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Статті в журналах з теми "Quantitative Neuroscience"
Höller, Yvonne. "Quantitative EEG in Cognitive Neuroscience." Brain Sciences 11, no. 4 (April 19, 2021): 517. http://dx.doi.org/10.3390/brainsci11040517.
Повний текст джерелаHu, Chenfei, and Gabriel Popescu. "Quantitative Phase Imaging (QPI) in Neuroscience." IEEE Journal of Selected Topics in Quantum Electronics 25, no. 1 (January 2019): 1–9. http://dx.doi.org/10.1109/jstqe.2018.2869613.
Повний текст джерелаMilton, John G. "Quantitative Neuroscience: From Chalk Board to Bedside." Mathematical Modelling of Natural Phenomena 5, no. 2 (2010): 1–4. http://dx.doi.org/10.1051/mmnp/20105299.
Повний текст джерелаWebster, Gregory D. "Evolutionary Theory in Cognitive Neuroscience: A 20-Year Quantitative Review of Publication Trends." Evolutionary Psychology 5, no. 3 (July 1, 2007): 147470490700500. http://dx.doi.org/10.1177/147470490700500304.
Повний текст джерелаDuffus, Dwight, and Andrei Olifer. "Introductory Life Science Mathematics and Quantitative Neuroscience Courses." CBE—Life Sciences Education 9, no. 3 (September 2010): 370–77. http://dx.doi.org/10.1187/cbe.10-03-0026.
Повний текст джерелаField, Thomas A., Eric T. Beeson, Chad Luke, Michelle Ghoston, and Nedeljko Golubovic. "Counselors' Neuroscience Conceptualizations of Depression." Journal of Mental Health Counseling 41, no. 3 (July 1, 2019): 260–79. http://dx.doi.org/10.17744/mehc.41.3.05.
Повний текст джерелаBloomingdale, Peter, Tatiana Karelina, Murat Cirit, Sarah F. Muldoon, Justin Baker, William J. McCarty, Hugo Geerts, and Sreeraj Macha. "Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies." CPT: Pharmacometrics & Systems Pharmacology 10, no. 5 (March 29, 2021): 412–19. http://dx.doi.org/10.1002/psp4.12607.
Повний текст джерелаZinchuk, Vadim, and Olga Grossenbacher-Zinchuk. "Recent advances in quantitative colocalization analysis: Focus on neuroscience." Progress in Histochemistry and Cytochemistry 44, no. 3 (October 2009): 125–72. http://dx.doi.org/10.1016/j.proghi.2009.03.001.
Повний текст джерелаCofré, Rodrigo, Cesar Maldonado, and Bruno Cessac. "Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics." Entropy 22, no. 11 (November 23, 2020): 1330. http://dx.doi.org/10.3390/e22111330.
Повний текст джерелаCherniak, Christopher. "The Bounded Brain: Toward Quantitative Neuroanatomy." Journal of Cognitive Neuroscience 2, no. 1 (January 1990): 58–68. http://dx.doi.org/10.1162/jocn.1990.2.1.58.
Повний текст джерелаДисертації з теми "Quantitative Neuroscience"
Ganau, Mario. "Nanotechnology Applications in Quantitative Neuroscience: Proteomic Analysis of Malignant Gliomas." Doctoral thesis, Università degli studi di Trieste, 2013. http://hdl.handle.net/10077/8575.
