Relevant bibliographies by topics / Biomedical labeling

Academic literature on the topic 'Biomedical labeling'

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Published: 7 September 2023

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Journal articles on the topic "Biomedical labeling"

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Sabot, Cyrille, and Péter Kele. "Novel Approaches in Biomolecule Labeling." Biomolecules 11, no. 12 (December 2, 2021): 1809. http://dx.doi.org/10.3390/biom11121809.

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The selective functionalization of biomolecules such as proteins, nucleic acids, lipids or carbohydrates is a focus of persistent interest due to their widespread use, ranging from basic chemical biology research to gain insight into biological processes to the most promising biomedical applications, including the development of diagnostics or targeted therapies [...]
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Brown, Cedric, Jill Marion, and Anchal Kaushiva. "Medical Device Labeling." Journal of Clinical Engineering 41, no. 3 (2016): 134–36. http://dx.doi.org/10.1097/jce.0000000000000164.

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Heinrich, Kevin E., Michael W. Berry, and Ramin Homayouni. "Gene Tree Labeling Using Nonnegative Matrix Factorization on Biomedical Literature." Computational Intelligence and Neuroscience 2008 (2008): 1–12. http://dx.doi.org/10.1155/2008/276535.

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Identifying functional groups of genes is a challenging problem for biological applications. Text mining approaches can be used to build hierarchical clusters or trees from the information in the biological literature. In particular, the nonnegative matrix factorization (NMF) is examined as one approach to label hierarchical trees. A generic labeling algorithm as well as an evaluation technique is proposed, and the effects of different NMF parameters with regard to convergence and labeling accuracy are discussed. The primary goals of this study are to provide a qualitative assessment of the NMF and its various parameters and initialization, to provide an automated way to classify biomedical data, and to provide a method for evaluating labeled data assuming a static input tree. As a byproduct, a method for generatinggold standardtrees is proposed.
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Dahlmeier, D., and H. T. Ng. "Domain adaptation for semantic role labeling in the biomedical domain." Bioinformatics 26, no. 8 (February 23, 2010): 1098–104. http://dx.doi.org/10.1093/bioinformatics/btq075.

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Santosh, K. C., Laurent Wendling, Sameer Antani, and George R. Thoma. "Overlaid Arrow Detection for Labeling Regions of Interest in Biomedical Images." IEEE Intelligent Systems 31, no. 3 (May 2016): 66–75. http://dx.doi.org/10.1109/mis.2016.24.

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Mao, Chenyi, Min Yen Lee, Jing-Ru Jhan, Aaron R. Halpern, Marcus A. Woodworth, Adam K. Glaser, Tyler J. Chozinski, et al. "Feature-rich covalent stains for super-resolution and cleared tissue fluorescence microscopy." Science Advances 6, no. 22 (May 2020): eaba4542. http://dx.doi.org/10.1126/sciadv.aba4542.

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Fluorescence microscopy is a workhorse tool in biomedical imaging but often poses substantial challenges to practitioners in achieving bright or uniform labeling. In addition, while antibodies are effective specific labels, their reproducibility is often inconsistent, and they are difficult to use when staining thick specimens. We report the use of conventional, commercially available fluorescent dyes for rapid and intense covalent labeling of proteins and carbohydrates in super-resolution (expansion) microscopy and cleared tissue microscopy. This approach, which we refer to as Fluorescent Labeling of Abundant Reactive Entities (FLARE), produces simple and robust stains that are modern equivalents of classic small-molecule histology stains. It efficiently reveals a wealth of key landmarks in cells and tissues under different fixation or sample processing conditions and is compatible with immunolabeling of proteins and in situ hybridization labeling of nucleic acids.
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An, Dongdong, Linlin Shi, Tianyu Li, Hong-Yu Zhang, Yahong Chen, Xin-Qi Hao, and Mao-Ping Song. "Tailored Supramolecular Cage for Efficient Bio-Labeling." International Journal of Molecular Sciences 24, no. 3 (January 21, 2023): 2147. http://dx.doi.org/10.3390/ijms24032147.

