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Articles de revues sur le sujet « Intrinsic labels »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Yu, Guangya, Qi Ye, and Tong Ruan. "Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label." Bioengineering 11, no. 3 (2024): 225. http://dx.doi.org/10.3390/bioengineering11030225.

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The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledg

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Thomas, Dean E., Ilyssa E. Ramos, Kyle S. Kinskie, Kyle M. Brown, James M. Cherry, and Alexandra R. Hyler. "Abstract 4585: Exploiting intrinsic bioelectrical signatures for label-free immune cell sorting." Cancer Research 85, no. 8_Supplement_1 (2025): 4585. https://doi.org/10.1158/1538-7445.am2025-4585.

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Abstract Immune cell separation from heterogeneous samples is crucial for various downstream research applications. Standard methods for cell purification are laborious and require additional fluorescent or magnetic labels to identify cells of interest. Approaches contingent upon the use of labels may interfere with downstream testing and analysis, like single cell sequencing or downstream oncology research. Label-free cell sorting by electrokinetic dielectrophoresis (DEP) has recently been used to characterize and separate target subpopulations from complex samples. Available literature descr

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Zhang, Pengfei, and Rui Wang. "Label-Free Biosensor." Biosensors 13, no. 5 (2023): 556. http://dx.doi.org/10.3390/bios13050556.

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Label-free biosensors have become an indispensable tool for analyzing intrinsic molecular properties, such as mass, and quantifying molecular interactions without interference from labels, which is critical for the screening of drugs, detecting disease biomarkers, and understanding biological processes at the molecular level [...]

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Chen, Boli, Xin Huang, Lin Xiao, Zixin Cai, and Liping Jing. "Hyperbolic Interaction Model for Hierarchical Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7496–503. http://dx.doi.org/10.1609/aaai.v34i05.6247.

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Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main re

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Zhao, Tianna, Yuanjian Zhang, and Witold Pedrycz. "Robust Multi-Label Classification with Enhanced Global and Local Label Correlation." Mathematics 10, no. 11 (2022): 1871. http://dx.doi.org/10.3390/math10111871.

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Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness is preliminary. Low-quality data is one of the main reasons that model robustness degrades. Aiming at the cases with noisy features and missing labels, we develop a novel method called robust global and local label correlation (RGLC). In this model, subspace learning reconstructs intrinsic latent features immune from feature noise. The manifold learning ensures that outputs obtained by matrix factoriza

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Fairweather-Tait, Susan J., Thomas E. Fox, S. Gabrielle Wharf, John Eagles, Helen M. Crews, and Robert Massey. "Apparent zinc absorption by rats from foods labelled intrinsically and extrinsically with 67Zn." British Journal of Nutrition 66, no. 1 (1991): 65–71. http://dx.doi.org/10.1079/bjn19910010.

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A variety of foods (peas (Pisum sativum), chicken meat, eggs, goat's milk, human milk) enriched with the stable isotope 67Zn were prepared by means of intrinsic- and extrinsic-labelling procedures. They were fed to rats and apparent absorption of 67Zn determined from faecal excretion measurements using thermal ionization mass spectrometry. There were significant differences in the absorption of the extrinsic and intrinsic label which differed in magnitude between the foods tested. The extrinsic 67Zn was less well absorbed in peas, chicken meat, eggs, and human milk than intrinsic 67Zn, but in

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Xing, Yuying, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, and Maozu Guo. "Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5508–15. http://dx.doi.org/10.1609/aaai.v33i01.33015508.

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Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance.\ In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instanc

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Huang, Shaoli, Xinchao Wang, and Dacheng Tao. "SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1628–36. http://dx.doi.org/10.1609/aaai.v35i2.16255.

