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Journal articles on the topic 'Biophysical data'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Yatskou, M. M., and V. V. Apanasovich. "Data analysis in complex biomolecular systems." Informatics 18, no. 1 (2021): 105–22. http://dx.doi.org/10.37661/1816-0301-2021-18-1-105-122.

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The biomolecular technology progress is directly related to the development of effective methods and algorithms for processing a large amount of information obtained by modern high-throughput experimental equipment. The priority task is the development of promising computational tools for the analysis and interpretation of biophysical information using the methods of big data and computer models. An integrated approach to processing large datasets, which is based on the methods of data analysis and simulation modelling, is proposed. This approach allows to determine the parameters of biophysic

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Pennefather, Peter S., and West Suhanic. "The Value of Encrypting Biophysical Data." Biophysical Journal 110, no. 3 (2016): 150a. http://dx.doi.org/10.1016/j.bpj.2015.11.845.

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Sikosek, Tobias, and Hue Sun Chan. "Biophysics of protein evolution and evolutionary protein biophysics." Journal of The Royal Society Interface 11, no. 100 (2014): 20140419. http://dx.doi.org/10.1098/rsif.2014.0419.

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The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance

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Burnecki, Krzysztof. "FARIMA processes with application to biophysical data." Journal of Statistical Mechanics: Theory and Experiment 2012, no. 05 (2012): P05015. http://dx.doi.org/10.1088/1742-5468/2012/05/p05015.

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Presse, Steve. "Inferring Quantitative Models from Noisy Biophysical Data." Biophysical Journal 106, no. 2 (2014): 195a. http://dx.doi.org/10.1016/j.bpj.2013.11.1155.

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Linaro, Daniele, Matthew J. Levy, and David L. Hunt. "Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons." PLOS Computational Biology 18, no. 4 (2022): e1010071. http://dx.doi.org/10.1371/journal.pcbi.1010071.

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The transformation of synaptic input into action potential output is a fundamental single-cell computation resulting from the complex interaction of distinct cellular morphology and the unique expression profile of ion channels that define the cellular phenotype. Experimental studies aimed at uncovering the mechanisms of the transfer function have led to important insights, yet are limited in scope by technical feasibility, making biophysical simulations an attractive complementary approach to push the boundaries in our understanding of cellular computation. Here we take a data-driven approach

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-, Aprisal, Junaidi -, Oktanis Emalinda, Nadia Ramadani, and Mila Isanova. "ANALYSIS OF BIOPHYSICAL FACTORS AND THEIR INFLUENCE ON INFILTRATION IN SOME LAND USES OF THE ULAKAN SUB-WATERSHED IN THE CENTRAL PART." Water Conservation & Management 8, no. 3 (2024): 374–79. https://doi.org/10.26480/wcm.03.2024.374.379.

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Biophysical changes in land in sub-watershed areas (DAS) due to uncontrolled population pressure cause a decrease in the biophysical characteristics of the watershed. This has caused the loss of the hydro-organological attributes of the watershed. This can be seen from the frequent surface flows, erosion, and flooding in this watershed area. This condition is also supported by the nature of the watershed morphology in the upstream region, which is steeper than in the middle and downstream parts. This research aims to see the influence of what biophysical properties are dominant in determining

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Wali, Emal, Masahiro Tasumi, and Masao Moriyama. "Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan." Remote Sensing 12, no. 1 (2020): 189. http://dx.doi.org/10.3390/rs12010189.

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This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were invest

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Kinz-Thompson, Colin D., Korak Kumar Ray, and Ruben L. Gonzalez. "Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments." Annual Review of Biophysics 50, no. 1 (2021): 191–208. http://dx.doi.org/10.1146/annurev-biophys-082120-103921.

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Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The

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Wiegand, C. L., A. H. Gerbermann, K. P. Gallo, B. L. Blad, and D. Dusek. "Multisite analyses of spectral-biophysical data for corn." Remote Sensing of Environment 33, no. 1 (1990): 1–16. http://dx.doi.org/10.1016/0034-4257(90)90051-m.

