Добірка наукової літератури з теми "Neural border"
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Статті в журналах з теми "Neural border"
Li, Yongbin, Di Zhao, Takeo Horie, Geng Chen, Hongcun Bao, Siyu Chen, Weihong Liu, et al. "Conserved gene regulatory module specifies lateral neural borders across bilaterians." Proceedings of the National Academy of Sciences 114, no. 31 (July 17, 2017): E6352—E6360. http://dx.doi.org/10.1073/pnas.1704194114.
Повний текст джерелаZaaboub, Wala, Lotfi Tlig, Mounir Sayadi, and Basel Solaiman. "Neural Network-based System for Automatic Passport Stamp Classification." Information Technology And Control 49, no. 4 (December 19, 2020): 583–607. http://dx.doi.org/10.5755/j01.itc.49.4.25919.
Повний текст джерелаCraft, Edward, Hartmut Schütze, Ernst Niebur, and Rüdiger von der Heydt. "A Neural Model of Figure–Ground Organization." Journal of Neurophysiology 97, no. 6 (June 2007): 4310–26. http://dx.doi.org/10.1152/jn.00203.2007.
Повний текст джерелаMilet, Cécile, and Anne H. Monsoro-Burq. "Neural crest induction at the neural plate border in vertebrates." Developmental Biology 366, no. 1 (June 2012): 22–33. http://dx.doi.org/10.1016/j.ydbio.2012.01.013.
Повний текст джерелаShen, Jianjun. "Research on the International Trade Performance Evaluation of Cross-Border e-Commerce Based on the Deep Neural Network Model." Journal of Sensors 2022 (October 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/3006907.
Повний текст джерелаBirgbauer, E., J. Sechrist, M. Bronner-Fraser, and S. Fraser. "Rhombomeric origin and rostrocaudal reassortment of neural crest cells revealed by intravital microscopy." Development 121, no. 4 (April 1, 1995): 935–45. http://dx.doi.org/10.1242/dev.121.4.935.
Повний текст джерелаRideaux, Reuben, and William J. Harrison. "Border ownership-dependent tilt aftereffect for shape defined by binocular disparity and motion parallax." Journal of Neurophysiology 121, no. 5 (May 1, 2019): 1917–23. http://dx.doi.org/10.1152/jn.00111.2019.
Повний текст джерелаLi, Yanting. "A Cloud Computing-Based Intelligent Forecasting Method for Cross-Border E-Commerce Logistics Costs." Advances in Mathematical Physics 2022 (March 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/3838293.
Повний текст джерелаLong, Gerald M., and Philip M. Garvey. "The Effects of Target Borders on Dynamic Visual Acuity: Practical and Theoretical Implications." Perception 17, no. 6 (December 1988): 745–51. http://dx.doi.org/10.1068/p170745.
Повний текст джерелаZhao, ShuTong, Zhenjie Yin, and Pingping Xie. "Multi-angle perception and convolutional neural network for service quality evaluation of cross-border e-commerce logistics enterprise." PeerJ Computer Science 10 (February 29, 2024): e1911. http://dx.doi.org/10.7717/peerj-cs.1911.
Повний текст джерелаДисертації з теми "Neural border"
Blair, Joel. "Building a better Placode: Modeling Neural Plate Border interactions with hPSCs." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663141272833.
Повний текст джерелаPatthey, Cédric. "Induction of the isthmic organizer and specification of the neural plate border." Doctoral thesis, Umeå universitet, Umeå centrum för molekylär medicin (UCMM), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1811.
Повний текст джерелаPatthey, Cédric. "Induction of the isthmic organizer and specification of the neural plate border /." Umeå : Univ, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1811.
Повний текст джерелаHerng, Eduardo Wu Jyh. "Detecção de bordas em imagens de ecocardiografia utilizando redes neurais artificiais." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/98/98131/tde-04062012-083028/.
Повний текст джерелаBeing non-invasive and having low cost, the echocardiography has been largely applied as diagnostic technique for left ventricle systolic and diastolic volumes determination that indirectly are used to calculate the left ventricle ejection volume, the cardiac cavities muscular contraction, the regional and global ejection fraction, the myocardial thickness, the ventricular mass, etc. For this reason, the detection of the left ventricle endocardial borders become necessary, but hampered by the noise that impairs the echocardiography images definition. In spite of having many image segmentation techniques, this work intend to detect the borders of left ventricle on echocardiography images by using a artificial neural network to recognize border patterns. To accelerate the process and facilitate the procedure, the operator uses the mouse to define a rectangular region inside the acoustic window of the pacient where all analyses and border recognitions will be accomplished. After labeling the recognized points as \'border\', gradient techniques and mobile boundary are used to connect the points of greater probability and delineate the left ventricle border. This technique has proved to be efficient when compared to the borders traced by the specialist. The ability of the operator is important in choosing of the region to be analyzed. After training with 50 samples of \"border\" pattern and 10 samples of \"no-border\" pattern, this technique was tested on 108 images, achieving good results on precision and velocitiy when we compared the calculated left ventricle area with the results of other techniques published on national and international literature.