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Data mixing augmentation has proved effective in training deep models. Recent methods mix labels mainly according to the mixture proportion of image pixels. Due to the major discriminative information of a fine-grained image usually resides in subtle regions, these methods tend to introduce heavy label noise in fine-grained recognition. We propose Semantically Proportional Mixing (SnapMix) that exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data. SnapMix generates the target label for a mixed image by estimating its intrinsic semantic composition. This

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Jia, Jinyuan, Xiaoyu Cao, and Neil Zhenqiang Gong. "Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7961–69. http://dx.doi.org/10.1609/aaai.v35i9.16971.

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In a data poisoning attack, an attacker modifies, deletes, and/or inserts some training examples to corrupt the learnt machine learning model. Bootstrap Aggregating (bagging) is a well known ensemble learning method, which trains multiple base models on random subsamples of a training dataset using a base learning algorithm and uses majority vote to predict labels of testing examples. We prove the intrinsic certified robustness of bagging against data poisoning attacks. Specifically, we show that bagging with an arbitrary base learning algorithm provably predicts the same label for a testing e

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Xiao, Qianpeng, Changbin Shao, Sen Xu, Xibei Yang, and Hualong Yu. "CC<i>k</i>EL: Compensation-based correlated <i>k</i>-labelsets for classifying imbalanced multi-label data." Electronic Research Archive 32, no. 5 (2024): 3038–58. http://dx.doi.org/10.3934/era.2024139.

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<abstract> <p>Imbalanced data distribution and label correlation are two intrinsic characteristics of multi-label data. This occurs because in this type of data, instances associated with certain labels may be sparse, and some labels may be associated with others, posing a challenge for traditional machine learning techniques. To simultaneously adapt imbalanced data distribution and label correlation, this study proposed a novel algorithm called compensation-based correlated <italic>k</italic>-labelsets (CC<italic>k</italic>EL). First, for each label, the CC

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Gómez-Carmona, Diego, Serafín Cruces-Montes, Pedro Pablo Marín-Dueñas, César Serrano-Domínguez, Alberto Paramio, and Antonio Zayas García. "Do You See It Clearly? The Effect of Packaging and Label Format on Google Ads." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 5 (2021): 1648–66. http://dx.doi.org/10.3390/jtaer16050093.

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The nature of e-commerce prevents the perception of the intrinsic and sensory attributes of wine. In the virtual environment, visual cues allow consumers to perceive the product, determine their attitude and form a preference. Users will choose one product or another based on the visual appeal of the advertisements they have seen. Wine marketers must consider the importance of the advertisement elements and attract the consumer’s attention. Optimizing the elements included in these messages can help capture consumers’ attention and achieve a higher click-through rate on the ads. The main objec

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Sijtsema, Nanna M., Johannes J. Duindam, Gerwin J. Puppels, Cees Otto, and Jan Greve. "Imaging with Extrinsic Raman Labels." Applied Spectroscopy 50, no. 5 (1996): 545–51. http://dx.doi.org/10.1366/0003702963905989.

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In two separate examples we demonstrate the use of extrinsic Raman scattering probes for imaging of biological samples. First, the distribution of cholesterol in a rat eye lens is determined with the use of the Raman scattered light from filipin, a molecule which binds specifically to cholesterol. The protein distribution in the same eye lens was obtained by using the 1450-cm-1 CH2 and CH3 bending modes as an intrinsic marker for protein. It appears that the cholesterol is concentrated in the membranes of the eye lens fibers, whereas the protein is distributed more evenly. Second, we demonstra

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Gallaher, D. D., P. E. Johnson, J. R. Hunt, G. I. Lykken, and M. J. Marchello. "Bioavailability in humans of zinc from beef: intrinsic vs extrinsic labels." American Journal of Clinical Nutrition 48, no. 2 (1988): 350–54. http://dx.doi.org/10.1093/ajcn/48.2.350.

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Cellar, Douglas F., and Gerald V. Barrett. "Script processing and intrinsic motivation: The cognitive sets underlying cognitive labels." Organizational Behavior and Human Decision Processes 40, no. 1 (1987): 115–35. http://dx.doi.org/10.1016/0749-5978(87)90008-2.

