Academic literature on the topic 'Biological framework'
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Journal articles on the topic "Biological framework"
Sciberras, Josette, Raymond Zammit, and Patricia Vella Bonanno. "The European framework for intellectual property rights for biological medicines." Generics and Biosimilars Initiative Journal 10, no. 4 (December 15, 2021): 172–83. http://dx.doi.org/10.5639/gabij.2021.1004.022.
Full textBelova, D. A. "Legal Framework for Reproductive Biological Material." Lex Russica, no. 7 (July 19, 2021): 111–21. http://dx.doi.org/10.17803/1729-5920.2021.176.7.111-121.
Full textGutschick, Vincent P., and Hormoz BassiriRad. "Biological Extreme Events: A Research Framework." Eos, Transactions American Geophysical Union 91, no. 9 (2010): 85. http://dx.doi.org/10.1029/2010eo090001.
Full textSong, Cheng Long, Chen Zou, Wen Ke Wang, and Si Kun Li. "An Integrated Framework for Biological Data Visualization." Advanced Materials Research 846-847 (November 2013): 1145–48. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1145.
Full textM. Colombo, Rinaldo, and Elena Rossi. "A modeling framework for biological pest control." Mathematical Biosciences and Engineering 17, no. 2 (2020): 1413–27. http://dx.doi.org/10.3934/mbe.2020072.
Full textWEBB, BARBARA. "A FRAMEWORK FOR MODELS OF BIOLOGICAL BEHAVIOUR." International Journal of Neural Systems 09, no. 05 (October 1999): 375–81. http://dx.doi.org/10.1142/s0129065799000356.
Full textHorie, Ryota. "An optimization framework of biological dynamical systems." Journal of Theoretical Biology 253, no. 1 (July 2008): 45–54. http://dx.doi.org/10.1016/j.jtbi.2008.02.029.
Full textMaloney, Laurence T. "A mathematical framework for biological color vision." Behavioral and Brain Sciences 15, no. 1 (March 1992): 45–46. http://dx.doi.org/10.1017/s0140525x00067467.
Full textSchipper, Harvey, Eva A. Turley, and Michael Baum. "A new biological framework for cancer research." Lancet 348, no. 9035 (October 1996): 1149–51. http://dx.doi.org/10.1016/s0140-6736(96)06184-3.
Full textBlackburn, Tim M., Petr Pyšek, Sven Bacher, James T. Carlton, Richard P. Duncan, Vojtěch Jarošík, John R. U. Wilson, and David M. Richardson. "A proposed unified framework for biological invasions." Trends in Ecology & Evolution 26, no. 7 (July 2011): 333–39. http://dx.doi.org/10.1016/j.tree.2011.03.023.
Full textDissertations / Theses on the topic "Biological framework"
Tagger, B. "A framework for the management of changing biological experimentation." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/147616/.
Full textLinsley, Drew. "A revised framework for human scene recognition." Thesis, Boston College, 2016. http://hdl.handle.net/2345/bc-ir:106986.
Full textFor humans, healthy and productive living depends on navigating through the world and behaving appropriately along the way. But in order to do this, humans must first recognize their visual surroundings. The technical difficulty of this task is hard to comprehend: the number of possible scenes that can fall on the retina approaches infinity, and yet humans often effortlessly and rapidly recognize their surroundings. Understanding how humans accomplish this task has long been a goal of psychology and neuroscience, and more recently, has proven useful in inspiring and constraining the development of new algorithms for artificial intelligence (AI). In this thesis I begin by reviewing the current state of scene recognition research, drawing upon evidence from each of these areas, and discussing an unchallenged assumption in the literature: that scene recognition emerges from independently processing information about scenes’ local visual features (i.e. the kinds of objects they contain) and global visual features (i.e., spatial parameters. ). Over the course of several projects, I challenge this assumption with a new framework for scene recognition that indicates a crucial role for information sharing between these resources. Development and validation of this framework will expand our understanding of scene recognition in humans and provide new avenues for research by expanding these concepts to other domains spanning psychology, neuroscience, and AI
Thesis (PhD) — Boston College, 2016
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Psychology
Keane, John F. "A framework for molecular signal processing and detection in biological cells /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/6126.
