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Статті в журналах з теми "Proton sensing"
Mashiko, Misaki, Aya Kurosawa, Yuki Tani, Takashi Tsuji, and Shigeki Takeda. "GPR31 and GPR151 are activated under acidic conditions." Journal of Biochemistry 166, no. 4 (May 23, 2019): 317–22. http://dx.doi.org/10.1093/jb/mvz042.
Повний текст джерелаSherwood, Thomas W., Erin N. Frey, and Candice C. Askwith. "Structure and activity of the acid-sensing ion channels." American Journal of Physiology-Cell Physiology 303, no. 7 (October 1, 2012): C699—C710. http://dx.doi.org/10.1152/ajpcell.00188.2012.
Повний текст джерелаSakata, Souhei, Tatsuki Kurokawa, Morten H. H. Nørholm, Masahiro Takagi, Yoshifumi Okochi, Gunnar von Heijne, and Yasushi Okamura. "Functionality of the voltage-gated proton channel truncated in S4." Proceedings of the National Academy of Sciences 107, no. 5 (December 14, 2009): 2313–18. http://dx.doi.org/10.1073/pnas.0911868107.
Повний текст джерелаWobig, Lea, Thérèse Wolfenstetter, Sylvia Fechner, Wolfgang Bönigk, Heinz G. Körschen, Jan F. Jikeli, Christian Trötschel, et al. "A family of hyperpolarization-activated channels selective for protons." Proceedings of the National Academy of Sciences 117, no. 24 (May 28, 2020): 13783–91. http://dx.doi.org/10.1073/pnas.2001214117.
Повний текст джерелаCarvacho, Ingrid, I. Scott Ramsey, and David E. Clapham. "Voltage and proton gradient sensing in Hv1 proton channels." Biophysical Journal 96, no. 3 (February 2009): 484a. http://dx.doi.org/10.1016/j.bpj.2008.12.2495.
Повний текст джерелаOsmakov, Dmitry I., Sergey G. Koshelev, Igor A. Ivanov, Yaroslav A. Andreev, and Sergey A. Kozlov. "Endogenous Neuropeptide Nocistatin Is a Direct Agonist of Acid-Sensing Ion Channels (ASIC1, ASIC2 and ASIC3)." Biomolecules 9, no. 9 (August 22, 2019): 401. http://dx.doi.org/10.3390/biom9090401.
Повний текст джерелаSisignano, Marco, Michael J. M. Fischer, and Gerd Geisslinger. "Proton-Sensing GPCRs in Health and Disease." Cells 10, no. 8 (August 10, 2021): 2050. http://dx.doi.org/10.3390/cells10082050.
Повний текст джерелаLudwig, Marie-Gabrielle, Miroslava Vanek, Danilo Guerini, Jürg A. Gasser, Carol E. Jones, Uwe Junker, Hans Hofstetter, Romain M. Wolf, and Klaus Seuwen. "Proton-sensing G-protein-coupled receptors." Nature 425, no. 6953 (September 2003): 93–98. http://dx.doi.org/10.1038/nature01905.
Повний текст джерелаShapira, Barak, Eran Avraham, and Doron Aurbach. "Proton-selective electrode for pH sensing." Electrochemistry Communications 73 (December 2016): 80–84. http://dx.doi.org/10.1016/j.elecom.2016.11.007.
Повний текст джерелаRandolph, Aaron L., Carlos A. Villalba-Galea, and I. Scott Ramsey. "Voltage Sensing in Hv1 Proton Channels." Biophysical Journal 104, no. 2 (January 2013): 207a. http://dx.doi.org/10.1016/j.bpj.2012.11.1173.
Повний текст джерелаДисертації з теми "Proton sensing"
Xin, Xie Zhu Da-Ming. "Current sensing atomic force microscope study of proton exchange membranes." Diss., UMK access, 2006.
