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Journal articles on the topic 'Sequential labeling'

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Author: Grafiati

Published: 6 September 2023

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1

Sumathi, P., and G. Geetha Ramani. "Arithmetic Sequential Graceful Labeling on Star Related Graphs." Indian Journal Of Science And Technology 15, no. 44 (November 28, 2022): 2356–62. http://dx.doi.org/10.17485/ijst/v15i44.1863.

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2

Kantabutra, Sanpawat. "Fast Sequential and Parallel Vertex Relabelings of Km,m." International Journal of Foundations of Computer Science 26, no. 01 (January 2015): 33–50. http://dx.doi.org/10.1142/s0129054115500021.

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Given an undirected, connected, simple graph G = (V,E), two vertex labelings LV and L'V of the vertices of G, and a label flip operation that interchanges a pair of labels on adjacent vertices, the Vertex Relabeling Problem is to transform G from LV into L'V using the flip operation. Agnarsson et al. showed solving the Vertex Relabeling Problem on arbitrary graphs can be done in θ(n2), where n is the number of vertices in G. In this article we study the Vertex Relabeling Problem on graphs Km,m and introduce the concept of parity and precise labelings. We show that, when we consider the parity labeling, the problem on graphs Km,m can be solved quickly in O(log m) time using m processors on an EREW PRAM. Additionally, we also show that the number of processors can be further reduced to [Formula: see text] in this case while the time complexity does not change. When the labeling is precise, the parallel time complexity increases by a factor of log m while the processor complexities remain m and [Formula: see text]. We also show that, when graphs are restricted to Km,m, this problem can be solved optimally in O(m) time when the labeling is parity, and can be solved in O(m log m) time when the labeling is precise, thereby improving the result in Agnarsson et al. for this specific case. Moreover, we generalize the result in the case of precise labeling to the cases when LV and L'V can be any configuration. In the end we give a conclusion and a list of some interesting open problems.

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3

Wu, Xian, Wei Fan, and Yong Yu. "Sembler: Ensembling Crowd Sequential Labeling for Improved Quality." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1713–19. http://dx.doi.org/10.1609/aaai.v26i1.8351.

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Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of expertise (e.g., labeling gene names from plain text requires corresponding domain knowledge); (2) ignored or omitted annotations due to carelessness or low confidence; (3) noisy annotations due to cheating or vandalism. To correct these mistakes, we present Sembler, a statistical model for ensembling crowd sequential labelings. Sembler considers three types of statistical information: (1) the majority agreement that proves the correctness of an annotation; (2) correct annotation that improves the credibility of the corresponding annotator; (3) correct annotation that enhances the correctness of other annotations which share similar linguistic or contextual features. We evaluate the proposed model on a real Twitter and a synthetical biological data set, and find that Sembler is particularly accurate when more than half of annotators make mistakes.

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4

Wang, Xiuying, Bo Xu, Changliang Li, and Wendong Ge. "Labeling Sequential Data Based on Word Representations and Conditional Random Fields." International Journal of Machine Learning and Computing 5, no. 6 (December 2015): 439–44. http://dx.doi.org/10.18178/ijmlc.2015.5.6.548.

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5

Kang, Qiyu, and Wee Peng Tay. "Sequential Multi-Class Labeling in Crowdsourcing." IEEE Transactions on Knowledge and Data Engineering 31, no. 11 (November 1, 2019): 2190–99. http://dx.doi.org/10.1109/tkde.2018.2874003.

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6

Cserép, Gergely B., András Herner, Otto S. Wolfbeis, and Péter Kele. "Tyrosine specific sequential labeling of proteins." Bioorganic & Medicinal Chemistry Letters 23, no. 21 (November 2013): 5776–78. http://dx.doi.org/10.1016/j.bmcl.2013.09.002.

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7

A. Manonmani and R. Savithiri. "Double quadrilateral snakes on k-odd sequential harmonious labeling of graphs." Malaya Journal of Matematik 3, no. 04 (October 1, 2015): 607–11. http://dx.doi.org/10.26637/mjm304/019.

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The objective of this paper is to investigate some $k$-odd sequential harmonious labeling of graphs. In particular, we show that $k$-odd sequential harmonious labeling of double quadrilateral snakes $\left(2 Q_x\right.$-snakes) for each $x \geq 1$. We also prove that, $2 m Q_x$-snakes are $k$-odd sequential harmonious labeling of graphs for each $m, x \geq 1$. Finally, we present some examples and verified to illustrate proposed theories.

