A coarse-grained biophysical model of sequence evolution and the population size dependence of the speciation rate

PubMed Central

Khatri, Bhavin S.; Goldstein, Richard A.

2015-01-01

Speciation is fundamental to understanding the huge diversity of life on Earth. Although still controversial, empirical evidence suggests that the rate of speciation is larger for smaller populations. Here, we explore a biophysical model of speciation by developing a simple coarse-grained theory of transcription factor-DNA binding and how their co-evolution in two geographically isolated lineages leads to incompatibilities. To develop a tractable analytical theory, we derive a Smoluchowski equation for the dynamics of binding energy evolution that accounts for the fact that natural selection acts on phenotypes, but variation arises from mutations in sequences; the Smoluchowski equation includes selection due to both gradients in fitness and gradients in sequence entropy, which is the logarithm of the number of sequences that correspond to a particular binding energy. This simple consideration predicts that smaller populations develop incompatibilities more quickly in the weak mutation regime; this trend arises as sequence entropy poises smaller populations closer to incompatible regions of phenotype space. These results suggest a generic coarse-grained approach to evolutionary stochastic dynamics, allowing realistic modelling at the phenotypic level. PMID:25936759

Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

PubMed Central

Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A.; Przekwas, Andrzej; Francis, Joseph T.; Lytton, William W.

2015-01-01

Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics. PMID:26635598

The Amazon rainforest, climate change, and drought: How will what is below the surface affect the climate of tropical South America?

NASA Astrophysics Data System (ADS)

Harper, A.; Denning, A. S.; Baker, I.; Randall, D.; Dazlich, D.

2008-12-01

Several climate models have predicted an increase in long-term droughts in tropical South America due to increased greenhouse gases in the atmosphere. Although the Amazon rainforest is resilient to seasonal drought, multi-year droughts pose a definite problem for the ecosystem's health. Furthermore, drought- stressed vegetation participates in feedbacks with the atmosphere that can exacerbate drought. Namely, reduced evapotranspiration further dries out the atmosphere and affects the regional climate. Trees in the rainforest survive seasonal drought by using deep roots to access adequate stores of soil moisture. We investigate the climatic impacts of deep roots and soil moisture by coupling the Simple Biosphere (SiB3) model to Colorado State University's general circulation model (BUGS5). We compare two versions of SiB3 in the GCM during years with anomalously low rainfall. The first has strong vegetative stress due to soil moisture limitations. The second experiences less stress and has more realistic representations of surface biophysics. In the model, basin-wide reductions in soil moisture stress result in increased evapotranspiration, precipitation, and moisture recycling in the Amazon basin. In the savannah region of southeastern Brazil, the unstressed version of SiB3 produces decreased precipitation and weaker moisture flux, which is more in-line with observations. The improved simulation of precipitation and evaporation also produces a more realistic Bolivian high and Nordeste low. These changes highlight the importance of subsurface biophysics for the Amazonian climate. The presence of deep roots and soil moisture will become even more important if climate change brings more frequent droughts to this region in the future.

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions

PubMed Central

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage. PMID:27468262

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

PubMed

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

The hydrological cycle at European Fluxnet sites: modeling seasonal water and energy budgets at local scale.

NASA Astrophysics Data System (ADS)

Stockli, R.; Vidale, P. L.

2003-04-01

The importance of correctly including land surface processes in climate models has been increasingly recognized in the past years. Even on seasonal to interannual time scales land surface - atmosphere feedbacks can play a substantial role in determining the state of the near-surface climate. The availability of soil moisture for both runoff and evapotranspiration is dependent on biophysical processes occuring in plants and in the soil acting on a wide time-scale from minutes to years. Fluxnet site measurements in various climatic zones are used to drive three generations of LSM's (land surface models) in order to assess the level of complexity needed to represent vegetation processes at the local scale. The three models were the Bucket model (Manabe 1969), BATS 1E (Dickinson 1984) and SiB 2 (Sellers et al. 1996). Evapotranspiration and runoff processes simulated by these models range from simple one-layer soils and no-vegetation parameterizations to complex multilayer soils, including realistic photosynthesis-stomatal conductance models. The latter is driven by satellite remote sensing land surface parameters inheriting the spatiotemporal evolution of vegetation phenology. In addition a simulation with SiB 2 not only including vertical water fluxes but also lateral soil moisture transfers by downslope flow is conducted for a pre-alpine catchment in Switzerland. Preliminary results are presented and show that - depending on the climatic environment and on the season - a realistic representation of evapotranspiration processes including seasonally and interannually-varying state of vegetation is significantly improving the representation of observed latent and sensible heat fluxes on the local scale. Moreover, the interannual evolution of soil moisture availability and runoff is strongly dependent on the chosen model complexity. Biophysical land surface parameters from satellite allow to represent the seasonal changes in vegetation activity, which has great impact on the yearly budget of transpiration fluxes. For some sites, however, the hydrological cycle is simulated reasonably well even with simple land surface representations.

Cellular and Network Mechanisms Underlying Information Processing in a Simple Sensory System

NASA Technical Reports Server (NTRS)

Jacobs, Gwen; Henze, Chris; Biegel, Bryan (Technical Monitor)

2002-01-01

Realistic, biophysically-based compartmental models were constructed of several primary sensory interneurons in the cricket cercal sensory system. A dynamic atlas of the afferent input to these cells was used to set spatio-temporal parameters for the simulated stimulus-dependent synaptic inputs. We examined the roles of dendritic morphology, passive membrane properties, and active conductances on the frequency tuning of the neurons. The sensitivity of narrow-band low pass interneurons could be explained entirely by the electronic structure of the dendritic arbors and the dynamic sensitivity of the SIZ. The dynamic characteristics of interneurons with higher frequency sensitivity required models with voltage-dependent dendritic conductances.

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells.

PubMed

Masoli, Stefano; Rizza, Martina F; Sgritta, Martina; Van Geit, Werner; Schürmann, Felix; D'Angelo, Egidio

2017-01-01

In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (G i-max ) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of G i-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of G i-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental G i-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.

A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

PubMed

Floares, Alexandru George

2008-01-01

Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

BeatBox-HPC simulation environment for biophysically and anatomically realistic cardiac electrophysiology.

PubMed

Antonioletti, Mario; Biktashev, Vadim N; Jackson, Adrian; Kharche, Sanjay R; Stary, Tomas; Biktasheva, Irina V

2017-01-01

The BeatBox simulation environment combines flexible script language user interface with the robust computational tools, in order to setup cardiac electrophysiology in-silico experiments without re-coding at low-level, so that cell excitation, tissue/anatomy models, stimulation protocols may be included into a BeatBox script, and simulation run either sequentially or in parallel (MPI) without re-compilation. BeatBox is a free software written in C language to be run on a Unix-based platform. It provides the whole spectrum of multi scale tissue modelling from 0-dimensional individual cell simulation, 1-dimensional fibre, 2-dimensional sheet and 3-dimensional slab of tissue, up to anatomically realistic whole heart simulations, with run time measurements including cardiac re-entry tip/filament tracing, ECG, local/global samples of any variables, etc. BeatBox solvers, cell, and tissue/anatomy models repositories are extended via robust and flexible interfaces, thus providing an open framework for new developments in the field. In this paper we give an overview of the BeatBox current state, together with a description of the main computational methods and MPI parallelisation approaches.

Neural simulations on multi-core architectures.

PubMed

Eichner, Hubert; Klug, Tobias; Borst, Alexander

2009-01-01

Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i.e. user-transparent load balancing.

Neural Simulations on Multi-Core Architectures

PubMed Central

Eichner, Hubert; Klug, Tobias; Borst, Alexander

2009-01-01

Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i.e. user-transparent load balancing. PMID:19636393

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation. Copyright © 2015 the authors 0270-6474/15/3510112-23$15.00/0.

Quantitative Analysis of Hepatitis C NS5A Viral Protein Dynamics on the ER Surface.

PubMed

Knodel, Markus M; Nägel, Arne; Reiter, Sebastian; Vogel, Andreas; Targett-Adams, Paul; McLauchlan, John; Herrmann, Eva; Wittum, Gabriel

2018-01-08

Exploring biophysical properties of virus-encoded components and their requirement for virus replication is an exciting new area of interdisciplinary virological research. To date, spatial resolution has only rarely been analyzed in computational/biophysical descriptions of virus replication dynamics. However, it is widely acknowledged that intracellular spatial dependence is a crucial component of virus life cycles. The hepatitis C virus-encoded NS5A protein is an endoplasmatic reticulum (ER)-anchored viral protein and an essential component of the virus replication machinery. Therefore, we simulate NS5A dynamics on realistic reconstructed, curved ER surfaces by means of surface partial differential equations (sPDE) upon unstructured grids. We match the in silico NS5A diffusion constant such that the NS5A sPDE simulation data reproduce experimental NS5A fluorescence recovery after photobleaching (FRAP) time series data. This parameter estimation yields the NS5A diffusion constant. Such parameters are needed for spatial models of HCV dynamics, which we are developing in parallel but remain qualitative at this stage. Thus, our present study likely provides the first quantitative biophysical description of the movement of a viral component. Our spatio-temporal resolved ansatz paves new ways for understanding intricate spatial-defined processes central to specfic aspects of virus life cycles.

Quantitative Analysis of Hepatitis C NS5A Viral Protein Dynamics on the ER Surface

PubMed Central

Nägel, Arne; Reiter, Sebastian; Vogel, Andreas; McLauchlan, John; Herrmann, Eva; Wittum, Gabriel

2018-01-01

Exploring biophysical properties of virus-encoded components and their requirement for virus replication is an exciting new area of interdisciplinary virological research. To date, spatial resolution has only rarely been analyzed in computational/biophysical descriptions of virus replication dynamics. However, it is widely acknowledged that intracellular spatial dependence is a crucial component of virus life cycles. The hepatitis C virus-encoded NS5A protein is an endoplasmatic reticulum (ER)-anchored viral protein and an essential component of the virus replication machinery. Therefore, we simulate NS5A dynamics on realistic reconstructed, curved ER surfaces by means of surface partial differential equations (sPDE) upon unstructured grids. We match the in silico NS5A diffusion constant such that the NS5A sPDE simulation data reproduce experimental NS5A fluorescence recovery after photobleaching (FRAP) time series data. This parameter estimation yields the NS5A diffusion constant. Such parameters are needed for spatial models of HCV dynamics, which we are developing in parallel but remain qualitative at this stage. Thus, our present study likely provides the first quantitative biophysical description of the movement of a viral component. Our spatio-temporal resolved ansatz paves new ways for understanding intricate spatial-defined processes central to specfic aspects of virus life cycles. PMID:29316722

The Role of Parvalbumin, Sarcoplasmatic Reticulum Calcium Pump Rate, Rates of Cross-Bridge Dynamics, and Ryanodine Receptor Calcium Current on Peripheral Muscle Fatigue: A Simulation Study

PubMed Central

Neumann, Verena

2016-01-01

A biophysical model of the excitation-contraction pathway, which has previously been validated for slow-twitch and fast-twitch skeletal muscles, is employed to investigate key biophysical processes leading to peripheral muscle fatigue. Special emphasis hereby is on investigating how the model's original parameter sets can be interpolated such that realistic behaviour with respect to contraction time and fatigue progression can be obtained for a continuous distribution of the model's parameters across the muscle units, as found for the functional properties of muscles. The parameters are divided into 5 groups describing (i) the sarcoplasmatic reticulum calcium pump rate, (ii) the cross-bridge dynamics rates, (iii) the ryanodine receptor calcium current, (iv) the rates of binding of magnesium and calcium ions to parvalbumin and corresponding dissociations, and (v) the remaining processes. The simulations reveal that the first two parameter groups are sensitive to contraction time but not fatigue, the third parameter group affects both considered properties, and the fourth parameter group is only sensitive to fatigue progression. Hence, within the scope of the underlying model, further experimental studies should investigate parvalbumin dynamics and the ryanodine receptor calcium current to enhance the understanding of peripheral muscle fatigue. PMID:27980606

Neuromorphic Silicon Neuron Circuits

PubMed Central

Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

2011-01-01

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

A single-cell spiking model for the origin of grid-cell patterns

PubMed Central

Kempter, Richard

2017-01-01

Spatial cognition in mammals is thought to rely on the activity of grid cells in the entorhinal cortex, yet the fundamental principles underlying the origin of grid-cell firing are still debated. Grid-like patterns could emerge via Hebbian learning and neuronal adaptation, but current computational models remained too abstract to allow direct confrontation with experimental data. Here, we propose a single-cell spiking model that generates grid firing fields via spike-rate adaptation and spike-timing dependent plasticity. Through rigorous mathematical analysis applicable in the linear limit, we quantitatively predict the requirements for grid-pattern formation, and we establish a direct link to classical pattern-forming systems of the Turing type. Our study lays the groundwork for biophysically-realistic models of grid-cell activity. PMID:28968386

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue.