Знайти повний текст джерела"A thesis in physics." Typescript. Advisor: Da-Ming Zhu. Vita. Title from "catalog record" of the print edition Description based on contents viewed Nov. 12, 2007. Includes bibliographical references (leaves 50-51). Online version of the print edition.
Randolph, Aaron L. "Voltage Sensing Mechanism in the Voltage-gated and Proton (H+)-selective Ion Channel Hv1." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/582.
Повний текст джерелаCampion, Katherine. "Characterisation of calcium-sensing receptor extracellular pH sensitivity and intracellular signal integration." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/characterisation-of-calciumsensing-receptor-extracellular-ph-sensitivity-and-intracellular-signal-integration(e11adf01-4748-42ed-8679-f8b990d79dea).html.
Повний текст джерелаNassios, Anaïs [Verfasser], and Stephan [Akademischer Betreuer] Schreml. "Expression of proton-sensing G-protein-coupled receptors in selected skin tumors / Anaïs Nassios ; Betreuer: Stephan Schreml." Regensburg : Universitätsbibliothek Regensburg, 2020. http://d-nb.info/121309612X/34.
Повний текст джерелаShvadchak, Volodymyr. "Two-color fluorescent dyes for sensing peptide interactions : application to the retroviral proteins." Strasbourg, 2009. https://publication-theses.unistra.fr/public/theses_doctorat/2009/SHVADCHAK_Volodymyr_2009.pdf.
Повний текст джерелаLguensat, Redouane. "Learning from ocean remote sensing data." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0050/document.
Повний текст джерелаReconstructing geophysical fields from noisy and partial remote sensing observations is a classical problem well studied in the literature. Data assimilation is one class of popular methods to address this issue, and is done through the use of classical stochastic filtering techniques, such as ensemble Kalman or particle filters and smoothers. They proceed by an online evaluation of the physical modelin order to provide a forecast for the state. Therefore, the performanceof data assimilation heavily relies on the definition of the physical model. In contrast, the amount of observation and simulation data has grown very quickly in the last decades. This thesis focuses on performing data assimilation in a data-driven way and this without having access to explicit model equations. The main contribution of this thesis lies in developing and evaluating the Analog Data Assimilation(AnDA), which combines analog methods (nearest neighbors search) and stochastic filtering methods (Kalman filters, particle filters, Hidden Markov Models). Through applications to both simplified chaotic models and real ocean remote sensing case-studies (sea surface temperature, along-track sea level anomalies), we demonstrate the relevance of AnDA for missing data interpolation of nonlinear and high dimensional dynamical systems from irregularly-sampled and noisy observations. Driven by the rise of machine learning in the recent years, the last part of this thesis is dedicated to the development of deep learning models for the detection and tracking of ocean eddies from multi-source and/or multi-temporal data (e.g., SST-SSH), the general objective being to outperform expert-based approaches
Bin, Khayat Mohd Ezuan. "Protein kinase involvement in wild-type and mutant calcium-sensing receptor signalling." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/protein-kinase-involvement-in-wildtype-and-mutant-calciumsensing-receptor-signalling(b0189d85-400e-4b65-9412-bb0b3527b01d).html.
Повний текст джерелаMaggiori, Emmanuel. "Approches d'apprentissage pour la classification à large échelle d'images de télédétection." Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4041/document.
Повний текст джерелаThe analysis of airborne and satellite images is one of the core subjects in remote sensing. In recent years, technological developments have facilitated the availability of large-scale sources of data, which cover significant extents of the earth’s surface, often at impressive spatial resolutions. In addition to the evident computational complexity issues that arise, one of the current challenges is to handle the variability in the appearance of the objects across different geographic regions. For this, it is necessary to design classification methods that go beyond the analysis of individual pixel spectra, introducing higher-level contextual information in the process. In this thesis, we first propose a method to perform classification with shape priors, based on the optimization of a hierarchical subdivision data structure. We then delve into the use of the increasingly popular convolutional neural networks (CNNs) to learn deep hierarchical contextual features. We investigate CNNs from multiple angles, in order to address the different points required to adapt them to our problem. Among other subjects, we propose different solutions to output high-resolution classification maps and we study the acquisition of training data. We also created a dataset of aerial images over dissimilar locations, and assess the generalization capabilities of CNNs. Finally, we propose a technique to polygonize the output classification maps, so as to integrate them into operational geographic information systems, thus completing the typical processing pipeline observed in a wide number of applications. Throughout this thesis, we experiment on hyperspectral, atellite and aerial images, with scalability, generalization and applicability goals in mind
Matteo, Lionel. "De l’image optique "multi-stéréo" à la topographie très haute résolution et la cartographie automatique des failles par apprentissage profond." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4099.