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8

Seoud, M. A., M. El-Zekey, and E. F. El-Gazar. "Mean, Odd Sequential and Triangular Sum Graphs." Circulation in Computer Science 2, no. 4 (May 20, 2017): 40–52. http://dx.doi.org/10.22632/ccs-2017-252-08.

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In this paper, we prove that all odd sequential graphs are mean graphs, but not all mean graphs are an odd sequential graph. We show that some new families generated by some graph operations on some standard graphs are admitting mean labeling and odd sequential labeling. Finally, we conclude some new results in triangular sum graphs.

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9

Qin, Jie, Li Liu, Zhaoxiang Zhang, Yunhong Wang, and Ling Shao. "Compressive Sequential Learning for Action Similarity Labeling." IEEE Transactions on Image Processing 25, no. 2 (February 2016): 756–69. http://dx.doi.org/10.1109/tip.2015.2508600.

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10

Maoying Qiao, Wei Bian, Richard Yi Da Xu, and Dacheng Tao. "Diversified Hidden Markov Models for Sequential Labeling." IEEE Transactions on Knowledge and Data Engineering 27, no. 11 (November 1, 2015): 2947–60. http://dx.doi.org/10.1109/tkde.2015.2433262.

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11

Elloumi, Samir. "An adaptive model for sequential labeling systems." Multimedia Tools and Applications 78, no. 16 (April 12, 2019): 22183–97. http://dx.doi.org/10.1007/s11042-019-7558-8.

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12

Brushart, Thomas M. E. "Preferential motor reinnervation: a sequential double-labeling study." Restorative Neurology and Neuroscience 1, no. 3,4 (1990): 281–87. http://dx.doi.org/10.3233/rnn-1990-13416.

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13

Glass, George, Jason A. Papin, and James W. Mandell. "Simple: A Sequential Immunoperoxidase Labeling and Erasing Method." Journal of Histochemistry & Cytochemistry 57, no. 10 (April 13, 2009): 899–905. http://dx.doi.org/10.1369/jhc.2009.953612.

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14

Row, R. David, Hui-Wen Shih, Austin T. Alexander, Ryan A. Mehl, and Jennifer A. Prescher. "Cyclopropenones for Metabolic Targeting and Sequential Bioorthogonal Labeling." Journal of the American Chemical Society 139, no. 21 (May 17, 2017): 7370–75. http://dx.doi.org/10.1021/jacs.7b03010.

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15

Yu, Dong, Shizhen Wang, and Li Deng. "Sequential Labeling Using Deep-Structured Conditional Random Fields." IEEE Journal of Selected Topics in Signal Processing 4, no. 6 (December 2010): 965–73. http://dx.doi.org/10.1109/jstsp.2010.2075990.

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16

Zhang, Guopeng, Massimo Piccardi, and Ehsan Zare Borzeshi. "Sequential Labeling With Structural SVM Under Nondecomposable Losses." IEEE Transactions on Neural Networks and Learning Systems 29, no. 9 (September 2018): 4177–88. http://dx.doi.org/10.1109/tnnls.2017.2757504.

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17

Payet, Nadia, and Sinisa Todorovic. "SLEDGE: Sequential Labeling of Image Edges for Boundary Detection." International Journal of Computer Vision 104, no. 1 (February 2, 2013): 15–37. http://dx.doi.org/10.1007/s11263-013-0612-5.

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18

OBA, Takanobu, Takaaki HORI, and Atsushi NAKAMURA. "Improved Sequential Dependency Analysis Integrating Labeling-Based Sentence Boundary Detection." IEICE Transactions on Information and Systems E93-D, no. 5 (2010): 1272–81. http://dx.doi.org/10.1587/transinf.e93.d.1272.

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19

Senthil, P., and M. Ganeshkumar. "K - ODD SEQUENTIAL HARMONIOUS LABELING OF DOUBLE M - TRIANGULAR SNAKES." Advances in Mathematics: Scientific Journal 9, no. 8 (August 20, 2020): 6377–84. http://dx.doi.org/10.37418/amsj.9.8.105.

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20

Selvaraju, P., P. Balaganesan, L. Vasu, and J. Renuka. "Even sequential harmonious labeling on path and cycle related graphs." Applied Mathematical Sciences 8 (2014): 4723–28. http://dx.doi.org/10.12988/ams.2014.46405.

