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5. LASIGE and UNICAGE solution to the NASA LitCoin NLP Competition

Author

Ruas, Pedro, Sousa, Diana F., Neves, André, Cruz, Carlos, and Couto, Francisco M.

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Performance
Abstract

Biomedical Natural Language Processing (NLP) tends to become cumbersome for most researchers, frequently due to the amount and heterogeneity of text to be processed. To address this challenge, the industry is continuously developing highly efficient tools and creating more flexible engineering solutions. This work presents the integration between industry data engineering solutions for efficient data processing and academic systems developed for Named Entity Recognition (LasigeUnicage\_NER) and Relation Extraction (BiOnt). Our design reflects an integration of those components with external knowledge in the form of additional training data from other datasets and biomedical ontologies. We used this pipeline in the 2022 LitCoin NLP Challenge, where our team LasigeUnicage was awarded the 7th Prize out of approximately 200 participating teams, reflecting a successful collaboration between the academia (LASIGE) and the industry (Unicage). The software supporting this work is available at \url{https://github.com/lasigeBioTM/Litcoin-Lasige_Unicage}.

Published
2023

13. The platelet transcriptome and proteome in Alzheimers disease and aging: an exploratory cross-sectional study.

Author

de Sousa, Diana, Poupardin, Rodolphe, Villeda, Saul, Fröhlich, Thomas, Frey, Vanessa, Staffen, Wolfgang, Mrowetz, Heike, Altendorfer, Barbara, Unger, Michael, Iglseder, Bernhard, Paulweber, Bernhard, Trinka, Eugen, Cadamuro, Janne, Drerup, Martin, Schallmoser, Katharina, Aigner, Ludwig, Kniewallner, Kathrin, and Schroer, Adam

Subjects
Alzheimer’s disease, aging, platelets, proteomics, transcriptomics
Abstract

Introduction: Alzheimers disease (AD) and aging are associated with platelet hyperactivity. However, the mechanisms underlying abnormal platelet function in AD and aging are yet poorly understood. Methods: To explore the molecular profile of AD and aged platelets, we investigated platelet activation (i.e., CD62P expression), proteome and transcriptome in AD patients, non-demented elderly, and young individuals as controls. Results: AD, aged and young individuals showed similar levels of platelet activation based on CD62P expression. However, AD and aged individuals had a proteomic signature suggestive of increased platelet activation compared with young controls. Transcriptomic profiling suggested the dysregulation of proteolytic machinery involved in regulating platelet function, particularly the ubiquitin-proteasome system in AD and autophagy in aging. The functional implication of these transcriptomic alterations remains unclear and requires further investigation. Discussion: Our data strengthen the evidence of enhanced platelet activation in aging and provide a first glimpse of the platelet transcriptomic changes occurring in AD.

Published
2023

14. Outcome Prediction in Cerebral Venous Thrombosis: The IN-REvASC Score.

Author

Klein, Piers, Shu, Liqi, Nguyen, Thanh, Siegler, James, Omran, Setareh, Simpkins, Alexis, Heldner, Mirjam, Havenon, Adam, Aparicio, Hugo, Abdalkader, Mohamad, Psychogios, Marios, Vedovati, Maria, Paciaroni, Maurizio, Martial, Rascha, Liebeskind, David, Sousa, Diana, Coutinho, Jonathan, and Yaghi, Shadi

Subjects
Prognosis, Sinus thrombosis, intracranial, Stroke
Abstract

BACKGROUND: We identified risk factors, derived and validated a prognostic score for poor neurological outcome and death for use in cerebral venous thrombosis (CVT). METHODS: We performed an international multicenter retrospective study including consecutive patients with CVT from January 2015 to December 2020. Demographic, clinical, and radiographic characteristics were collected. Univariable and multivariable logistic regressions were conducted to determine risk factors for poor outcome, mRS 3-6. A prognostic score was derived and validated. RESULTS: A total of 1,025 patients were analyzed with median 375 days (interquartile range [IQR], 180 to 747) of follow-up. The median age was 44 (IQR, 32 to 58) and 62.7% were female. Multivariable analysis revealed the following factors were associated with poor outcome at 90- day follow-up: active cancer (odds ratio [OR], 11.20; 95% confidence interval [CI], 4.62 to 27.14; P