Повний текст джерелаSeismogenic faults are the source of earthquakes. The study of their properties thus provides information on some of the properties of the large earthquakes they might produce. Faults are 3D features, forming complex networks generally including one master fault and myriads of secondary faults and fractures that intensely dissect the master fault embedding rocks. I aim in my thesis to develop approaches to help studying this intense secondary faulting/fracturing. To identify, map and measure the faults and fractures within dense fault networks, I have handled two challenges:1) Faults generally form steep topographic escarpments at the ground surface that enclose narrow, deep corridors or canyons, where topography, and hence fault traces, are difficult to measure using the available standard methods (such as stereo and tri-stereo of optical satellite images). To address this challenge, I have thus used multi-stéréo acquisitions with different configuration such as different roll and pitch angles, different date of acquisitions and different mode of acquisitions (mono and tri-stéréo). Our dataset amounting 37 Pléiades images in three different tectonic sites within Western USA (Valley of Fire, Nevada; Granite Dells, Arizona; Bishop Tuff, California) allow us to test different configuration of acquisitions to calculate the topography with three different approaches. Using the free open-source software Micmac (IGN ; Rupnik et al., 2017), I have calculated the topography in the form of Digital Surface Models (DSM): (i) with the combination of 2 to 17 Pleiades images, (ii) stacking and merging DSM built from individual stéréo or tri-stéréo acquisitions avoiding the use of multi-dates combinations, (iii) stacking and merging point clouds built from tri-stereo acquisitions following the multiview pipeline developped by Rupnik et al., 2018. We used the recent multiview stereo pipeling CARS (CNES/CMLA) developped by Michel et al., 2020 as a last approach (iv), combnining tri-stereo acquisitions. From the four different approaches, I have thus calculated more than 200 DSM and my results suggest that combining two tri-stéréo acquisitions or one stéréo and one tri-stéréo acquisitions with opposite roll angles leads to the most accurate DSM (with the most complete and precise topography surface).2) Commonly, faults are mapped manually in the field or from optical images and topographic data through the recognition of the specific curvilinear traces they form at the ground surface. However, manual mapping is time-consuming, which limits our capacity to produce complete representations and measurements of the fault networks. To overcome this problem, we have adopted a machine learning approach, namely a U-Net Convolutional Neural Network, to automate the identification and mapping of fractures and faults in optical images and topographic data. Intentionally, we trained the CNN with a moderate amount of manually created fracture and fault maps of low resolution and basic quality, extracted from one type of optical images (standard camera photographs of the ground surface). Based on the results of a number of performance tests, we select the best performing model, MRef, and demonstrate its capacity to predict fractures and faults accurately in image data of various types and resolutions (ground photographs, drone and satellite images and topographic data). The MRef predictions thus enable the statistical analysis of the fault networks. MRef exhibits good generalization capacities, making it a viable tool for fast and accurate extraction of fracture and fault networks from image and topographic data
Girard, Nicolas. "Approches d'apprentissage et géométrique pour l'extraction automatique d'objets à partir d'images de télédétection." Thesis, Université Côte d'Azur, 2020. https://tel.archives-ouvertes.fr/tel-03177997.