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21

Zhao, Zhongtang, Xuezhuan Zhao, and Lingling Li. "Self labeling online sequential extreme learning machine and it’s application." Journal of Intelligent & Fuzzy Systems 37, no. 4 (October 25, 2019): 4485–91. http://dx.doi.org/10.3233/jifs-179281.

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22

Suzuki, Kenji, Isao Horiba, and Noboru Sugie. "Linear-time connected-component labeling based on sequential local operations." Computer Vision and Image Understanding 89, no. 1 (January 2003): 1–23. http://dx.doi.org/10.1016/s1077-3142(02)00030-9.

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23

Zong, Hong, Sascha N. Goonewardena, Huai-Ning Chang, James B. Otis, and James R. Baker. "Sequential and parallel dual labeling of nanoparticles using click chemistry." Bioorganic & Medicinal Chemistry 22, no. 21 (November 2014): 6288–96. http://dx.doi.org/10.1016/j.bmc.2014.07.015.

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24

Rehm, Fabian B. H., Thibault J. Harmand, Kuok Yap, Thomas Durek, David J. Craik, and Hidde L. Ploegh. "Site-Specific Sequential Protein Labeling Catalyzed by a Single Recombinant Ligase." Journal of the American Chemical Society 141, no. 43 (October 2019): 17388–93. http://dx.doi.org/10.1021/jacs.9b09166.

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25

Zhou, Xiangzeng, Lei Xie, Peng Zhang, and Yanning Zhang. "Online object tracking based on BLSTM-RNN with contextual-sequential labeling." Journal of Ambient Intelligence and Humanized Computing 8, no. 6 (June 14, 2017): 861–70. http://dx.doi.org/10.1007/s12652-017-0514-4.

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26

Sui, Guodong, Cheng-Chung Lee, Ken-Ichiro Kamei, Hua-Jung Li, Jin-Yi Wang, Jun Wang, Harvey R. Herschman, and Hsian-Rong Tseng. "A microfluidic platform for sequential ligand labeling and cell binding analysis." Biomedical Microdevices 9, no. 3 (December 29, 2006): 301–5. http://dx.doi.org/10.1007/s10544-006-9033-3.

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27

Kele, Péter, Gábor Mezö, Daniela Achatz, and Otto S Wolfbeis. "Dual Labeling of Biomolecules by Using Click Chemistry: A Sequential Approach." Angewandte Chemie International Edition 48, no. 2 (January 2, 2009): 344–47. http://dx.doi.org/10.1002/anie.200804514.

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28

Kele, Péter, Gábor Mezö, Daniela Achatz, and Otto S Wolfbeis. "Dual Labeling of Biomolecules by Using Click Chemistry: A Sequential Approach." Angewandte Chemie 121, no. 2 (January 2, 2009): 350–53. http://dx.doi.org/10.1002/ange.200804514.

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29

Patel, Amit A., Yan Zhang, James N. Fullerton, Lies Boelen, Anthony Rongvaux, Alexander A. Maini, Venetia Bigley, et al. "The fate and lifespan of human monocyte subsets in steady state and systemic inflammation." Journal of Experimental Medicine 214, no. 7 (June 12, 2017): 1913–23. http://dx.doi.org/10.1084/jem.20170355.

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In humans, the monocyte pool comprises three subsets (classical, intermediate, and nonclassical) that circulate in dynamic equilibrium. The kinetics underlying their generation, differentiation, and disappearance are critical to understanding both steady-state homeostasis and inflammatory responses. Here, using human in vivo deuterium labeling, we demonstrate that classical monocytes emerge first from marrow, after a postmitotic interval of 1.6 d, and circulate for a day. Subsequent labeling of intermediate and nonclassical monocytes is consistent with a model of sequential transition. Intermediate and nonclassical monocytes have longer circulating lifespans (∼4 and ∼7 d, respectively). In a human experimental endotoxemia model, a transient but profound monocytopenia was observed; restoration of circulating monocytes was achieved by the early release of classical monocytes from bone marrow. The sequence of repopulation recapitulated the order of maturation in healthy homeostasis. This developmental relationship between monocyte subsets was verified by fate mapping grafted human classical monocytes into humanized mice, which were able to differentiate sequentially into intermediate and nonclassical cells.