Повний текст джерелаAbstract (English) The current limit of knowledge advancement in proteomic analysis of gliomas, the most common primary malignant brain tumors, is related to the high sensitivity required to detect specific biomarkers within few cells volumes. To address this problem we developed a quantitative approach to eventually enable precise, high throughput and low cost analysis of glial cells with potential capability of real-time pathological screening and subtyping of brain tumors. A device consisting in micro-fabricated wells capable to isolate and host living astrocytes was designed and functionalized. Then for the fabrication of a nanobiosensor, able to detect in small volumes the presence of specific biomarkers, ideally for multiplexing assays and meant to fit within the small dimensions of this microdevice, an approach consisting in DNA-directed-immobilization (DDI) of biotinylated antibodies (Abs) on a single stranded DNA (ssDNA) nanoarray, produced by Atomic Force Microscopy (AFM) nanografting, was carefully optimized. The proof of concept was realized with Abs specific for Glial Fibrillary Acidic Protein (GFAP), a biomarker which belongs to the family of intermediate filaments and is crucial in cell’s differentiation, within a platform ready for parallelization. Nanosized patches of thiol modified ssDNA were prepared by AFM-based nanografting inside a matrix of self assembled monolayers (SAM) of alkanethiol-modified gold surfaces. Subsequently a complementary DNA strand (cDNA) conjugated to streptavidin (STV) was allowed to covalently bind to the patch by sequence specific DNA hybridization. Finally the biotin binding sites of STV were exploited to immobilize biotinylated monoclonal GFAP Abs (already in use for ELISA assays) on the top of those nanopatches. The efficiency of those nano-immuno arrays was tested by successfully obtaining the immobilization of purified recombinant GFAP protein, down to a concentration of 4 nM, firstly in standard PBS then in multicells’ lysate obtained from U87 glial cultures. The immobilization was detected by means of AFM measuring step by step the increases in the height of the patches and excluding modification of the roughness of both the SAM and the nanopatches after incubation with the cells’ lysate through a signal to noise ratio analysis. Titration curves for a comparison of sensitivity between this technique and the conventional ELISA assays are provided, they indeed confirm that the sensitivity of our nanosensors is at least that of ELISA, with the advantage of the scalability of the device.
Abstract (Italiano) L’attuale limite di avanzamento dello stato dell’arte dell’analisi proteomica dei gliomi cerebrali, la classe istologica di tumori cerebrali più frequente ed aggressiva, è legato alla difficoltà di individuare specifici biomarkers in piccoli volumi cellulari. Per superare questo limite si è deciso di sviluppare un approccio nanoquantitativo che consenta un’analisi proteomica precisa, ad alta sensibilità e basso costo, degli astrociti tumorali, con potenzialità di screening in tempo reale e sottotipizzazione di tumori cerebrali. Previa fabbricazione e funzionalizzazione di micro pozzetti idonei ad ospitare cellule astrocitarie, ci si è dedicati alla realizzazione di biosensori in grado di riconoscere specifici biomarkers e di essere accoppiati ai micro pozzetti. Al fine di immobilizzare anticorpi specifici per proteine di interesse in ambito neuroncologico, è stato scelto un approccio basato sul nanografting con Microscopio a Forza Atomica (AFM) e sull’immobilizzazione diretta sul DNA di anticorpi (DDI). In particolare la prova concettuale è stata condotta con anticorpi specifici per la Glial Fibrillary Acidic Protein (GFAP), un marcatore della differenziazione astrocitaria appartenente alla famiglia dei filamenti intermedi intracellulari, su una piattaforma atta ad una successiva parallelizzazione. I nanocostrutti responsabili del riconoscimento della proteina d’interesse, sono stati realizzati partendo da molecole di DNA a singola elica (ssDNA) graftate in una matrice di monostrati autoassemblati (SAM) di superfici d’oro alchiltiolo modificato. Al fine di sfruttare la capacità della streptavidina (STV) di legarsi ad anticorpi biotinilati è stata successivamente indotta l’ibridazione di un filamento di DNA complementare (cDNA) a quello precedentemente immobilizzato sulla superficie nanoassemblata che presentasse anche una coda di STV. I siti di legame per la biotina intrinseci al tetramero di STV sono quindi stati sfruttati per immobilizzare sulla superficie dei nanocostrutti degli anticorpi monoclonali biotinilati specifici per GFAP (già in uso per i protocolli ELISA). L’efficienza dei nano-immuno costrutti così ottenuti è stata testata ottenendo l’immobilizzazione di GFAP ricombinante anche a basse concentrazioni (fino a 4nM), sia in presenza di standard PBS, sia in presenza di un lisato multicellulare ottenuto da colture gliali di cellule U87. L’immobilizzazione di GFAP è stata confermata dall’incremento in altezza dei nanocostrutti misurato all’AFM escludendo modificazioni del rapporto segnale/rumore sia del SAM che dei nanocostrutti prima e dopo aggiunta di lisato multicellulare. Il limite di sensibilità del prototipo così ottenuto è stato confrontato con quello raggiungibile con protocolli standard ELISA, mostrando una sensibilità almeno comparabile all’ELISA a fronte di un maggiore potenziale diagnostico legato alla sua scalabilità.