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Dong, Biqin, Luay M. Almassalha, Yolanda Stypula-Cyrus, et al. "Superresolution intrinsic fluorescence imaging of chromatin utilizing native, unmodified nucleic acids for contrast." Proceedings of the National Academy of Sciences 113, no. 35 (2016): 9716–21. http://dx.doi.org/10.1073/pnas.1602202113.

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Visualizing the nanoscale intracellular structures formed by nucleic acids, such as chromatin, in nonperturbed, structurally and dynamically complex cellular systems, will help expand our understanding of biological processes and open the next frontier for biological discovery. Traditional superresolution techniques to visualize subdiffractional macromolecular structures formed by nucleic acids require exogenous labels that may perturb cell function and change the very molecular processes they intend to study, especially at the extremely high label densities required for superresolution. Howev

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Luo, Yong, Dacheng Tao, Chang Xu, Dongchen Li, and Chao Xu. "Vector-Valued Multi-View Semi-Supervsed Learning for Multi-Label Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 647–53. http://dx.doi.org/10.1609/aaai.v27i1.8589.

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Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e.g. a pedestrian, bicycle and tree) and multiple visual features (e.g. color, texture and shape). Currently available tools tend to use either labels or features for classification, but both are necessary to describe the image properly. There have been recent successes in using vector-valued functions, which construct matrix-valued kernels, to explore the multi-label structure in the output space. This has motivated us to develop multi-view vector-valued manifold r

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Perfilov, Maxim M., Alexey S. Gavrikov, Konstantin A. Lukyanov, and Alexander S. Mishin. "Transient Fluorescence Labeling: Low Affinity—High Benefits." International Journal of Molecular Sciences 22, no. 21 (2021): 11799. http://dx.doi.org/10.3390/ijms222111799.

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Fluorescent labeling is an established method for visualizing cellular structures and dynamics. The fundamental diffraction limit in image resolution was recently bypassed with the development of super-resolution microscopy. Notably, both localization microscopy and stimulated emission depletion (STED) microscopy impose tight restrictions on the physico-chemical properties of labels. One of them—the requirement for high photostability—can be satisfied by transiently interacting labels: a constant supply of transient labels from a medium replenishes the loss in the signal caused by photobleachi

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Pan, Qingtao, Wenhao Qiao, Jingjiao Lou, Bing Ji, and Shuo Li. "DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6299–307. https://doi.org/10.1609/aaai.v39i6.32674.

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Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it often suffers from error supervision from low-quality pseudo labels. Vision-Language Model (VLM) has great potential to enhance pseudo labels by introducing text prompt guided multimodal supervision information. It nevertheless faces the cross-modal problem: the obtained messages tend to correspond to multiple targets. To address aforementioned problems, we propose a Dual Semantic Similarity-Supervised VLM (DuSSS) f

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Xu, Shuo, Yuefu Zhang, Xin An, and Sainan Pi. "Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets." Journal of Data and Information Science 9, no. 2 (2024): 81–103. http://dx.doi.org/10.2478/jdis-2024-0014.

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Abstract Purpose Many science, technology and innovation (STI) resources are attached with several different labels. To assign automatically the resulting labels to an interested instance, many approaches with good performance on the benchmark datasets have been proposed for multilabel classification task in the literature. Furthermore, several open-source tools implementing these approaches have also been developed. However, the characteristics of real-world multilabel patent and publication datasets are not completely in line with those of benchmark ones. Therefore, the main purpose of this

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Egan, C. B., F. G. Smith, R. S. Houk, and R. E. Serfass. "Zinc absorption in women: comparison of intrinsic and extrinsic stable-isotope labels." American Journal of Clinical Nutrition 53, no. 2 (1991): 547–53. http://dx.doi.org/10.1093/ajcn/53.2.547.