PubMed

D'Angelo, Egidio; Antonietti, Alberto; Casali, Stefano; Casellato, Claudia; Garrido, Jesus A; Luque, Niceto Rafael; Mapelli, Lisa; Masoli, Stefano; Pedrocchi, Alessandra; Prestori, Francesca; Rizza, Martina Francesca; Ros, Eduardo

2016-01-01

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate "realistic" models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

Optimisation of a Generic Ionic Model of Cardiac Myocyte Electrical Activity

PubMed Central

Guo, Tianruo; Al Abed, Amr; Lovell, Nigel H.; Dokos, Socrates

2013-01-01

A generic cardiomyocyte ionic model, whose complexity lies between a simple phenomenological formulation and a biophysically detailed ionic membrane current description, is presented. The model provides a user-defined number of ionic currents, employing two-gate Hodgkin-Huxley type kinetics. Its generic nature allows accurate reconstruction of action potential waveforms recorded experimentally from a range of cardiac myocytes. Using a multiobjective optimisation approach, the generic ionic model was optimised to accurately reproduce multiple action potential waveforms recorded from central and peripheral sinoatrial nodes and right atrial and left atrial myocytes from rabbit cardiac tissue preparations, under different electrical stimulus protocols and pharmacological conditions. When fitted simultaneously to multiple datasets, the time course of several physiologically realistic ionic currents could be reconstructed. Model behaviours tend to be well identified when extra experimental information is incorporated into the optimisation. PMID:23710254

Spatiotemporal canards in neural field equations

NASA Astrophysics Data System (ADS)

Avitabile, D.; Desroches, M.; Knobloch, E.

2017-04-01

Canards are special solutions to ordinary differential equations that follow invariant repelling slow manifolds for long time intervals. In realistic biophysical single-cell models, canards are responsible for several complex neural rhythms observed experimentally, but their existence and role in spatially extended systems is largely unexplored. We identify and describe a type of coherent structure in which a spatial pattern displays temporal canard behavior. Using interfacial dynamics and geometric singular perturbation theory, we classify spatiotemporal canards and give conditions for the existence of folded-saddle and folded-node canards. We find that spatiotemporal canards are robust to changes in the synaptic connectivity and firing rate. The theory correctly predicts the existence of spatiotemporal canards with octahedral symmetry in a neural field model posed on the unit sphere.

Induction and modulation of persistent activity in a layer V PFC microcircuit model.

PubMed

Papoutsi, Athanasia; Sidiropoulou, Kyriaki; Cutsuridis, Vassilis; Poirazi, Panayiota

2013-01-01

Working memory refers to the temporary storage of information and is strongly associated with the prefrontal cortex (PFC). Persistent activity of cortical neurons, namely the activity that persists beyond the stimulus presentation, is considered the cellular correlate of working memory. Although past studies suggested that this type of activity is characteristic of large scale networks, recent experimental evidence imply that small, tightly interconnected clusters of neurons in the cortex may support similar functionalities. However, very little is known about the biophysical mechanisms giving rise to persistent activity in small-sized microcircuits in the PFC. Here, we present a detailed biophysically-yet morphologically simplified-microcircuit model of layer V PFC neurons that incorporates connectivity constraints and is validated against a multitude of experimental data. We show that (a) a small-sized network can exhibit persistent activity under realistic stimulus conditions. (b) Its emergence depends strongly on the interplay of dADP, NMDA, and GABAB currents. (c) Although increases in stimulus duration increase the probability of persistent activity induction, variability in the stimulus firing frequency does not consistently influence it. (d) Modulation of ionic conductances (I h , I D , I sAHP, I caL, I caN, I caR) differentially controls persistent activity properties in a location dependent manner. These findings suggest that modulation of the microcircuit's firing characteristics is achieved primarily through changes in its intrinsic mechanism makeup, supporting the hypothesis of multiple bi-stable units in the PFC. Overall, the model generates a number of experimentally testable predictions that may lead to a better understanding of the biophysical mechanisms of persistent activity induction and modulation in the PFC.

Multiple Ion Binding Equilibria, Reaction Kinetics, and Thermodynamics in Dynamic Models of Biochemical Pathways

PubMed Central

Vinnakota, Kalyan C.; Wu, Fan; Kushmerick, Martin J.; Beard, Daniel A.

2009-01-01

The operation of biochemical systems in vivo and in vitro is strongly influenced by complex interactions between biochemical reactants and ions such as H+, Mg2+, K+, and Ca2+. These are important second messengers in metabolic and signaling pathways that directly influence the kinetics and thermodynamics of biochemical systems. Herein we describe the biophysical theory and computational methods to account for multiple ion binding to biochemical reactants and demonstrate the crucial effects of ion binding on biochemical reaction kinetics and thermodynamics. In simulations of realistic systems, the concentrations of these ions change with time due to dynamic buffering and competitive binding. In turn, the effective thermodynamic properties vary as functions of cation concentrations and important environmental variables such as temperature and overall ionic strength. Physically realistic simulations of biochemical systems require incorporating all of these phenomena into a coherent mathematical description. Several applications to physiological systems are demonstrated based on this coherent simulation framework. PMID:19216922

Enhanced and effective conformational sampling of protein molecular systems for their free energy landscapes.

PubMed

Higo, Junichi; Ikebe, Jinzen; Kamiya, Narutoshi; Nakamura, Haruki

2012-03-01

Protein folding and protein-ligand docking have long persisted as important subjects in biophysics. Using multicanonical molecular dynamics (McMD) simulations with realistic expressions, i.e., all-atom protein models and an explicit solvent, free-energy landscapes have been computed for several systems, such as the folding of peptides/proteins composed of a few amino acids up to nearly 60 amino-acid residues, protein-ligand interactions, and coupled folding and binding of intrinsically disordered proteins. Recent progress in conformational sampling and its applications to biophysical systems are reviewed in this report, including descriptions of several outstanding studies. In addition, an algorithm and detailed procedures used for multicanonical sampling are presented along with the methodology of adaptive umbrella sampling. Both methods control the simulation so that low-probability regions along a reaction coordinate are sampled frequently. The reaction coordinate is the potential energy for multicanonical sampling and is a structural identifier for adaptive umbrella sampling. One might imagine that this probability control invariably enhances conformational transitions among distinct stable states, but this study examines the enhanced conformational sampling of a simple system and shows that reasonably well-controlled sampling slows the transitions. This slowing is induced by a rapid change of entropy along the reaction coordinate. We then provide a recipe to speed up the sampling by loosening the rapid change of entropy. Finally, we report all-atom McMD simulation results of various biophysical systems in an explicit solvent.

Biophysics of olfaction

NASA Astrophysics Data System (ADS)

Marques Simoes de Souza, Fabio; Antunes, Gabriela

2007-03-01

The majority of the biophysical models of olfaction have been focused on the electrical properties of the system, which is justified by the relative facility of recording the electrical activity of the olfactory cells. However, depending on the level of detail utilized, a biophysical model can explore molecular, cellular and network phenomena. This review presents the state of the art of the biophysical approach to understanding olfaction. The reader is introduced to the principal problems involving the study of olfaction and guided gradually to comprehend why it is important to develop biophysical models to investigate olfaction. A large number of representative biophysical efforts in olfaction, their main contributions, the trends for the next generations of biophysical models and the improvements that may be explored by future biophysicists of olfaction have been reviewed.

A recurrent network mechanism of time integration in perceptual decisions.

PubMed

Wong, Kong-Fatt; Wang, Xiao-Jing

2006-01-25

Recent physiological studies using behaving monkeys revealed that, in a two-alternative forced-choice visual motion discrimination task, reaction time was correlated with ramping of spike activity of lateral intraparietal cortical neurons. The ramping activity appears to reflect temporal accumulation, on a timescale of hundreds of milliseconds, of sensory evidence before a decision is reached. To elucidate the cellular and circuit basis of such integration times, we developed and investigated a simplified two-variable version of a biophysically realistic cortical network model of decision making. In this model, slow time integration can be achieved robustly if excitatory reverberation is primarily mediated by NMDA receptors; our model with only fast AMPA receptors at recurrent synapses produces decision times that are not comparable with experimental observations. Moreover, we found two distinct modes of network behavior, in which decision computation by winner-take-all competition is instantiated with or without attractor states for working memory. Decision process is closely linked to the local dynamics, in the "decision space" of the system, in the vicinity of an unstable saddle steady state that separates the basins of attraction for the two alternative choices. This picture provides a rigorous and quantitative explanation for the dependence of performance and response time on the degree of task difficulty, and the reason for which reaction times are longer in error trials than in correct trials as observed in the monkey experiment. Our reduced two-variable neural model offers a simple yet biophysically plausible framework for studying perceptual decision making in general.

From Spiking Neuron Models to Linear-Nonlinear Models

PubMed Central

Ostojic, Srdjan; Brunel, Nicolas

2011-01-01

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates. PMID:21283777

From spiking neuron models to linear-nonlinear models.

PubMed

Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

Modeling Physiological Events in 2D vs. 3D Cell Culture

PubMed Central

Duval, Kayla; Grover, Hannah; Han, Li-Hsin; Mou, Yongchao; Pegoraro, Adrian F.; Fredberg, Jeffery

2017-01-01

Cell culture has become an indispensable tool to help uncover fundamental biophysical and biomolecular mechanisms by which cells assemble into tissues and organs, how these tissues function, and how that function becomes disrupted in disease. Cell culture is now widely used in biomedical research, tissue engineering, regenerative medicine, and industrial practices. Although flat, two-dimensional (2D) cell culture has predominated, recent research has shifted toward culture using three-dimensional (3D) structures, and more realistic biochemical and biomechanical microenvironments. Nevertheless, in 3D cell culture, many challenges remain, including the tissue-tissue interface, the mechanical microenvironment, and the spatiotemporal distributions of oxygen, nutrients, and metabolic wastes. Here, we review 2D and 3D cell culture methods, discuss advantages and limitations of these techniques in modeling physiologically and pathologically relevant processes, and suggest directions for future research. PMID:28615311

An Off-Lattice Hybrid Discrete-Continuum Model of Tumor Growth and Invasion

PubMed Central

Jeon, Junhwan; Quaranta, Vito; Cummings, Peter T.

2010-01-01

Abstract We have developed an off-lattice hybrid discrete-continuum (OLHDC) model of tumor growth and invasion. The continuum part of the OLHDC model describes microenvironmental components such as matrix-degrading enzymes, nutrients or oxygen, and extracellular matrix (ECM) concentrations, whereas the discrete portion represents individual cell behavior such as cell cycle, cell-cell, and cell-ECM interactions and cell motility by the often-used persistent random walk, which can be depicted by the Langevin equation. Using this framework of the OLHDC model, we develop a phenomenologically realistic and bio/physically relevant model that encompasses the experimentally observed superdiffusive behavior (at short times) of mammalian cells. When systemic simulations based on the OLHDC model are performed, tumor growth and its morphology are found to be strongly affected by cell-cell adhesion and haptotaxis. There is a combination of the strength of cell-cell adhesion and haptotaxis in which fingerlike shapes, characteristic of invasive tumor, are observed. PMID:20074513

Multiscale modeling and simulation of brain blood flow

NASA Astrophysics Data System (ADS)

Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em

2016-02-01

The aim of this work is to present an overview of recent advances in multi-scale modeling of brain blood flow. In particular, we present some approaches that enable the in silico study of multi-scale and multi-physics phenomena in the cerebral vasculature. We discuss the formulation of continuum and atomistic modeling approaches, present a consistent framework for their concurrent coupling, and list some of the challenges that one needs to overcome in achieving a seamless and scalable integration of heterogeneous numerical solvers. The effectiveness of the proposed framework is demonstrated in a realistic case involving modeling the thrombus formation process taking place on the wall of a patient-specific cerebral aneurysm. This highlights the ability of multi-scale algorithms to resolve important biophysical processes that span several spatial and temporal scales, potentially yielding new insight into the key aspects of brain blood flow in health and disease. Finally, we discuss open questions in multi-scale modeling and emerging topics of future research.

Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging.

PubMed

Lacerda, Luis M; Sperl, Jonathan I; Menzel, Marion I; Sprenger, Tim; Barker, Gareth J; Dell'Acqua, Flavio

2016-12-01

Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized. Here we have simulated different tissue configurations, sampling schemes, and processing steps to evaluate DSI performances' under realistic biophysical conditions. A novel approach to compute the orientation distribution function (ODF) has also been developed to include biophysical constraints, namely integration ranges compatible with axial fiber diffusivities. Performed simulations identified several DSI configurations that consistently show aliasing artifacts caused by fast diffusion components for both isotropic diffusion and fiber configurations. The proposed method for ODF computation showed some improvement in reducing such artifacts and improving the ability to resolve crossings, while keeping the quantitative nature of the ODF. In this study, we identified an important limitation of current DSI implementations, specifically the presence of aliasing due to fast diffusion components like those from pathological tissues, which are not well characterized, and can lead to artifactual fiber reconstructions. To minimize this issue, a new way of computing the ODF was introduced, which removes most of these artifacts and offers improved angular resolution. Magn Reson Med 76:1837-1847, 2016. © 2015 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2015 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating cardiac electrophysiology.