Full textHwang, Daehee 1971. "A statistical framework for extraction of structured knowledge from biological/biotechnological systems." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29603.
Full textIncludes bibliographical references (leaves 203-215).
Despite enormous efforts to understand complex biological/biotechnological systems, a significant amount of knowledge has still remained unraveled. However, recent advances in high throughput technologies have offered new opportunities to understand these complex systems by providing us with huge amounts of data about these systems. Unlike traditional tools, these high throughput detection tools: (1) permit large-scale screening of formulations to find the optimal condition, and (2) provide us with a global scale of measurement for a given system. Thus, there has been a strong need for computational tools that effectively extract useful knowledge about systems behavior from the vast amount of data. This thesis presents a comprehensive set of computational tools that enables us to extract important information (called structured knowledge) from this huge amount of data to improve our understanding of biological and biotechnological systems. Then, in several case studies, this extracted knowledge is used to optimize these systems. These tools include: (1) optimal design of experiments (DOE) for efficient investigation of systems, and (2) various statistical methods for effective analyses of the data to capture all structured knowledge in the data. These tools have been applied to various biological and biotechnological systems for identification of: (1) discriminatory characteristics for several diseases from gene expression data to construct disease classifiers; (2) rules to improve plasma absorptions of drugs from high-throughput screening data; (3) binding rules of epitopes to MHC molecules from binding assay data to artificially activate immune responses involving these MHC molecules; (4) rules for pre-conditioning and plasma supplementation from metabolic profiling data to improve the bio-artificial liver (BAL) device;
(cont.) (5) rules to facilitate protein crystallizations from high-throughput screening data to find the optimal condition for crystallization; (6) a new clinical index from metabolic profiling through serum data to improve the diagnostic resolution of liver failure. The results from these applications demonstrate that the developed tools successfully extracted important information to understand systems behavior from various high-throughput data and suggested rules to improve systems performance. In the first case study, the statistical methods helped us identify a drug target for Multiple Scleroses disease through analyses of gene expression data and, then, facilitated finding a peptide drug to inhibit the drug target. In the fifth case study, the methodology enabled us to find large protein crystals for several test proteins difficult to crystallize. The rules identified from the other case studies are being validated for improvement of the systems behavior.
by Daehee Hwang.
Sc.D.
Yates, Phillip. "An Inferential Framework for Network Hypothesis Tests: With Applications to Biological Networks." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2200.
Full textAlkhairy, Samiya Ashraf. "A modeling framework and toolset for simulation and characterization of the cochlea within the auditory system." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67201.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 50-53).
Purpose: This research develops a modeling approach and an implementation toolset to simulate reticular lamina displacement in response to excitation at the ear canal and to characterize the cochlear system in the frequency domain. Scope The study develops existing physical models covering the outer, middle, and inner ears. The range of models are passive linear, active linear, and active nonlinear. These models are formulated as differential algebraic equations, and solved for impulse and tone excitations to determine responses. The solutions are mapped into tuning characteristics as a function of position within the cochlear partition. Objectives The central objective of simulation is to determine the characteristic frequency (CF)-space map, equivalent rectangular bandwidth (ERB), and sharpness of tuning (QERB) of the cochlea. The focus of this research is on getting accurate characteristics, with high time and space resolution. The study compares the simulation results to empirical measurements and to predictions of a model that utilizes filter theory and coherent reflection theory. Method We develop lumped and distributed physical models based on mechanical, acoustic, and electrical phenomena. The models are structured in the form of differential-algebraic equations (DAE), discretized in the space domain. This is in contrast to existing methods that solve a set of algebraic equations discretized in both space and time. The DAEs are solved using numerical differentiation formulas (NDFs) to compute the displacement of the reticular lamina and intermediate variables such as displacement of stapes in response to impulse and tone excitations at the ear canal. The inputs and outputs of the cochlear partition are utilized in determining its resonances and tuning characteristics. Transfer functions of the cochlear system with impulse excitation are calculated for passive and active linear models to determine resonance and tuning of the cochlear partition. Output characteristics are utilized for linear systems with tone excitation and for nonlinear models with stimuli of various amplitudes. Stability of the system is determined using generalized eigenvalues and the individual subsystems are stabilized based on their poles and zeros. Results The passive system has CF map ranging from 20 kHz at the base to 10 Hz at the apex of the cochlear partition, and has the strongest resonant frequency corresponding to that of the middle ear. The ERB is on the order of the CF, and the QERB is on the order of 1. The group delay decreases with CF which is in contradiction with findings from Stimulus Frequency Otoacoustic Emissions (SFOAE) experiments. The tuning characteristics of the middle ear correspond well to experimental observations. The stability of the system varies greatly with the choice of parameters, and number of space sections used for both the passive and active implementations. Implication Estimates of cochlear partition tuning based on solution of differential algebraic equations have better time and space resolution compared to existing methods that solve discretized set of equations. Domination of the resonance frequency of the reticular lamina by that of the middle ear rather than the resonant frequency of the cochlea at that position for the passive model is in contradiction with Bekesys measurements on human cadavers. Conclusion The methodology used in the thesis demonstrate the benefits of developing models and formulating the problem as differential-algebraic equations and solving it using the NDFs. Such an approach facilitates computation of responses and transfer functions simultaneously, studying stability of the system, and has good accuracy (controlled directly by error tolerance) and resolution.