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Davidsson, Lena, Ake Cederblad, Bo Lönnerdal, and Brittmarie Sandström. "Manganese absorption from mangold (Beta vulgaris): comparison of intrinsic and extrinsic labels." Journal of Nutritional Biochemistry 2, no. 6 (1991): 323–26. http://dx.doi.org/10.1016/0955-2863(91)90075-g.

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Song, Zhenjiang, Mengting Hu, Mingni Leng, and Shubin Zhu. "Exploring the Effects of Low-Carbon Labels on Purchase Intentions for Green Agricultural Products." Sustainability 16, no. 17 (2024): 7313. http://dx.doi.org/10.3390/su16177313.

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Expedited economic expansion and innovative technological advancement have precipitated considerable environmental predicaments, giving rise to extensive ecological conundrums worldwide. In response to the pressing situation, numerous countries have implemented policies aimed at reducing carbon emissions. One such initiative is the “low-carbon label”, which signifies a commitment to lowering carbon emissions. On one hand, it offers consumers an effective means to identify low-carbon products, thereby enhancing their willingness towards green consumption. On the other hand, it motivates busines

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Wang, Lei, Lingmu Peng, Hanyu Hong, Shuwei Zhao, Qiwen Lv, and Rong Gui. "Semi-supervised PolSAR Image Change Detection using Similarity Matching." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 655–61. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-655-2024.

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Abstract. The lack of precisely labeled data limits the development of supervised polarimetric synthetic aperture radar (PolSAR) image change detection. Therefore, semi-supervised deep learning methods have recently demonstrated their significant capability for PolSAR image change detection. Similarity Matching (SimMatch) improves the performance of semi-supervised learning tasks across different benchmark datasets and different settings. Introducing SimMatch into the field of PolSAR image change detection can improve the performance of semi-supervised PolSAR image change detection under limit

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Vitale, Sergio, Federica Biondo, Cristina Giosuè, et al. "Consumers’ Perception and Willingness to Pay for Eco-Labeled Seafood in Italian Hypermarkets." Sustainability 12, no. 4 (2020): 1434. http://dx.doi.org/10.3390/su12041434.

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The aim of this paper is to discuss eco-labels for fish products, which are a useful tool to improve both seafood markets and strategies for sustainability management. In this study, 560 consumer-surveys were conducted in selected towns in northern and southern Italy. Both probit and linear regression modeling were used to measure consumers’ perception and willingness to pay for anchovy eco-labels. Italian hypermarket consumers demonstrated attention to environmental features as well as to eco-products, at times, independent of income. The results of this study show that willingness to pay rel

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Zhai, Xinlong, Chunchen Wang, Ruijia Wang, et al. "Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22336–44. https://doi.org/10.1609/aaai.v39i21.34389.

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Drug-target interaction prediction (DTI) is essential in various applications including drug discovery and clinical application. There are two perspectives of input data widely used in DTI prediction: Intrinsic data represents how drugs or targets are constructed, and extrinsic data represents how drugs or targets are related to other biological entities. However, any of the two perspectives of input data can be scarce for some drugs or targets, especially for those unpopular or newly discovered. Furthermore, ground-truth labels for specific interaction types can also be scarce. Therefore, we

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Zhu, Yue. "Beyond Labels: A Comprehensive Review of Self-Supervised Learning and Intrinsic Data Properties." Journal of Science & Technology 4, no. 4 (2023): 65–84. http://dx.doi.org/10.55662/jst.2023.4403.

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Self-supervised learning (SSL) has become a transformative approach in the field of machine learning, offering a powerful means to harness the vast amounts of unlabeled data available across various domains. By creating auxiliary tasks that generate supervisory signals directly from the data, SSL mitigates the dependency on large, labeled datasets, thereby expanding the applicability of machine learning models. This paper provides a comprehensive exploration of SSL techniques applied to diverse data types, including images, text, audio, and time-series data. We delve into the underlying princi

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Zhu, Yue. "Beyond Labels: A Comprehensive Review of Self-Supervised Learning and Intrinsic Data Properties." Journal of Science & Technology 5, no. 4 (2024): 132–51. http://dx.doi.org/10.55662/jst.2024.5406.