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Yan, Haiyan. "Quantitative EEG changes in excessive daytime sleepiness." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0017/MQ57169.pdf.
Повний текст джерелаColetta, Annette Lisa. "A Quantitative Assessment of Empathy After an Art Prime with Counseling Students." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6717.
Повний текст джерелаMunoz, Maniega Susana. "Diffusion tensor MRI of human ischaemic stroke : quantitative measurements, acquisition and registration issues." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/1955.
Повний текст джерелаSegerdahl, Andrew Reilly. "Investigation of the neural correlates of ongoing pain states using quantitative perfusion arterial spin labelling." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:e55cc4a1-cbd3-477d-a7c2-0935349914f1.
Повний текст джерелаLancione, Marta. "Structural and functional neuroimaging using quantitative susceptibility mapping and ultra-high field magnetic resonance imaging." Thesis, IMT Alti Studi Lucca, 2021. http://e-theses.imtlucca.it/339/1/Lancione_phdthesis.pdf.
Повний текст джерелаMasri, Rania. "Neurons of the primate retina: A qualitative and quantitative analysis." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/21165.
Повний текст джерелаTziortzi, Andri. "Quantitative dopamine imaging in humans using magnetic resonance and positron emission tomography." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:26b8b4c2-0237-4c40-8c84-9ae818a0dabf.
Повний текст джерелаHengenius, James B. "Quantitative modeling of spatiotemporal systems| Simulation of biological systems and analysis of error metric effects on model fitting." Thesis, Purdue University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3687049.
Повний текст джерелаUnderstanding the biophysical processes underlying biological and biotechnological processes is a prerequisite for therapeutic treatments and technological innovation. With the exponential growth of computational processing speed, experimental findings in these fields have been complemented by dynamic simulations of developmental signaling and genetic interactions. Models provide means to evaluate "emergent" properties of systems sometimes inaccessible by reductionist approaches, making them test beds for biological inference and technological refinement.
The complexity and interconnectedness of biological processes pose special challenges to modelers; biological models typically possess a large number of unknown parameters relative to their counterparts in other physical sciences. Estimating these parameter values requires iterative testing of parameter values to find values that produce low error between model and data. This is a task whose length grows exponentially with the number of unknown parameters. Many biological systems require spatial representation (i.e., they are not well-mixed systems and change over space and time). Adding spatial dimensions complicates parameter estimation by increasing computational time for each model evaluation. Defining error for model-data comparison is also complicated on spatial domains. Different metrics compare different features of data and simulation, and the desired features are dependent on the underlying research question.
This dissertation documents the modeling, parameter estimation, and simulation of two spatiotemporal modeling studies. Each study addresses an unanswered research question in the respective experimental system. The former is a 3D model of a nanoscale amperometric glucose biosensor; the model was used to optimize the sensor's design for improved sensitivity to glucose. The latter is a 3D model of the developmental gap gene system that helps establish the bodyplan of Drosophila melanogaster; I wished to determine if the embryo's geometry alone was capable of accounting for observed spatial distributions of gap gene products and to infer feasible genetic regulatory networks (GRNs) via parameter estimation of the GRN interaction terms. Simulation of the biosensor successfully predicted an optimal electrode density on the biosensor surface, allowing us to fabricate improved biosensors. Simulation of the gap gene system on 1D and 3D embryonic demonstrated that geometric effects were insufficient to produce observed distributions when simulated with previously reported GRNs. Noting the effects of the error definition on the outcome of parameter estimation, I conclude with a characterization of assorted error definitions (objective functions), describe data characteristics to which they are sensitive, and end with a suggested procedure for objective function selection. Choice of objective function is important in parameter estimation of spatiotemporal system models in varied biological and biotechnological disciplines.