by Samiya Ashraf Alkhairy.
M.Eng.
De, Blocq Andrew Dirk. "Estimating spotted hyaena (Crocuta crocuta) population density using camera trap data in a spatially-explicit capture-recapture framework." Bachelor's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/13053.
Full textSpecies-specific population data are important for the effective management and conservation of wildlife populations within protected areas. However such data are often logistically difficult and expensive to attain for species that are rare and have large ranges. Camera trap surveys provide a non-invasive, inexpensive and effective method for obtaining population level data on wildlife species. Provided that species can be individually identified, a photographic capture-recapture framework can be used to provide density estimates. Spatially-explicit capture-recapture (SECR) models have recently been developed, and are currently considered the most robust method for analysing capture-recapture data. Camera trap data sourced from a leopard survey performed in uMkhuze Game Reserve, KwaZulu-Natal, South Africa, was analysed using SPACECAP, a Bayesian inference-based SECR modelling program. Overall hyaena density for the reserve was estimated at 10.59 (sd=2.10) hyaenas/100 km2, which is comparable to estimates obtained using other methods for this reserve and some other protected areas in southern Africa. SECR methods are typically conservative in comparison to other methods of measuring large carnivore populations, which is somewhat supported by higher estimates in other nearby reserves. However, large gaps in time between studies and the variety of historical methods used confound comparisons between estimates. The findings from this study provide support for both camera trap surveys and SECR models in terms of deriving robust population data for spotted hyaenas and other individually recognisable species. Such data allows for studies on the drivers of population and distribution changes for such species in addition to temporal and spatial activity patterns and habitat preference for select species. The generation of accurate population data for ecologically important predators provides reserve managers with robust data upon which to make informed management decisions. This study shows that estimates for spotted hyaenas can be produced from an existing survey of leopards, which makes photographic capture-recapture methods a sensible and cost-effective option for the less charismatic species. The implementation of standardized and scientifically robust population estimation methods such as SECR using camera trap data would contribute appreciably to the conservation of important wildlife species and the ecological processes they support.
Kalantari, John I. "A general purpose artificial intelligence framework for the analysis of complex biological systems." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5953.
Full textGriesel, Gerhard. "Development and management framework for the Gouritz River Catchment." Pretoria : [s.n.], 2003. http://upetd.up.ac.za/thesis/available/etd-11202003-155742.
Full textMoxley, Courtney. "Characterization of biotic and sodic lawns of the Kruger National Park using the framework of the positive feedback loop / Courtney Moxley." Bachelor's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/14020.
Full textBooks on the topic "Biological framework"
Development, New Zealand Ministry of Economic. Bioprospecting: Harnessing benefits for New Zealand : a policy framework discussion. Wellington [N.Z.]: Ministry of Economic Development, 2007.
Find full text(Ireland), Heritage Council. Towards a national framework for the management of biological data. Dublin: Heritage Council, 2002.
Find full textPhantom menace or looming threat?: A new framework for assessing bioweapons threats. Baltimore: Johns Hopkins University Press, 2013.
