Distinctive antibody responses to Mycobacterium tuberculosis in pulmonary and brain infection by Spatola, M, Nziza, N, Irvine, EB, Cizmeci, D, Jung, W, Van, LH, Nhat, LTH, Ha, VTN, Phu, NH, Nghia, HDT, Thwaites, GE, Lauffenburger, DA, Fortune, S, Thuong, NTT, Alter, G

“…Antibody studies included analysis of immunoglobulin isotypes (IgG, IgM, IgA) and subclass levels (IgG1–4) and the capacity of <i>M. tuberculosis</i>-specific antibodies to bind to Fc receptors or C1q and to activate innate immune effector functions (complement and natural killer cell activation; monocyte or neutrophil phagocytosis). Machine learning methods were applied to characterize serum and CSF responses in TBM, identify prognostic factors associated with disease severity, and define the key antibody features that distinguish TBM from pulmonary TB. …”

Microbial communities: network reconstruction and control by Fu, A

“…It proposes adaptive learning methods and experimental design rules to transform PAG-inferred structures into fully identified causal models, thus enhancing our understanding of microbial dynamics and providing a systematic approach for future research in causal inference within complex biological systems. …”

Measuring the predictability of life outcomes with a scientific mass collaboration by Salganik, Matthew J., Lundberg, Ian, Kindel, Alexander T., Ahearn, Caitlin E., Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M., Brand, Jennie E., Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J., Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C., Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E., Roberts, Claudia V., Stanescu, Diana M., Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H., Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B., Argyle, Lisa P., Baer-Bositis, Livia, Buchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagne ́, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P., Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M., Hummel, Lisa M., Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, E. H., Leizman, Ben, Liu, Naijia, Moser, Malte, Mack, Andrew E., Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M., Porter, Ethan, Porter, Kristin E., Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D., Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K., Woon, Wei Lee, Wu, James, Wu, Catherine, Yang, Kengran, Yin, Jingwen, Zhao, Bingyu, Zhu, Chenyun, Brooks-Gunn, Jeanne, Engelhardt, Barbara E., Hardt, Moritz, Knox, Dean, Levy, Karen, Narayanan, Arvind, Stewart, Brandon M., Watts, Duncan J., McLanahan, Sara

“…Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. …”
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Feature extraction from EEG signals and regularization for brain-computer interface by Mishuhina, Vasilisa

“…The goal of this research is to improve feature extraction and regularization of EEG signals using machine learning methods and hence achieve better results during the classification of the signals for motor imagery BCI (MI-BCI). …”
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Natural robustness of machine learning in the open world by Wei, Hongxin

“…Secondly, classic machine learning methods are built on the i.i.d. assumption that training and testing data are independent and identically distributed. …”
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Sensor-based human activity recognition via zero-shot learning by Wang, Wei

“…For problems under this problem setting, as there are no labeled training instances belonging to the unseen classes, the zero-shot learning methods are used. We focus on three problems under this setting. …”
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Offline eLearning for undergraduates in health professions : a systematic review of the impact on knowledge, skills, attitudes and satisfaction by Wark, Petra A., Rasmussen, Kristine, Belisario, José Marcano, Molina, Joseph Antonio, Loong, Stewart Lee, Cotic, Ziva, Papachristou, Nikos, Riboli–Sasco, Eva, Car, Lorainne Tudor, Musulanov, Eve Marie, Zhang, Yanfeng, Kunz, Holger, George, Pradeep Paul, Heng, Bee Hoon, Wheeler, Erica Lynette, Al Shorbaji, Najeeb, Svab, Igor, Atun, Rifat, Majeed, Azeem, Car, Josip