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Fluorescent chemosensors are powerful imaging tools used in a broad range of biomedical fields. However, the application of fluorescent dyes in bioimaging still remains challenging, with small Stokes shifts, interfering signals, background noise, and self-quenching on current microscope configurations. In this work, we reported a supramolecular cage (CA) by coordination-driven self-assembly of benzothiadiazole derivatives and Eu(OTf)3. The CA exhibited high fluorescence with a quantum yield (QY) of 38.57%, good photoluminescence (PL) stability, and a large Stokes shift (153 nm). Furthermore, the CCK-8 assay against U87 glioblastoma cells verified the low cytotoxicity of CA. We revealed that the designed probes could be used as U87 cells targeting bioimaging.
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Hsiao, Jong-Kai, Chung-Yi Yang, Yiao-Hong Wang, Chen-Wen Lu, Borade Prajakta Uttam, Hon-Man Liu, and Jaw-Lin Wang. "MAGNETIC NANOPARTICLE LABELING OF CULTURED CANCER CELL LINE WITHOUT TRANSFECTION AGENT." Biomedical Engineering: Applications, Basis and Communications 20, no. 04 (August 2008): 259–65. http://dx.doi.org/10.4015/s1016237208000854.

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Magnetic nanoparticle (MNP) labeling of stem cell has been proved its efficacy for cell trafficking. Most of the labeling technique requires mixture of iron oxide nanoparticles and transfection agent. Stem cells with ionic MNP without the aid of transfection agent were labeled previously. The possibility of high efficiency labeling of cultured cancer cell, HeLa cell, by using ionic MNP is proposed. The labeled cell morphology was observed and the intracellular iron content was determined by spectrophotometry. The cell character change was evaluated by flow cytometry where front scattering count and side scattering count (SSC) were recorded. The imaging ability of the labeling method was determined by T2 weighted magnetic resonance (MR) imaging. Labeled MNPs were accumulated at cytoplasm is observed and the iron content of labeled cell could reach 27 pg/cell. There is no cell diameter change but the cell granularity increased according to SSC data from flow cytometry. Under clinical 1.5T MR imaging, we could detect labeled cells easily were detected at the cell number of 1 × 105. It is concluded that labeling of cancer cell with ionic MNPs without transfection agent is an efficient labeling method which will provide non-invasive imaging method for monitoring cancer behavior.
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Schutera, Mark, Luca Rettenberger, Christian Pylatiuk, and Markus Reischl. "Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels." PLOS ONE 17, no. 2 (February 8, 2022): e0263656. http://dx.doi.org/10.1371/journal.pone.0263656.

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Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish’s position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition.
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Sadek, Hesham, Shuaib Latif, Robert Collins, Mary G. Garry, and Daniel J. Garry. "Use of ferumoxides for stem cell labeling." Regenerative Medicine 3, no. 6 (November 2008): 807–16. http://dx.doi.org/10.2217/17460751.3.6.807.

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Dissertations / Theses on the topic "Biomedical labeling"

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Barnickel, Thorsten. "Large scale knowledge extraction from biomedical literature based on semantic role labeling." kostenfrei, 2009. https://mediatum2.ub.tum.de/node?id=802669.

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Clonda, Diego. "Automatic thalamic labeling for image-guided neurosurgery." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0007/MQ44150.pdf.

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Driscoll, Harry. "Improving the sensitivity of aptamer-driven fluorescent protein complementation for RNA labeling and detection." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21147.

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In eukaryotic cells, some mRNAs localize to distinct areas of the cell where RNA is translated and the encoded protein is specifically localized. Recent studies have suggested that even though prokaryotic cells lack internal compartmentalization, different RNAs can localize to distinct regions of the bacterial cell. Our lab is developing methods for labeling and detecting RNA with the goal of determining localization of endogenous RNAs within single cells. We currently employ an eIF4a protein-specific aptamer for RNA labeling using one of two methods. (1) Target RNA is tagged with the aptamer sequence at the 3' end and the aptamer triggers protein complementation of two fusion proteins, each containing split EGFP and split eIF4A proteins. (2) Two RNA probes, each containing a half of a split eIF4a-specific aptamer and an antisense sequence complementary to the target RNA, bind the unmodified transcript through complementary interactions. This binding brings the two fragments of the split aptamer in close proximity and allows proper folding of a split aptamer. A fluorescent signal is generated by the aptamer-driven reassociation of the fusion proteins. In this work, we investigate the sensitivity of the first method for detecting transcripts expressed from their natural chromosomal loci, and describe attempts to increase the sensitivity of the method by using multiple aptamer tagging. We also present results suggesting that the second method, combining protein complementation and split aptamer approach, provides high sensitivity enabling detection of endogenous bacterial RNAs expressed at low level.
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Long, Philip S. "NEUROCALCIN PROTEIN LABELING REVEALS A DIMORPHISM WITHIN THE DEVELOPING ZEBRA FINCH BRAIN: POSSBIBLE REGULATION BY ESTRADIOL." Kent State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=kent1279571034.