Find full textBarber, Ben. Building Illinois' biological memory: A framework for long-term ecosystem monitoring. Springfield, Ill: Illinois Dept. of Natural Resources, Energy and Environmental Assessment Division, 2000.
Find full textVladimir, Krever, World Wildlife Fund (U.S.), and Russia (Federation). Ministerstvo okhrany okruzhai͡u︡shcheĭ sredy i prirodnykh resursov., eds. Conserving Russia's biological diversity: An analytical framework and initial investment portfolio. Washington, D.C: World Wildlife Fund, 1994.
Find full textNational Research Council (U.S.). Board on Life Sciences. Reopening public facilities after a biological attack: A decision making framework. Washington, D.C: National Academies Press, 2005.
Find full textMoshe, Shachak, ed. Biodiversity in drylands: Toward a unified framework. Oxford: Oxford University Press, 2005.
Find full textIndian Institute of Management, Ahmedabad., ed. Empowering conservators of biodiversity and associated knowledge systems: An intellectual property based framework. Ahmedabad: Indian Institute of Management, 2002.
Find full textJenkins, Kurt Jeffrey. A framework for long-term ecological monitoring in Olympic National Park: Prototype for the Coniferous Forest Biome. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2003.
Find full textJenkins, Kurt Jeffrey. A framework for long-term ecological monitoring in Olympic National Park: Prototype for the Coniferous Forest Biome. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2003.
Find full textBook chapters on the topic "Biological framework"
Mabberley, D. J. "The Changing Biological Framework." In Tropical Rain Forest Ecology, 52–79. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3672-7_4.
Full textMabberley, D. J. "The Changing Biological Framework." In Tropical Rain Forest Ecology, 52–79. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-3048-6_4.
Full textKumar, Pramod, Vandana Mishra, and Subarna Roy. "Machine Learning Framework: Predicting Protein Structural Features." In Soft Computing for Biological Systems, 121–41. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7455-4_8.
Full textLoucks, Orie L. "Policy Framework Issues for Protecting Biological Diversity." In Air Pollution Effects on Biodiversity, 263–79. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3538-6_12.
Full textPossas, Arícia, Letícia Ungaretti Haberbeck, and Fernando Pérez-Rodríguez. "Food Risk Assessment Framework." In Risk Assessment Methods for Biological and Chemical Hazards in Food, 3–16. Boca Raton : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429083525-2.
Full textMoolgavkar, Suresh, and Georg Luebeck. "Multistage Carcinogenesis: A Unified Framework for Cancer Data Analysis." In Statistical Modeling for Biological Systems, 117–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34675-1_7.
Full textUttley, Chris. "The Water Framework Directive and the Habitats and Birds Directives." In Biological Monitoring in Freshwater Habitats, 23–29. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9278-7_3.
Full textHokkanen, Heikki M. T., Franz Bigler, Giovanni Burgio, Joop C. Van Lenteren, and Matt B. Thomas. "Ecological Risk Assessment Framework for Biological Control Agents." In Environmental Impacts of Microbial Insecticides, 1–14. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-1441-9_1.
Full textBadura, Jens, Broder Breckling, Thomas Potthast, and Jan Barkmann. "Conclusions: A Generalizing Framework for Biological Orientation Orientation." In Eco Targets, Goal Functions, and Orientors, 355–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58769-6_23.
Full textCrupi, Vincenzo. "Measures of Biological Diversity: Overview and Unified Framework." In History, Philosophy and Theory of the Life Sciences, 123–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10991-2_6.
Full textConference papers on the topic "Biological framework"
Krishnamoorthy, K., and T. Mathew. "217. Statistical Framework for Biological Monitoring." In AIHce 2006. AIHA, 2006. http://dx.doi.org/10.3320/1.2758928.
Full textSingh, Shivani. "An efficient framework for mining biological network." In 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IEEE, 2015. http://dx.doi.org/10.1109/icacea.2015.7164735.
Full textAbdi, Afshin, Arash Einolghozati, and Faramarz Fekri. "Computing framework in biological cells via stochastic methods." In 2017 IEEE Information Theory Workshop (ITW). IEEE, 2017. http://dx.doi.org/10.1109/itw.2017.8278005.