PubMed

Sachetto Oliveira, Rafael; Martins Rocha, Bernardo; Burgarelli, Denise; Meira, Wagner; Constantinides, Christakis; Weber Dos Santos, Rodrigo

2018-02-01

The use of computer models as a tool for the study and understanding of the complex phenomena of cardiac electrophysiology has attained increased importance nowadays. At the same time, the increased complexity of the biophysical processes translates into complex computational and mathematical models. To speed up cardiac simulations and to allow more precise and realistic uses, 2 different techniques have been traditionally exploited: parallel computing and sophisticated numerical methods. In this work, we combine a modern parallel computing technique based on multicore and graphics processing units (GPUs) and a sophisticated numerical method based on a new space-time adaptive algorithm. We evaluate each technique alone and in different combinations: multicore and GPU, multicore and GPU and space adaptivity, multicore and GPU and space adaptivity and time adaptivity. All the techniques and combinations were evaluated under different scenarios: 3D simulations on slabs, 3D simulations on a ventricular mouse mesh, ie, complex geometry, sinus-rhythm, and arrhythmic conditions. Our results suggest that multicore and GPU accelerate the simulations by an approximate factor of 33×, whereas the speedups attained by the space-time adaptive algorithms were approximately 48. Nevertheless, by combining all the techniques, we obtained speedups that ranged between 165 and 498. The tested methods were able to reduce the execution time of a simulation by more than 498× for a complex cellular model in a slab geometry and by 165× in a realistic heart geometry simulating spiral waves. The proposed methods will allow faster and more realistic simulations in a feasible time with no significant loss of accuracy. Copyright © 2017 John Wiley & Sons, Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perdikaris, Paris, E-mail: parisp@mit.edu; Grinberg, Leopold, E-mail: leopoldgrinberg@us.ibm.com; Karniadakis, George Em, E-mail: george-karniadakis@brown.edu

The aim of this work is to present an overview of recent advances in multi-scale modeling of brain blood flow. In particular, we present some approaches that enable the in silico study of multi-scale and multi-physics phenomena in the cerebral vasculature. We discuss the formulation of continuum and atomistic modeling approaches, present a consistent framework for their concurrent coupling, and list some of the challenges that one needs to overcome in achieving a seamless and scalable integration of heterogeneous numerical solvers. The effectiveness of the proposed framework is demonstrated in a realistic case involving modeling the thrombus formation process takingmore » place on the wall of a patient-specific cerebral aneurysm. This highlights the ability of multi-scale algorithms to resolve important biophysical processes that span several spatial and temporal scales, potentially yielding new insight into the key aspects of brain blood flow in health and disease. Finally, we discuss open questions in multi-scale modeling and emerging topics of future research.« less

The simulation approach to lipid-protein interactions.

PubMed

Paramo, Teresa; Garzón, Diana; Holdbrook, Daniel A; Khalid, Syma; Bond, Peter J

2013-01-01

The interactions between lipids and proteins are crucial for a range of biological processes, from the folding and stability of membrane proteins to signaling and metabolism facilitated by lipid-binding proteins. However, high-resolution structural details concerning functional lipid/protein interactions are scarce due to barriers in both experimental isolation of native lipid-bound complexes and subsequent biophysical characterization. The molecular dynamics (MD) simulation approach provides a means to complement available structural data, yielding dynamic, structural, and thermodynamic data for a protein embedded within a physiologically realistic, modelled lipid environment. In this chapter, we provide a guide to current methods for setting up and running simulations of membrane proteins and soluble, lipid-binding proteins, using standard atomistically detailed representations, as well as simplified, coarse-grained models. In addition, we outline recent studies that illustrate the power of the simulation approach in the context of biologically relevant lipid/protein interactions.

Simulation of Radiation Damage to Neural Cells with the Geant4-DNA Toolkit

NASA Astrophysics Data System (ADS)

Bayarchimeg, Lkhagvaa; Batmunkh, Munkhbaatar; Belov, Oleg; Lkhagva, Oidov

2018-02-01

To help in understanding the physical and biological mechanisms underlying effects of cosmic and therapeutic types of radiation on the central nervous system (CNS), we have developed an original neuron application based on the Geant4 Monte Carlo simulation toolkit, in particular on its biophysical extension Geant4-DNA. The applied simulation technique provides a tool for the simulation of physical, physico-chemical and chemical processes (e.g. production of water radiolysis species in the vicinity of neurons) in realistic geometrical model of neural cells exposed to ionizing radiation. The present study evaluates the microscopic energy depositions and water radiolysis species yields within a detailed structure of a selected neuron taking into account its soma, dendrites, axon and spines following irradiation with carbon and iron ions.

Linking ecosystem characteristics to final ecosystem services for public policy.

PubMed

Wong, Christina P; Jiang, Bo; Kinzig, Ann P; Lee, Kai N; Ouyang, Zhiyun

2015-01-01

Governments worldwide are recognising ecosystem services as an approach to address sustainability challenges. Decision-makers need credible and legitimate measurements of ecosystem services to evaluate decisions for trade-offs to make wise choices. Managers lack these measurements because of a data gap linking ecosystem characteristics to final ecosystem services. The dominant method to address the data gap is benefit transfer using ecological data from one location to estimate ecosystem services at other locations with similar land cover. However, benefit transfer is only valid once the data gap is adequately resolved. Disciplinary frames separating ecology from economics and policy have resulted in confusion on concepts and methods preventing progress on the data gap. In this study, we present a 10-step approach to unify concepts, methods and data from the disparate disciplines to offer guidance on overcoming the data gap. We suggest: (1) estimate ecosystem characteristics using biophysical models, (2) identify final ecosystem services using endpoints and (3) connect them using ecological production functions to quantify biophysical trade-offs. The guidance is strategic for public policy because analysts need to be: (1) realistic when setting priorities, (2) attentive to timelines to acquire relevant data, given resources and (3) responsive to the needs of decision-makers. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

Linking ecosystem characteristics to final ecosystem services for public policy

PubMed Central

Wong, Christina P; Jiang, Bo; Kinzig, Ann P; Lee, Kai N; Ouyang, Zhiyun

2015-01-01

Governments worldwide are recognising ecosystem services as an approach to address sustainability challenges. Decision-makers need credible and legitimate measurements of ecosystem services to evaluate decisions for trade-offs to make wise choices. Managers lack these measurements because of a data gap linking ecosystem characteristics to final ecosystem services. The dominant method to address the data gap is benefit transfer using ecological data from one location to estimate ecosystem services at other locations with similar land cover. However, benefit transfer is only valid once the data gap is adequately resolved. Disciplinary frames separating ecology from economics and policy have resulted in confusion on concepts and methods preventing progress on the data gap. In this study, we present a 10-step approach to unify concepts, methods and data from the disparate disciplines to offer guidance on overcoming the data gap. We suggest: (1) estimate ecosystem characteristics using biophysical models, (2) identify final ecosystem services using endpoints and (3) connect them using ecological production functions to quantify biophysical trade-offs. The guidance is strategic for public policy because analysts need to be: (1) realistic when setting priorities, (2) attentive to timelines to acquire relevant data, given resources and (3) responsive to the needs of decision-makers. PMID:25394857

Object localization through the lateral line system of fish: theory and experiment.

PubMed

Goulet, Julie; Engelmann, Jacob; Chagnaud, Boris P; Franosch, Jan-Moritz P; Suttner, Maria D; van Hemmen, J Leo

2008-01-01

Fish acquire information about their aquatic environment by means of their mechanosensory lateral-line system. This system consists of superficial and canal neuromasts that sense perturbations in the water surrounding them. Based on a hydrodynamic model presented here, we propose a mechanism through which fish can localize the source of these perturbations. In doing so we include the curvature of the fish body, a realistic lateral line canal inter-pore distance for the lateral-line canals, and the surface boundary layer. Using our model to explore receptor behavior based on experimental data of responses to dipole stimuli we suggest that superficial and canal neuromasts employ the same mechanism, hence provide the same type of input to the central nervous system. The analytical predictions agree well with spiking responses recorded experimentally from primary lateral-line nerve fibers. From this, and taking into account the central organization of the lateral-line system, we present a simple biophysical model for determining the distance to a source.

Research frontiers for improving our understanding of drought‐induced tree and forest mortality

USGS Publications Warehouse

Hartmann, Henrik; Moura, Catarina; Anderegg, William R. L.; Ruehr, Nadine; Salmon, Yann; Allen, Craig D.; Arndt, Stefan K.; Breshears, David D.; Davi, Hendrik; Galbraith, David; Ruthrof, Katinka X.; Wunder, Jan; Adams, Henry D.; Bloemen, Jasper; Cailleret, Maxime; Cobb, Richard; Gessler, Arthur; Grams, Thorsten E. E.; Jansen, Steven; Kautz, Markus; Lloret, Francisco; O’Brien, Michael

2018-01-01

Accumulating evidence highlights increased mortality risks for trees during severe drought, particularly under warmer temperatures and increasing vapour pressure deficit (VPD). Resulting forest die‐off events have severe consequences for ecosystem services, biophysical and biogeochemical land–atmosphere processes. Despite advances in monitoring, modelling and experimental studies of the causes and consequences of tree death from individual tree to ecosystem and global scale, a general mechanistic understanding and realistic predictions of drought mortality under future climate conditions are still lacking. We update a global tree mortality map and present a roadmap to a more holistic understanding of forest mortality across scales. We highlight priority research frontiers that promote: (1) new avenues for research on key tree ecophysiological responses to drought; (2) scaling from the tree/plot level to the ecosystem and region; (3) improvements of mortality risk predictions based on both empirical and mechanistic insights; and (4) a global monitoring network of forest mortality. In light of recent and anticipated large forest die‐off events such a research agenda is timely and needed to achieve scientific understanding for realistic predictions of drought‐induced tree mortality. The implementation of a sustainable network will require support by stakeholders and political authorities at the international level.

Computational Modeling of 3D Tumor Growth and Angiogenesis for Chemotherapy Evaluation

PubMed Central

Tang, Lei; van de Ven, Anne L.; Guo, Dongmin; Andasari, Vivi; Cristini, Vittorio; Li, King C.; Zhou, Xiaobo

2014-01-01

Solid tumors develop abnormally at spatial and temporal scales, giving rise to biophysical barriers that impact anti-tumor chemotherapy. This may increase the expenditure and time for conventional drug pharmacokinetic and pharmacodynamic studies. In order to facilitate drug discovery, we propose a mathematical model that couples three-dimensional tumor growth and angiogenesis to simulate tumor progression for chemotherapy evaluation. This application-oriented model incorporates complex dynamical processes including cell- and vascular-mediated interstitial pressure, mass transport, angiogenesis, cell proliferation, and vessel maturation to model tumor progression through multiple stages including tumor initiation, avascular growth, and transition from avascular to vascular growth. Compared to pure mechanistic models, the proposed empirical methods are not only easy to conduct but can provide realistic predictions and calculations. A series of computational simulations were conducted to demonstrate the advantages of the proposed comprehensive model. The computational simulation results suggest that solid tumor geometry is related to the interstitial pressure, such that tumors with high interstitial pressure are more likely to develop dendritic structures than those with low interstitial pressure. PMID:24404145

Linking biophysical models and public preferences for ecosystem service assessments: a case study for the Southern Rocky Mountains

USGS Publications Warehouse

Bagstad, Kenneth J.; Reed, James; Semmens, Darius J.; Sherrouse, Ben C.; Troy, Austin

2016-01-01

Through extensive research, ecosystem services have been mapped using both survey-based and biophysical approaches, but comparative mapping of public values and those quantified using models has been lacking. In this paper, we mapped hot and cold spots for perceived and modeled ecosystem services by synthesizing results from a social-values mapping study of residents living near the Pike–San Isabel National Forest (PSI), located in the Southern Rocky Mountains, with corresponding biophysically modeled ecosystem services. Social-value maps for the PSI were developed using the Social Values for Ecosystem Services tool, providing statistically modeled continuous value surfaces for 12 value types, including aesthetic, biodiversity, and life-sustaining values. Biophysically modeled maps of carbon sequestration and storage, scenic viewsheds, sediment regulation, and water yield were generated using the Artificial Intelligence for Ecosystem Services tool. Hotspots for both perceived and modeled services were disproportionately located within the PSI’s wilderness areas. Additionally, we used regression analysis to evaluate spatial relationships between perceived biodiversity and cultural ecosystem services and corresponding biophysical model outputs. Our goal was to determine whether publicly valued locations for aesthetic, biodiversity, and life-sustaining values relate meaningfully to results from corresponding biophysical ecosystem service models. We found weak relationships between perceived and biophysically modeled services, indicating that public perception of ecosystem service provisioning regions is limited. We believe that biophysical and social approaches to ecosystem service mapping can serve as methodological complements that can advance ecosystem services-based resource management, benefitting resource managers by showing potential locations of synergy or conflict between areas supplying ecosystem services and those valued by the public.

3D multicellular model of shock wave-cell interaction.

PubMed

Li, Dongli; Hallack, Andre; Cleveland, Robin O; Jérusalem, Antoine

2018-05-01

Understanding the interaction between shock waves and tissue is critical for ad- vancing the use of shock waves for medical applications, such as cancer therapy. This work aims to study shock wave-cell interaction in a more realistic environment, relevant to in vitro and in vivo studies, by using 3D computational models of healthy and cancerous cells. The results indicate that for a single cell embedded in an extracellular environment, the cellular geometry does not influence significantly the membrane strain but does influence the von Mises stress. On the contrary, the presence of neighbouring cells has a strong effect on the cell response, by increasing fourfold both quantities. The membrane strain response of a cell converges with more than three neighbouring cell layers, indicating that a cluster of four layers of cells is sufficient to model the membrane strain in a large domain of tissue. However, a full 3D tissue model is needed if the stress evaluation is of main interest. A tumour mimicking multicellular spheroid model is also proposed to study mutual interaction between healthy and cancer cells and shows that cancer cells can be specifically targeted in an early stage tumour-mimicking environment. This work presents 3D computational models of shock-wave/cell interaction in a biophysically realistic environment using real cell morphology in tissue-mimicking phantom and multicellular spheroid. Results show that cell morphology does not strongly influence the membrane strain but influences the von Mises stress. While the presence of neighbouring cells significantly increases the cell response, four cell layers are enough to capture the membrane strain change in tissue. However, a full tissue model is necessary if accurate stress analysis is needed. The work also shows that cancer cells can be specifically targetted in early stage tumourmimicking environment. This work is a step towards realistic modelling of shock-wave/cell interactions in tissues and provides insight on the use of 3D models for different scenarios. Copyright © 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Induction and modulation of persistent activity in a layer V PFC microcircuit model

PubMed Central

Papoutsi, Athanasia; Sidiropoulou, Kyriaki; Cutsuridis, Vassilis; Poirazi, Panayiota

2013-01-01

Working memory refers to the temporary storage of information and is strongly associated with the prefrontal cortex (PFC). Persistent activity of cortical neurons, namely the activity that persists beyond the stimulus presentation, is considered the cellular correlate of working memory. Although past studies suggested that this type of activity is characteristic of large scale networks, recent experimental evidence imply that small, tightly interconnected clusters of neurons in the cortex may support similar functionalities. However, very little is known about the biophysical mechanisms giving rise to persistent activity in small-sized microcircuits in the PFC. Here, we present a detailed biophysically—yet morphologically simplified—microcircuit model of layer V PFC neurons that incorporates connectivity constraints and is validated against a multitude of experimental data. We show that (a) a small-sized network can exhibit persistent activity under realistic stimulus conditions. (b) Its emergence depends strongly on the interplay of dADP, NMDA, and GABAB currents. (c) Although increases in stimulus duration increase the probability of persistent activity induction, variability in the stimulus firing frequency does not consistently influence it. (d) Modulation of ionic conductances (Ih, ID, IsAHP, IcaL, IcaN, IcaR) differentially controls persistent activity properties in a location dependent manner. These findings suggest that modulation of the microcircuit's firing characteristics is achieved primarily through changes in its intrinsic mechanism makeup, supporting the hypothesis of multiple bi-stable units in the PFC. Overall, the model generates a number of experimentally testable predictions that may lead to a better understanding of the biophysical mechanisms of persistent activity induction and modulation in the PFC. PMID:24130519

Exploring the biophysical option space for feeding the world without deforestation.