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30

Shibui, S., T. Hoshino, M. Vanderlaan, and J. W. Gray. "Double labeling with iodo- and bromodeoxyuridine for cell kinetics studies." Journal of Histochemistry & Cytochemistry 37, no. 7 (July 1989): 1007–11. http://dx.doi.org/10.1177/37.7.2659659.

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The rate of progression through the cell cycle was determined in five human glioma cell lines by a new sequential immunohistochemical staining technique. The cells were labeled first with iododeoxyuridine (IdUrd) for 1-3 hr and then with bromodeoxyuridine (BrdUrd) for 30 min. Labeled cells were identified with Br-3, a monoclonal antibody that recognizes only BrdUrd, and with IU-4, an antibody that recognizes both IdUrd and BrdUrd. Each slide was stained sequentially, first with the immunoperoxidase method for Br-3 and then with the alkaline phosphatase-anti-alkaline phosphatase method for IU-4. Cells that were positive only for IU-4 represented the fraction of S-phase cells that passed into the G2 phase during the period of incubation with IdUrd. The rates of progression measured by this method were constant in each cell line and resulted in smaller standard errors than were obtained by measurements from specimens stained singly for IdUrd and BrdUrd in different slides. The duration of the S-phase calculated from this fraction in the five cell lines ranged from 8-13 hr; the estimated potential doubling times were 25-32 hr and were very similar to the actual doubling times.

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31

Qian, Chen, Fuli Feng, Lijie Wen, Zhenpeng Chen, Li Lin, Yanan Zheng, and Tat-Seng Chua. "Solving Sequential Text Classification as Board-Game Playing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8640–48. http://dx.doi.org/10.1609/aaai.v34i05.6388.

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Sequential Text Classification (STC) aims to classify a sequence of text fragments (e.g., words in a sentence or sentences in a document) into a sequence of labels. In addition to the intra-fragment text contents, considering the inter-fragment context dependencies is also important for STC. Previous sequence labeling approaches largely generate a sequence of labels in left-to-right reading order. However, the need for context information in making decisions varies across different fragments and is not strictly organized in a left-to-right order. Therefore, it is appealing to label the fragments that need less consideration of context information first before labeling the fragments that need more. In this paper, we propose a novel model that labels a sequence of fragments in jumping order. Specifically, we devise a dedicated board-game to develop a correspondence between solving STC and board-game playing. By defining proper game rules and devising a game state evaluator in which context clues are injected, at each round, each player is effectively pushed to find the optimal move without position restrictions via considering the current game state, which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues. The final game-end state is viewed as the optimal label sequence. Extensive results on three representative datasets show that the proposed approach outperforms the state-of-the-art methods with statistical significance.

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32

Li, Minghai, Wenying Fan, Qinyang Li, and Yuzhi Meng. "Automatic labeling algorithms and their applications in weld seam of oil pipeline." MATEC Web of Conferences 309 (2020): 03005. http://dx.doi.org/10.1051/matecconf/202030903005.

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Building information model (BIM) technology has become an important tool for construction practitioners to improve the engineering design, construction and management. However, it has not been widely used in petrochemical industry, such as oil pipeline construction. This paper presents an automatic labeling algorithm based on BIM technology. We used simulated annealing algorithm to optimize the overlap of pipe weld label positions, resulting in the sequential pipe welds. We used this algorithm in oil pipeline construction and verified its automatic labeling effect on pipe welds.

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33

Júnior, Bruno König, and Cláudia de Carvalho Lopes. "Bone remodeling analysis after placement of dental implants using polyfluorochrome sequential labeling." Annals of Anatomy - Anatomischer Anzeiger 184, no. 3 (May 2002): 241–44. http://dx.doi.org/10.1016/s0940-9602(02)80114-5.

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34

Sun, Xiaozheng, Yanhua Xue, Jianye Li, Yu Yang, Yu Bai, and Yujia Chen. "Fluorescent labeling and characterization of dicarboxylic cellulose nanocrystals prepared by sequential periodate–chlorite oxidation." RSC Advances 11, no. 40 (2021): 24694–701. http://dx.doi.org/10.1039/d1ra04812k.

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A new method to synthesize fluorescent cellulose nanocrystals (FCNC) using 7-amino-4-methylcoumarin (AMC) and dicarboxylic cellulose nanocrystals (CNC), prepared by sequential periodate–chlorite oxidation.