Mumuni, Abdul Nashirudeen. "Investigation of brain tissue water NMR response by optimised quantitative single-voxel proton magnetic resonance spectroscopy." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4717/.
Повний текст джерелаКниги з теми "Quantitative Neuroscience"
Pardalos, P. M., J. C. Sackellares, P. R. Carney, and L. D. Iasemidis, eds. Quantitative Neuroscience. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4613-0225-4.
Повний текст джерелаEllis, Carson Richard, Daube-Witherspoon Margaret E, and Herscovitch Peter, eds. Quantitative functional brain imaging with positron emission tomography. San Diego, Calif: Academic Press, 1998.
Знайти повний текст джерелаTranquillo, Joseph Vincent. Quantitative neurophysiology. San Rafael, Calif. (1537 Fourth St, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2008.
Знайти повний текст джерелаPlonsey, Robert. Bioelectricity: A Quantitative Approach. Boston, MA: Springer US, 2000.
Знайти повний текст джерелаHabib, Zaidi, ed. Quantitative analysis of nuclear medicine images. New York: Springer, 2005.
Знайти повний текст джерелаEvans, Stephen M., Ann Marie Janson, and Jens Randel Nyengaard. Quantitative Methods in Neuroscience: A Neuroanatomical Approach. Oxford University Press, 2004.
Знайти повний текст джерела(Editor), Stephen M. Evans, Ann Marie Janson (Editor), and Jens Randel Nyengaard (Editor), eds. Quantitative Methods in Neuroscience: A Neuroanatomical Approach. Oxford University Press, USA, 2004.
Знайти повний текст джерелаQuantitative neuroscience: Models, algorithms, diagnostics, and therapeutic applications. Boston: Kluwer Academic, 2004.
Знайти повний текст джерелаPardalos, P. M. Quantitative Neuroscience: Models, Algorithms, Diagnostics, and Therapeutic Applications. Springer, 2011.
Знайти повний текст джерелаPardalos, P. M. Quantitative Neuroscience: Models, Algorithms, Diagnostics, and Therapeutic Applications. Springer London, Limited, 2013.
Знайти повний текст джерелаЧастини книг з теми "Quantitative Neuroscience"
Thomassen, Arnold J. W. M., and Hein J. C. M. Tibosch. "A Quantitative Model of Graphic Production." In Tutorials in Motor Neuroscience, 269–81. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3626-6_22.
Повний текст джерелаZornoza, Teodoro, María José Cano-Cebrián, Ana Polache, and Luis Granero. "Quantitative In Vivo Microdialysis in Pharmacokinetic Studies." In Microdialysis Techniques in Neuroscience, 103–20. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-173-8_6.
Повний текст джерелаMizusawa, Hidehiro. "Prism Adaptation Test (PAT): A Practical and Quantitative Method to Evaluate Cerebellar Function." In Contemporary Clinical Neuroscience, 445–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75817-2_22.
Повний текст джерелаAsherson, Philip, and Hugh Gurling. "Quantitative and Molecular Genetics of ADHD." In Behavioral Neuroscience of Attention Deficit Hyperactivity Disorder and Its Treatment, 239–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/7854_2011_155.
Повний текст джерелаMilošević, Nebojša. "The Morphology of the Brain Neurons: Box-Counting Method in Quantitative Analysis of 2D Image." In Springer Series in Computational Neuroscience, 109–26. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3995-4_7.
Повний текст джерелаHaralanov, Svetlozar, Evelina Haralanova, Emil Milushev, and Diana Shkodrova. "Locomotor Movement-Pattern Analysis as an Individualized Objective and Quantitative Approach in Psychiatry and Psychopharmacology: Clinical and Theoretical Implications." In Psychiatry and Neuroscience Update, 387–416. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95360-1_32.
Повний текст джерелаWang, Yue-Ting, Sujeewa C. Piyankarage, and Gregory R. J. Thatcher. "Quantitative Profiling of Reversible Cysteome Modification Under Nitrosative Stress." In Analysis of Post-Translational Modifications and Proteolysis in Neuroscience, 55–72. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/7657_2015_88.