PubMed

Erb, Karl-Heinz; Lauk, Christian; Kastner, Thomas; Mayer, Andreas; Theurl, Michaela C; Haberl, Helmut

2016-04-19

Safeguarding the world's remaining forests is a high-priority goal. We assess the biophysical option space for feeding the world in 2050 in a hypothetical zero-deforestation world. We systematically combine realistic assumptions on future yields, agricultural areas, livestock feed and human diets. For each scenario, we determine whether the supply of crop products meets the demand and whether the grazing intensity stays within plausible limits. We find that many options exist to meet the global food supply in 2050 without deforestation, even at low crop-yield levels. Within the option space, individual scenarios differ greatly in terms of biomass harvest, cropland demand and grazing intensity, depending primarily on the quantitative and qualitative aspects of human diets. Grazing constraints strongly limit the option space. Without the option to encroach into natural or semi-natural land, trade volumes will rise in scenarios with globally converging diets, thereby decreasing the food self-sufficiency of many developing regions.

Evaluation and attribution of vegetation contribution to seasonal climate predictability

NASA Astrophysics Data System (ADS)

Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

2015-04-01

The land surface model of EC-Earth has been modified to include dependence of vegetation densities on the Leaf Area Index (LAI), based on the Lambert-Beer formulation. Effective vegetation fractional coverage can now vary at seasonal and interannual time-scales and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. The modified model is used to perform a real predictability seasonal hindcast experiment. LAI is prescribed using a recent observational dataset based on the third generation GIMMS and MODIS satellite data. Hindcast setup is: 7 months forecast length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Hindcast results demonstrate that a realistic representation of vegetation significantly improves the forecasts of temperature and precipitation. The sensitivity is particularly large for temperature during boreal winter over central North America and Central Asia. This may be attributed in particular to the effect of the high vegetation component on the snow cover. Summer forecasts are improved in particular for precipitation over Europe, Sahel, North America, West Russia and Nordeste. Correlation improvements depends on the links between targets (temperature and precipitation) and drivers (surface heat fluxes, albedo, soil moisture, evapotranspiration, moisture divergence) which varies from region to region.

Development of HEATHER for cochlear implant stimulation using a new modeling workflow.

PubMed

Tran, Phillip; Sue, Andrian; Wong, Paul; Li, Qing; Carter, Paul

2015-02-01

The current conduction pathways resulting from monopolar stimulation of the cochlear implant were studied by developing a human electroanatomical total head reconstruction (namely, HEATHER). HEATHER was created from serially sectioned images of the female Visible Human Project dataset to encompass a total of 12 different tissues, and included computer-aided design geometries of the cochlear implant. Since existing methods were unable to generate the required complexity for HEATHER, a new modeling workflow was proposed. The results of the finite-element analysis agree with the literature, showing that the injected current exits the cochlea via the modiolus (14%), the basal end of the cochlea (22%), and through the cochlear walls (64%). It was also found that, once leaving the cochlea, the current travels to the implant body via the cranial cavity or scalp. The modeling workflow proved to be robust and flexible, allowing for meshes to be generated with substantial user control. Furthermore, the workflow could easily be employed to create realistic anatomical models of the human head for different bioelectric applications, such as deep brain stimulation, electroencephalography, and other biophysical phenomena.

Spiral-wave dynamics in ionically realistic mathematical models for human ventricular tissue: the effects of periodic deformation.

PubMed

Nayak, Alok R; Pandit, Rahul

2014-01-01

We carry out an extensive numerical study of the dynamics of spiral waves of electrical activation, in the presence of periodic deformation (PD) in two-dimensional simulation domains, in the biophysically realistic mathematical models of human ventricular tissue due to (a) ten-Tusscher and Panfilov (the TP06 model) and (b) ten-Tusscher, Noble, Noble, and Panfilov (the TNNP04 model). We first consider simulations in cable-type domains, in which we calculate the conduction velocity θ and the wavelength λ of a plane wave; we show that PD leads to a periodic, spatial modulation of θ and a temporally periodic modulation of λ; both these modulations depend on the amplitude and frequency of the PD. We then examine three types of initial conditions for both TP06 and TNNP04 models and show that the imposition of PD leads to a rich variety of spatiotemporal patterns in the transmembrane potential including states with a single rotating spiral (RS) wave, a spiral-turbulence (ST) state with a single meandering spiral, an ST state with multiple broken spirals, and a state SA in which all spirals are absorbed at the boundaries of our simulation domain. We find, for both TP06 and TNNP04 models, that spiral-wave dynamics depends sensitively on the amplitude and frequency of PD and the initial condition. We examine how these different types of spiral-wave states can be eliminated in the presence of PD by the application of low-amplitude pulses by square- and rectangular-mesh suppression techniques. We suggest specific experiments that can test the results of our simulations.

Spiral-wave dynamics in ionically realistic mathematical models for human ventricular tissue: the effects of periodic deformation

PubMed Central

Nayak, Alok R.; Pandit, Rahul

2014-01-01

We carry out an extensive numerical study of the dynamics of spiral waves of electrical activation, in the presence of periodic deformation (PD) in two-dimensional simulation domains, in the biophysically realistic mathematical models of human ventricular tissue due to (a) ten-Tusscher and Panfilov (the TP06 model) and (b) ten-Tusscher, Noble, Noble, and Panfilov (the TNNP04 model). We first consider simulations in cable-type domains, in which we calculate the conduction velocity θ and the wavelength λ of a plane wave; we show that PD leads to a periodic, spatial modulation of θ and a temporally periodic modulation of λ; both these modulations depend on the amplitude and frequency of the PD. We then examine three types of initial conditions for both TP06 and TNNP04 models and show that the imposition of PD leads to a rich variety of spatiotemporal patterns in the transmembrane potential including states with a single rotating spiral (RS) wave, a spiral-turbulence (ST) state with a single meandering spiral, an ST state with multiple broken spirals, and a state SA in which all spirals are absorbed at the boundaries of our simulation domain. We find, for both TP06 and TNNP04 models, that spiral-wave dynamics depends sensitively on the amplitude and frequency of PD and the initial condition. We examine how these different types of spiral-wave states can be eliminated in the presence of PD by the application of low-amplitude pulses by square- and rectangular-mesh suppression techniques. We suggest specific experiments that can test the results of our simulations. PMID:24959148

Developing a generalized allometric equation for aboveground biomass estimation

NASA Astrophysics Data System (ADS)

Xu, Q.; Balamuta, J. J.; Greenberg, J. A.; Li, B.; Man, A.; Xu, Z.

2015-12-01

A key potential uncertainty in estimating carbon stocks across multiple scales stems from the use of empirically calibrated allometric equations, which estimate aboveground biomass (AGB) from plant characteristics such as diameter at breast height (DBH) and/or height (H). The equations themselves contain significant and, at times, poorly characterized errors. Species-specific equations may be missing. Plant responses to their local biophysical environment may lead to spatially varying allometric relationships. The structural predictor may be difficult or impossible to measure accurately, particularly when derived from remote sensing data. All of these issues may lead to significant and spatially varying uncertainties in the estimation of AGB that are unexplored in the literature. We sought to quantify the errors in predicting AGB at the tree and plot level for vegetation plots in California. To accomplish this, we derived a generalized allometric equation (GAE) which we used to model the AGB on a full set of tree information such as DBH, H, taxonomy, and biophysical environment. The GAE was derived using published allometric equations in the GlobAllomeTree database. The equations were sparse in details about the error since authors provide the coefficient of determination (R2) and the sample size. A more realistic simulation of tree AGB should also contain the noise that was not captured by the allometric equation. We derived an empirically corrected variance estimate for the amount of noise to represent the errors in the real biomass. Also, we accounted for the hierarchical relationship between different species by treating each taxonomic level as a covariate nested within a higher taxonomic level (e.g. species < genus). This approach provides estimation under incomplete tree information (e.g. missing species) or blurred information (e.g. conjecture of species), plus the biophysical environment. The GAE allowed us to quantify contribution of each different covariate in estimating the AGB of trees. Lastly, we applied the GAE to an existing vegetation plot database - Forest Inventory and Analysis database - to derive per-tree and per-plot AGB estimations, their errors, and how much the error could be contributed to the original equations, the plant's taxonomy, and their biophysical environment.

Raman biophysical markers in skin cancer diagnosis.

PubMed

Feng, Xu; Moy, Austin J; Nguyen, Hieu T M; Zhang, Yao; Zhang, Jason; Fox, Matthew C; Sebastian, Katherine R; Reichenberg, Jason S; Markey, Mia K; Tunnell, James W

2018-05-01

Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

The development and modeling of devices and paradigms for transcranial magnetic stimulation

PubMed Central

Goetz, Stefan M.; Deng, Zhi-De

2017-01-01

Magnetic stimulation is a noninvasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modeling. PMID:28443696

The development and modelling of devices and paradigms for transcranial magnetic stimulation.

PubMed

Goetz, Stefan M; Deng, Zhi-De

2017-04-01

Magnetic stimulation is a non-invasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain, as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modelling.

ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms.

PubMed

Hagen, Espen; Ness, Torbjørn V; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne; Hafting, Torkel; Franke, Felix; Einevoll, Gaute T

2015-04-30

New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Modeling Pharmacological Clock and Memory Patterns of Interval Timing in a Striatal Beat-Frequency Model with Realistic, Noisy Neurons

PubMed Central

Oprisan, Sorinel A.; Buhusi, Catalin V.

2011-01-01

In most species, the capability of perceiving and using the passage of time in the seconds-to-minutes range (interval timing) is not only accurate but also scalar: errors in time estimation are linearly related to the estimated duration. The ubiquity of scalar timing extends over behavioral, lesion, and pharmacological manipulations. For example, in mammals, dopaminergic drugs induce an immediate, scalar change in the perceived time (clock pattern), whereas cholinergic drugs induce a gradual, scalar change in perceived time (memory pattern). How do these properties emerge from unreliable, noisy neurons firing in the milliseconds range? Neurobiological information relative to the brain circuits involved in interval timing provide support for an striatal beat frequency (SBF) model, in which time is coded by the coincidental activation of striatal spiny neurons by cortical neural oscillators. While biologically plausible, the impracticality of perfect oscillators, or their lack thereof, questions this mechanism in a brain with noisy neurons. We explored the computational mechanisms required for the clock and memory patterns in an SBF model with biophysically realistic and noisy Morris–Lecar neurons (SBF–ML). Under the assumption that dopaminergic drugs modulate the firing frequency of cortical oscillators, and that cholinergic drugs modulate the memory representation of the criterion time, we show that our SBF–ML model can reproduce the pharmacological clock and memory patterns observed in the literature. Numerical results also indicate that parameter variability (noise) – which is ubiquitous in the form of small fluctuations in the intrinsic frequencies of neural oscillators within and between trials, and in the errors in recording/retrieving stored information related to criterion time – seems to be critical for the time-scale invariance of the clock and memory patterns. PMID:21977014

The dual-state theory of prefrontal cortex dopamine function with relevance to catechol-o-methyltransferase genotypes and schizophrenia.

PubMed

Durstewitz, Daniel; Seamans, Jeremy K

2008-11-01

There is now general consensus that at least some of the cognitive deficits in schizophrenia are related to dysfunctions in the prefrontal cortex (PFC) dopamine (DA) system. At the cellular and synaptic level, the effects of DA in PFC via D1- and D2-class receptors are highly complex, often apparently opposing, and hence difficult to understand with regard to their functional implications. Biophysically realistic computational models have provided valuable insights into how the effects of DA on PFC neurons and synaptic currents as measured in vitro link up to the neural network and cognitive levels. They suggest the existence of two discrete dynamical regimes, a D1-dominated state characterized by a high energy barrier among different network patterns that favors robust online maintenance of information and a D2-dominated state characterized by a low energy barrier that is beneficial for flexible and fast switching among representational states. These predictions are consistent with a variety of electrophysiological, neuroimaging, and behavioral results in humans and nonhuman species. Moreover, these biophysically based models predict that imbalanced D1:D2 receptor activation causing extremely low or extremely high energy barriers among activity states could lead to the emergence of cognitive, positive, and negative symptoms observed in schizophrenia. Thus, combined experimental and computational approaches hold the promise of allowing a detailed mechanistic understanding of how DA alters information processing in normal and pathological conditions, thereby potentially providing new routes for the development of pharmacological treatments for schizophrenia.