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Richardson, A. J., C. L. Wiegand, D. F. Wanjura, D. Dusek, and J. L. Steiner. "Multisite analyses of spectral-biophysical data for Sorghum." Remote Sensing of Environment 41, no. 1 (1992): 71–82. http://dx.doi.org/10.1016/0034-4257(92)90062-o.

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Wiegand, C. L., S. J. Maas, J. K. Aase, et al. "Multisite analyses of spectral-biophysical data for wheat." Remote Sensing of Environment 42, no. 1 (1992): 1–21. http://dx.doi.org/10.1016/0034-4257(92)90064-q.

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Lovynska, Viktoriia, Yuriy Buchavyi, Petro Lakyda, Svitlana Sytnyk, Yuriy Gritzan, and Roman Sendziuk. "Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data." Journal of Forest Science 66, No. 8 (2020): 339–48. http://dx.doi.org/10.17221/28/2020-jfs.

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The present study offers the results of the spectral characteristics, calculated vegetative indices and biophysical parameters of pine stands of the Northern Steppe of Ukraine region obtained using Sentinel-2 data. For the development of regression models with the prediction of the biomass of pine forests using the obtained spectral characteristics, we used the results of the assessment of the aboveground biomass by the method of field surveys. The results revealed the highest correlation relations between the parameters of the general and trunk biomass with the normalised difference vegetatio

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Hagen, Espen, Steinn H. Magnusson, Torbjørn V. Ness, et al. "Brain signal predictions from multi-scale networks using a linearized framework." PLOS Computational Biology 18, no. 8 (2022): e1010353. http://dx.doi.org/10.1371/journal.pcbi.1010353.

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Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited comput

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Yailymov, Bohdan, Andrii Shelestov, Mikhail Emelyanov, and Oleksandr Parkhomchuk. "VALIDATION OF LAND DEGRADATION CARDS ON THE BASIS OF GEOSPATIAL DATA." Journal of Automation and Information sciences 1 (January 1, 2022): 112–25. http://dx.doi.org/10.34229/1028-0979-2022-1-10.

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Today there is a lot of satellite data and products based on it in the public domain. By integrating them with heterogeneous socio-economic information and soil maps, model biophysical data using modern machine learning methods and modern approaches to geospatial data processing, it becomes possible to create maps of land degradation. Considering that classification maps, productivity maps and deforestation maps are the main intellectual components to create a degradation map, it is these products that affect the overall reliability of the results. For their validation, the necessary quality m

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de Sá, Nuno César, Mitra Baratchi, Leon T. Hauser, and Peter van Bodegom. "Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data." Remote Sensing 13, no. 4 (2021): 648. http://dx.doi.org/10.3390/rs13040648.

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Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data

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Rounsevell, Ross W. S., Annette Steward, and Jane Clarke. "Biophysical Investigations of Engineered Polyproteins: Implications for Force Data." Biophysical Journal 88, no. 3 (2005): 2022–29. http://dx.doi.org/10.1529/biophysj.104.053744.

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Rosenqvist, A., A. K. Milne, and R. Zimmermann. "Systematic data acquisitiona prerequisite for meaningful biophysical parameter retrieval?" IEEE Transactions on Geoscience and Remote Sensing 41, no. 7 (2003): 1709–11. http://dx.doi.org/10.1109/tgrs.2003.813358.

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Hughes, C. S., E. Longo, M. K. Phillips-Jones, and R. Hussain. "Quality control and biophysical characterisation data of VanS A." Data in Brief 14 (October 2017): 41–47. http://dx.doi.org/10.1016/j.dib.2017.07.012.

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Digonnet-Kerhoas, Catherine, Gilles Gay, Jean Claude Duplan, and Christian Dumas. "Viability of Cururbita pepo pollen: biophysical and structural data." Planta 179, no. 2 (1989): 165–70. http://dx.doi.org/10.1007/bf00393686.