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Self-supervised learning (SSL) has become a transformative approach in the field of machine learning, offering a powerful means to harness the vast amounts of unlabeled data available across various domains. By creating auxiliary tasks that generate supervisory signals directly from the data, SSL mitigates the dependency on large, labeled datasets, thereby expanding the applicability of machine learning models. This paper provides a comprehensive exploration of SSL techniques applied to diverse data types, including images, text, audio, and time-series data. We delve into the underlying princi

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Wang, Lichen, Zhengming Ding, and Yun Fu. "Generic Multi-label Annotation via Adaptive Graph and Marginalized Augmentation." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (2021): 1–20. http://dx.doi.org/10.1145/3451884.

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Multi-label learning recovers multiple labels from a single instance. It is a more challenging task compared with single-label manner. Most multi-label learning approaches need large-scale well-labeled samples to achieve high accurate performance. However, it is expensive to build such a dataset. In this work, we propose a generic multi-label learning framework based on Adaptive Graph and Marginalized Augmentation (AGMA) in a semi-supervised scenario. Generally speaking, AGMA makes use of a small amount of labeled data associated with a lot of unlabeled data to boost the learning performance.

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Kang, Cheng, Xujing Yao, and Daniel Novak. "Fuzzy Windows with Gaussian Processed Labels for Ordinal Image Scoring Tasks." Applied Sciences 13, no. 6 (2023): 4019. http://dx.doi.org/10.3390/app13064019.

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In this paper, we propose a Fuzzy Window with the Gaussian Processed Label (FW-GPL) method to mitigate the overlap problem in the neighboring ordinal category when scoring images. Many published conventional methods treat this challenge as a traditional regression problem and make a strong assumption that each ordinal category owns an adequate intrinsic rank to outline its distribution. Our FW-GPL method aims to refine the ordinal label pattern by using two novel techniques: (1) assembling fuzzy logic to the fully connected layer of convolution neural networks and (2) transforming the ordinal

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Yamada, Keiko, Michiko Shimodaira, Seiko Chida, et al. "Degradation of Vitamin B12 in Dietary Supplements." International Journal for Vitamin and Nutrition Research 78, no. 45 (2008): 195–203. http://dx.doi.org/10.1024/0300-9831.78.45.195.

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Beverages and solid dietary supplements rich in various added vitamins and minerals have recently become available. It seems reasonable to consider that the intake of these foods is convenient for easy ingestion of nutrients, but problems caused by blending different nutrients in high concentrations have arisen. We focused on vitamin B12 (B12) among vitamins and determinded the B12 contents of beverages and solid dietary supplements purchased from a retail shop. The B12 contents of three of five beverges were less than stated on the labels. On the other hand, certain beverages unexpectedly con

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Liu, Zhen, Qianli Ma, Peitian Ma, and Linghao Wang. "Temporal-Frequency Co-training for Time Series Semi-supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8923–31. http://dx.doi.org/10.1609/aaai.v37i7.26072.

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Semi-supervised learning (SSL) has been actively studied due to its ability to alleviate the reliance of deep learning models on labeled data. Although existing SSL methods based on pseudo-labeling strategies have made great progress, they rarely consider time-series data's intrinsic properties (e.g., temporal dependence). Learning representations by mining the inherent properties of time series has recently gained much attention. Nonetheless, how to utilize feature representations to design SSL paradigms for time series has not been explored. To this end, we propose a Time Series SSL framewor

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Bao, Wei-Xuan, Yong Rui, and Min-Ling Zhang. "Disentangled Partial Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11007–15. http://dx.doi.org/10.1609/aaai.v38i10.28976.