A competitive binding model predicts the response of mammalian olfactory receptors to mixtures

NASA Astrophysics Data System (ADS)

Singh, Vijay; Murphy, Nicolle; Mainland, Joel; Balasubramanian, Vijay

Most natural odors are complex mixtures of many odorants, but due to the large number of possible mixtures only a small fraction can be studied experimentally. To get a realistic understanding of the olfactory system we need methods to predict responses to complex mixtures from single odorant responses. Focusing on mammalian olfactory receptors (ORs in mouse and human), we propose a simple biophysical model for odor-receptor interactions where only one odor molecule can bind to a receptor at a time. The resulting competition for occupancy of the receptor accounts for the experimentally observed nonlinear mixture responses. We first fit a dose-response relationship to individual odor responses and then use those parameters in a competitive binding model to predict mixture responses. With no additional parameters, the model predicts responses of 15 (of 18 tested) receptors to within 10 - 30 % of the observed values, for mixtures with 2, 3 and 12 odorants chosen from a panel of 30. Extensions of our basic model with odorant interactions lead to additional nonlinearities observed in mixture response like suppression, cooperativity, and overshadowing. Our model provides a systematic framework for characterizing and parameterizing such mixing nonlinearities from mixture response data.

A coupled biophysical model for the distribution of the great scallop Pecten maximus in the English Channel

NASA Astrophysics Data System (ADS)

Le Goff, Clément; Lavaud, Romain; Cugier, Philippe; Jean, Fred; Flye-Sainte-Marie, Jonathan; Foucher, Eric; Desroy, Nicolas; Fifas, Spyros; Foveau, Aurélie

2017-03-01

In this paper we used a modelling approach integrating both physical and biological constraints to understand the biogeographical distribution of the great scallop Pecten maximus in the English Channel during its whole life cycle. A 3D bio-hydrodynamical model (ECO-MARS3D) providing environmental conditions was coupled to (i) a population dynamics model and (ii) an individual ecophysiological model (Dynamic Energy Budget model). We performed the coupling sequentially, which underlined the respective role of biological and physical factors in defining P. maximus distribution in the English Channel. Results show that larval dispersion by hydrodynamics explains most of the scallop distribution and enlighten the main known hotspots for the population, basically corresponding to the main fishing areas. The mechanistic description of individual bioenergetics shows that food availability and temperature control growth and reproduction and explain how populations may maintain themselves in particular locations. This last coupling leads to more realistic densities and distributions of adults in the English Channel. The results of this study improves our knowledge on the stock and distribution dynamics of P. maximus, and provides grounds for useful tools to support management strategies.

Changes of mind in an attractor network of decision-making.

PubMed

Albantakis, Larissa; Deco, Gustavo

2011-06-01

Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks. Especially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes them from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we demonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the choice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task. Based on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the network predicts neural activity during changes of mind and accurately simulates reaction times, performance and percentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming activity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation, which up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of attractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor from linear diffusion models.

Physics Based Modeling and Rendering of Vegetation in the Thermal Infrared

NASA Technical Reports Server (NTRS)

Smith, J. A.; Ballard, J. R., Jr.

1999-01-01

We outline a procedure for rendering physically-based thermal infrared images of simple vegetation scenes. Our approach incorporates the biophysical processes that affect the temperature distribution of the elements within a scene. Computer graphics plays a key role in two respects. First, in computing the distribution of scene shaded and sunlit facets and, second, in the final image rendering once the temperatures of all the elements in the scene have been computed. We illustrate our approach for a simple corn scene where the three-dimensional geometry is constructed based on measured morphological attributes of the row crop. Statistical methods are used to construct a representation of the scene in agreement with the measured characteristics. Our results are quite good. The rendered images exhibit realistic behavior in directional properties as a function of view and sun angle. The root-mean-square error in measured versus predicted brightness temperatures for the scene was 2.1 deg C.

Biophysics of protein evolution and evolutionary protein biophysics

PubMed Central

Sikosek, Tobias; Chan, Hue Sun

2014-01-01

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 of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence–structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by ‘hidden’ conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution. PMID:25165599

A note on the roles of quantum and mechanical models in social biophysics.

PubMed

Takahashi, Taiki; Kim, Song-Ju; Naruse, Makoto

2017-11-01

Recent advances in the applications of quantum models into various disciplines such as cognitive science, social sciences, economics, and biology witnessed enormous achievements and possible future progress. In this paper, we propose one of the most promising directions in the applications of quantum models: the combination of quantum and mechanical models in social biophysics. The possible resulting discipline may be called as experimental quantum social biophysics and could foster our understandings of the relationships between the society and individuals. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Computational Framework for Realistic Retina Modeling.

PubMed

Martínez-Cañada, Pablo; Morillas, Christian; Pino, Begoña; Ros, Eduardo; Pelayo, Francisco

2016-11-01

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

USDA-ARS?s Scientific Manuscript database

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be ...

Remote sensing and modeling to fill the “gap” in missing natural capital

USGS Publications Warehouse

Bagstad, Kenneth J.; Willcock, Simon; Lange, Glenn-Marie

2018-01-01

This chapter reviews recent advances in remote sensing and environmental modeling that address the first step in ecosystem accounting: biophysical quantification of ecosystem services. The chapter focuses on those ecosystem services in which the most rapid advances are likely, including crop pollination, sediment regulation, carbon sequestration and storage, and coastal flood regulation. The discussion highlights data sources and modeling approaches that can support wealth accounting, next steps for mapping and biophysical modeling of ecosystem services, and considerations for integrating biophysical modeling and monetary valuation. These approaches could make the inclusion of some ecosystem services increasingly feasible in future versions of wealth accounts.

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

PubMed Central

Zur, Hadas; Tuller, Tamir

2016-01-01

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field. PMID:27591251

A predictive biophysical model of translational coupling to coordinate and control protein expression in bacterial operons

PubMed Central

Tian, Tian; Salis, Howard M.

2015-01-01

Natural and engineered genetic systems require the coordinated expression of proteins. In bacteria, translational coupling provides a genetically encoded mechanism to control expression level ratios within multi-cistronic operons. We have developed a sequence-to-function biophysical model of translational coupling to predict expression level ratios in natural operons and to design synthetic operons with desired expression level ratios. To quantitatively measure ribosome re-initiation rates, we designed and characterized 22 bi-cistronic operon variants with systematically modified intergenic distances and upstream translation rates. We then derived a thermodynamic free energy model to calculate de novo initiation rates as a result of ribosome-assisted unfolding of intergenic RNA structures. The complete biophysical model has only five free parameters, but was able to accurately predict downstream translation rates for 120 synthetic bi-cistronic and tri-cistronic operons with rationally designed intergenic regions and systematically increased upstream translation rates. The biophysical model also accurately predicted the translation rates of the nine protein atp operon, compared to ribosome profiling measurements. Altogether, the biophysical model quantitatively predicts how translational coupling controls protein expression levels in synthetic and natural bacterial operons, providing a deeper understanding of an important post-transcriptional regulatory mechanism and offering the ability to rationally engineer operons with desired behaviors. PMID:26117546

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

PubMed Central

D’Angelo, Egidio; Antonietti, Alberto; Casali, Stefano; Casellato, Claudia; Garrido, Jesus A.; Luque, Niceto Rafael; Mapelli, Lisa; Masoli, Stefano; Pedrocchi, Alessandra; Prestori, Francesca; Rizza, Martina Francesca; Ros, Eduardo

2016-01-01

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems. PMID:27458345

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

2017-03-01

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

Radiation dosimetry and biophysical models of space radiation effects

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Wu, Honglu; Shavers, Mark R.; George, Kerry

2003-01-01

Estimating the biological risks from space radiation remains a difficult problem because of the many radiation types including protons, heavy ions, and secondary neutrons, and the absence of epidemiology data for these radiation types. Developing useful biophysical parameters or models that relate energy deposition by space particles to the probabilities of biological outcomes is a complex problem. Physical measurements of space radiation include the absorbed dose, dose equivalent, and linear energy transfer (LET) spectra. In contrast to conventional dosimetric methods, models of radiation track structure provide descriptions of energy deposition events in biomolecules, cells, or tissues, which can be used to develop biophysical models of radiation risks. In this paper, we address the biophysical description of heavy particle tracks in the context of the interpretation of both space radiation dosimetry and radiobiology data, which may provide insights into new approaches to these problems.

Generating synchrony from the asynchronous: compensation for cochlear traveling wave delays by the dendrites of individual brainstem neurons

PubMed Central

McGinley, Matthew J.; Liberman, M. Charles; Bal, Ramazan; Oertel, Donata

2012-01-01

Broadband transient sounds, such as clicks and consonants, activate a traveling wave in the cochlea. This wave evokes firing in auditory nerve fibers that are tuned to high frequencies several milliseconds earlier than in fibers tuned to low frequencies. Despite this substantial traveling wave delay, octopus cells in the brainstem receive broadband input and respond to clicks with submillisecond temporal precision. The dendrites of octopus cells lie perpendicular to the tonotopically organized array of auditory nerve fibers, placing the earliest arriving inputs most distally and the latest arriving closest to the soma. Here, we test the hypothesis that the topographic arrangement of synaptic inputs on dendrites of octopus cells allows octopus cells to compensate the traveling wave delay. We show that in mice the full cochlear traveling wave delay is 1.6 ms. Because the dendrites of each octopus cell spread across about one third of the tonotopic axis, a click evokes a soma directed sweep of synaptic input lasting 0.5 ms in individual octopus cells. Morphologically and biophysically realistic, computational models of octopus cells show that soma-directed sweeps with durations matching in vivo measurements result in the largest and sharpest somatic excitatory postsynaptic potentials (EPSPs). A low input resistance and activation of a low-voltage-activated potassium conductance that are characteristic of octopus cells are important determinants of sweep sensitivity. We conclude that octopus cells have dendritic morphologies and biophysics tailored to accomplish the precise encoding of broadband transient sounds. PMID:22764237

Modeling the response of small myelinated axons in a compound nerve to kilohertz frequency signals

NASA Astrophysics Data System (ADS)

Pelot, N. A.; Behrend, C. E.; Grill, W. M.

2017-08-01

Objective. There is growing interest in electrical neuromodulation of peripheral nerves, particularly autonomic nerves, to treat various diseases. Electrical signals in the kilohertz frequency (KHF) range can produce different responses, including conduction block. For example, EnteroMedics’ vBloc® therapy for obesity delivers 5 kHz stimulation to block the abdominal vagus nerves, but the mechanisms of action are unclear. Approach. We developed a two-part computational model, coupling a 3D finite element model of a cuff electrode around the human abdominal vagus nerve with biophysically-realistic electrical circuit equivalent (cable) model axons (1, 2, and 5.7 µm in diameter). We developed an automated algorithm to classify conduction responses as subthreshold (transmission), KHF-evoked activity (excitation), or block. We quantified neural responses across kilohertz frequencies (5-20 kHz), amplitudes (1-8 mA), and electrode designs. Main results. We found heterogeneous conduction responses across the modeled nerve trunk, both for a given parameter set and across parameter sets, although most suprathreshold responses were excitation, rather than block. The firing patterns were irregular near transmission and block boundaries, but otherwise regular, and mean firing rates varied with electrode-fibre distance. Further, we identified excitation responses at amplitudes above block threshold, termed ‘re-excitation’, arising from action potentials initiated at virtual cathodes. Excitation and block thresholds decreased with smaller electrode-fibre distances, larger fibre diameters, and lower kilohertz frequencies. A point source model predicted a larger fraction of blocked fibres and greater change of threshold with distance as compared to the realistic cuff and nerve model. Significance. Our findings of widespread asynchronous KHF-evoked activity suggest that conduction block in the abdominal vagus nerves is unlikely with current clinical parameters. Our results indicate that compound neural or downstream muscle force recordings may be unreliable as quantitative measures of neural activity for in vivo studies or as biomarkers in closed-loop clinical devices.

Modeling the response of small myelinated axons in a compound nerve to kilohertz frequency signals.

PubMed

Pelot, N A; Behrend, C E; Grill, W M

2017-08-01

There is growing interest in electrical neuromodulation of peripheral nerves, particularly autonomic nerves, to treat various diseases. Electrical signals in the kilohertz frequency (KHF) range can produce different responses, including conduction block. For example, EnteroMedics' vBloc ® therapy for obesity delivers 5 kHz stimulation to block the abdominal vagus nerves, but the mechanisms of action are unclear. We developed a two-part computational model, coupling a 3D finite element model of a cuff electrode around the human abdominal vagus nerve with biophysically-realistic electrical circuit equivalent (cable) model axons (1, 2, and 5.7 µm in diameter). We developed an automated algorithm to classify conduction responses as subthreshold (transmission), KHF-evoked activity (excitation), or block. We quantified neural responses across kilohertz frequencies (5-20 kHz), amplitudes (1-8 mA), and electrode designs. We found heterogeneous conduction responses across the modeled nerve trunk, both for a given parameter set and across parameter sets, although most suprathreshold responses were excitation, rather than block. The firing patterns were irregular near transmission and block boundaries, but otherwise regular, and mean firing rates varied with electrode-fibre distance. Further, we identified excitation responses at amplitudes above block threshold, termed 're-excitation', arising from action potentials initiated at virtual cathodes. Excitation and block thresholds decreased with smaller electrode-fibre distances, larger fibre diameters, and lower kilohertz frequencies. A point source model predicted a larger fraction of blocked fibres and greater change of threshold with distance as compared to the realistic cuff and nerve model. Our findings of widespread asynchronous KHF-evoked activity suggest that conduction block in the abdominal vagus nerves is unlikely with current clinical parameters. Our results indicate that compound neural or downstream muscle force recordings may be unreliable as quantitative measures of neural activity for in vivo studies or as biomarkers in closed-loop clinical devices.