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Yakushevich, L. V. "Nonlinear dynamics of biopolymers: theoretical models, experimental data." Quarterly Reviews of Biophysics 26, no. 2 (1993): 201–23. http://dx.doi.org/10.1017/s0033583500004078.

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Nonlinear dynamics of biopolymers is a new and rapidly developing field of biophysical science. It can be considered as a part of the general dynamics which deals with the internal mobility of biopolymers. Theoreticians define it also as the next (anharmonic or nonlinear) approximation after the first harmonic or linear one.

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Harfenmeister, Katharina, Sibylle Itzerott, Cornelia Weltzien, and Daniel Spengler. "Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data." Remote Sensing 13, no. 4 (2021): 575. http://dx.doi.org/10.3390/rs13040575.

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The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric pa

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Addisie, Meseret B., Gashaw Molla, Menberu Teshome, and Gebiaw T. Ayele. "Evaluating Biophysical Conservation Practices with Dynamic Land Use and Land Cover in the Highlands of Ethiopia." Land 11, no. 12 (2022): 2187. http://dx.doi.org/10.3390/land11122187.

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Ethiopia is one of the sub-Saharan countries affected by land degradation, notably by soil erosion. The government of Ethiopia has launched an extensive biophysical soil and water conservation (SWC) effort each year to address the problem. These practices were installed on varying land use and land cover (LULC) systems. Despite the fact that the interventions covered the majority of the landmasses, there were no quantitative data on the scale of biophysical measures with the change in land use and land cover. Therefore, the objective of this study was to evaluate biophysical conservation pract

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Laidler, Gita J., and Paul Treitz. "Biophysical remote sensing of arctic environments." Progress in Physical Geography: Earth and Environment 27, no. 1 (2003): 44–68. http://dx.doi.org/10.1191/0309133303pp358ra.

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Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical

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Taufiq, Azhary, Melya Riniarti, Duryat Duryat, and Slamet Budi Yuwono. "Biophysical Studies In Khilau Watershed." EnviroScienteae 15, no. 1 (2019): 1. http://dx.doi.org/10.20527/es.v15i1.6317.

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Khilau Watershed (DAS) is one of the sub-watersheds with the status of should be restored. The biophysical conditions of the watershed must be assessed, to determine the suitable actions for land rehabilitation. The purpose of this study was to provide a comparative analysis of baseline data on the biophysical conditions of the Khilau Sub-watershed area based on edaphic and climatic parameters. Data collection method was using cluster sampling in five types of land cover. The data analysis used spatial and laboratory analysis. The results showed that agroforests and annual crops mostly were on

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Grendaitė, Dalia, and Edvinas Stonevičius. "Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data." Water 14, no. 11 (2022): 1732. http://dx.doi.org/10.3390/w14111732.

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Inland waters are dynamic systems that are under pressure from anthropogenic activities, thus constant observation of these waters is essential. Remote sensing provides a great opportunity to have frequent observations of inland waters. The aim of this study was to create a data-driven model that uses a machine learning algorithm and Sentinel-2 data to classify lake observations into four biophysical classes: Clear, Moderate, Chla-dominated, and Turbid. We used biophysical variables such as water transparency, chlorophyll concentration, and suspended matter to define these classes. We tested s

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Bonebrake, Timothy C., Carol L. Boggs, Jeannie A. Stamberger, Curtis A. Deutsch, and Paul R. Ehrlich. "From global change to a butterfly flapping: biophysics and behaviour affect tropical climate change impacts." Proceedings of the Royal Society B: Biological Sciences 281, no. 1793 (2014): 20141264. http://dx.doi.org/10.1098/rspb.2014.1264.

