patients with major depressive disorder (MDD). The GR-FKBP51 complex is also elevated in fear-conditioned mice, an aversive learning paradigm that models some aspects of PTSD. Both PTSD patients and fear-conditioned mice had decreased GR phosphorylation, decreased nuclear GR, and lower expression of 14-3-3[epsilon], a gene regulated by GR. We created a peptide that disrupts GR-FKBP51 binding and reverses behavioral and molecular changes induced by fear conditioning. This peptide reduces freezing time and increases GR phosphorylation, GR-FKBP52 binding, GR nuclear translocation, and 14-3-3[epsilon] expression in fear-conditioned mice. These experiments demonstrate a molecular mechanism contributing to PTSD and suggest that the GR-FKBP51 complex may be a diagnostic biomarker and a potential therapeutic target for preventing or treating PTSD., Introduction Posttraumatic stress disorder (PTSD) was originally described in soldiers exposed to horrific battlefield events, but the conception of psychological trauma has since been expanded to include any life- or [...]

Published
2020
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50. CpG-creating mutations are costly in many human viruses

Author

Caudill, Victoria R., Qin, Sarina, Winstead, Ryan, Kaur, Jasmeen, Tisthammer, Kaho, Pineda, E. Geo, Solis, Caroline, Cobey, Sarah, Bedford, Trevor, Carja, Oana, Eggo, Rosalind M., Koelle, Katia, Lythgoe, Katrina, Regoes, Roland, Roy, Scott, Allen, Nicole, Aviles, Milo, Baker, Brittany A., Bauer, William, Bermudez, Shannel, Carlson, Corey, Castellanos, Edgar, Catalan, Francisca L., Chemel, Angeline Katia, Elliot, Jacob, Evans, Dwayne, Fiutek, Natalie, Fryer, Emily, Goodfellow, Samuel Melvin, Hecht, Mordecai, Hopp, Kellen, Hopson, Jr., E. Deshawn, Jaberi, Amirhossein, Kinney, Christen, Lao, Derek, Le, Adrienne, Lo, Jacky, Lopez, Alejandro G., López, Andrea, Lorenzo, Fernando G., Luu, Gordon T., Mahoney, Andrew R., Melton, Rebecca L., Nascimento, Gabriela Do, Pradhananga, Anjani, Rodrigues, Nicole S., Shieh, Annie, Sims, Jasmine, Singh, Rima, Sulaeman, Hasan, Thu, Ricky, Tran, Krystal, Tran, Livia, Winters, Elizabeth J., Wong, Albert, and Pennings, Pleuni S.

Published
2020
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