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35

Fan, Tijun, Yang Song, Huan Cao, and Haiyang Xia. "Optimal eco-labeling strategy with imperfectly informed consumers." Industrial Management & Data Systems 119, no. 6 (July 8, 2019): 1166–88. http://dx.doi.org/10.1108/imds-06-2018-0256.

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Purpose The purpose of this paper is to find the optimal environmental quality criteria for a strategic eco-labeling authority with three objectives (i.e. maximizing the aggregate environmental quality, maximizing the industry profit and maximizing the social welfare). Particularly, the authors investigate how the existence of imperfectly informed consumers affects labeling criteria determination and competition among firms. Design/methodology/approach A game-theoretic modeling approach was adopted in this paper. A three-stage sequential game was modeled and backward induction was used to solve for a subgame perfect Nash equilibrium. To investigate the impacts of the existence of imperfectly informed consumers, the equilibrium, if all consumers are perfectly informed of the eco-label, was studied as a benchmark. Findings A more strict eco-labeling criterion improves revenues for both the labeled and unlabeled firms. It is interesting to find that the eco-labeling criteria to maximize industry profits are stricter than the criteria to maximize social welfare. Moreover, when the fraction of imperfectly informed consumers increases, the eco-labeling criteria to maximize aggregate environmental quality or industry profits will be more strict, while the criteria to maximize the social welfare will be looser. Originality/value The authors analyze the equilibrium strategies for firms against the eco-labeling criteria certified by authority with different objectives. The obtained optimal labeling strategies could provide insightful guidelines for the certifying authority to select the best suitable labeling criteria to achieve its goals.

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36

Plamont, Marie-Aude, Emmanuelle Billon-Denis, Sylvie Maurin, Carole Gauron, Frederico M. Pimenta, Christian G. Specht, Jian Shi, et al. "Small fluorescence-activating and absorption-shifting tag for tunable protein imaging in vivo." Proceedings of the National Academy of Sciences 113, no. 3 (December 28, 2015): 497–502. http://dx.doi.org/10.1073/pnas.1513094113.

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This paper presents Yellow Fluorescence-Activating and absorption-Shifting Tag (Y-FAST), a small monomeric protein tag, half as large as the green fluorescent protein, enabling fluorescent labeling of proteins in a reversible and specific manner through the reversible binding and activation of a cell-permeant and nontoxic fluorogenic ligand (a so-called fluorogen). A unique fluorogen activation mechanism based on two spectroscopic changes, increase of fluorescence quantum yield and absorption red shift, provides high labeling selectivity. Y-FAST was engineered from the 14-kDa photoactive yellow protein by directed evolution using yeast display and fluorescence-activated cell sorting. Y-FAST is as bright as common fluorescent proteins, exhibits good photostability, and allows the efficient labeling of proteins in various organelles and hosts. Upon fluorogen binding, fluorescence appears instantaneously, allowing monitoring of rapid processes in near real time. Y-FAST distinguishes itself from other tagging systems because the fluorogen binding is highly dynamic and fully reversible, which enables rapid labeling and unlabeling of proteins by addition and withdrawal of the fluorogen, opening new exciting prospects for the development of multiplexing imaging protocols based on sequential labeling.

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37

Repka, A. M., and R. Adler. "Accurate determination of the time of cell birth using a sequential labeling technique with [3H]-thymidine and bromodeoxyuridine ("window labeling")." Journal of Histochemistry & Cytochemistry 40, no. 7 (July 1992): 947–53. http://dx.doi.org/10.1177/40.7.1607643.

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During tissue embryogenesis, precursor cells divide actively and eventually withdraw from the mitotic cycle before differentiation. Accurate information about the time of terminal mitosis ("birthdate") of precursors is of vital importance for studying relationships between cell proliferation and differentiation. Methods presently available for birthdate determination, based on "pulse" or "cumulative" labeling with either tritiated thymidine (3HT) or bromodeoxyuridine (BrDU) incorporated into DNA during the mitotic cycle, allow only the approximate timing of terminal mitosis. To overcome this limitation, we have developed a "window labeling" technique based on the sequential administration of 3HT and BrDU. Chick retinal precursor cell cultures were first exposed to 3HT and, after a specified time interval, also to BrDU. After 6 days the cultures were fixed and processed for BrDU immunocytochemistry and 3HT autoradiography. Three populations of cells could be easily identified: (a) unlabeled cells, representing post-mitotic cells before label exposure; (b) BrDU-labeled cells [either 3HT (+)/BrDU (+) or 3HT (-)/BrDU (+)], representing those that continue dividing after the addition of BrDU; and (c) "window-labeled" cells, 3HT (+)/BrDU (-), which are those undergoing their last round of DNA synthesis during the interval between 3HT and BrDU administration. Control experiments demonstrated that this method allows birthdate determinations with a resolution of hours or minutes and is essentially free of deleterious effects on precursor cell survival and differentiation.