Rossi, Christy Cortez. "Early development of two cell populations at the neural plate border : rohon-beard sensory neurons and neural crest cells /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2008.
Знайти повний текст джерелаIncludes bibliographical references (leaves 112-120). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
Liu, Boqi. "The gene regulatory network in the anterior neural plate border of ascidian embryos." Kyoto University, 2020. http://hdl.handle.net/2433/253119.
Повний текст джерелаWhite, Cory B. "A Neural Network Approach to Border Gateway Protocol Peer Failure Detection and Prediction." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/215.
Повний текст джерелаGrieves, Roderick McKinlay. "The neural basis of a cognitive map." Thesis, University of Stirling, 2015. http://hdl.handle.net/1893/21878.
Повний текст джерелаAn, Min. "Positional cloning and functional analysis of the SF3B1gene in zebrafish." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180528932.
Повний текст джерелаGhimouz, Rym. "Caractérisation du rôle des facteurs de transcription Homez et CBFbeta au cours de la neurogenèse et de la formation de la crête neurale chez Xenopus laevis." Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209568.
Повний текст джерелаLe premier clone d’ADNc code pour l’homologue du facteur de transcription Homez, contenant trois homéodomaines et deux motifs leucine zipper et dont la fonction est inconnue. Mes résultats ont montré que chez l’embryon de xénope au stade neurula, Homez est exprimé préférentiellement dans la plaque neurale, l’expression la plus forte étant détectée dans les domaines où les neurones primaires apparaissent. Plus tard, Homez est détecté dans le tube neural dans des cellules neurales postmitotiques en cours de différenciation. En accord avec ce profil d’expression, j’ai observé que Homez est régulé positivement par l’atténuation des signaux BMPs et par le facteur proneural Ngnr1 et négativement par la voie Notch. Bien que le facteur Homez ne soit pas suffisant pour induire une expression ectopique de marqueurs neuronaux dans l’embryon de xénope, j’ai pu montrer, en utilisant une approche de morpholino antisens, que celui-ci est requis en aval du facteur Ngnr1 pour la différenciation des précurseurs neuraux en neurones primaires.
Le deuxième clone code pour l’homologue du facteur CBFβ qui s’associe avec une famille de protéines CBFα1-3/Aml1-3/Runx1-3 pour former un complexe hétérodimérique liant l’ADN. Alors que chez la souris, les facteurs Runx1 et Runx3 jouent un rôle important dans la neurogenèse dans les ganglions spinaux et que chez le xénope, Runx1 est requis pour la formation des neurones Rohon-Beard, le rôle de CBFβ au cours du développement du système nerveux est actuellement mal connu. Mes résultats ont montré que chez l’embryon de xénope au stade neurula, CBFβ est coexprimé avec les facteurs Runx1-3 en bordure de la plaque neurale, mais de manière plus étendue et plus précoce. Comme attendu pour un marqueur de la bordure de la plaque neurale, j’ai observé que l’expression de CBFβ est régulée par les signaux BMP, Wnt, FGF et Notch. De manière intéressante, son expression est induite par les facteurs proneuraux alors que celle de Runx1 est inhibée. Des expériences de perte de fonction à l’aide de morpholinos antisens bloquant la traduction de CBFβ ont été réalisées. Ces expériences suggèrent que le facteur CBFβ est nécessaire à la mise en place de la CN et à la différenciation des neurones de Rohon-Beard.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Книги з теми "Neural border"
Chappell, Michael, Bradley MacIntosh, and Thomas Okell. Introduction to Perfusion Quantification using Arterial Spin Labelling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.001.0001.
Повний текст джерелаRijpma, Jorrit J. Brave New Borders: The EU’s Use of New Technologies for the Management of Migration and Asylum. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198807216.003.0007.
Повний текст джерелаAltman, Meryl. The Grand Rectification. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190608811.003.0008.
Повний текст джерелаAgius, Christine. Rescuing the State? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190644031.003.0005.
Повний текст джерелаKurevska, Līga. Designing Regulatory Framework for Demand Response Service Integration in Baltic Electricity Markets. RTU Press, 2022. http://dx.doi.org/10.7250/9789934227974.
Повний текст джерелаBindemann, Markus, ed. Forensic Face Matching. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198837749.001.0001.
Повний текст джерелаЧастини книг з теми "Neural border"
Nakata, Yusuke, and Ko Sakai. "Structures of Surround Modulation for the Border-Ownership Selectivity of V2 Cells." In Neural Information Processing, 383–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34475-6_46.
Повний текст джерелаZainal, Zaem Arif, and Shunji Satoh. "Formulation of Border-Ownership Assignment in Area V2 as an Optimization Problem." In Neural Information Processing, 859–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70090-8_87.
Повний текст джерелаBronner-Fraser, Marianne. "The Neural Crest: Migrating from the Border." In Cell Migration in Development and Disease, 155–71. Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527604669.ch9.
Повний текст джерелаSakai, Ko, and Shunsuke Michii. "Latency Modulation of Border Ownership Selective Cells in V1-V2 Feed-Forward Model." In Neural Information Processing, 291–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42051-1_37.
