Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis by Ennio Idrobo-Avila, Gergo Bognar, Dagmar Krefting, Thomas Penzel, Peter Kovacs, Nicolai Spicher

“…Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. …”
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Culturally appropriate terminologies in health research: a participatory study with pregnant and breastfeeding mothers in Uganda by Ritah Nakijoba, Adelline Twimukye, Josephine Bayigga, Aida N. Kawuma, Simon Peter Asiimwe, Fredrick Byenume, Jacqueline Kyeyune, Sylvia Nabukenya, Francis Williams Ojara, Francis Williams Ojara, Catriona Waitt, Catriona Waitt

“…Language defining a person on the basis of anatomical or physiological characteristics was considered ‘embarrassing’ and labelling individuals based on their conditions was construed as stigmatising. …”
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The cerebral blood flow response to neuroactivation is reduced in cognitively normal men with β-amyloid accumulation by Mark Bitsch Vestergaard, Aftab Bakhtiari, Merete Osler, Erik Lykke Mortensen, Ulrich Lindberg, Ian Law, Martin Lauritzen, Krisztina Benedek, Henrik Bo Wiberg Larsson

“…ΔCBFVis.Act. was measured using arterial spin labelling (ASL) combined with blood-oxygen-level-dependent (BOLD) MRI. …”
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Electrocardiography-based artificial intelligence predicts the upcoming future of heart failure with mildly reduced ejection fraction by Dae-Young Kim, Sang-Won Lee, Dong-Ho Lee, Sang-Chul Lee, Sang-Chul Lee, Ji-Hun Jang, Sung-Hee Shin, Dae-Hyeok Kim, Dae-Hyeok Kim, Wonik Choi, Wonik Choi, Yong-Soo Baek, Yong-Soo Baek, Yong-Soo Baek

“…The AI-ECG encompasses a novel model that combines an automatic labelling preprocessing method with a transformer architecture incorporating a triplet loss for HFmrEF analysis.ResultsThe receiver operating characteristic analyses revealed that the area under the curve of AI-ECG for identifying all types of HF was acceptable [0.873, 95% confidence interval (CI): 0.864–0.893], while that for identifying patients with HFmrEF was relatively lower (0.824, 95% CI: 0.794–0.863) than that for those with HF with reduced ejection fraction (EF) (0.875, 95% CI: 0.844–0.912) and those with normal EF (0.870, 95% CI: 0.842–0.894). …”
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Nobiletin Attenuates Pathological Cardiac Remodeling after Myocardial Infarction via Activating PPARγ and PGC1α by Yufei Zhou, Ting Yin, Mengsha Shi, Mengli Chen, Xiaodong Wu, Kai Wang, Iokfai Cheang, Yanxiu Li, Hongcai Shang, Haifeng Zhang, Xinli Li

“…Cardiac function (echocardiography, survival rate, Evans blue, and triphenyl tetrazolium chloride staining), fibrosis (Masson’s trichrome staining, quantitative real-time polymerase chain reaction (qRT-PCR), and western blot (WB)), hypertrophy (haematoxylin-eosin staining, wheat germ agglutinin staining, and qRT-PCR), and apoptosis (WB and terminal deoxynucleotidyl transferase dUTP nick-end labelling (TUNEL) staining) were evaluated. Hypoxia-induced apoptosis (TUNEL, WB) and phenylephrine- (PE-) induced pathological hypertrophy (immunofluorescence staining, qRT-PCR) models were established in primary neonatal rat ventricular myocytes (NRVMs). …”
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Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists by Xin Chen, Wei Xu, Yong Huang, Wei Jin, Gang Xu, Jing Yuan, Zhigang Song, Chunkai Yu, Shuangmei Zou, Wenmiao Wang, Xiaohui Ding, Jinhong Liu, Liwei Shao, Xiangnan Gou, Zhanbo Wang, Huang Chen, Cancheng Liu, Zhuo Sun, Calvin Ku, Yongqiang Zhang, Xianghui Dong, Shuhao Wang, Ning Lv, Huaiyin Shi

“…The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study.Design The deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. …”
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Towards a common approach for managing food allergy and serious allergic reactions (anaphylaxis) at school. GA2LEN and EFA consensus statement by Antoine Deschildre, Montserrat Alvaro‐Lozano, Antonella Muraro, Marcia Podesta, Debra deSilva, Mattia Giovannini, Simona Barni, Timothy E. Dribin, Mónica Sandoval‐Ruballos, Aikaterini Anagnostou, Alessandro Fiocchi, Alice Toniolo, Andrew Bird, Angel Sánchez Sanz, Anna Asarnoj, Anna Nowak‐Wegrzyn, Berber Vlieg‐Boerstra, Brian P. Vickery, Carina Venter, Caroline Nilsson, Cecilia Parente, Céline Demoulin, David M. Fleischer, Diola Bijlhout, Edward F. Knol, Eleanor Garrow, Emma E. Cook, Fallon Schultz, Francesca Lazzarotto, Francesca Mori, Gary Wong, Gideon Lack, Graham Roberts, Gustavo Andres Marino, H. N. G. Oude Elberink, Helen A. Brough, Hugh A. Sampson, Jay Lieberman, Jennifer Gerdts, Jing Zhao, Josefine Gradman, Julia E. M. Upton, Julie Wang, Kati Palosuo, Kirsi M. Järvinen, Kirsten Beyer, Kunling Shen, Laura Polloni, Lianne Mandelbaum, Luciana Kase Tanno, Lucy A. Bilaver, Marcus S. Shaker, Margitta Worm, Maria Said, Mary Kelly, Mary Jane Marchisotto, Michael Makris, Mikaela Odemyr, Montserrat Fernandez‐Rivas, Motohiro Ebisawa, Nandinee Patel, Pablo Rodríguez del Río, Pakit Vichyanond, Paul Turner, Pete Smith, Pilar Morón Gaspar, R. Sharon Chinthrajah, Rima Rachid, Roberta Bonaguro, Ruchi Gupta, Sabine Schnadt, Sakura Sato, Stefania Arasi, Stephanie Leonard, Sung Poblete, Susanne Halken, Thuy‐My Le, Guillaume Pouessel, Tracey Dunn, Victoria Cardona, Torsten Zuberbier