Повний текст джерелаCreating a digital double of the Earth in the form of maps has many applications in e.g. autonomous driving, automated drone delivery, urban planning, telecommunications, and disaster management. Geographic Information Systems (GIS) are the frameworks used to integrate geolocalized data and represent maps. They represent shapes of objects in a vector representation so that it is as sparse as possible while representing shapes accurately, as well as making it easier to edit than raster data. With the increasing amount of satellite and aerial images being captured every day, automatic methods are being developed to transfer the information found in those remote sensing images into Geographic Information Systems. Deep learning methods for image segmentation are able to delineate the shapes of objects found in images however they do so with a raster representation, in the form of a mask. Post-processing vectorization methods then convert that raster representation into a vector representation compatible with GIS. Another challenge in remote sensing is to deal with a certain type of noise in the data, which is the misalignment between different layers of geolocalized information (e.g. between images and building cadaster data). This type of noise is frequent due to various errors introduced during the processing of remote sensing data. This thesis develops combined learning and geometric approaches with the purpose to improve automatic GIS mapping from remote sensing images.We first propose a method for correcting misaligned maps over images, with the first motivation for them to match, but also with the motivation to create remote sensing datasets for image segmentation with alignment-corrected ground truth. Indeed training a model on misaligned ground truth would not lead to great performance, whereas aligned ground truth annotations will result in better models. During this work we also observed a denoising effect of our alignment model and use it to denoise a misaligned dataset in a self-supervised manner, meaning only the misaligned dataset was used for training.We then propose a simple approach to use a neural network to directly output shape information in the vector representation, in order to by-pass the post-processing vectorization step. Experimental results on a dataset of solar panels show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs. Our simple method is limited to predicting polygons with a fixed number of vertices though.While more recent methods for learning directly in the vector representation do not have this limitation, they still have other limitations in terms of the type of object shapes they can predict. More complex topological cases such as objects with holes or buildings touching each other (with a common wall which is very typical of European city centers) are not handled by these fully deep learning methods. We thus propose a hybrid approach alleviating those limitations by training a neural network to output a segmentation probability map as usual and also to output a frame field aligned with the contours of detected objects (buildings in our case). That frame field constitutes additional shape information learned by the network. We then propose our highly parallelizable polygonization method for leveraging that frame field information to vectorize the segmentation probability map efficiently. Because our polygonization method has access to additional information in the form of a frame field, it can be less complex than other advanced vectorization methods and is thus faster. Lastly, requiring an image segmentation network to also output a frame field only adds two convolutional layers and virtually does not increase inference time, making the use of a frame field only beneficial
Книги з теми "Proton sensing"
lantbruksuniversitet, Sveriges, ed. Isolation, cloning and tissue distribution of a 500 kDa protein with Ca²⁺ sensing properties. Uppsala: Swedish University of Agricultural Sciences, Genetic Center, Dept. of Cell Research, 1995.
Знайти повний текст джерелаRaju, Raghavan, and Irshad H. Chaudry. The host response to hypoxia in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0305.
Повний текст джерелаNewell-Price, John, Alia Munir, and Miguel Debono. Primary hyperparathyroidism. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0187.
Повний текст джерелаSimpson, Stephen J., Carlos Ribeiro, and Daniel González-Tokman. Feeding behavior. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797500.003.0008.
Повний текст джерелаЧастини книг з теми "Proton sensing"
Seuwen, Klaus, and Marie-Gabrielle Ludwig. "Proton-Sensing GPCRs." In Encyclopedia of Molecular Pharmacology, 1309–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57401-7_200.
Повний текст джерелаSeuwen, Klaus, and Marie-Gabrielle Ludwig. "Proton-Sensing GPCRs." In Encyclopedia of Molecular Pharmacology, 1–5. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-21573-6_200-1.
Повний текст джерелаSun, Wei-Hsin, and Shih-Ping Dai. "Tackling Pain Associated with Rheumatoid Arthritis: Proton-Sensing Receptors." In Advances in Pain Research: Mechanisms and Modulation of Chronic Pain, 49–64. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1756-9_5.
