Highly Tunable, Nanomaterial‐Functionalized Structural Templating of Intracellular Protein Structures Within Biological Species by Dae‐Hyeon Song, Chang Woo Song, Seunghee H. Cho, Tae Yoon Kwon, Hoeyun Jung, Ki Hyun Park, Jiyun Kim, Junyoung Seo, Jaeyoung Yoo, Minjoon Kim, Gyu Rac Lee, Jisung Hwang, Hyuck Mo Lee, Jonghwa Shin, Jennifer H. Shin, Yeon Sik Jung, Jae‐Byum Chang

“…In this study, Conversion to Advanced Materials via labeled Biostructures (CamBio), an integrated biotemplating platform that involves labeling target protein structures with antibodies followed by the growth of functional materials, ensuring outstanding nanostructure tunability is proposed. …”
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Perceived Healthfulness, Nutrient Content Awareness, Consumption, and Intention to Purchase Selected Ultraprocessed Products Among Adults in South Africa by Makoma Bopape, Jeroen De Man, Lindsey Smith Taillie, Rina Swart

“…Conclusion and Implications: Intervention strategies such as simplified front-of-pack labeling may have a role in improving nutrition awareness and discouraging UPP consumption.…”
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Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering by Giles M. Foody

“…., training data for a supervised classifier) and/or an output (e.g., predicted class label). Whether as an input to or output from a classification, little if any information beyond a class label is typically available for a pixel. …”
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A Y178C rhodopsin mutation causes aggregation and comparatively severe retinal degeneration by Sreelakshmi Vasudevan, Paul S.–H. Park

“…Aggregates of the Y178C rhodopsin mutant labeled by the dye PROTEOSTAT were morphologically similar to those formed by both the P23H and G188R rhodopsin mutants. …”
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Attention-based interactive multi-level feature fusion for named entity recognition by Yiwu Xu, Yun Chen

“…Finally, the fused features are fed into the sequence labeling layer to predict the word labels. We conducted generous comparative experiments on three datasets, and the experimental results showed that our model achieved better performance than several state-of-the-art models.…”
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Semantic segmentation using synthetic images of underwater marine-growth by Christian Mai, Jesper Liniger, Simon Pedersen

“…However, collecting and precisely labeling submerged data is challenging due to uncontrollable and harsh environmental factors. …”
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Peer-review Blinded Assay Test (P-BAT): a framework for trustless laboratory quality assurance for state-regulated cannabis markets by Stuart Procter, Grayson L. Baird, Jason Iannuccilli

“…The following proposed framework called the Peer-review Blinded Assay Test (P-BAT), is a validation process where each laboratory tests products from competing labs and their own lab, but in a blinded fashion to ensure that the label values of said products and the labs that produced said labels, are unknown. …”
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Altered Thyroid Function Tests Observed in Hypophosphatasia Patients Treated with Asfotase Alfa by Hajime Kato, Naoko Hidaka, Minae Koga, Yuka Kinoshita, Masaomi Nangaku, Noriko Makita, Nobuaki Ito

“…Thyroid hormone levels measured using five different immunoassays with or without ALP as a labeling enzyme during asfotase alfa treatment were evaluated. …”
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Preclinical Evaluation of Ga-DOTA-Minigastrin for the Detection of Cholecystokinin-2/Gastrin Receptor-Positive Tumors by Maarten Brom, Lieke Joosten, Peter Laverman, Wim J.G. Oyen, Martin Béhé, Martin Gotthardt, Otto C. Boerman

“…We investigated whether the 68 Ga-labeled gastrin analogue DOTA-MG0 is suited for positron emission tomography (PET), which could improve image quality. …”
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Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning by Minjuan Zhong, Zhenjin Li, Shengzong Liu, Bo Yang, Rui Tan, Xilong Qu

“…Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…The development of automated models requires extensive labeled and incredibly abnormal data to accurately identify and analyze abnormalities, which are difficult to obtain in sufficient quantities. …”
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SiCLAT: simultaneous imaging of chromatin loops and active transcription in living cells by Xin Wan, Jie Kong, Xiaodi Hu, Lulu Liu, Yuanping Yang, Hu Li, Gaoao Liu, Xingchen Niu, Fengling Chen, Dan Zhang, Dahai Zhu, Yong Zhang

“…Using SiCLAT, we accurately labeled chromatin loop anchor interactions and associated gene transcription during myogenic differentiation. …”
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Protocol to identify endogenous proximal proteins using biotinylation by antibody recognition by Camilla Rega, Mercedes Pardo, Lesley-Ann Martin, Jyoti Choudhary

“…We describe steps for defining proximity labeling reaction conditions, assessing enrichment using western blot, and sample preparation for mass spectroscopy analysis. …”
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Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis by Paul Wighton, Tim K. Lee, Greg Mori, Harvey Lui, David I. McLean, M. Stella Atkins

“…The first model is based on independent pixel labeling using maximum a-posteriori (MAP) estimation. …”
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Adjuvant Use of Ivabradine in Acute Heart Failure due to Myocarditis by Jennifer Franke, Dorothee Schmahl, Stephanie Lehrke, Regina Pribe, Raffi Bekeredjian, Andreas O. Doesch, Philipp Ehlermann, Philipp Schnabel, Hugo A. Katus, Christian Zugck

“…In both patients, the If-channel inhibitor ivabradine was administered off-label to provide selective heart rate reduction, and thus support hemodynamic stabilization. …”
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Image recognition technology for bituminous concrete reservoir panel cracks based on deep learning. by Kai Hu, Yang Ling, Jie Liu

“…A large dataset of panel images was collected and processed using denoising, standardization, and data augmentation techniques, with crack areas labeled via LabelImg software. The core model is an improved Xception network, enhanced with an adaptive activation function, dynamic attention mechanism, and multi-level residual connections. …”
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Image classification with rotation-invariant variational quantum circuits by Paul San Sebastian Sein, Mikel Cañizo, Román Orús

“…In this work, an equivariant architecture for variational quantum classifiers is introduced to create a label-invariant model for image classification with C_{4} rotational label symmetry. …”
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Human action recognition method based on multi-view semi-supervised ensemble learning by Shengnan CHEN, Xinmin FAN

“…Mass labeled data are hard to get in mobile devices.Inadequate training leads to bad performance of classifiers in human action recognition.To tackle this problem, a multi-view semi-supervised ensemble learning method was proposed.First, data of two different inertial sensors was used to construct two feature views.Two feature views and two base classifiers were combined to construct co-training framework.Then, the confidence degree was redefined in multi-class task and was combined with active learning method to control predict pseudo-label result in each iteration.Finally, extended training data was used as input to train LightGBM.Experiments show that the method has good performance in precision rate, recall rate and F1 value, which can effectively detect different human action.…”
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The Full m Index Sets of P2×Pn by Zhizhong Liu, Jinmeng Liu, Yurong Ji

“…This work expands the scope of previous graph labeling studies and provides new insights into determining the full m index set of product graphs. …”
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