“…We built shared expectations around: 1. training all school staff about what food allergy is, the symptoms of allergic reactions, what to do in an emergency, and when and how to use and store devices that laypeople can use to administer adrenaline (epinephrine). 2. preventing allergic reactions by using clear labelling on school menus and prepacked and non‐prepacked foods and regular cleaning where students eat. 3. preparing for serious allergic reactions, with written emergency action plans for every student with food allergies, legislation allowing schools to store adrenaline for anyone who needs it in an emergency (not just those prescribed it), and training and legal safeguards for staff administering adrenaline. 4. including affected students by discussing food allergy in the curriculum, raising awareness among all students and caregivers and reviewing school processes regularly. …”
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Level-8 Ph.D. Programs In Medical Sciences: High Time For The Higher Education Commission (HEC) Of Pakistan To Establish A Medical Wing by Wafa Omer

“…A physician cannot be asked to opt out of clinical setups and hospitals upon completion of a PhD degree labelling it as an academic or research degree because the doctors who are opting for this level-8 degree are primarily physicians and they cannot be detached from the clinical environment. …”
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A Decision-Making Model for Deterring Food Vendors from Selling Harmless Low-Quality Foods as High-Quality Foods to Consumers by Po-Yu Chen

“…For certain types of foods, food vendors often label low-quality foods that are harmless to human health as foods of excellent quality and sell these falsely labeled products to consumers. …”
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Comparison of Tumor Non-specific and PD-L1 Specific Imaging by Near-Infrared Fluorescence/Cerenkov Luminescence Dual-Modality In-situ Imaging by Linhan Zhang MD, Lianmeng Zhao MS, Xue Lin MD, Sheng Zhao MD, Wenbin Pan MS, Dandan Wang MD, Zhongqi Sun MD, Jinping Li MD, Zonghui Liang MD, Rongjun Zhang MS, Huijie Jiang MD

“…Background Labeled antibodies are excellent imaging agents in oncology to non-invasively visualize cancer-related antigens expression levels. …”
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A Novel Approach for Cyclic Decompositions of Balanced Complete Bipartite Graphs into Infinite Graph Classes by A. El-Mesady, Omar Bazighifan, S. S. Askar

“…In the present paper, we propose a technique for labeling the vertices of a bipartite graph G with n edges, called orthogonal labeling, to yield cyclic decompositions of balanced complete bipartite graphs Kn,n by the graph G. …”
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Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes by Wei Song, Guang Hu, Liuqing OuYang, Zhenjie Zhu

“…Meanwhile, to exploit the correlations between labels in the label space as well, this paper uses the partial differential equation random field to inscribe the correlations between different kinds of labels in the label space and the correlations between hidden features and labels. …”
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A Novel Semi-Supervised Learning Method Based on Fast Search and Density Peaks by Fei Gao, Teng Huang, Jinping Sun, Amir Hussain, Erfu Yang, Huiyu Zhou

“…Then, a labeling method based on clustering information (LCI) is designed to label the unlabeled samples. …”
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A Fine-Grained Mobile Data Security Model by Yubin Yang, Liming Cheng

“…The creating, modifying, storing and transmitting of the data label and application label were controlled and managed by the label control management module. …”
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EM-AUC: A Novel Algorithm for Evaluating Anomaly Based Network Intrusion Detection Systems by Kevin Z. Bai, John M. Fossaceca

“…Although unsupervised models do not require labels during the training and testing phases, the assessment of their performance metrics during the evaluation phase still requires comparing anomaly scores against labels. …”
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A problem-agnostic approach to feature selection and analysis using SHAP by John T. Hancock, Taghi M. Khoshgoftaar, Qianxin Liang

“…When no labeled data is available, unsupervised learners should be used. …”
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Structure Properties of Koch Networks Based on Networks Dynamical Systems by Yinhu Zhai, Jia-Bao Liu, Shaohui Wang

“…We introduce an informative labeling algorithm for the vertices of a family of Koch networks. …”
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UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification by Zeyu Ren, Xiangyu Kong, Yudong Zhang, Shuihua Wang

“…However, obtaining ground truth labels for unlabeled medical images is challenging as they often outnumber labeled images. …”
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