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38

Iadarola, Linda, and Paul Webster. "Can Microwave Ovens Reduce Immunocytochemical Labeling Times?" Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 38–39. http://dx.doi.org/10.1017/s042482010016265x.

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In recent years the use of microwave ovens in biomedical microscopy laboratories has contributed to reducing the times of fixation and resin embedding. Reports of the use of microwaves for histochemsitry and immunocytochemistry led us to investigate the possible use of a microwave oven to reduce immunocytochemical labeling protocols.The application of specific antibodies to thawed cryosections of aldehyde-fixed material is becoming more accessible to research and service laboratories. These detection methods, routinely performed in our laboratory, were used to study the effect of microwaves on labeling protocols using affinity purified, polyclonal antibodies and protein A-gold.Cells containing 3-(2,4-dinitroanilino)-3-arnino-N-methyldipropylamine (DAMP), a compound which accumulates in low pH compartments, were aldehyde-fixed, cryosectioned and then labeled with rabbit antibodies to dinitrophenol (which bind to DAMP) and 10nm protein-A gold. Regular sequential labeling protocols were compared with protocols using a microwave oven operating at 100% power, where the antibody incubation and washing times were reduced. The effect of microwaves on the labeling efficiency was investigated using simple quantitative methods. The protocol which produced reduced incubation times with no loss of labeling efficiency was then applied to sections in the absence of microwaves. The effect of reducing the final methyl cellulose-uranyl acetate contrasting step was also investigated.

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39

Zhao, Wenjun, Hong Geun Lee, Stephen L. Buchwald, and Jacob M. Hooker. "Direct 11CN-Labeling of Unprotected Peptides via Palladium-Mediated Sequential Cross-Coupling Reactions." Journal of the American Chemical Society 139, no. 21 (May 17, 2017): 7152–55. http://dx.doi.org/10.1021/jacs.7b02761.

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40

Miao, Frederick Jia-Pei, and Tony Jer-Fu Lee. "Cholinergic and VIPergic Innervation in Cerebral Arteries: A Sequential Double-Labeling Immunohistochemical Study." Journal of Cerebral Blood Flow & Metabolism 10, no. 1 (January 1990): 32–37. http://dx.doi.org/10.1038/jcbfm.1990.4.

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The possible co-localization of choline acetyltransferase (ChAT) and vasoactive intestinal polypeptide (VIP) in the nerve fibers of cat cerebral arteries was examined by a sequential double-labeling immunohistochemical method. Diaminobenzidine and tetramethylbenzidine were used as chromogens to distinguish ChAT (protein) and VIP (peptide) immunoreactivities. Since available fixatives often did not provide simultaneous preservation of optimal protein and peptide immunoreactivities, a new fixative, buffered periodate-paraformal-dehyde-picric acid-formaldehyde-lysine (PPPFL), was formulated and tested. PPPFL fixative is more reliable for simultaneously preserving ChAT and VIP immunoreactivities than were periodate-lysine-paraformaldehyde (PLP) fixative, Zamboni's fixative, or 2% paraformaldehyde solution alone. Using PPPFL as fixative, both ChAT immunoreactive (ChAT-I) and VIP-immunoreactive (VIP-I) fibers in cerebral arteries appeared as bundle and fine fibers. Most ChAT-I and VIP-I fibers were separate. Portions of ChAT-I and VIP-I fibers often ran closely in parallel or across each other. Overlaying of VIP-I on ChAT-I fibers and relay connections between them were also observed. These morphological data suggest the potential functional interactions between cholinergic and VIPergic innervations. In <5% of the fibers examined did ChAT and VIP immunoreactivities appear to be co-localized. These data therefore do not support the hypothesis that acetylcholine and VIP are co-localized in most fibers innervating the cerebral arterial wall.