Published
2022

17. Early Versus Late Initiation of Direct Oral Anticoagulants After Ischemic Stroke in People With Atrial Fibrillation and Hemorrhagic Transformation: Prespecified Subanalysis of the Randomized Controlled ELAN Trial

Author

Rohner, Roman, Kneihsl, Markus, Goeldlin, Martina B., Hakim, Arsany, Branca, Mattia, Abend, Stefanie, Valenzuela Pinilla, Waldo, Fenzl, Sabine, Rezny-Kasprzak, Beata, Strbian, Daniel, Trelle, Sven, Paciaroni, Maurizio, Thomalla, Götz, Michel, Patrik, Nedeltchev, Krassen, Gattringer, Thomas, Sandset, Else C., Bonati, Leo, Aguiar de Sousa, Diana, Sylaja, P.N., Ntaios, George, Koga, Masatoshi, Gdovinova, Zuzana, Lemmens, Robin, Bornstein, Natan M., Kelly, Peter, Katan, Mira, Horvath, Thomas, Dawson, Jesse, and Fischer, Urs

Published
2024
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18. Outcomes of Decompressive Surgery for Patients With Severe Cerebral Venous Thrombosis: DECOMPRESS2 Observational Study

Author

Aaron, Sanjit, Ferreira, Jorge M., Coutinho, Jonathan M., Canhão, Patrícia, Conforto, Adriana B., Arauz, Antonio, Carvalho, Marta, Masjuan, Jaime, Sharma, Vijay K., Putaala, Jukka, Uyttenboogaart, Maarten, Werring, David J., Bazan, Rodrigo, Mohindra, Sandeep, Weber, Jochen, Coert, Bert A., Kirubakaran, Prabhu, Sanchez van Kammen, Mayte, Singh, Pankaj, Aguiar de Sousa, Diana, Ferro, José M., Benjamin, Kenneth, Zuurbier, Yvonne, Winckler, Fernanda, MFerro, José, Costa, Marisa, Barboza, Miguel, Uribe Pacheco, Rodrigo, Alvis, Fernando, Serrano, Eunice, Menendez, Fernanda, Soriano Navarro, Eduardo, Wilson, Duncan, Vida, Mariann, and Greiner, Karolin

Published
2024
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20. External validation of the SI2NCAL2C score for outcomes following cerebral venous thrombosis

Author

Klein, Piers, Shu, Liqi, Lindgren, Erik, de Winter, Maria A., Siegler, James E., Simpkins, Alexis N., Omran, Satareh Salehi, Heldner, Mirjam R., de Havenon, Adam, Abdalkader, Mohamad, al Kasab, Sami, Stretz, Christoph, Wu, Teddy Y., Wilson, Duncan, Asad, Syed Daniyal, Almallouhi, Eyad, Frontera, Jennifer, Kuohn, Lindsey, Rothstein, Aaron, Bakradze, Ekaterina, Henninger, Nils, Zubair, Adeel S., Sharma, Richa, Kerrigan, Deborah, Aziz, Yasmin, Mistry, Eva A., van Kammen, Mayte Sanchez, Tatlisumak, Turgut, Krzywicka, Katarzyna, Aguiar de Sousa, Diana, Jood, Katarina, Field, Thalia S., Yaghi, Shadi, Coutinho, Jonathan M., and Nguyen, Thanh N.

Published
2024
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28. Antithrombotic Treatment for Stroke Prevention in Cervical Artery Dissection: The STOP-CAD Study