Повний текст джерелаPesci, Everett C., and Barbara H. Iglewski. "Quorum Sensing." In Bacterial Protein Toxins, 55–65. Washington, DC, USA: ASM Press, 2014. http://dx.doi.org/10.1128/9781555817893.ch4.
Повний текст джерелаHanus, Kasper, and Emilia Smagur. "Pre- and Proto-Historic Anthropogenic Landscape Modifications in Siem Reap Province (Cambodia) as Seen Through Satellite Imagery." In Digital Methods and Remote Sensing in Archaeology, 229–46. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40658-9_10.
Повний текст джерелаPicking, William D., and Michael L. Barta. "The Tip Complex: From Host Cell Sensing to Translocon Formation." In Bacterial Type III Protein Secretion Systems, 173–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/82_2019_171.
Повний текст джерелаJastrzebska, Beata. "Class A GPCR: Light Sensing G Protein-Coupled Receptor – Focus on Rhodopsin Dimer." In G-Protein-Coupled Receptor Dimers, 79–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60174-8_4.
Повний текст джерелаFindakly, T. "PROTON-EXCHANGED INTEGRATED OPTICAL COMPONENTS." In Optical Fiber Rotation Sensing, 337–51. Elsevier, 1994. http://dx.doi.org/10.1016/b978-0-12-146075-4.50016-5.
Повний текст джерелаKoltai, Tomas, Larry Fliegel, Fátima Baltazar, Stephan J. Reshkin, Khalid O. Alfarouk, Rosa Angela Cardone, and Julieta Afonso. "Membrane proton sensing potentiates the pro-tumoral effects of extracellular acidity." In pH Deregulation as the Eleventh Hallmark of Cancer, 163–72. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-443-15461-4.00012-6.
Повний текст джерелаAanniz, Tarik, Wissal Bakri, Safae El Mazouri, Hajar Wakrim, Ilham Kandoussi, Lahcen Belyamani, Mouna Ouadghiri, and Azeddine Ibrahimi. "Biofilm and Quorum Sensing in Helicobacter pylori." In Bacterial Biofilms [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104568.
Повний текст джерелаТези доповідей конференцій з теми "Proton sensing"
Vu, Paul, Boyd Fowler, Brian Rodricks, Janusz Balicki, Steve Mims, and Wang Li. "Evaluation of 10MeV proton irradiation on 5.5 Mpixel scientific CMOS image sensor." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2010. http://dx.doi.org/10.1117/12.868160.
Повний текст джерелаСурнин and S. Surnin. "Crystal structure of a proton." In XXIV International Conference. Москва: Infra-m, 2016. http://dx.doi.org/10.12737/22881.
Повний текст джерелаBoucher, Richard H., Warren F. Woodward, Terrence S. Lomheim, Ralph M. Shima, David J. Asman, Kevin M. Killian, Jason LeGrand, and Gregory J. Goellner. "Proton-induced degradation in interferometric fiber optic gyroscopes." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Edward W. Taylor. SPIE, 1995. http://dx.doi.org/10.1117/12.210543.
Повний текст джерелаTang, Xiling, Kurtis Remmel, Daniel Sandker, Zhi Xu, and Junhang Dong. "Proton conducting perovskite-type ceramics for fiber optic sensors for hydrogen monitoring at high temperature." In SPIE Defense, Security, and Sensing, edited by Xudong Fan, Hai Xiao, and Anbo Wang. SPIE, 2010. http://dx.doi.org/10.1117/12.850919.
Повний текст джерелаTsurumaki, Hiroaki, Chihiro Mogi, Haruka Saito-Aoki, Koichi Sato, Takashi Nakakura, Masakiyo Yatomi, Yasuhiko Koga, et al. "Absence of proton-sensing TDAG8 protects against ovalbumin-induced allergic airway inflammation." In ERS International Congress 2017 abstracts. European Respiratory Society, 2017. http://dx.doi.org/10.1183/1393003.congress-2017.pa2020.