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Difficulty in characterizing the relationship between climatic variability and climate change vulnerability arises when we consider the multiple scales at which this variation occurs, be it temporal (from minute to annual) or spatial (from centimetres to kilometres). We studied populations of a single widely distributed butterfly species, Chlosyne lacinia , to examine the physiological, morphological, thermoregulatory and biophysical underpinnings of adaptation to tropical and temperate climates. Microclimatic and morphological data along with a biophysical model documented the importance of s

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Fang, Meichen, Gennady Gorin, and Lior Pachter. "Trajectory inference from single-cell genomics data with a process time model." PLOS Computational Biology 21, no. 1 (2025): e1012752. https://doi.org/10.1371/journal.pcbi.1012752.

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Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of “pseudotime” that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of “process time” via a principl

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Cong, Yao, Julio A. Kovacs, and Willy Wriggers. "2D fast rotational matching for image processing of biophysical data." Journal of Structural Biology 144, no. 1-2 (2003): 51–60. http://dx.doi.org/10.1016/j.jsb.2003.09.017.

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Rivera, S., P. Martinez de Anguita, R. D. Ramsey, and T. A. Crowl. "Spatial Modeling of Tropical Deforestation Using Socioeconomic and Biophysical Data." Small-scale Forestry 12, no. 2 (2012): 321–34. http://dx.doi.org/10.1007/s11842-012-9214-2.

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Idris, Md Suffian, Hing Lee Siang, and Roswati Md Amin. "Data on sea surface biophysical parameters during different monsoon seasons." Data in Brief 28 (February 2020): 104982. http://dx.doi.org/10.1016/j.dib.2019.104982.

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Ferraro, Paul J. "Conservation Contracting in Heterogeneous Landscapes: An Application to Watershed Protection with Threshold Constraints." Agricultural and Resource Economics Review 32, no. 1 (2003): 53–64. http://dx.doi.org/10.1017/s1068280500002501.

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A key issue in the design of land use policy is how to integrate information about spatially variable biophysical and economic conditions into a cost-effective conservation plan. Using common biophysical scoring methods, in combination with economic data and simple optimization methods, an illustration is provided for how to identify a set of priority land parcels for conservation investment. This study also demonstrates a way in which conservation agencies can incorporate concerns about biophysical thresholds in the identification of their priority land parcels. These methods are applied usin

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LUBSKYI, M. S., A. V. KHYZHNIAK, T. A. ORLENKO, and S. I. GOLUBOV. "MODELING THE BIOPHYSICAL CONDITION OF THE UKRAINIAN STEPPE ZONE USING REMOTE SENSING DATA." Kosmìčna nauka ì tehnologìâ 31, no. 2 (2025): 20–31. https://doi.org/10.15407/knit2025.02.020.

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The steppe zone of Ukraine is facing significant pressure due to global climate change and anthropogenic activities related to agricul- ture and mining. In addition, the full-scale russian invasion has caused war crimes that have had catastrophic consequences for the steppe ecosystems and the nature reserves, making conservation efforts even more urgent. In this regard, an urgent scientific task arises to develop a comprehensive approach to geospatial modeling of the biophysical condition of the steppe zone and identifying areas most vulnerable to climate challenges and damage. The paper intro

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Blodgett, Clayton, and Mark Jakubauskas. "Remote Sensing of Coniferous Forest Structure in Grand Teton National Park." UW National Parks Service Research Station Annual Reports 19 (January 1, 1995): 3–7. http://dx.doi.org/10.13001/uwnpsrc.1995.3227.

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Satellite remotely sensed multispectral data provides a systematic, synoptic means for broad-area spatially-explicit estimation of biophysically important variables. By using ground measurements of biotic properties to calibrate remotely sensed multispectral data, vegetation properties measured at sample points can be extrapolated across a large geographic region (Graetz, 1990). Biophysical variables derived by this empirical method may include the successional state of the vegetation, or an intrinsic property of the vegetation, such as biomass, leaf area index, cover, or moisture content (Jen

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Tripathy, Shreejoy J., Shawn D. Burton, Matthew Geramita, Richard C. Gerkin, and Nathaniel N. Urban. "Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types." Journal of Neurophysiology 113, no. 10 (2015): 3474–89. http://dx.doi.org/10.1152/jn.00237.2015.