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Partial label learning (PLL) induces a multi-class classifier from training examples each associated with a set of candidate labels, among which only one is valid. The formation of real-world data typically arises from heterogeneous entanglement of series latent explanatory factors, which are considered intrinsic properties for discriminating between different patterns. Though learning disentangled representation is expected to facilitate label disambiguation for partial-label (PL) examples, few existing works were dedicated to addressing this issue. In this paper, we make the first attempt to

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Kan, Yulong, Yunjing Qi, Zhongxiao Zhang, Xikeng Liang, Weihao Wang, and Shuilin Jin. "Integration of unpaired single cell omics data by deep transfer graph convolutional network." PLOS Computational Biology 21, no. 1 (2025): e1012625. https://doi.org/10.1371/journal.pcbi.1012625.

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The rapid advance of large-scale atlas-level single cell RNA sequences and single-cell chromatin accessibility data provide extraordinary avenues to broad and deep insight into complex biological mechanism. Leveraging the datasets and transfering labels from scRNA-seq to scATAC-seq will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the lower capable of preserving fine-grained cell populations and intrinsic or extrinsic heterogeneity between datasets. Here, we present a robust deep transfer model based gra

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Valades Cruz, Cesar Augusto, Haitham Ahmed Shaban, Alla Kress, et al. "Quantitative nanoscale imaging of orientational order in biological filaments by polarized superresolution microscopy." Proceedings of the National Academy of Sciences 113, no. 7 (2016): E820—E828. http://dx.doi.org/10.1073/pnas.1516811113.

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Essential cellular functions as diverse as genome maintenance and tissue morphogenesis rely on the dynamic organization of filamentous assemblies. For example, the precise structural organization of DNA filaments has profound consequences on all DNA-mediated processes including gene expression, whereas control over the precise spatial arrangement of cytoskeletal protein filaments is key for mechanical force generation driving animal tissue morphogenesis. Polarized fluorescence is currently used to extract structural organization of fluorescently labeled biological filaments by determining the

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Palacios, Juan J. "Too many Labels, Just a Few Concepts: The Intrinsic Properties of Industrial Agglomeration Archetypes." Growth and Change 47, no. 4 (2016): 433–60. http://dx.doi.org/10.1111/grow.12154.

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Olsen, Nina Veflen, Elena Menichelli, Christine Meyer, and Tormod Næs. "Consumers liking of private labels. An evaluation of intrinsic and extrinsic orange juice cues." Appetite 56, no. 3 (2011): 770–77. http://dx.doi.org/10.1016/j.appet.2011.03.004.

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German, Benjamin D., Jeffrey Allen Borgia, Ankur D. Naqib, et al. "RNA pathway enrichment in serum-based mass spectroscopy prognostic analyses." Journal of Clinical Oncology 38, no. 15_suppl (2020): e15197-e15197. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e15197.

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e15197 Background: Veristrat(VS) good and poor labels were found to be prognostic in the front-line NSCLC setting with or without immune checkpoint inhibitors. Little is known about RNA expression data corresponding to these good and poor prognostic labels. Methods: Raw RNA seq expression data was obtained from TEMPUS XP RNA analysis. Sequences were filtered for allowing only genes that had an overall count of at least 20 across all samples. Samples were then processed through the EdgeR analysis in the R programming environment. Along with a Z-scored heatmap, PCA plot was also generated. Diffe

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Chen, Liang, Qianjin Du, Yihang Lou, Jianzhong He, Tao Bai, and Minghua Deng. "Mutual Nearest Neighbor Contrast and Hybrid Prototype Self-Training for Universal Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6248–57. http://dx.doi.org/10.1609/aaai.v36i6.20574.