Author

Yaghi, Shadi, Shu, Liqi, Mandel, Daniel, Leon Guerrero, Christopher R., Henninger, Nils, Muppa, Jayachandra, Affan, Muhammad, Ul Haq Lodhi, Omair, Heldner, Mirjam R., Antonenko, Kateryna, Seiffge, David, Arnold, Marcel, Salehi Omran, Setareh, Crandall, Ross, Lester, Evan, Lopez Mena, Diego, Arauz, Antonio, Nehme, Ahmad, Boulanger, Marion, Touze, Emmanuel, Sousa, Joao Andre, Sargento-Freitas, Joao, Barata, Vasco, Castro-Chaves, Paulo, Brito, Maria Teresa, Khan, Muhib, Mallick, Dania, Rothstein, Aaron, Khazaal, Ossama, Kaufmann, Josefin E., Engelter, Stefan T., Traenka, Christopher, Aguiar de Sousa, Diana, Soares, Mafalda, Rosa, Sara, Zhou, Lily W., Gandhi, Preet, Field, Thalia S., Mancini, Steven, Metanis, Issa, Leker, Ronen R., Pan, Kelly, Dantu, Vishnu, Baumgartner, Karl, Burton, Tina, Von Rennenberg, Regina, Nolte, Christian H., Choi, Richard, MacDonald, Jason, Bavarsad Shahripour, Reza, Guo, Xiaofan, Ghannam, Malik, Almajali, Mohammad, Samaniego, Edgar A., Sanchez, Sebastian, Rioux, Bastien, Zine-Eddine, Faycal, Poppe, Alexandre, Fonseca, Ana Catarina, Baptista, Maria Fortuna, Cruz, Diana, Romoli, Michele, De Marco, Giovanna, Longoni, Marco, Keser, Zafer, Griffin, Kim, Kuohn, Lindsey, Frontera, Jennifer, Amar, Jordan, Giles, James, Zedde, Marialuisa, Pascarella, Rosario, Grisendi, Ilaria, Nzwalo, Hipolito, Liebeskind, David S., Molaie, Amir, Cavalier, Annie, Kam, Wayneho, Mac Grory, Brian, Al Kasab, Sami, Anadani, Mohammad, Kicielinski, Kimberly, Eltatawy, Ali, Chervak, Lina, Chulluncuy-Rivas, Roberto, Aziz, Yasmin, Bakradze, Ekaterina, Tran, Thanh Lam, Rodrigo-Gisbert, Marc, Requena, Manuel, Saleh Velez, Faddi, Ortiz Gracia, Jorge, Mudassani, Varsha, de Havenon, Adam, Vishnu, Venugopalan Y., Yaddanapudi, Sridhara, Adams, Latasha, Browngoehl, Abigail, Ranasinghe, Tamra, Dunston, Randy, Lynch, Zachary, Penckofer, Mary, Siegler, James, Mayer, Silvia, Willey, Joshua, Zubair, Adeel, Cheng, Yee Kuang, Sharma, Richa, Marto, João Pedro, Mendes Ferreira, Vítor, Klein, Piers, Nguyen, Thanh N., Asad, Syed Daniyal, Sarwat, Zoha, Balabhadra, Anvesh, Patel, Shivam, Secchi, Thais, Martins, Sheila, Mantovani, Gabriel, Kim, Young Dae, Krishnaiah, Balaji, Elangovan, Cheran, Lingam, Sivani, Quereshi, Abid, Fridman, Sebastian, Alvarado, Alonso, Khasiyev, Farid, Linares, Guillermo, Mannino, Marina, Terruso, Valeria, Vassilopoulou, Sofia, Tentolouris, Vasilis, Martinez-Marino, Manuel, Carrasco Wall, Victor, Indraswari, Fransisca, El Jamal, Sleiman, Liu, Shilin, Alvi, Muhammad, Ali, Farman, Sarvath, Mohammed, Morsi, Rami Z., Kass-Hout, Tareq, Shi, Feina, Zhang, Jinhua, Sokhi, Dilraj, Said, Jamil, Simpkins, Alexis N., Gomez, Roberto, Sen, Shayak, Ghani, Mohammad, Elnazeir, Marwa, Xiao, Han, Kala, Narendra, Khan, Farhan, Stretz, Christoph, Mohammadzadeh, Nahid, Goldstein, Eric, and Furie, Karen

Published
2024
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31. Generating Biomedical Question Answering Corpora from Q&A forums

Author

Lamurias, Andre, Sousa, Diana, and Couto, Francisco M.

Subjects
Computer Science - Information Retrieval
Abstract

Question Answering (QA) is a natural language processing task that aims at obtaining relevant answers to user questions. While some progress has been made in this area, biomedical questions are still a challenge to most QA approaches, due to the complexity of the domain and limited availability of training sets. We present a method to automatically extract question-article pairs from Q\&A web forums, which can be used for document retrieval, a crucial step of most QA systems. The proposed framework extracts from selected forums the questions and the respective answers that contain citations. This way, QA systems based on document retrieval can be developed and evaluated using the question-article pairs annotated by users of these forums. We generated the BiQA corpus by applying our framework to three forums, obtaining 7,453 questions and 14,239 question-article pairs. We evaluated how the number of articles associated with each question and the number of votes on each answer affects the performance of baseline document retrieval approaches. Also, we demonstrated that the articles given as answers are significantly similar to the questions and trained a state-of-the-art deep learning model that obtained similar performance to using a dataset manually annotated by experts. The proposed framework can be used to update the BiQA corpus from the same forums as new posts are made, and from other forums that support their answers with documents. The BiQA corpus and the framework used to generate it are available at \url{https://github.com/lasigeBioTM/BiQA}.