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41

Weeg-Aerssens, Els, James M. Tiedje, and Bruce A. Averill. "Evidence from isotope labeling studies for a sequential mechanism for dissimilatory nitrite reduction." Journal of the American Chemical Society 110, no. 20 (September 1988): 6851–56. http://dx.doi.org/10.1021/ja00228a039.

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42

Frisch, T., M. S. Sørensen, S. Overgaard, M. Lind, and P. Bretlau. "Volume-Referent Bone Turnover Estimated From the Interlabel Area Fraction After Sequential Labeling." Bone 22, no. 6 (June 1998): 677–82. http://dx.doi.org/10.1016/s8756-3282(98)00050-7.

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43

Tomohiro, Takenori, Hirotsugu Inoguchi, Souta Masuda, and Yasumaru Hatanaka. "Affinity-based fluorogenic labeling of ATP-binding proteins with sequential photoactivatable cross-linkers." Bioorganic & Medicinal Chemistry Letters 23, no. 20 (October 2013): 5605–8. http://dx.doi.org/10.1016/j.bmcl.2013.08.041.

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44

Luchinat, Enrico, Erica Secci, Francesca Cencetti, and Paola Bruni. "Sequential protein expression and selective labeling for in-cell NMR in human cells." Biochimica et Biophysica Acta (BBA) - General Subjects 1860, no. 3 (March 2016): 527–33. http://dx.doi.org/10.1016/j.bbagen.2015.12.023.

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Kemter, Elisabeth, Andreas Müller, Martin Neukam, Anna Ivanova, Nikolai Klymiuk, Simone Renner, Kaiyuan Yang, et al. "Sequential in vivo labeling of insulin secretory granule pools in INS-SNAP transgenic pigs." Proceedings of the National Academy of Sciences 118, no. 37 (September 10, 2021): e2107665118. http://dx.doi.org/10.1073/pnas.2107665118.

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Abstract:

β cells produce, store, and secrete insulin upon elevated blood glucose levels. Insulin secretion is a highly regulated process. The probability for insulin secretory granules to undergo fusion with the plasma membrane or being degraded is correlated with their age. However, the molecular features and stimuli connected to this behavior have not yet been fully understood. Furthermore, our understanding of β cell function is mostly derived from studies of ex vivo isolated islets in rodent models. To overcome this translational gap and study insulin secretory granule turnover in vivo, we have generated a transgenic pig model with the SNAP-tag fused to insulin. We demonstrate the correct targeting and processing of the tagged insulin and normal glycemic control of the pig model. Furthermore, we show specific single- and dual-color granular labeling of in vivo–labeled pig pancreas. This model may provide unprecedented insights into the in vivo insulin secretory granule behavior in an animal close to humans.

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Souza, Vinicius M. A., Rafael G. Rossi, Gustavo E. A. P. A. Batista, and Solange O. Rezende. "Unsupervised active learning techniques for labeling training sets: An experimental evaluation on sequential data." Intelligent Data Analysis 21, no. 5 (October 10, 2017): 1061–95. http://dx.doi.org/10.3233/ida-163075.

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Raijmakers, Reinout, Celia R. Berkers, Annemieke de Jong, Huib Ovaa, Albert J. R. Heck, and Shabaz Mohammed. "Automated Online Sequential Isotope Labeling for Protein Quantitation Applied to Proteasome Tissue-specific Diversity." Molecular & Cellular Proteomics 7, no. 9 (June 4, 2008): 1755–62. http://dx.doi.org/10.1074/mcp.m800093-mcp200.

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Frisch, Thomas, Søren Overgaard, Mads Sølvsten Sørensen, and Poul Bretlau. "Estimation of Volume Referent Bone Turnover in the Otic Capsule after Sequential Point Labeling." Annals of Otology, Rhinology & Laryngology 109, no. 1 (January 2000): 33–39. http://dx.doi.org/10.1177/000348940010900106.

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Senn, Hans, Gottfried Otting, and Kurt Wuethrich. "Protein structure and interactions by combined use of sequential NMR assignments and isotope labeling." Journal of the American Chemical Society 109, no. 4 (February 1987): 1090–92. http://dx.doi.org/10.1021/ja00238a016.

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Holth, Per, and Erik Arntzen. "Symmetry Versus Sequentiality Related to Prior Training, Sequential Dependency of Stimuli, and Verbal Labeling." Psychological Record 48, no. 2 (April 1998): 293–315. http://dx.doi.org/10.1007/bf03395271.

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