“…To inform investments in offline eLearning, we need to establish its effectiveness in terms of gaining knowledge and skills, students’ satisfaction and attitudes towards eLearning. Methods: We conducted a systematic review of offline eLearning for students enrolled in undergraduate, health–related university degrees. …”
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Digital problem-based learning in health professions : systematic review and meta-analysis by the digital health education collaboration by Car, Lorainne Tudor, Kyaw, Bhone Myint, Dunleavy, Gerard, Smart, Neil A., Semwal, Monika, Rotgans, Jerome Ingmar, Low-Beer, Naomi, Campbell, James

“…We included studies that compared the effectiveness of DPBL with traditional learning methods or other forms of digital education in improving health professionals’ knowledge, skills, attitudes, and satisfaction. …”
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Brain Informatics : 14th International Conference, BI 2021, Virtual Event, September 17-19, 2021, Proceedings / by International Conference on Brain Informatics (14th : 2021 : Online), organiser 643024, Mahmud, Mufti, editor 643025, Kaiser, M. Shamim, editor 643026, Vassanelli, Stefano, editor 643027, Dai, Qionghai, 1964-, editor 643028, Zhong, Ning, 1956-, editor 643029

Brain computer interface for post-stroke motor rehabilitation by Mane, Ravikiran Tanaji

“…Moving ahead, we analyze the classification performance of proposed and baseline deep learning architectures and traditional machine learning methods for MI detection in 25 chronic stroke patients undergoing three different BCI-based motor rehabilitation interventions for 2/4 weeks. …”
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Creativity in Intelligent Technologies and Data Science : 4th International Conference, CIT&DS 2021, Volgograd, Russia, September 20-23, 2021 : Proceedings / by CIT&DS (Conference) (4th : 2021 : Volgograd, Russia), organiser 642924, Kravets, Alla G., editor 642925, Shcherbakov, Maxim, editor 642926, Parygin, Danila, editor 642927, Groumpos, Peter P., editor 642928

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures by Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J. Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., Goya-Maldonado, Roberto

“…Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.…”
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Night vision with unmanned surface vehicle (USV) by Ang, Claryl Hui Ern

“…Besides that, this research also involves sourcing code on a deep learning method for transferring night-time images to daytime images. …”
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Active learning for ontological event extraction incorporating named entity recognition and unknown word handling by Han, Xu, Kim, Jung-jae, Kwoh, Chee Keong

“…We also apply our active learning method for the task of named entity recognition. …”
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Query cost estimation in DBMS with deep learning by Acharya, Atul

“…Our experiments showed that the TreeGBM was ∼120 times faster than state-of-the-art learned methods while maintaining good prediction scores. …”
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Comparison of character recognition performance - bayes classifier and neural network methods by Low, Siew Eng.

“…In this project, two learning schemes had been tested and verified, in the recognition unit within the image processing environment of the Machine Vision Tool, namely the Bayes classifier method and the neural network-error back propagation gradient descent learning method. Their performances were analysed and compared. …”
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Building student preparedness and confidence: Insights from the global classroom mock job interview by Hanisah, Kasbon, Azimah, Ahmad Zaki, Farah Liyana, Ahmad A'azmey

“…Yet, a merely traditional formal learning method can be uninteresting, thus a more effective learning experience can be introduced including international collaborative approach. …”
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Factor modeling for clustering high-dimensional time series by Zhang, Bo, Pan, Guangming, Yao, Qiwei, Zhou, Wang

“…We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. …”
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Value investing with machine learning: the South American market by Chen, Ye

“…The primary machine learning method employed in this study is LSTM (Long Short-Term Memory), which performs well with time series data such as financial data of companies. …”
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An artificial sensory neuron with tactile perceptual learning by Wan, Changjin, Chen, Geng, Fu, Yangming, Wang, Ming, Matsuhisa, Naoji, Pan, Shaowu, Pan, Liang, Yang, Hui, Wan, Qing, Zhu, Liqiang, Chen, Xiaodong

“…Furthermore, the recognition error rate can be dramatically decreased from 44% to 0.4% by integrating with the machine learning method. This work represents a step toward the design and use of neuromorphic electronic skin with artificial intelligence for robotics and prosthetics.…”
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