Published
2020
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32. BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction

Author

Sousa, Diana and Couto, Francisco M.

Subjects
Computer Science - Information Retrieval
Abstract

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt., Comment: ECIR 2020

Published
2020
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33. Global Impact of COVID-19 on Stroke Care and IV Thrombolysis.

Author

Nogueira, Raul, Qureshi, Muhammad, Abdalkader, Mohamad, Martins, Sheila, Yamagami, Hiroshi, Qiu, Zhongming, Mansour, Ossama, Sathya, Anvitha, Czlonkowska, Anna, Tsivgoulis, Georgios, Aguiar de Sousa, Diana, Demeestere, Jelle, Mikulik, Robert, Vanacker, Peter, Siegler, James, Kõrv, Janika, Biller, Jose, Liang, Conrad, Sangha, Navdeep, Zha, Alicia, Czap, Alexandra, Holmstedt, Christine, Turan, Tanya, Ntaios, George, Malhotra, Konark, Tayal, Ashis, Loochtan, Aaron, Ranta, Annamarei, Mistry, Eva, Alexandrov, Anne, Huang, David, Yaghi, Shadi, Raz, Eytan, Sheth, Sunil, Mohammaden, Mahmoud, Frankel, Michael, Bila Lamou, Eric, Aref, Hany, Elbassiouny, Ahmed, Hassan, Farouk, Menecie, Tarek, Mustafa, Wessam, Shokri, Hossam, Roushdy, Tamer, Sarfo, Fred, Alabi, Tolulope, Arabambi, Babawale, Nwazor, Ernest, Sunmonu, Taofiki, Wahab, Kolawole, Yaria, Joseph, Mohammed, Haytham, Adebayo, Philip, Riahi, Anis, Sassi, Samia, Gwaunza, Lenon, Ngwende, Gift, Sahakyan, David, Rahman, Aminur, Ai, Zhibing, Bai, Fanghui, Duan, Zhenhui, Hao, Yonggang, Huang, Wenguo, Li, Guangwen, Li, Wei, Liu, Ganzhe, Luo, Jun, Shang, Xianjin, Sui, Yi, Tian, Ling, Wen, Hongbin, Wu, Bo, Yan, Yuying, Yuan, Zhengzhou, Zhang, Hao, Zhang, Jun, Zhao, Wenlong, Zi, Wenjie, Leung, Thomas, Chugh, Chandril, Huded, Vikram, Menon, Bindu, Pandian, Jeyaraj, Sylaja, P, Usman, Fritz, Farhoudi, Mehdi, Hokmabadi, Elyar, Horev, Anat, Reznik, Anna, Sivan Hoffmann, Rotem, Ohara, Nobuyuki, Sakai, Nobuyuki, Watanabe, Daisuke, Yamamoto, Ryoo, Doijiri, Ryosuke, Tokuda, Naoki, Yamada, Takehiro, Terasaki, Tadashi, and Yazawa, Yukako

Subjects
COVID-19, Cross-Sectional Studies, Hospitalization, Humans, Pandemics, Retrospective Studies, SARS-CoV-2, Stroke, Thrombolytic Therapy
Abstract

OBJECTIVE: To measure the global impact of COVID-19 pandemic on volumes of IV thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with 2 control 4-month periods. METHODS: We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases. RESULTS: There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95% confidence interval [CI] -11.7 to -11.3, p < 0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95% CI -13.8 to -12.7, p < 0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95% CI -13.7 to -10.3, p = 0.001). Recovery of stroke hospitalization volume (9.5%, 95% CI 9.2-9.8, p < 0.0001) was noted over the 2 later (May, June) vs the 2 earlier (March, April) pandemic months. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was noted in 3.3% (1,722/52,026) of all stroke admissions. CONCLUSIONS: The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID-19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months.