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Universal domain adaptation (UniDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain under domain shift and category shift. Without prior category overlap information, it is challenging to simultaneously align the common categories between two domains and separate their respective private categories. Additionally, previous studies utilize the source classifier's prediction to obtain various known labels and one generic "unknown" label of target samples. However, over-reliance on learned classifier knowledge is inevitably biased to source data, ignor

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Žganec, Matjaž, Ajda Taler Verčič, Igor Muševič, Miha Škarabot, and Eva Žerovnik. "Amyloid Fibrils of Stefin B Show Anisotropic Properties." International Journal of Molecular Sciences 24, no. 4 (2023): 3737. http://dx.doi.org/10.3390/ijms24043737.

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Human stefin B, a member of the cystatin family of cysteine protease inhibitors, tends to form amyloid fibrils under relatively mild conditions, which is why it is used as a model protein to study amyloid fibrillation. Here, we show for the first time that bundles of amyloid fibrils, i.e., helically twisted ribbons, formed by human stefin B exhibit birefringence. This physical property is commonly observed in amyloid fibrils when stained with Congo red. However, we show that the fibrils arrange in regular anisotropic arrays and no staining is required. They share this property with anisotropic

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Kim, Hannah, Jaegul Choo, Chandan K. Reddy, and Haesun Park. "Doubly supervised embedding based on class labels and intrinsic clusters for high-dimensional data visualization." Neurocomputing 150 (February 2015): 570–82. http://dx.doi.org/10.1016/j.neucom.2014.09.064.

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Båve, Arvid. "Truth-Deflationism and Truth-Theoretic Semantics: One Way to Make Them Clash." Revista Portuguesa de Filosofia 79, no. 3 (2023): 1067–72. http://dx.doi.org/10.17990/rpf/2023_79_3_1067.

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Deflationism about truth is often said to be incompatible with truth-theoretic semantics. However, both of these labels are ambiguous, making the truth of the incompatibility claim dependent on interpretation. I provide one pair of natural interpretations, on which both views relate essentially to grounding and on which they are indeed incompatible. This result has some intrinsic interest as well as paving the way for further needed clarifications in the debate about the relationship between the views.

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Herrera González, Daniel. "Influence of Variables to Reduce the Intention and Behaviour Purchase Gaps in Eco-Label Food Products." Multidisciplinary Business Review 13, no. 1 (2020): 71–81. http://dx.doi.org/10.35692/07183992.13.1.7.

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Academic researchers have sought to examine the variables that affect purchasing behaviour and the development of models for ethical consumer behaviour due to the fact that consumers are increasingly aware of the impact that daily food products can have on the environment. As a result, the demand for environmentally friendly food has increased rapidly and the companies have created different eco-labels to encourage consumers to buy their products. However, there is still a large gap between the intent to purchase eco-labeled products and the actual purchasing behaviour. Thus, this paper explai

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YANG, LILY L., MIRAH L. KHALID, MINH D. DUONG, et al. "Consumer Response to Mechanically Tenderized Beef (MTB) and MTB Labels: An Exploratory Focus Group Study." Journal of Food Protection 82, no. 9 (2019): 1484–95. http://dx.doi.org/10.4315/0362-028x.jfp-19-099.

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ABSTRACT In May 2016, labeling of certain nonintact mechanically tenderized beef (MTB) products was mandated in the United States. MTB products should be handled differently by the consumer because pathogens can be transferred from the exterior to the interior of the meat during the tenderization process. Without labeling, it is difficult to visually distinguish between some intact beef and MTB products, which is a concern because MTB products require higher internal cooking temperatures for safety. An exploratory study was conducted to understand consumer understanding of MTB products and con

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Lerner, B. Rosie. "READ THE LABEL, A PESTICIDE SAFETY VIDEO TAPE." HortScience 27, no. 6 (1992): 578f—578. http://dx.doi.org/10.21273/hortsci.27.6.578f.