Full textOKUMURA, TOSHIYUKI, SUSUMU DATE, YOICHI TAKENAKA, and HIDEO MATSUDA. "A FRAMEWORK FOR BIOLOGICAL ANALYSIS ON THE GRID." In Proceedings of the 2nd International Life Science Grid Workshop, LSGRID 2005. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772503_0007.
Full text"An improved stochastic modelling framework for biological networks." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.a1.altarawni.
Full textda Silva, Bruno S., Taua M. Cabreira, Bruno J. O. De Souza, Nicholas R. Matias, Ricardo A. O. Machado, Lucio Andre C. Jorge, and Paulo Roberto Ferreira. "Framework for Biological Control with Unmanned Aerial Vehicles." In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE). IEEE, 2022. http://dx.doi.org/10.1109/lars/sbr/wre56824.2022.9995875.
Full textAmami, Maha, Rim Faiz, and Aymen Elkhlifi. "A framework for biological event extraction from text." In the 2nd International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2254129.2254193.
Full textBriscoe, Jayson, Leah Appelhans, Sean Smith, K. Westlake, Igal Brener, and Jeremy Wright. "Zirconium metal-organic framework functionalized plasmonic sensor." In Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX, edited by Jason A. Guicheteau and Chris R. Howle. SPIE, 2019. http://dx.doi.org/10.1117/12.2519134.
Full textPetsios, Stefanos Konstantinos D., and Dimitrios I. Fotiadis. "A Computational Framework for the Analysis of Biological Models." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4352488.
Full text"Inverse modeling of biological processes in sensitivity operator framework." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-666.
Full textReports on the topic "Biological framework"
Dzenitis, J. Acceptance Criteria Framework for Autonomous Biological Detectors. Office of Scientific and Technical Information (OSTI), December 2006. http://dx.doi.org/10.2172/902228.
Full textShakya, B., E. Sharma, J. Gurung, and N. Chettri. The Landscape Approach in Biodiversity Conservation; A Regional Cooperation Framework for Implementation of the Convention on Biological Diversity in the Kangchenjunga Landscape. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2007. http://dx.doi.org/10.53055/icimod.479.
Full textShakya, B., E. Sharma, J. Gurung, and N. Chettri. The Landscape Approach in Biodiversity Conservation; A Regional Cooperation Framework for Implementation of the Convention on Biological Diversity in the Kangchenjunga Landscape. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2007. http://dx.doi.org/10.53055/icimod.479.
Full textSeale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.
Full textSkalski, John R., and Roger F. Ngouenet. Monitoring and Evaluation of Smolt Migration in the Columbia Basin : Volume VII : Evaluation of the Compliance Testing Framework for RPA Improvement as Stated in the 2000 Federal Columbia River Power System (FCRPS) Biological Opinion. Office of Scientific and Technical Information (OSTI), May 2001. http://dx.doi.org/10.2172/961873.
Full textFleishman, Erica. Sixth Oregon climate assessment. Oregon Climate Change Research Institute, Oregon State University, 2023. http://dx.doi.org/10.5399/osu/1161.
Full textDalton, Meghan M., and Erica Fleishman. Fifth Oregon climate assessment. Oregon Climate Change Research Institute, Oregon State University, 2021. http://dx.doi.org/10.5399/osu/1160.
Full textYurovskaya, M. V., and A. V. Yushmanova. Complex Investigations of the World Ocean. Proceedings of the VI Russian Scientific Conference of Young Scientists. Edited by D. A. Alekseev, A. Yu Andreeva, I. M. Anisimov, A. V. Bagaev, Yu S. Bayandina, E. M. Bezzubova, D. F. Budko, et al. Shirshov Institute Publishing House, April 2021. http://dx.doi.org/10.29006/978-5-6045110-3-9.
Full textSerre, Thomas, Lior Wolf, and Tomaso Poggio. A New Biologically Motivated Framework for Robust Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada454724.
Full textKapulnik, Yoram, and Donald A. Phillips. Isoflavonoid Regulation of Root Bacteria. United States Department of Agriculture, January 1996. http://dx.doi.org/10.32747/1996.7570561.bard.
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