Published
2021

36. Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization–Lancet Neurology Commission

Author

Feigin, Valery L, Abd-Allah, Foad, Akinyemi, Rufus O, Bhattacharjee, Natalia V, Brainin, Michael, Cao, Jackie, Caso, Valeria, Dalton, Bronte, Davis, Alan, Dempsey, Robert, Duprey, Joseph, Feng, Wuwei, Ford, Gary A, Gall, Seana, Gandhi, Dorcas, Good, David C, Hachinski, Vladimir, Hacke, Werner, Hankey, Graeme J, Ishida, Marie, Johnson, Walter, Kim, Julie, Lavados, Pablo, Lindsay, Patrice, Mahal, Ajay, Martins, Sheila, Murray, Christopher, Nguyen, Thuy Phuong, Norrving, Bo, Olaiya, Muideen T, Olalusi, Oladotun V, Pandian, Jeyaraj, Phan, Hoang, Platz, Thomas, Ranta, Anna, Rehman, Sabah, Roth, Greg, Sebastian, Ivy Anne, Smith, Amanda E, Suwanwela, Nijasri C, Sylaja, P N, Thapa, Rajshree, Thrift, Amanda G, Uvere, Ezinne, Vollset, Stein Emil, Yavagal, Dileep, Yaria, Joseph, Owolabi, Mayowa O, Abera, Semaw Ferede, Akinyemi, Rufus, Dempsey, Robert J, Ibrahim, Norlinah Mohamed, Liu, Liping, Ovbiagele, Bruce, Piradov, Michael, Suwanwela, Nijasri, Abanto, Carlos, Addissie, Adamu, Adeleye, Amos O, Adilbekov, Yerzhan, Adilbekova, Bibigul, Adoukonou, Thierry A, Aguiar de Sousa, Diana, Akhmetzhanova, Zauresh, Akpalu, Albert, El Alaoui-Faris, Mustapha, Ameriso, Sebastian F, Andonova, Silva, Arsovska, Anita, Awoniyi, Folorunso E, Bakhiet, Moiz, Barboza, Miguel A, Basri, Hamidon, Bath, Philip M, Bereczki, Daniel, Beretta, Simone, Berkowitz, Aaron L, Bernhardt, Julie, Berzina, Guna, Bhavsar, Bhavan, Bisharyan, Mher S, Bohara, Manoj, Bovet, Pascal, Budincevic, Hrvoje, Cadilhac, Dominique A, Čerimagić, Denis, Charway-Felli, Augustina, Chen, Christopher, Chin, Jerome H, Christensen, Hanne, Chwojnicki, Kamil, Conforto, Adriana B, Correia, Manuel, Mora Cuervo, Daissy L, Członkowska, Anna, D'Amelio, Marco, Danielyan, Kristine E, Davis, Stephen, Demarin, Vida, Demchuk, Andrew M, Dichgans, Martin, Dokova, Klara, Donnan, Geoffrey, Duran, Juan Carlos, Ekeng, Gloria, Elkind, Mitchell S, Endres, Matthias, Fischer, Urs, Flomin, Yuriy, Gankpe, Fortuné, Gavidia, Martin, Gaye Saavedra, Andrés, Gebreyohanns, Mehari, George, Mary, Gierlotka, Marek, Giroud, Maurice, Gnedovskaya, Elena V, Gonçalves, Ivete Pillo, Gongora-Rivera, Fernando, Gunaratne, Padma S, Hamadeh, Randah R, Hamzat, Tal-hatu K, Heldner, Mirjam R, Ibrahim, Etedal, Ihle-Hansen, Hanne, Jee, Sungju, Jiann-Shing, Jeng, Johnston, S Clay, Jovanovic, Dejana, Jurjāns, Kristaps, Kalani, Rizwan, Kalkonde, Yogeshwar, Kamenova, Saltanat, Karaszewski, Bartosz, Kelly, Peter, Kiechl, Stefan, Kondybayeva, Aida, Kõrv, Janika, Kozera, Grzegorz, Kravchenko, Michael, Krespi, Yakup, Krishnamurthi, Rita, Kruja, Jera, Kutluk, Kursad, Langhorne, Peter, Law, Zhe K, Lebedynets, Dmytro, Lee, Tsong-Hai, Leung, Thomas W, Liebeskind, David S, López-Jaramillo, Patricio, Lotufo, Paulo A, Machline-Carrion, M Julia, Maia, Luis F, Malojcic, Branko, Markus, Hugh S, Marquez-Romero, Juan M, Medina, Marco T, Medukhanova, Sabina, Mehndiratta, Man Mohan, Miglāne, Evija, Mihejeva, Illa, Mikulik, Robert, Mirrakhimov, Erkin, Mohl, Stephanie, Munakomi, Sunil, Murphy, Sean, Musa, Kamarul I, Nasreldein, Ahmed, Nogueira, Raul G, Nolte, Christian H, Noubiap, Jean Jacques, Novarro-Escudero, Nelson, Ocampo, Cassandra, O'Donnell, Martin, Ogun, Yomi, Ogunniyi, Adesola, Oraby, Mohammed I, Ōrken, Dilek N, Ōzdemir, Atilla O, Ozturk, Serefnur, Paccot, Mélanie, Pereira, Telmo, Peeters, André, Potpara, Tatjana, Proios, Hariklia, Rathore, Farooq A, Sacco, Ralph L, Sahathevan, Ramesh, Sandset, Else S, Renato Santos, Irving, Saposnik, Gustavo, Sarfo, Fred S, Sargento-Freitas, João, Sharma, Mukul, Shaw, Louise, Sheth, Kevin N, Shin, Yong-Il, Shobhana, A, Silva, S Nishan, Tedim Cruz, Vitor, Thakur, Kiran, Thapa, Lekh Jung, Toni, Danilo, Topcuoglu, Mehmetakif A, Torales, Julio, Towfighi, Amytis, Truelsen, Thomas, Tsiskaridze, Alexander, Tulloch-Reid, Marshall, Useche, Juan N, Vanacker, Peter, Vassilopoulou, Sophia, Vukorepa, Gorana, Vuletic, Vladimira, Wahab, Kolawole W, Wang, Wenzhi, Wijeratne, Tissa, Wojtyniak, Bogdan, Wolfe, Charles, Yacouba, Mapoure N, Yang, Jie, Yifru, Yared M, Yock-Corrales, Adriana, Yonemoto, Naohiro, Yperzeele, Laetitia, and Zagożdżon, Pawel