Повний текст джерелаCherniatiev, B. V., Grigorii M. Chernyavskiy, Nicholas L. Johnson, and Darren S. McKnight. "Identification and resolution of an orbital debris problem with the proton launch vehicle." In Optical Engineering and Photonics in Aerospace Sensing, edited by Firooz A. Allahdadi. SPIE, 1993. http://dx.doi.org/10.1117/12.156549.
Повний текст джерелаKissa, Karl M., Hogan Eng, David K. Lewis, Vincent D. Rodino, Paul G. Suchoski, Jr., and Nancy A. Koziarz. "Reliability of lithium niobate Annealed Proton Exchanged integrated optical circuits." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Andrew R. Pirich. SPIE, 1995. http://dx.doi.org/10.1117/12.212700.
Повний текст джерелаTaylor, Edward W., S. P. Chapman, Anthony D. Sanchez, Michael A. Kelly, Jonathan Stohs, and Douglas M. Craig. "Radiation-induced crosstalk in a proton-exchanged LiNbO3 directional coupler." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Edward W. Taylor. SPIE, 1995. http://dx.doi.org/10.1117/12.210541.
Повний текст джерелаLee, Yu Jin, Kyun Heo, Soo-Ah Park, Dong-Young Noh, Kyong-Tai Kim, Sung Ho Ryu, and Pann-Ghill Suh Suh. "Abstract 3950: Extracellular protons promote the metastasis of breast cancerviaactivation of the proton-sensing receptor G-protein coupled receptor 4." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-3950.
Повний текст джерелаTsurumaki, Hiroaki, Takeshi Hisada, Chihiro Mogi, Haruka Saito-Aoki, Masakiyo Yatomi, Yousuke Kamide, Masayuki Tobo, et al. "Proton-sensing TDAG8 exhibits the protective role in lipopolysaccharide-induced acute lung injury." In ERS International Congress 2016 abstracts. European Respiratory Society, 2016. http://dx.doi.org/10.1183/13993003.congress-2016.pa943.
Повний текст джерелаЗвіти організацій з теми "Proton sensing"
Fernando, P. U. Ashvin Iresh, Gilbert Kosgei, Matthew Glasscott, Garrett George, Erik Alberts, and Lee Moores. Boronic acid functionalized ferrocene derivatives towards fluoride sensing. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44762.
Повний текст джерелаTian, Yongchi, and Jia Chen. Protein-Assisted Redox Sensing at Biomimetic Electrode. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada412000.
Повний текст джерелаSohn, Lydia, T. C. Messina, L. N. Dunkleberger, G. A. Mensing, A. S. Kalmbach, Ron Weiss, and D. J. Beebe. Probing Interactions at the Nanoscale. Sensing Protein Molecules. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/940829.
Повний текст джерелаKhaneja, Navin. Intelligent Sensing and Probing with Applications to Protein NMR Spectroscopy and Laser Chemistry. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada463606.
Повний текст джерелаPerdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.
Повний текст джерелаBorrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.
Повний текст джерелаMcInerney, Michael K., and John M. Carlyle. : Demonstration of Acoustic Sensing Techniques for Fuel-Distribution System Condition Monitoring : Final Report on Project F07-AR07. Engineer Research and Developmenter Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/39560.
Повний текст джерелаZarrieß, Benjamin, and Jens Claßen. Verification of Knowledge-Based Programs over Description Logic Actions. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.216.
Повний текст джерелаDouglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41222.
Повний текст джерелаCoplin, David L., Shulamit Manulis, and Isaac Barash. roles Hrp-dependent effector proteins and hrp gene regulation as determinants of virulence and host-specificity in Erwinia stewartii and E. herbicola pvs. gypsophilae and betae. United States Department of Agriculture, June 2005. http://dx.doi.org/10.32747/2005.7587216.bard.
Повний текст джерела