Повний текст джерелаBiercewicz, Konrad, and Mariusz Borawski. "Examining the Degree of Engagement of a Participant in Economic Games Using Cognitive Neuroscience Techniques." In Experimental and Quantitative Methods in Contemporary Economics, 201–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30251-1_15.
Повний текст джерелаCottrell, Marie, and Patrick Rousset. "The Kohonen algorithm: A powerful tool for analysing and representing multidimensional quantitative and qualitative data." In Biological and Artificial Computation: From Neuroscience to Technology, 861–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032546.
Повний текст джерелаKager, Klara. "Language Aptitude in Relation to Handedness, Hemispheric Dominance, Cognitive Learning Strategies and Non-verbal IQ: A Combined Quantitative and Qualitative Study." In Exploring Language Aptitude: Views from Psychology, the Language Sciences, and Cognitive Neuroscience, 167–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91917-1_9.
Повний текст джерелаТези доповідей конференцій з теми "Quantitative Neuroscience"
Saha Roy, Tiasha, Jesse Breedlove, Ghislain St-Yves, Kendrick Kay, and Thomas Naselaris. "Quantitative comparison of imagery and perception." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1310-0.
Повний текст джерелаThorburn, Craig, Naomi Feldman, and Thomas Schatz. "A quantitative model of the language familiarity effect in infancy." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1353-0.
Повний текст джерелаRajalingham, Rishi, Hyodong Lee, and James J. DiCarlo. "Selective behavioral deficits from focal inactivation of primate inferior temporal (IT) cortex: a new quantitative constraint for models of core object recognition." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1056-0.
Повний текст джерелаKlimenkov, Igor, Nikolai Sudakov, Mikhail Pastukhov, Mikhail Svinov, and Nikolai Kositsyn. "QUANTITATIVE INDICATORS OF STIMULUSDEPENDENT APOPTOSIS AND PROLIFERATION OF CELLS IN THE OLFACTORY EPITHELIUM IN FISH." In XIV International interdisciplinary congress "Neuroscience for Medicine and Psychology". LLC MAKS Press, 2018. http://dx.doi.org/10.29003/m186.sudak.ns2018-14/249-250.
Повний текст джерелаHarahap, Iskandar Azmy, Abdullah Rasyid,, and Masteria Yunovilsa Putra. "Estimation of quantitative risk assessment of dietary exposure to lead (Pb) from sea cucumbers in Indonesia." In THE FIRST INTERNATIONAL CONFERENCE ON NEUROSCIENCE AND LEARNING TECHNOLOGY (ICONSATIN 2021). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0118424.
Повний текст джерелаVasilyeva, Valentina, and Nina Shumeyko. "QUANTITATIVE CHANGES IN THE FIBROUS STRUCTURES OF THE VISUAL AND MOTOR AREAS OF THE CEREBRAL CORTEX OF CHILDREN FROM BIRTH TO 7 YEARS." In XVIII INTERNATIONAL INTERDISCIPLINARY CONGRESS NEUROSCIENCE FOR MEDICINE AND PSYCHOLOGY. LCC MAKS Press, 2022. http://dx.doi.org/10.29003/m2705.sudak.ns2022-18/87-88.
Повний текст джерелаM. Bahgat, Mohamed, Ashraf Elsafty, and Ashraf Shaarawy. "Validating the Impact of FIRST as a New Learner Experience Framework for Teachers Professional Development." In International Conference on Education. The International Institute of Knowledge Management, 2020. http://dx.doi.org/10.17501/24246700.2020.6204.
Повний текст джерелаDaube, Christoph, Bruno Giordano, Phillippe Schyns, and Robin Ince. "Quantitatively comparing predictive models with the Partial Information Decomposition." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1142-0.
Повний текст джерелаЗвіти організацій з теми "Quantitative Neuroscience"
Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.
Повний текст джерелаBurnett, Cathy. Scoping the field of literacy research: how might a range of research be valuable to primary teachers? Sheffield Hallam University, 2022. http://dx.doi.org/10.7190/shu-working-papers/2201.
Повний текст джерела