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For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature-based database of electrophysiological properties ( www.neuroelectro.org ), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (

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Verrelst, Jochem, Juan Rivera Caicedo, Jorge Vicent, Pablo Morcillo Pallarés, and José Moreno. "Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation." Remote Sensing 11, no. 2 (2019): 157. http://dx.doi.org/10.3390/rs11020157.

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Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical

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Zeunert, Joshua, Scott Hawken, and Josh Gowers. "Visualising and Valuing Urban Agriculture for Land Use Planning: A Critical GIS Analysis of Sydney and Neighbouring Regions." Land 14, no. 4 (2025): 854. https://doi.org/10.3390/land14040854.

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The loss of a city’s agricultural lands due to land use change through urban development is a global problem, as local food production is an essential green infrastructure for intergenerational sustainability. Like many cities, much of Sydney’s rapid urban development occurs on land previously used for food production. Sydney has one of the highest rates of urban growth among Western cities and a planning strategy that marginalises its agricultural productivity. To better understand and advocate for Sydney’s capacity for food production we explore all available government datasets containing a

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Van Bree, Ron, Diego Marcos, and Ioannis N. Athanasiadis. "Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28458–66. https://doi.org/10.1609/aaai.v39i27.35068.

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Biophysical models offer valuable insights into climate-phenology relationships in both natural and agricultural settings. However, there are substantial structural discrepancies across models which require site-specific recalibration, often yielding inconsistent predictions under similar climate scenarios. Machine learning methods offer data-driven solutions, but often lack interpretability and alignment with existing knowledge. We present a phenology model describing dormancy in fruit trees, integrating conventional biophysical models with a neural network to address their structural dispari

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Hines, Keegan E., Thomas R. Middendorf, and Richard W. Aldrich. "Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach." Journal of General Physiology 143, no. 3 (2014): 401–16. http://dx.doi.org/10.1085/jgp.201311116.

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A major goal of biophysics is to understand the physical mechanisms of biological molecules and systems. Mechanistic models are evaluated based on their ability to explain carefully controlled experiments. By fitting models to data, biophysical parameters that cannot be measured directly can be estimated from experimentation. However, it might be the case that many different combinations of model parameters can explain the observations equally well. In these cases, the model parameters are not identifiable: the experimentation has not provided sufficient constraining power to enable unique est

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Dahms, Thorsten, Sylvia Seissiger, Christopher Conrad, and Erik Borg. "MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 171–75. http://dx.doi.org/10.5194/isprs-archives-xli-b2-171-2016.

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Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. <br><br> In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and t

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Dahms, Thorsten, Sylvia Seissiger, Christopher Conrad, and Erik Borg. "MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 171–75. http://dx.doi.org/10.5194/isprsarchives-xli-b2-171-2016.

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Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. &lt;br&gt;&lt;br&gt; In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area i

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Albinet, C., P. Borderies, and J. P. Gastellu-Etchegorry. "Characterization of Maritime Pine Forests with Combination of Simulated P-Band SAR Data and Hyperspectral Data." International Journal of Antennas and Propagation 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/409512.

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This paper describes a sensitivity study performed on simulated radar and optical remote sensing forest data. It presents how the dual model has been built up. The first step is a forest growth model fed with biophysical parameters. The geometrical description is then the input of an optical hyperspectral model, giving reflectance spectra, and a Synthetic Aperture Radar (SAR) model, giving the polarimetric and interferometric observables. As an illustration, the first results obtained by both models outputs are presented, and fusions of these outputs are performed.

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Copeland, Renee, Oluwamayokun T. Oshinowo, Yumiko Sakurai, et al. "A Comparative Medicine Study of Platelet Biophysics Among Hemostasis Models of Different Species." Blood 132, Supplement 1 (2018): 869. http://dx.doi.org/10.1182/blood-2018-99-113107.