Implementing the Simple Biosphere Model (SiB) in a general circulation model: Methodologies and results

NASA Technical Reports Server (NTRS)

Sato, N.; Sellers, P. J.; Randall, D. A.; Schneider, E. K.; Shukla, J.; Kinter, J. L., III; Hou, Y.-T.; Albertazzi, E.

1989-01-01

The Simple Biosphere MOdel (SiB) of Sellers et al., (1986) was designed to simulate the interactions between the Earth's land surface and the atmosphere by treating the vegetation explicitly and relistically, thereby incorporating biophysical controls on the exchanges of radiation, momentum, sensible and latent heat between the two systems. The steps taken to implement SiB in a modified version of the National Meteorological Center's spectral GCM are described. The coupled model (SiB-GCM) was used with a conventional hydrological model (Ctl-GCM) to produce summer and winter simulations. The same GCM was used with a conventional hydrological model (Ctl-GCM) to produce comparable 'control' summer and winter variations. It was found that SiB-GCM produced a more realistic partitioning of energy at the land surface than Ctl-GCM. Generally, SiB-GCM produced more sensible heat flux and less latent heat flux over vegetated land than did Ctl-GCM and this resulted in the development of a much deeper daytime planetary boundary and reduced precipitation rates over the continents in SiB-GCM. In the summer simulation, the 200 mb jet stream and the wind speed at 850 mb were slightly weakened in the SiB-GCM relative to the Ctl-GCM results and equivalent analyses from observations.

Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series

NASA Astrophysics Data System (ADS)

Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik

2016-06-01

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. 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 the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.

Soil Surface Runoff Scheme for Improving Land-Hydrology and Surface Fluxes in Simple SiB (SSiB)

NASA Technical Reports Server (NTRS)

Sud, Y. C.; Mocko, David M.

1999-01-01

Evapotranspiration on land is hard to measure and difficult to simulate. On the scale of a GCM grid, there is large subgrid-scale variability of orography, soil moisture, and vegetation. Our hope is to be able to tune the biophysical constants of vegetation and soil parameters to get the most realistic space-averaged diurnal cycle of evaporation and its climatology. Field experiments such as First ISLSCP Field Experiment (FIFE), Boreal Ecosystem-Atmosphere Study (BOREAS), and LBA help a great deal in improving our evapotranspiration schemes. However, these improvements have to be matched with, and coupled to, consistent improvement in land-hydrology; otherwise, the runoff problems will intrinsically reflect on the soil moisture and evapotranspiration errors. Indeed, a realistic runoff simulation also ensures a reasonable evapotranspiration simulation provided the precipitation forcing is reliable. We have been working on all of the above problems to improve the simulated hydrologic cycle. Through our participation in the evaluation and intercomparison of land-models under the behest of Global Soil Wetness Project (GSWP), we identified a few problems with Simple SiB (SSIB; Xue et al., 1991) hydrology in regions of significant snowmelt. Sud and Mocko (1999) show that inclusion of a separate snowpack model, with its own energy budget and fluxes with the atmosphere aloft and soil beneath, helps to ameliorate some of the deficiencies of delayed snowmelt and excessive spring season runoff. Thus, much more realistic timing of melt water generation was simulated with the new snowpack model in the subsequent GSWP re-evaluations using 2 years of ISLSCP Initiative I forcing data for 1987 and 1988. However, we noted an overcorrection of the low meltwater infiltration of SSiB. While the improvement in snowmelt timing was found everywhere, the snowmelt infiltration has became excessive in some regions, e.g., Lena river basin. This leads to much reduced runoff in many basins as compared to observations. We believe this is a consequence of neglect of the influence of subgrid-scale variations in orography that affects the production of surface runoff.

Sequentially distant but structurally similar proteins exhibit fold specific patterns based on their biophysical properties.

PubMed

Rajendran, Senthilnathan; Jothi, Arunachalam

2018-05-16

The Three-dimensional structure of a protein depends on the interaction between their amino acid residues. These interactions are in turn influenced by various biophysical properties of the amino acids. There are several examples of proteins that share the same fold but are very dissimilar at the sequence level. For proteins to share a common fold some crucial interactions should be maintained despite insignificant sequence similarity. Since the interactions are because of the biophysical properties of the amino acids, we should be able to detect descriptive patterns for folds at such a property level. In this line, the main focus of our research is to analyze such proteins and to characterize them in terms of their biophysical properties. Protein structures with sequence similarity lesser than 40% were selected for ten different subfolds from three different mainfolds (according to CATH classification) and were used for this analysis. We used the normalized values of the 49 physio-chemical, energetic and conformational properties of amino acids. We characterize the folds based on the average biophysical property values. We also observed a fold specific correlational behavior of biophysical properties despite a very low sequence similarity in our data. We further trained three different binary classification models (Naive Bayes-NB, Support Vector Machines-SVM and Bayesian Generalized Linear Model-BGLM) which could discriminate mainfold based on the biophysical properties. We also show that among the three generated models, the BGLM classifier model was able to discriminate protein sequences coming under all beta category with 81.43% accuracy and all alpha, alpha-beta proteins with 83.37% accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Direct Scaling of Leaf-Resolving Biophysical Models from Leaves to Canopies

NASA Astrophysics Data System (ADS)

Bailey, B.; Mahaffee, W.; Hernandez Ochoa, M.

2017-12-01

Recent advances in the development of biophysical models and high-performance computing have enabled rapid increases in the level of detail that can be represented by simulations of plant systems. However, increasingly detailed models typically require increasingly detailed inputs, which can be a challenge to accurately specify. In this work, we explore the use of terrestrial LiDAR scanning data to accurately specify geometric inputs for high-resolution biophysical models that enables direct up-scaling of leaf-level biophysical processes. Terrestrial LiDAR scans generate "clouds" of millions of points that map out the geometric structure of the area of interest. However, points alone are often not particularly useful in generating geometric model inputs, as additional data processing techniques are required to provide necessary information regarding vegetation structure. A new method was developed that directly reconstructs as many leaves as possible that are in view of the LiDAR instrument, and uses a statistical backfilling technique to ensure that the overall leaf area and orientation distribution matches that of the actual vegetation being measured. This detailed structural data is used to provide inputs for leaf-resolving models of radiation, microclimate, evapotranspiration, and photosynthesis. Model complexity is afforded by utilizing graphics processing units (GPUs), which allows for simulations that resolve scales ranging from leaves to canopies. The model system was used to explore how heterogeneity in canopy architecture at various scales affects scaling of biophysical processes from leaves to canopies.

3D-modelling of radon-induced cellular radiobiological effects in bronchial airway bifurcations: direct versus bystander effects.

PubMed

Szőke, István; Farkas, Arpád; Balásházy, Imre; Hofmann, Werner; Madas, Balázs G; Szőke, Réka

2012-06-01

The primary objective of this paper was to investigate the distribution of radiation doses and the related biological responses in cells of a central airway bifurcation of the human lung of a hypothetical worker of the New Mexico uranium mines during approximately 12 hours of exposure to short-lived radon progenies. State-of-the-art computational modelling techniques were applied to simulate the relevant biophysical and biological processes in a central human airway bifurcation. The non-uniform deposition pattern of inhaled radon daughters caused a non-uniform distribution of energy deposition among cells, and of related cell inactivation and cell transformation probabilities. When damage propagation via bystander signalling was assessed, it produced more cell killing and cell transformation events than did direct effects. If bystander signalling was considered, variations of the average probabilities of cell killing and cell transformation were supra-linear over time. Our results are very sensitive to the radiobiological parameters, derived from in vitro experiments (e.g., range of bystander signalling), applied in this work and suggest that these parameters may not be directly applicable to realistic three-dimensional (3D) epithelium models.

Organization of Lipids in the Tear Film: A Molecular-Level View

PubMed Central

Wizert, Alicja; Iskander, D. Robert; Cwiklik, Lukasz

2014-01-01

Biophysical properties of the tear film lipid layer are studied at the molecular level employing coarse grain molecular dynamics (MD) simulations with a realistic model of the human tear film. In this model, polar lipids are chosen to reflect the current knowledge on the lipidome of the tear film whereas typical Meibomian-origin lipids are included in the thick non-polar lipids subphase. Simulation conditions mimic those experienced by the real human tear film during blinks. Namely, thermodynamic equilibrium simulations at different lateral compressions are performed to model varying surface pressure, and the dynamics of the system during a blink is studied by non-equilibrium MD simulations. Polar lipids separate their non-polar counterparts from water by forming a monomolecular layer whereas the non-polar molecules establish a thick outermost lipid layer. Under lateral compression, the polar layer undulates and a sorting of polar lipids occurs. Moreover, formation of three-dimensional aggregates of polar lipids in both non-polar and water subphases is observed. We suggest that these three-dimensional structures are abundant under dynamic conditions caused by the action of eye lids and that they act as reservoirs of polar lipids, thus increasing stability of the tear film. PMID:24651175

Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention

PubMed Central

Nestor, P.G.; Han, S.D.; Niznikiewicz, M.; Salisbury, D.; Spencer, K.; Shenton, M.E.; McCarley, R.W.

2010-01-01

We view schizophrenia as producing a failure of attentional modulation that leads to a breakdown in the selective enhancement or inhibition of semantic/lexical representations whose biological substrata are widely distributed across left (dominant) temporal and frontal lobes. Supporting behavioral evidence includes word recall studies that have pointed to a disturbance in connectivity (associative strength) but not network size (number of associates) in patients with schizophrenia. Paralleling these findings are recent neural network simulation studies of the abnormal connectivity effect in schizophrenia through ‘lesioning’ network connection weights while holding constant network size. Supporting evidence at the level of biology are in vitro studies examining N-methyl-d-aspartate (NMDA) receptor antagonists on recurrent inhibition; simulations in neural populations with realistically modeled biophysical properties show NMDA antagonists produce a schizophrenia-like disturbance in pattern association. We propose a similar failure of NMDA-mediated recurrent inhibition as a candidate biological substrate for attention and semantic anomalies of schizophrenia. PMID:11454433

Improving Access to MODIS Biophysical Science Products for NACP Investigators

NASA Technical Reports Server (NTRS)

Wolfe, Robert E.; Gao, Feng; Morisette, Jeffrey T.; Ederer, Gregory A.; Pedelty, Jeffrey A.

2007-01-01

MODIS 4 NACP is a NASA-funded project supporting the North American Carbon Program (NACP). The purpose of this Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) project is to provide researchers with Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical data products that are custom tailored for use in NACP model studies. Standard MODIS biophysical products provide used to improve our understanding on the climate and ecosystem changes. However, direct uses of the MODIS biophysical parameters are constrained by retrieval quality and cloud contamination. Another challenge that NACP users face is acquiring MODIS data in formats and at spatial-temporal resolutions consistent with other data sets they use. We have been working closely with key NACP users to tailor the MODIS products to fit their needs. First, we provide new temporally smoothed and spatially continuous MODIS biophysical data sets. Second, we are distributing MODIS data at suitable spatial-temporal resolutions and in formats consistent with other data integration into model studies.

An integrated modelling framework for neural circuits with multiple neuromodulators.

PubMed

Joshi, Alok; Youssofzadeh, Vahab; Vemana, Vinith; McGinnity, T M; Prasad, Girijesh; Wong-Lin, KongFatt

2017-01-01

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. © 2017 The Authors.

An agent-based hydroeconomic model to evaluate water policies in Jordan

NASA Astrophysics Data System (ADS)

Yoon, J.; Gorelick, S.

2014-12-01

Modern water systems can be characterized by a complex network of institutional and private actors that represent competing sectors and interests. Identifying solutions to enhance water security in such systems calls for analysis that can adequately account for this level of complexity and interaction. Our work focuses on the development of a hierarchical, multi-agent, hydroeconomic model that attempts to realistically represent complex interactions between hydrologic and multi-faceted human systems. The model is applied to Jordan, one of the most water-poor countries in the world. In recent years, the water crisis in Jordan has escalated due to an ongoing drought and influx of refugees from regional conflicts. We adopt a modular approach in which biophysical modules simulate natural and engineering phenomena, and human modules represent behavior at multiple scales of decision making. The human modules employ agent-based modeling, in which agents act as autonomous decision makers at the transboundary, state, organizational, and user levels. A systematic nomenclature and conceptual framework is used to characterize model agents and modules. Concepts from the Unified Modeling Language (UML) are adopted to promote clear conceptualization of model classes and process sequencing, establishing a foundation for full deployment of the integrated model in a scalable object-oriented programming environment. Although the framework is applied to the Jordanian water context, it is generalizable to other regional human-natural freshwater supply systems.

An integrated modelling framework for neural circuits with multiple neuromodulators

PubMed Central

Vemana, Vinith

2017-01-01

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. PMID:28100828

Software for Teaching Physiology and Biophysics.

ERIC Educational Resources Information Center

Weiss, Thomas F.; And Others

1992-01-01

Describes a software library developed to teach biophysics and physiology undergraduates that includes software on (1) the Hodgkin-Huxley model for excitation of action potentials in electrically excitable cells; (2) a random-walk model of diffusion; (3) single voltage-gated ion channels; (4) steady-state chemically mediated transport; and (5)…

A Biophysical Modeling Framework for Assessing the Environmental Impact of Biofuel Production

NASA Astrophysics Data System (ADS)

Zhang, X.; Izaurradle, C.; Manowitz, D.; West, T. O.; Post, W. M.; Thomson, A. M.; Nichols, J.; Bandaru, V.; Williams, J. R.