Published
2023
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38. Genome-guided analysis allows the identification of novel physiological traits in Trichococcus species

Author

Strepis, Nikolaos, Naranjo, Henry D, Meier-Kolthoff, Jan, Göker, Markus, Shapiro, Nicole, Kyrpides, Nikos, Klenk, Hans-Peter, Schaap, Peter J, Stams, Alfons JM, and Sousa, Diana Z

Subjects
Microbiology, Biological Sciences, Genetics, Biotechnology, Infection, Bacterial Typing Techniques, Carnobacteriaceae, Phenotype, Phylogeny, Propylene Glycols, RNA, Ribosomal, 16S, Sequence Analysis, DNA, Comparative genomics, Protein domains, Halophilic, Psychrophilic, 1, 3-propanediol, 1, 3-propanediol, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundThe genus Trichococcus currently contains nine species: T. flocculiformis, T. pasteurii, T. palustris, T. collinsii, T. patagoniensis, T. ilyis, T. paludicola, T. alkaliphilus, and T. shcherbakoviae. In general, Trichococcus species can degrade a wide range of carbohydrates. However, only T. pasteurii and a non-characterized strain of Trichococcus, strain ES5, have the capacity of converting glycerol to mainly 1,3-propanediol. Comparative genomic analysis of Trichococcus species provides the opportunity to further explore the physiological potential and uncover novel properties of this genus.ResultsIn this study, a genotype-phenotype comparative analysis of Trichococcus strains was performed. The genome of Trichococcus strain ES5 was sequenced and included in the comparison with the other nine type strains. Genes encoding functions related to e.g. the utilization of different carbon sources (glycerol, arabinan and alginate), antibiotic resistance, tolerance to low temperature and osmoregulation could be identified in all the sequences analysed. T. pasteurii and Trichococcus strain ES5 contain a operon with genes encoding necessary enzymes for 1,3-PDO production from glycerol. All the analysed genomes comprise genes encoding for cold shock domains, but only five of the Trichococcus species can grow at 0 °C. Protein domains associated to osmoregulation mechanisms are encoded in the genomes of all Trichococcus species, except in T. palustris, which had a lower resistance to salinity than the other nine studied Trichococcus strains.ConclusionsGenome analysis and comparison of ten Trichococcus strains allowed the identification of physiological traits related to substrate utilization and environmental stress resistance (e.g. to cold and salinity). Some substrates were used by single species, e.g. alginate by T. collinsii and arabinan by T. alkaliphilus. Strain ES5 may represent a subspecies of Trichococcus flocculiformis and contrary to the type strain (DSM 2094T), is able to grow on glycerol with the production of 1,3-propanediol.