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Abstract Background: Animal models of hemostasis play a crucial role in understanding hemostasis and developing novel therapeutic treatments. Hemostasis is a mechanical phenomenon and recent studies have demonstrated that biophysical parameters, such as platelet margination (Walton et al. Blood 2017), thrombus porosity (Welsh et al. Blood 2014), shear forces (Nesbitt et al. Nat Med 2009), compression forces (Ju et al. Nat Comm 2018), and single platelet forces, as measured by platelet contraction cytometry, developed by our laboratory, (Myers et al. Nat Mat. 2017) affect hemostatic processes a

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Jelev, Georgi, Petar Dimitrov, and Eugenia Roumenina. "Within-Field Mapping of Winter Wheat Biophysical Variables Using Multispectral Images from UAV." Aerospace Research in Bulgaria 34 (2022): 13–28. http://dx.doi.org/10.3897/arb.v34.e02.

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The paper presents the results from a study aiming to map the dynamic of biophysical variables of winter wheat crops in different phenological growth stages (PGSs) using multispectral camera data acquired by Unmanned Aerial Vehicle (UAV). The studied biophysical variables are Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and fraction of vegetation cover (fCover). During agricultural year 2016/2017, 4 field campaigns (FCs) were carried out in 6 farmer-managed fields sown with two winter wheat varieties. During the FCs, 8 UAV flight missions were accompl

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Nicoletti, Martina, Letizia Chiodo, Alessandro Loppini, et al. "Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families." PLOS ONE 19, no. 3 (2024): e0298105. http://dx.doi.org/10.1371/journal.pone.0298105.

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The nematode Caenorhabditis elegans is a widely used model organism for neuroscience. Although its nervous system has been fully reconstructed, the physiological bases of single-neuron functioning are still poorly explored. Recently, many efforts have been dedicated to measuring signals from C. elegans neurons, revealing a rich repertoire of dynamics, including bistable responses, graded responses, and action potentials. Still, biophysical models able to reproduce such a broad range of electrical responses lack. Realistic electrophysiological descriptions started to be developed only recently,

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Jesus, Janisson Batista de, and Tatiana Mora Kuplich. "APPLICATIONS OF SAR DATA TO ESTIMATE FOREST BIOPHYSICAL VARIABLES IN BRAZIL." CERNE 26, no. 1 (2020): 88–97. http://dx.doi.org/10.1590/01047760202026012656.

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Yankeelov, Thomas E. "Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer." ISRN Biomathematics 2012 (December 26, 2012): 1–12. http://dx.doi.org/10.5402/2012/287394.

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While there is a mature literature on biomathematical and biophysical modeling in cancer, many of the existing approaches are not of clinical utility, as they require input data that are extremely difficult to obtain in an intact organism, and/or require a large number of assumptions on the free parameters included in the models. Thus, there has only been very limited application of such models to solve problems of clinical import. More recently, however, there has been increased activity at the interface of quantitative, noninvasive imaging data, and tumor mathematical modeling. In addition t

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Railian, Brigita, and Edvinas Stonevičius. "Evaluation of Biophysical Vegetation Parameters Based on Sentinel-2 Satellite Data." Vilnius University Proceedings 10 (May 21, 2020): 50. http://dx.doi.org/10.15388/klimatokaita.2020.42.

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Минакова, Н. Н., and Е. В. Божич. "Multivariate Data Analysis of Polygraph Charts for Assessment of Biophysical Characteristics." Izvestiya of Altai State University Journal, no. 1(99) (March 6, 2018): 34. http://dx.doi.org/10.14258/izvasu(2018)1-05.

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Schlerf, Martin, Clement Atzberger, and Joachim Hill. "Remote sensing of forest biophysical variables using HyMap imaging spectrometer data." Remote Sensing of Environment 95, no. 2 (2005): 177–94. http://dx.doi.org/10.1016/j.rse.2004.12.016.

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