2009-12-01

Long-term sustainability of a biofuel economy necessitates environmentally friendly biofuel production systems. We describe a biophysical modeling framework developed to understand and quantify the environmental value and impact (e.g. water balance, nutrients balance, carbon balance, and soil quality) of different biomass cropping systems. This modeling framework consists of three major components: 1) a Geographic Information System (GIS) based data processing system, 2) a spatially-explicit biophysical modeling approach, and 3) a user friendly information distribution system. First, we developed a GIS to manage the large amount of geospatial data (e.g. climate, land use, soil, and hydrograhy) and extract input information for the biophysical model. Second, the Environmental Policy Integrated Climate (EPIC) biophysical model is used to predict the impact of various cropping systems and management intensities on productivity, water balance, and biogeochemical variables. Finally, a geo-database is developed to distribute the results of ecosystem service variables (e.g. net primary productivity, soil carbon balance, soil erosion, nitrogen and phosphorus losses, and N2O fluxes) simulated by EPIC for each spatial modeling unit online using PostgreSQL. We applied this framework in a Regional Intensive Management Area (RIMA) of 9 counties in Michigan. A total of 4,833 spatial units with relatively homogeneous biophysical properties were derived using SSURGO, Crop Data Layer, County, and 10-digit watershed boundaries. For each unit, EPIC was executed from 1980 to 2003 under 54 cropping scenarios (eg. corn, switchgrass, and hybrid poplar). The simulation results were compared with historical crop yields from USDA NASS. Spatial mapping of the results show high variability among different cropping scenarios in terms of the simulated ecosystem services variables. Overall, the framework developed in this study enables the incorporation of environmental factors into economic and life-cycle analysis in order to optimize biomass cropping production scenarios.

Mechanism for Collective Cell Alignment in Myxococcus xanthus Bacteria

PubMed Central

Balagam, Rajesh; Igoshin, Oleg A.

2015-01-01

Myxococcus xanthus cells self-organize into aligned groups, clusters, at various stages of their lifecycle. Formation of these clusters is crucial for the complex dynamic multi-cellular behavior of these bacteria. However, the mechanism underlying the cell alignment and clustering is not fully understood. Motivated by studies of clustering in self-propelled rods, we hypothesized that M. xanthus cells can align and form clusters through pure mechanical interactions among cells and between cells and substrate. We test this hypothesis using an agent-based simulation framework in which each agent is based on the biophysical model of an individual M. xanthus cell. We show that model agents, under realistic cell flexibility values, can align and form cell clusters but only when periodic reversals of cell directions are suppressed. However, by extending our model to introduce the observed ability of cells to deposit and follow slime trails, we show that effective trail-following leads to clusters in reversing cells. Furthermore, we conclude that mechanical cell alignment combined with slime-trail-following is sufficient to explain the distinct clustering behaviors observed for wild-type and non-reversing M. xanthus mutants in recent experiments. Our results are robust to variation in model parameters, match the experimentally observed trends and can be applied to understand surface motility patterns of other bacterial species. PMID:26308508

Construction, MD simulation, and hydrodynamic validation of an all-atom model of a monoclonal IgG antibody.

PubMed

Brandt, J Paul; Patapoff, Thomas W; Aragon, Sergio R

2010-08-04

At 150 kDa, antibodies of the IgG class are too large for their structure to be determined with current NMR methodologies. Because of hinge-region flexibility, it is difficult to obtain atomic-level structural information from the crystal, and questions regarding antibody structure and dynamics in solution remain unaddressed. Here we describe the construction of a model of a human IgG1 monoclonal antibody (trastuzumab) from the crystal structures of fragments. We use a combination of molecular-dynamics (MD) simulation, continuum hydrodynamics modeling, and experimental diffusion measurements to explore antibody behavior in aqueous solution. Hydrodynamic modeling provides a link between the atomic-level details of MD simulation and the size- and shape-dependent data provided by hydrodynamic measurements. Eight independent 40 ns MD trajectories were obtained with the AMBER program suite. The ensemble average of the computed transport properties over all of the MD trajectories agrees remarkably well with the value of the translational diffusion coefficient obtained with dynamic light scattering at 20 degrees C and 27 degrees C, and the intrinsic viscosity measured at 20 degrees C. Therefore, our MD results likely represent a realistic sampling of the conformational space that an antibody explores in aqueous solution. 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Mechanism of cell alignment in groups of Myxococcus xanthus bacteria

NASA Astrophysics Data System (ADS)

Balgam, Rajesh; Igoshin, Oleg

2015-03-01

Myxococcus xanthus is a model for studying self-organization in bacteria. These flexible cylindrical bacteria move along. In groups, M. xanthus cells align themselves into dynamic cell clusters but the mechanism underlying their formation is unknown. It has been shown that steric interactions can cause alignment in self-propelled hard rods but it is not clear how flexibility and reversals affect the alignment and cluster formation. We have investigated cell alignment process using our biophysical model of M. xanthus cell in an agent-based simulation framework under realistic cell flexibility values. We observed that flexible model cells can form aligned cell clusters when reversals are suppressed but these clusters disappeared when reversals frequency becomes similar to the observed value. However, M. xanthus cells follow slime (polysaccharide gel like material) trails left by other cells and we show that implementing this into our model rescues cell clustering for reversing cells. Our results show that slime following along with periodic cell reversals act as positive feedback to reinforce existing slime trails and recruit more cells. Furthermore, we have observed that mechanical cell alignment combined with slime following is sufficient to explain the distinct clustering patterns of reversing and non-reversing cells as observed in recent experiments. This work is supported by NSF MCB 0845919 and 1411780.

The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter.

PubMed

Nilsson, Markus; van Westen, Danielle; Ståhlberg, Freddy; Sundgren, Pia C; Lätt, Jimmy

2013-08-01

Biophysical models that describe the outcome of white matter diffusion MRI experiments have various degrees of complexity. While the simplest models assume equal-sized and parallel axons, more elaborate ones may include distributions of axon diameters and axonal orientation dispersions. These microstructural features can be inferred from diffusion-weighted signal attenuation curves by solving an inverse problem, validated in several Monte Carlo simulation studies. Model development has been paralleled by microscopy studies of the microstructure of excised and fixed nerves, confirming that axon diameter estimates from diffusion measurements agree with those from microscopy. However, results obtained in vivo are less conclusive. For example, the amount of slowly diffusing water is lower than expected, and the diffusion-encoded signal is apparently insensitive to diffusion time variations, contrary to what may be expected. Recent understandings of the resolution limit in diffusion MRI, the rate of water exchange, and the presence of microscopic axonal undulation and axonal orientation dispersions may, however, explain such apparent contradictions. Knowledge of the effects of biophysical mechanisms on water diffusion in tissue can be used to predict the outcome of diffusion tensor imaging (DTI) and of diffusion kurtosis imaging (DKI) studies. Alterations of DTI or DKI parameters found in studies of pathologies such as ischemic stroke can thus be compared with those predicted by modelling. Observations in agreement with the predictions strengthen the credibility of biophysical models; those in disagreement could provide clues of how to improve them. DKI is particularly suited for this purpose; it is performed using higher b-values than DTI, and thus carries more information about the tissue microstructure. The purpose of this review is to provide an update on the current understanding of how various properties of the tissue microstructure and the rate of water exchange between microenvironments are reflected in diffusion MRI measurements. We focus on the use of biophysical models for extracting tissue-specific parameters from data obtained with single PGSE sequences on clinical MRI scanners, but results obtained with animal MRI scanners are also considered. While modelling of white matter is the central theme, experiments on model systems that highlight important aspects of the biophysical models are also reviewed.

Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model.

PubMed

Neic, Aurel; Campos, Fernando O; Prassl, Anton J; Niederer, Steven A; Bishop, Martin J; Vigmond, Edward J; Plank, Gernot

2017-10-01

Anatomically accurate and biophysically detailed bidomain models of the human heart have proven a powerful tool for gaining quantitative insight into the links between electrical sources in the myocardium and the concomitant current flow in the surrounding medium as they represent their relationship mechanistically based on first principles. Such models are increasingly considered as a clinical research tool with the perspective of being used, ultimately, as a complementary diagnostic modality. An important prerequisite in many clinical modeling applications is the ability of models to faithfully replicate potential maps and electrograms recorded from a given patient. However, while the personalization of electrophysiology models based on the gold standard bidomain formulation is in principle feasible, the associated computational expenses are significant, rendering their use incompatible with clinical time frames. In this study we report on the development of a novel computationally efficient reaction-eikonal (R-E) model for modeling extracellular potential maps and electrograms. Using a biventricular human electrophysiology model, which incorporates a topologically realistic His-Purkinje system (HPS), we demonstrate by comparing against a high-resolution reaction-diffusion (R-D) bidomain model that the R-E model predicts extracellular potential fields, electrograms as well as ECGs at the body surface with high fidelity and offers vast computational savings greater than three orders of magnitude. Due to their efficiency R-E models are ideally suitable for forward simulations in clinical modeling studies which attempt to personalize electrophysiological model features.

Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model

NASA Astrophysics Data System (ADS)

Neic, Aurel; Campos, Fernando O.; Prassl, Anton J.; Niederer, Steven A.; Bishop, Martin J.; Vigmond, Edward J.; Plank, Gernot

2017-10-01

Anatomically accurate and biophysically detailed bidomain models of the human heart have proven a powerful tool for gaining quantitative insight into the links between electrical sources in the myocardium and the concomitant current flow in the surrounding medium as they represent their relationship mechanistically based on first principles. Such models are increasingly considered as a clinical research tool with the perspective of being used, ultimately, as a complementary diagnostic modality. An important prerequisite in many clinical modeling applications is the ability of models to faithfully replicate potential maps and electrograms recorded from a given patient. However, while the personalization of electrophysiology models based on the gold standard bidomain formulation is in principle feasible, the associated computational expenses are significant, rendering their use incompatible with clinical time frames. In this study we report on the development of a novel computationally efficient reaction-eikonal (R-E) model for modeling extracellular potential maps and electrograms. Using a biventricular human electrophysiology model, which incorporates a topologically realistic His-Purkinje system (HPS), we demonstrate by comparing against a high-resolution reaction-diffusion (R-D) bidomain model that the R-E model predicts extracellular potential fields, electrograms as well as ECGs at the body surface with high fidelity and offers vast computational savings greater than three orders of magnitude. Due to their efficiency R-E models are ideally suitable for forward simulations in clinical modeling studies which attempt to personalize electrophysiological model features.

Direct Numerical Simulation of Cellular-Scale Blood Flow in 3D Microvascular Networks.

PubMed

Balogh, Peter; Bagchi, Prosenjit

2017-12-19

We present, to our knowledge, the first direct numerical simulation of 3D cellular-scale blood flow in physiologically realistic microvascular networks. The vascular networks are designed following in vivo images and data, and are comprised of bifurcating, merging, and winding vessels. Our model resolves the large deformation and dynamics of each individual red blood cell flowing through the networks with high fidelity, while simultaneously retaining the highly complex geometric details of the vascular architecture. To our knowledge, our simulations predict several novel and unexpected phenomena. We show that heterogeneity in hemodynamic quantities, which is a hallmark of microvascular blood flow, appears both in space and time, and that the temporal heterogeneity is more severe than its spatial counterpart. The cells are observed to frequently jam at vascular bifurcations resulting in reductions in hematocrit and flow rate in the daughter and mother vessels. We find that red blood cell jamming at vascular bifurcations results in several orders-of-magnitude increase in hemodynamic resistance, and thus provides an additional mechanism of increased in vivo blood viscosity as compared to that determined in vitro. A striking result from our simulations is negative pressure-flow correlations observed in several vessels, implying a significant deviation from Poiseuille's law. Furthermore, negative correlations between vascular resistance and hematocrit are observed in various vessels, also defying a major principle of particulate suspension flow. To our knowledge, these novel findings are absent in blood flow in straight tubes, and they underscore the importance of considering realistic physiological geometry and resolved cellular interactions in modeling microvascular hemodynamics. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Steady state temperature distribution in dermal regions of an irregular tapered shaped human limb with variable eccentricity.

PubMed

Agrawal, M; Pardasani, K R; Adlakha, N

2014-08-01

The investigators in the past have developed some models of temperature distribution in the human limb assuming it as a regular circular or elliptical tapered cylinder. But in reality the limb is not of regular tapered cylindrical shape. The radius and eccentricity are not same throughout the limb. In view of above a model of temperature distribution in the irregular tapered elliptical shaped human limb is proposed for a three dimensional steady state case in this paper. The limb is assumed to be composed of multiple cylindrical substructures with variable radius and eccentricity. The mathematical model incorporates the effect of blood mass flow rate, metabolic activity and thermal conductivity. The outer surface is exposed to the environment and appropriate boundary conditions have been framed. The finite element method has been employed to obtain the solution. The temperature profiles have been computed in the dermal layers of a human limb and used to study the effect of shape, microstructure and biophysical parameters on temperature distribution in human limbs. The proposed model is one of the most realistic model as compared to conventional models as this can be effectively employed to every regular and nonregular structures of the body with variable radius and eccentricity to study the thermal behaviour. Copyright © 2014 Elsevier Ltd. All rights reserved.

Formulation and Implementation of Nonlinear Integral Equations to Model Neural Dynamics Within the Vertebrate Retina.