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The general public is in need of education regarding the responsible use of pesticides in home gardens. A 1990 California survey indicated that many individuals never read product labels and do not follow safety precautions when applying pesticides. A 1991 EPA study found that the most frequently detected pesticide in well water was a breakdown product of DCPA, a commonly used herbicide on home lawns. A 1988-89 National Gardening Survey found that 39% of US households purchased pesticide products. Excerpts of a video tape titled “Read the Label”, which specifically targets the home gardening a

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Chen, Mingxia, Jing Wang, Xueqing Li, and Xiaolong Sun. "Robust Semi-Supervised Manifold Learning Algorithm for Classification." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/2382803.

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In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional structures of the high-dimensional data. Given partially labeled data, the semi-supervised manifold learning algorithms are proposed to predict the labels of the unlabeled points, taking into account label information. However, these semi-supervised manifold learning algorithms are not robust against noisy points, especially when the labeled data contain noise. In this paper, we propose a frame

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Zhai, Yuyao, Liang Chen, and Minghua Deng. "Generalized Cell Type Annotation and Discovery for Single-Cell RNA-Seq Data." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5402–10. http://dx.doi.org/10.1609/aaai.v37i4.25672.

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The rapid development of single-cell RNA sequencing (scRNA-seq) technology allows us to study gene expression heterogeneity at the cellular level. Cell annotation is the basis for subsequent downstream analysis in single-cell data mining. Existing methods rarely explore the fine-grained semantic knowledge of novel cell types absent from the reference data and usually susceptible to batch effects on the classification of seen cell types. Taking into consideration these limitations, this paper proposes a new and practical task called generalized cell type annotation and discovery for scRNA-seq d

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Bador, R., H. Déchaud, F. Claustrat, and C. Desuzinges. "Europium and samarium as labels in time-resolved immunofluorometric assay of follitropin." Clinical Chemistry 33, no. 1 (1987): 48–51. http://dx.doi.org/10.1093/clinchem/33.1.48.

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Abstract This time-resolved immunofluorometric assay for human follitropin involves use of europium- or samarium-labeled monoclonal antibodies, with an average incorporation ratio of 3 mol of Eu3+ or Sm3+ per mole of antibody. These lanthanide ions are bound to the antibody molecules by means of the anhydride of diethylenetriaminepentaacetic acid. The solid-phase antibody is immobilized inside polystyrene tubes in which plasma samples were assayed in a one-step procedure. After incubation, the fluorescence intensity of Eu3+ or Sm3+ label is measured by time-resolved fluorometry, with a nitroge

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Zheng, Tingting, Kui Jiang, Hongxun Yao, Yi Xiao, and Zhongyuan Wang. "OODML: Whole Slide Image Classification Meets Online Pseudo-Supervision and Dynamic Mutual Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10626–34. https://doi.org/10.1609/aaai.v39i10.33154.

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Bag-label-based multi-instance learning (MIL) has demonstrated significant performance in whole slide image (WSI) analysis, particularly in pseudo-label-based learning schemes. However, due to inaccurate feature representation and interference, existing MIL methods often yield unreliable pseudo-labels, which spawn undesired predictions. To address these issues, we propose an Online Pseudo-Supervision and Dynamic Mutual Learning (OODML) framework that enhances pseudo-label generation and feature representation while exploring their mutual learning to improve bag-level prediction. Specifically,

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Song, H., and F. Abramson. "Drug metabolism studies using "intrinsic" and "extrinsic" labels. A demonstration using 15N vs. Cl in midazolam." Drug Metabolism and Disposition 21, no. 5 (1993): 868–73. https://doi.org/10.1016/s0090-9556(25)08176-0.

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Zeng, Liang, Lanqing Li, Ziqi Gao, Peilin Zhao, and Jian Li. "ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11138–46. http://dx.doi.org/10.1609/aaai.v37i9.26319.

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Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, the underlying class distribution of unlabeled nodes for the given graph is usually imbalanced. This highly imbalanced class distribution inevitably deteriorates the quality of learned node representations in GCL. Indeed, we empirically find that most state-of-the-art GCL methods cannot obtain discriminative representations and exhibit poor performance on imbalanced node classification. Motivated by this observation, w

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