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Asfaha, Timnit Yosef. "Clickable, Photoactive NAADP Analogs for Isolation and Purification of the Unknown NAADP Receptor." University of Toledo Health Science Campus / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=mco1471643537.

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Wright, Katherine L. "Measuring Perfusion with Magnetic Resonance Imaging using Novel Data Acquisition and Reconstruction Strategies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1412786849.

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Na, Sungsoo. "Effects of mechanical forces on cytoskeletal remodeling and stiffness of cultured smooth muscle cells." Thesis, [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1704.

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Leary, Dagmar Hajkova. "CIRCADIAN PROTEOME CHANGES IN PHOTORECEPTOR OUTER SEGMENTS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1264276011.

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Li, Wen. "Automated parcellation on the surface of human cerebral cortex generated from MR images." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2928.

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The human cerebral cortex is a highly foliated structure that supports the complex cognitive abilities of humans. The cortex is divided by its cytoarchitectural characteristics that can be approximated by the folding pattern of the cortex. Psychiatric and neurological diseases, such as Huntington's disease or schizophrenias, are often related with structural changes in the cerebral cortex. Detecting structural changes in different regions of cerebral cortex can provide insight into disease biology, progression and response to treatment. The delineation of anatomical regions on the cerebral cortex is time intensive if performed manually, therefore automated methods are needed to perform this delineation. Magnetic Resonance Imaging (MRI) is commonly used to explore the structural change in patients with psychiatric and neurological diseases. This dissertation proposes a fast and reliable method to automatically parcellate the cortical surface generated from MR images. A fully automated pipeline has been built to process MR images and generate cortical surfaces associated with parcellation labels. First, genus zero cortical surfaces for each hemisphere of a subject are generated from MR images. The surface is generated at the parametric boundary between gray matter and white matter. Geometry features are calculated for each cortical surface to as scalar values to drive a multi-resolution spherical registration that can align two cortical surfaces together in the spherical domain. Then, the labels on a subject's cortical surface are evaluated by registering a subject's cortical surface with a population atlas and combining the information of prior probabilities on the atlas with the subject's geometry features. The automated parcellation has been tested on a group of subjects with various cerebral cortex structures. It shows that the proposed method is fast (takes about 3 hours to parcellate at one hemisphere) and accurate (with the weighted average Dice ~0.86). The framework of this dissertation will be as follows: the first chapter is about the introduction, including motivation, background, and significance of the study. The second chapter describes the whole pipeline of the automated surface parcellation and focuses on technical details of every method used in the pipeline. The third chapter presents results achieved in this study and the fourth chapter discusses the results and draws a conclusion.
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Thalman, Scott William. "CALIBRATED SHORT TR RECOVERY MRI FOR RAPID MEASUREMENT OF BRAIN-BLOOD PARTITION COEFFICIENT AND CORRECTION OF QUANTITATIVE CEREBRAL BLOOD FLOW." UKnowledge, 2019. https://uknowledge.uky.edu/cbme_etds/59.