Published
2020

40. Using Neural Networks for Relation Extraction from Biomedical Literature

Author

Sousa, Diana, Lamurias, Andre, and Couto, Francisco M.

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results., Comment: Artificial Neural Networks book (Springer) - Chapter 14

Published
2019
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41. A Silver Standard Corpus of Human Phenotype-Gene Relations

Author

Sousa, Diana, Lamurias, Andre, and Couto, Francisco M.

Subjects
Computer Science - Computation and Language
Abstract

Human phenotype-gene relations are fundamental to fully understand the origin of some phenotypic abnormalities and their associated diseases. Biomedical literature is the most comprehensive source of these relations, however, we need Relation Extraction tools to automatically recognize them. Most of these tools require an annotated corpus and to the best of our knowledge, there is no corpus available annotated with human phenotype-gene relations. This paper presents the Phenotype-Gene Relations (PGR) corpus, a silver standard corpus of human phenotype and gene annotations and their relations. The corpus consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. We generated this corpus using Named-Entity Recognition tools, whose results were partially evaluated by eight curators, obtaining a precision of 87.01%. By using the corpus we were able to obtain promising results with two state-of-the-art deep learning tools, namely 78.05% of precision. The PGR corpus was made publicly available to the research community., Comment: NAACL 2019

Published
2019
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46. Medical Management Versus Endovascular Treatment for Large-Vessel Occlusion Anterior Circulation Stroke With Low NIHSS

Author

Safouris, Apostolos, Palaiodimou, Lina, Nardai, Sándor, Kargiotis, Odysseas, Magoufis, George, Psychogios, Klearchos, Matusevicius, Marius, Feil, Katharina, Ahmed, Niaz, Kellert, Lars, Spiliopoulos, Stavros, Brountzos, Elias, Szikora, István, Sarraj, Amrou, Goyal, Nitin, Aguiar de Sousa, Diana, Strbian, Daniel, Caso, Valeria, Alexandrov, Andrei V., and Tsivgoulis, Georgios

Published
2023
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47. Conversion of Carbon Monoxide to Chemicals Using Microbial Consortia

Author

Parera Olm, Ivette, Sousa, Diana Z., Scheper, Thomas, Series Editor, Belkin, Shimshon, Editorial Board Member, Bley, Thomas, Editorial Board Member, Bohlmann, Jörg, Editorial Board Member, Gu, Man Bock, Editorial Board Member, Hu, Wei Shou, Editorial Board Member, Mattiasson, Bo, Editorial Board Member, Olsson, Lisbeth, Editorial Board Member, Seitz, Harald, Editorial Board Member, Silva, Ana Catarina, Editorial Board Member, Ulber, Roland, Series Editor, Zeng, An-Ping, Editorial Board Member, Zhong, Jian-Jiang, Editorial Board Member, Zhou, Weichang, Editorial Board Member, and Claassens, Nico J., editor

Published
2022
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49. Study of Rivaroxaban for Cerebral Venous Thrombosis: A Randomized Controlled Feasibility Trial Comparing Anticoagulation With Rivaroxaban to Standard-of-Care in Symptomatic Cerebral Venous Thrombosis