Повний текст джерелаKikuchi, Masayuki, and Youhei Akashi. "A Model of Border-Ownership Coding in Early Vision." In Artificial Neural Networks — ICANN 2001, 1069–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_148.
Повний текст джерелаHosoya, Haruo. "Bayesian Interpretation of Border-Ownership Signals in Early Visual Cortex." In Neural Information Processing. Theory and Algorithms, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17537-4_1.
Повний текст джерелаWagatsuma, Nobuhiko, and Hirotoshi Konno. "The Effects of Feedback Signals Mediated by NMDA-Type Synapses for Modulating Border-Ownership Selective Neurons in Visual Cortex." In Neural Information Processing, 563–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04167-0_51.
Повний текст джерелаWagatsuma, Nobuhiko, and Ko Sakai. "Roles of Early Vision for the Dynamics of Border-Ownership Selective Neurons." In Neural Information Processing. Theory and Algorithms, 99–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17537-4_13.
Повний текст джерелаRana, Mashud, and Irena Koprinska. "Wavelet Neural Networks for Electricity Load Forecasting – Dealing with Border Distortion and Shift Invariance." In Artificial Neural Networks and Machine Learning – ICANN 2013, 571–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40728-4_71.
Повний текст джерелаJadhav, Ashwin R., Arun G. Ghontale, and Vimal K. Shrivastava. "Segmentation and Border Detection of Melanoma Lesions Using Convolutional Neural Network and SVM." In Computational Intelligence: Theories, Applications and Future Directions - Volume I, 97–108. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1132-1_8.
Повний текст джерелаТези доповідей конференцій з теми "Neural border"
Carlos-Roca, Laura Rodriguez, Isabelle Hupont Torres, and Carles Fernandez Tena. "Facial recognition application for border control." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489113.
Повний текст джерелаKarimi, Mohsen, Ali Jahanshahi, Abbas Mazloumi, and Hadi Zamani Sabzi. "Border Gateway Protocol Anomaly Detection Using Neural Network." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006201.
Повний текст джерелаHsu, Chih-Chieh, and Alice C. Parker. "Border ownership in a nano-neuromorphic circuit using nonlinear dendritic computations." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889690.
Повний текст джерелаQuan, Yu, Zhixin Li, Canlong Zhang, and Huifang Ma. "Object Detection by Integrating Scene-Level Semantic Information and Border Regression Reinforcement." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206636.
Повний текст джерелаLiu, Tianying, Yang Wang, Siyun Hou, Wengen Li, Jihong Guan, Shuigeng Zhou, and Rufu Qin. "RBA-CenterNet: Feature Enhancement by Rotated Border Alignment for Oriented Object Detection." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534400.
Повний текст джерелаMishra, A., K. Sudan, and H. Soliman. "Detecting border intrusion using wireless sensor network and artificial neural network." In 2010 International Conference on Distributed Computing in Sensor Systems Workshops (DCOSSW 2010). IEEE, 2010. http://dx.doi.org/10.1109/dcossw.2010.5593287.
Повний текст джерелаKrigel, Tina, RA Benjamin Schitze, and Jonathan Stoklas. "Legal, ethical and social impact on the use of computational intelligence based systems for land border crossings." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489349.
Повний текст джерелаPawlicki, Marek, Rafał Kozik, and Michał Choraś. "Improving Siamese Neural Networks with Border Extraction Sampling for the use in Real-Time Network Intrusion Detection." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191496.
Повний текст джерелаAli, Abder-Rahman, Jingpeng Li, Sally Jane O'Shea, Guang Yang, Thomas Trappenberg, and Xujiong Ye. "A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852134.
Повний текст джерелаOkel, Sanne E., Fons van der Sommen, Endi Selmanaj, Joost A. van der Putten, Maarten R. Struyvenberg, Jacques J. Bergman, and Peter H. N. de With. "Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks." In Computer-Aided Diagnosis, edited by Karen Drukker and Maciej A. Mazurowski. SPIE, 2021. http://dx.doi.org/10.1117/12.2579751.
Повний текст джерелаЗвіти організацій з теми "Neural border"
Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 1996. http://dx.doi.org/10.55274/r0010423.
Повний текст джерелаOlsen and Willson. L51916 Pressure Based Parametric Emission Monitoring Systems (PEMS). Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2002. http://dx.doi.org/10.55274/r0010181.
Повний текст джерелаIrudayaraj, Joseph, Ze'ev Schmilovitch, Amos Mizrach, Giora Kritzman, and Chitrita DebRoy. Rapid detection of food borne pathogens and non-pathogens in fresh produce using FT-IRS and raman spectroscopy. United States Department of Agriculture, October 2004. http://dx.doi.org/10.32747/2004.7587221.bard.
Повний текст джерелаUK, Ipsos. Survey of public attitudes towards precision breeding. Food Standards Agency, October 2022. http://dx.doi.org/10.46756/sci.fsa.ouv127.
Повний текст джерела