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The high prevalence and mortality of cerebrovascular disease has led to the development of several methods to measure cerebral blood flow (CBF) in vivo. One of these, arterial spin labeling (ASL), is a quantitative magnetic resonance imaging (MRI) technique with the advantage that it is completely non-invasive. The quantification of CBF using ASL requires correction for a tissue specific parameter called the brain-blood partition coefficient (BBPC). Despite regional and inter-subject variability in BBPC, the current recommended implementation of ASL uses a constant assumed value of 0.9 mL/g for all regions of the brain, all subjects, and even all species. The purpose of this dissertation is 1) to apply ASL to a novel population to answer an important clinical question in the setting of Down syndrome, 2) to demonstrate proof of concept of a rapid technique to measure BBPC in mice to improve CBF quantification, and 3) to translate the correction method by applying it to a population of healthy canines using equipment and parameters suitable for use with humans. Chapter 2 reports the results of an ASL study of adults with Down syndrome (DS). This population is unique for their extremely high prevalence of Alzheimer’s disease (AD) and very low prevalence of systemic cardiovascular risk factors like atherosclerosis and hypertension. This prompted the hypothesis that AD pathology would lead to the development of perfusion deficits in people with DS despite their healthy cardiovascular profile. The results demonstrate that perfusion is not compromised in DS participants until the middle of the 6th decade of life after which measured global CBF was reduced by 31% (p=0.029). There was also significantly higher prevalence of residual arterial signal in older participants with DS (60%) than younger DS participants (7%, p = 0.005) or non-DS controls (0%, p < 0.001). This delayed pattern of perfusion deficits in people with DS differs from observations in studies of sporadic AD suggesting that adults with DS benefit from an improved cardiovascular risk profile early in life. Chapter 3 introduces calibrated short TR recovery (CaSTRR) imaging as a rapid method to measure BBPC and its development in mice. This was prompted by the inability to account for potential changes in BBPC due to age, brain atrophy, or the accumulation of hydrophobic A-β plaques in the ASL study of people with DS in Chapter 2. The CaSTRR method reduces acquisition time of BBPC maps by 87% and measures a significantly higher BBPC in cortical gray matter (0.99±0.04 mL/g,) than white matter in the corpus callosum (0.93±0.05 mL/g, p=0.03). Furthermore, when CBF maps are corrected for BBPC, the contrast between gray and white matter regions of interest is improved by 14%. This demonstrates proof of concept for the CaSTRR technique. Chapter 4 describes the application of CaSTRR on healthy canines (age 5-8 years) using a 3T human MRI scanner. This represents a translation of the technique to a setting suitable for use with a human subject. Both CaSTRR and pCASL acquisitions were performed and further optimization brought the acquisition time of CaSTRR down to 4 minutes which is comparable to pCASL. Results again show higher BBPC in gray matter (0.83 ± 0.05 mL/g) than white matter (0.78 ± 0.04 mL/g, p = 0.007) with both values unaffected by age over the range studied. Also, gray matter CBF is negatively correlated with age (p = 0.003) and BBPC correction improved the contrast to noise ratio by 3.6% (95% confidence interval = 0.6 – 6.5%). In summary, the quantification of ASL can be improved using BBPC maps derived from the novel, rapid CaSTRR technique.
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Books on the topic "Biomedical labeling"

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Rangayyan, Rangaraj M. Color image processing with biomedical applications. Bellingham, Wash: SPIE Press, 2011.

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H, Gronemeyer, ed. Affinity labelling and cloning of steroid and thyroid hormone receptors. Weinheim, Federal Republic of Germany: VCH, 1988.

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Book chapters on the topic "Biomedical labeling"

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Park, Jane H., and Wolfgang E. Trommer. "Advantages of 15N and Deuterium Spin Probes for Biomedical Electron Paramagnetic Resonance Investigations." In Spin Labeling, 547–95. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0743-3_11.

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Lee, Il Joon, and Byeang Hyean Kim. "Labeling Oligonucleotides toward the Biomedical Probe." In Medicinal Chemistry of Nucleic Acids, 292–334. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118092804.ch8.

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Joshi, Anand A., David W. Shattuck, and Richard M. Leahy. "A Method for Automated Cortical Surface Registration and Labeling." In Biomedical Image Registration, 180–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31340-0_19.

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Hughes, David A. "Applications of Affinity Labeling in Biomedical Sciences." In Immunocytochemistry and In Situ Hybridization in the Biomedical Sciences, 223–53. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0139-7_11.

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Pattabiraman, Lalitha. "Unisum Labeling of Hydra Hexagons." In International Conference on Computing, Communication, Electrical and Biomedical Systems, 85–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-86165-0_8.

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Drozdzal, Michal, Eugene Vorontsov, Gabriel Chartrand, Samuel Kadoury, and Chris Pal. "The Importance of Skip Connections in Biomedical Image Segmentation." In Deep Learning and Data Labeling for Medical Applications, 179–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46976-8_19.

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Garcia Seco de Herrera, Alba, Roger Schaer, Sameer Antani, and Henning Müller. "Using Crowdsourcing for Multi-label Biomedical Compound Figure Annotation." In Deep Learning and Data Labeling for Medical Applications, 228–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46976-8_24.