Author

Field, Thalia S., Dizonno, Vanessa, Almekhlafi, Mohammed A., Bala, Fouzi, Alhabli, Ibrahim, Wong, Hubert, Norena, Monica, Villaluna, Maria Karina, King-Azote, Princess, Ratnaweera, Namali, Mancini, Steven, Van Gaal, Stephen C., Wilson, Laura K., Graham, Brett R., Sposato, Luciano A., Blacquiere, Dylan, Dewar, Brian M., Boulos, Mark I., Buck, Brian H., Odier, Celine, Perera, Kanjana S., Pikula, Aleksandra, Tkach, Aleksander, Medvedev, George, Canfield, Carolyn, Mortenson, W. Ben, Nadeau, Janel O., Alshimemeri, Sohaila, Benavente, Oscar R., Demchuk, Andrew M., Dowlatshahi, Dar, Lanthier, Sylvain, Lee, Agnes Y.Y., Mandzia, Jennifer, Suryanarayan, Deepa, Weitz, Jeffrey I., Hill, Michael D., Hill, Michael, Villaluna Murray, Karina, Jones, Andrea, Matsubara, Lauren, O’Neill, Zoe, Park, Sarah, Yuan, Michelle, Saluzzi, Marina, Dueck, Ashley, McKibben, K. Ingrid, Zhang, Qiao, Benavente (chair), Oscar, Butcher (chair), Ken, Bushnell, Cheryl, de Sousa, Diana Aguiar, Hill, Michael, Benavente, Oscar, Wilson, Dr Laura, Benavente, Oscar, Lloret, Mar, Lau, Hsien Lee, Maclean, Genoveva, Mann, Sharanpal, Murphy, Colleen, Smith, Jonathan, Teal, Philip, Tse, Ming Yin (Dominic), Yip, Samuel, Naidoo, VIshaya, Brar, Jaskiran, Chen, Shuo, Horton, Myles, Hill, Michael, Save, Supriya, Althubait, Shorog, Bogiatzi, Chrysi, Boyko, Matthew, Chatuverdi, Surbhi, Coutts, Shelagh, Ganesh, Aravind, Ghavami, Kimia, Harrison, Emme, Hu, Sherry, Jambula, Anitha, Joundi, Raed, Kenney, Carol, Klein, Gary, Lin, Katie, Mansoor, Salman, Marko, Martha, Ryckborst, Karla, Sage, Kayla, Singh, Nishita, Tse, Ming Yin (Dominic), Wadhwa, Ankur, Wasiliw, Sanchea, Graham, Brett, Bhavsar, Shrijal, Bold, Kala, Maley, Sharleen, Urroz, Lilian, Foster, Kaitlyn, Junk, Emily, Corley, Scott, Gardner, Aaron, McMullen, Jennifer, Whelan, Ruth, Duff, Whitney, Tyson, Cassandra, Cooley, Regan, Hunter, Gary, Magee, Fergall, Wasyliw, Sanchea, Beauchamp, Meribeth-Ann (Beth), Lambourn, Lindsay, Ayan, Diana, Khaw, Aleksander, Mai, Lauren, Bullrich, Maria Bres, Fridman, Sebastian, Daham, Zeinab, Fahad, Robert, Shamy, Michel, Stotts, Grant, Fatadawala, Idris, Lopes, Kaitlyn, Southwell, Alisia, Bhandari, Vinaya, Fitzpatrick, Tess, Gladstone, David, Hopyan, Julia, Kamra, Maneesha, Khosravani, Houman, Liddy, Anne-Marie, Liu, Zhongyu, Popel, Najla, Sivakumar, Keith, Swartz, Richard, Yu, Amy, Fairall, Paige, Ahmed, Farhat, Jabs, Juline, White, Leah, Piquette, Lori, Shepherd, Rekha, Ishaque, Noman, Odier, Céline, Simon, Nandy-Shelwine, Lapierre, Marlene, Bereznyakova, Olena, Caporuscio, Casey Boudreau, Cauchon, Francois, Côté, Valerie, Denault, Nicole, Desciantre, Yan, Gauthier, Lyne, Gioia, Laura, Jaquin, Grégory, Jadil, Nadia, Lim, Sothun, Poppe, Alexandra, Rodriguez, Caludia, Rodriguez, Marie-Christine, Stapf, Christian, Vandervelde, Cheyenne, Ng, Kuan Huei (Kelvin), Oczkowski, Wes, Lourenco, Diane, Moreau, Cathay, Jolie, Amber, Bhagraith, Vinai, Katsanos, Aristeidis, de Sa Boasquevisque, Danielle, Ratnayake, Kanchana, McLelland, Marie, Adderly, Coleen, Elamin, Elsadig, Galloway, Camille, Smith, Michelle, Topor, Tess, Singh, Shobha, To, William, Akthar, Farhana, Casaubon, Leanne, Cayley, Anne, Crelling, Lisa, Del Campo, Martin, Gao, Mingyang, Hanna, Cresti, Khalid, Muhammad, Pham, Nga, Schaafsma, Joanna, Silver, Frank, Stewart, TIm, Tiopanas, Patricia, Wigner, Rely, and Williams, Janice

Published
2023
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