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Albarqouni, Shadi, Stefan Matl, Maximilian Baust, Nassir Navab, and Stefanie Demirci. "Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research." In Deep Learning and Data Labeling for Medical Applications, 269–77. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46976-8_28.

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Avants, Brian B., Murray Grossman, and James C. Gee. "Symmetric Diffeomorphic Image Registration: Evaluating Automated Labeling of Elderly and Neurodegenerative Cortex and Frontal Lobe." In Biomedical Image Registration, 50–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11784012_7.

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Andermatt, Simon, Simon Pezold, and Philippe Cattin. "Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data." In Deep Learning and Data Labeling for Medical Applications, 142–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46976-8_15.

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Conference papers on the topic "Biomedical labeling"

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Ramponi, Alan, Rob van der Goot, Rosario Lombardo, and Barbara Plank. "Biomedical Event Extraction as Sequence Labeling." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.431.

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Xiao, Xudong, Jeanne P. Haushalter, Khalid Amin, Zishan Haroon, and Gregory W. Faris. "Tumor boundary optical labeling using fluorescence." In Biomedical Topical Meeting. Washington, D.C.: OSA, 2006. http://dx.doi.org/10.1364/bio.2006.sh18.

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Tseng, Po-Hang, Shu-Jen Chiang, Shean-Jen Chen, and Chen-Yuan Dong. "Reducing labeling time of fluorescent molecules in thick tissue sections." In European Conference on Biomedical Optics. Washington, D.C.: Optica Publishing Group, 2021. http://dx.doi.org/10.1364/ecbo.2021.em1a.2.

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In this work we investigated how changing the labeling construct of porcine liver tissue enhances labeling speed. Our results show that bi-directional labeling can indeed reduce labeling time with respect to the standard uni-directional labeling.
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Amoros, Oscar, Sergio Escalera, and Anna Puig. "OpenCL based machine learning labeling of biomedical datasets." In SPIE Medical Imaging, edited by Kenneth H. Wong and David R. Holmes III. SPIE, 2011. http://dx.doi.org/10.1117/12.877664.

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Tsai, Richard Tzong-Han, Wen-Chi Chou, Yu-Chun Lin, Cheng-Lung Sung, Wei Ku, Ying-Shan Su, Ting-Yi Sung, and Wen-Lian Hsu. "BIOSMILE: adapting semantic role labeling for biomedical verbs." In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1567619.1567629.

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Landry, James P., Xiangdong Zhu, Jeffrey P. Gregg, and Xiaowen Guo. "Detection of biomolecular microarrays without fluorescent labeling agents." In Biomedical Optics 2004, edited by Dan V. Nicolau and Ramesh Raghavachari. SPIE, 2004. http://dx.doi.org/10.1117/12.524554.

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Chai, Yaqiong, Adam Bush, Julie Coloigner, Xiaoping Qu, Jonathan Chia, Natasha Lepore, and John Wood. "An experimental investigation of labeling efficiency for pseudo-continuous arterial spin labeling." In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). IEEE, 2016. http://dx.doi.org/10.1109/isbi.2016.7493458.

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Strack, Rita L., Daniel E. Strongin, Dibyendu Bhattacharyya, Wen Tao, Allison Berman, Hal E. Broxmeyer, Robert J. Keenan, and Benjamin S. Glick. "A noncytotoxic DsRed variant for whole-cell labeling." In SPIE BiOS: Biomedical Optics, edited by Alexander P. Savitsky and Yingxiao Wang. SPIE, 2009. http://dx.doi.org/10.1117/12.808046.

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Agnew, Brian, Suzanne Buck, Tamara Nyberg, Jolene Bradford, Scott Clarke, and Kyle Gee. "Click chemistry for labeling and detection of biomolecules." In Biomedical Optics (BiOS) 2008, edited by Samuel Achilefu, Darryl J. Bornhop, and Ramesh Raghavachari. SPIE, 2008. http://dx.doi.org/10.1117/12.762175.

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Suffern, Diana, Samuel J. Clarke, C. Annette Hollmann, Daniel Bahcheli, Stephen E. Bradforth, and Jay L. Nadeau. "Labeling of subcellular redox potential with dopamine-conjugated quantum dots." In Biomedical Optics 2006, edited by Marek Osinski, Kenji Yamamoto, and Thomas M. Jovin. SPIE, 2006. http://dx.doi.org/10.1117/12.663332.

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