PubMed

Eshraghian, Jason K; Baek, Seungbum; Kim, Jun-Ho; Iannella, Nicolangelo; Cho, Kyoungrok; Goo, Yong Sook; Iu, Herbert H C; Kang, Sung-Mo; Eshraghian, Kamran

2018-02-13

Existing computational models of the retina often compromise between the biophysical accuracy and a hardware-adaptable methodology of implementation. When compared to the current modes of vision restoration, algorithmic models often contain a greater correlation between stimuli and the affected neural network, but lack physical hardware practicality. Thus, if the present processing methods are adapted to complement very-large-scale circuit design techniques, it is anticipated that it will engender a more feasible approach to the physical construction of the artificial retina. The computational model presented in this research serves to provide a fast and accurate predictive model of the retina, a deeper understanding of neural responses to visual stimulation, and an architecture that can realistically be transformed into a hardware device. Traditionally, implicit (or semi-implicit) ordinary differential equations (OES) have been used for optimal speed and accuracy. We present a novel approach that requires the effective integration of different dynamical time scales within a unified framework of neural responses, where the rod, cone, amacrine, bipolar, and ganglion cells correspond to the implemented pathways. Furthermore, we show that adopting numerical integration can both accelerate retinal pathway simulations by more than 50% when compared with traditional ODE solvers in some cases, and prove to be a more realizable solution for the hardware implementation of predictive retinal models.

Numerical simulation of a low-lying barrier island's morphological response to Hurricane Katrina

USGS Publications Warehouse

Lindemer, C.A.; Plant, N.G.; Puleo, J.A.; Thompson, D.M.; Wamsley, T.V.

2010-01-01

Tropical cyclones that enter or form in the Gulf of Mexico generate storm surge and large waves that impact low-lying coastlines along the Gulf Coast. The Chandeleur Islands, located 161. km east of New Orleans, Louisiana, have endured numerous hurricanes that have passed nearby. Hurricane Katrina (landfall near Waveland MS, 29 Aug 2005) caused dramatic changes to the island elevation and shape. In this paper the predictability of hurricane-induced barrier island erosion and accretion is evaluated using a coupled hydrodynamic and morphodynamic model known as XBeach. Pre- and post-storm island topography was surveyed with an airborne lidar system. Numerical simulations utilized realistic surge and wave conditions determined from larger-scale hydrodynamic models. Simulations included model sensitivity tests with varying grid size and temporal resolutions. Model-predicted bathymetry/topography and post-storm survey data both showed similar patterns of island erosion, such as increased dissection by channels. However, the model under predicted the magnitude of erosion. Potential causes for under prediction include (1) errors in the initial conditions (the initial bathymetry/topography was measured three years prior to Katrina), (2) errors in the forcing conditions (a result of our omission of storms prior to Katrina and/or errors in Katrina storm conditions), and/or (3) physical processes that were omitted from the model (e.g., inclusion of sediment variations and bio-physical processes). ?? 2010.

From global change to a butterfly flapping: biophysics and behaviour affect tropical climate change impacts.

PubMed

Bonebrake, Timothy C; Boggs, Carol L; Stamberger, Jeannie A; Deutsch, Curtis A; Ehrlich, Paul R

2014-10-22

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 solar radiation in predicting butterfly body temperature. We also integrated the biophysics with a physiologically based insect fitness model to quantify the influence of solar radiation, morphology and behaviour on warming impact projections. While warming is projected to have some detrimental impacts on tropical ectotherms, fitness impacts in this study are not as negative as models that assume body and air temperature equivalence would suggest. We additionally show that behavioural thermoregulation can diminish direct warming impacts, though indirect thermoregulatory consequences could further complicate predictions. With these results, at multiple spatial and temporal scales, we show the importance of biophysics and behaviour for studying biodiversity consequences of global climate change, and stress that tropical climate change impacts are likely to be context-dependent. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Modelling the influence of land-use changes on biophysical and biochemical interactions at regional and global scales.

PubMed

Devaraju, N; Bala, G; Nemani, R

2015-09-01

Land-use changes since the start of the industrial era account for nearly one-third of the cumulative anthropogenic CO2 emissions. In addition to the greenhouse effect of CO2 emissions, changes in land use also affect climate via changes in surface physical properties such as albedo, evapotranspiration and roughness length. Recent modelling studies suggest that these biophysical components may be comparable with biochemical effects. In regard to climate change, the effects of these two distinct processes may counterbalance one another both regionally and, possibly, globally. In this article, through hypothetical large-scale deforestation simulations using a global climate model, we contrast the implications of afforestation on ameliorating or enhancing anthropogenic contributions from previously converted (agricultural) land surfaces. Based on our review of past studies on this subject, we conclude that the sum of both biophysical and biochemical effects should be assessed when large-scale afforestation is used for countering global warming, and the net effect on global mean temperature change depends on the location of deforestation/afforestation. Further, although biochemical effects trigger global climate change, biophysical effects often cause strong local and regional climate change. The implication of the biophysical effects for adaptation and mitigation of climate change in agriculture and agroforestry sectors is discussed. © 2014 John Wiley & Sons Ltd.

From global change to a butterfly flapping: biophysics and behaviour affect tropical climate change impacts

PubMed Central

Bonebrake, Timothy C.; Boggs, Carol L.; Stamberger, Jeannie A.; Deutsch, Curtis A.; Ehrlich, Paul R.

2014-01-01

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 solar radiation in predicting butterfly body temperature. We also integrated the biophysics with a physiologically based insect fitness model to quantify the influence of solar radiation, morphology and behaviour on warming impact projections. While warming is projected to have some detrimental impacts on tropical ectotherms, fitness impacts in this study are not as negative as models that assume body and air temperature equivalence would suggest. We additionally show that behavioural thermoregulation can diminish direct warming impacts, though indirect thermoregulatory consequences could further complicate predictions. With these results, at multiple spatial and temporal scales, we show the importance of biophysics and behaviour for studying biodiversity consequences of global climate change, and stress that tropical climate change impacts are likely to be context-dependent. PMID:25165769

Geometric and material determinants of patterning efficiency by dielectrophoresis.

PubMed

Albrecht, Dirk R; Sah, Robert L; Bhatia, Sangeeta N

2004-10-01

Dielectrophoretic (DEP) forces have been used extensively to manipulate, separate, and localize biological cells and bioparticles via high-gradient electric fields. However, minimization of DEP exposure time is desirable, because of possible untoward effects on cell behavior. Toward this goal, this article investigates the geometric and material determinants of particle patterning kinetics and efficiency. In particular, the time required to achieve a steady-state pattern is theoretically modeled and experimentally validated for a planar, interdigitated bar electrode array energized in a standing-wave configuration. This measure of patterning efficiency is calculated from an improved Fourier series solution of DEP force, in which realistic boundary conditions and a finite chamber height are imposed to reflect typical microfluidic applications. The chamber height, electrode spacing, and fluid viscosity and conductivity are parameters that profoundly affect patterning efficiency, and optimization can reduce electric field exposure by orders of magnitude. Modeling strategies are generalizable to arbitrary electrode design as well as to conditions where DEP force may not act alone to cause particle motion. This improved understanding of DEP patterning kinetics provides a framework for new advances in the development of DEP-based biological devices and assays with minimal perturbation of cell behavior. Copyright 2004 Biophysical Society

A Monte Carlo model reveals independent signaling at central glutamatergic synapses.

PubMed Central

Franks, Kevin M; Bartol, Thomas M; Sejnowski, Terrence J

2002-01-01

We have developed a biophysically realistic model of receptor activation at an idealized central glutamatergic synapse that uses Monte Carlo techniques to simulate the stochastic nature of transmission following release of a single synaptic vesicle. For the a synapse with 80 AMPA and 20 NMDA receptors, a single quantum, with 3000 glutamate molecules, opened approximately 3 NMDARs and 20 AMPARs. The number of open receptors varied directly with the total number of receptors, and the fraction of open receptors did not depend on the ratio of co-localized AMPARs and NMDARs. Variability decreased with increases in either total receptor number or quantal size, and differences between the variability of AMPAR and NMDAR responses were due solely to unequal numbers of receptors at the synapse. Despite NMDARs having a much higher affinity for glutamate than AMPARs, quantal release resulted in similar occupancy levels in both receptor types. Receptor activation increased with number of transmitter molecules released or total receptor number, whereas occupancy levels were only dependent on quantal size. Tortuous diffusion spaces reduced the extent of spillover and the activation of extrasynaptic receptors. These results support the conclusion that signaling is spatially independent within and between central glutamatergic synapses. PMID:12414671

Biophysics of cadherin adhesion.

PubMed

Leckband, Deborah; Sivasankar, Sanjeevi

2012-01-01

Since the identification of cadherins and the publication of the first crystal structures, the mechanism of cadherin adhesion, and the underlying structural basis have been studied with a number of different experimental techniques, different classical cadherin subtypes, and cadherin fragments. Earlier studies based on biophysical measurements and structure determinations resulted in seemingly contradictory findings regarding cadherin adhesion. However, recent experimental data increasingly reveal parallels between structures, solution binding data, and adhesion-based biophysical measurements that are beginning to both reconcile apparent differences and generate a more comprehensive model of cadherin-mediated cell adhesion. This chapter summarizes the functional, structural, and biophysical findings relevant to cadherin junction assembly and adhesion. We emphasize emerging parallels between findings obtained with different experimental approaches. Although none of the current models accounts for all of the available experimental and structural data, this chapter discusses possible origins of apparent discrepancies, highlights remaining gaps in current knowledge, and proposes challenges for further study.

A biophysical basis for patchy mortality during heat waves.

PubMed

Mislan, K A S; Wethey, David S

2015-04-01

Extreme heat events cause patchy mortality in many habitats. We examine biophysical mechanisms responsible for patchy mortality in beds of the competitively dominant ecosystem engineer, the marine mussel Mytilus californianus, on the west coast of the United States. We used a biophysical model to predict daily fluctuations in body temperature at sites from southern California to Washington and used results of laboratory experiments on thermal tolerance to determine mortality rates from body temperature. In our model, we varied the rate of thermal conduction within mussel beds and found that this factor can account for large differences in body temperature and consequent mortality during heat waves. Mussel beds provide structural habitat for other species and increase local biodiversity, but, as sessile organisms, they are particularly vulnerable to extreme weather conditions. Identifying critical biophysical mechanisms related to mortality and ecological performance will improve our ability to predict the effects of climate change on these vulnerable ecosystems.

Biophysical characterization of α-synuclein and its controversial structure

PubMed Central

Alderson, T Reid; Markley, John L

2013-01-01

α-synuclein, a presynaptic protein of poorly defined function, constitutes the main component of Parkinson disease-associated Lewy bodies. Extensive biophysical investigations have provided evidence that isolated α-synuclein is an intrinsically disordered protein (IDP) in vitro. Subsequently serving as a model IDP in numerous studies, α-synuclein has aided in the development of many technologies used to characterize IDPs and arguably represents the most thoroughly analyzed IDP to date. Recent reports, however, have challenged the disordered nature of α-synuclein inside cells and have instead proposed a physiologically relevant helical tetramer. Despite α-synuclein’s rich biophysical history, a single coherent picture has not yet emerged concerning its in vivo structure, dynamics, and physiological role(s). We present herein a review of the biophysical discoveries, developments, and models pertinent to the characterization of α-synuclein’s structure and analysis of the native tetramer controversy. PMID:24634806

Biophysical models of protein evolution: Understanding the patterns of evolutionary sequence divergence

PubMed Central

Echave, Julian; Wilke, Claus O.

2018-01-01

For decades, rates of protein evolution have been interpreted in terms of the vague concept of “functional importance”. Slowly evolving proteins or sites within proteins were assumed to be more functionally important and thus subject to stronger selection pressure. More recently, biophysical models of protein evolution, which combine evolutionary theory with protein biophysics, have completely revolutionized our view of the forces that shape sequence divergence. Slowly evolving proteins have been found to evolve slowly because of selection against toxic misfolding and misinteractions, linking their rate of evolution primarily to their abundance. Similarly, most slowly evolving sites in proteins are not directly involved in function, but mutating them has large impacts on protein structure and stability. Here, we review the studies of the emergent field of biophysical protein evolution that have shaped our current understanding of sequence divergence patterns. We also propose future research directions to develop this nascent field. PMID:28301766

Representing biophysical landscape interactions in soil models by bridging disciplines and scales.

NASA Astrophysics Data System (ADS)

van der Ploeg, M. J.; Carranza, C.; Teixeira da Silva, R.; te Brake, B.; Baartman, J.; Robinson, D.

2017-12-01

The combination of climate change, population growth and soil threats including carbon loss, biodiversity decline and erosion, increasingly confront the global community (Schwilch et al., 2016). One major challenge in studying processes involved in soil threats, landscape resilience, ecosystem stability, sustainable land management and resulting economic consequences, is that it is an interdisciplinary field (Pelletier et al., 2012). Less stringent scientific disciplinary boundaries are therefore important (Liu et al., 2007), because as a result of disciplinary focus, ambiguity may arise on the understanding of landscape interactions. This is especially true in the interaction between a landscape's physical and biological processes (van der Ploeg et al. 2012). Biophysical landscape interactions are those biotic and abiotic processes in a landscape that have an influence on the developments within and evolution of a landscape. An important aspect in biophysical landscape interactions is the differences in scale related to the various processes that play a role in these systems. Moreover, the interplay between the physical landscape and the occurring vegetation, which often co-evolve, and the resulting heterogeneity and emerging patterns are the reason why it is so challenging to establish a theoretical basis to describe biophysical processes in landscapes (e.g. te Brake et al. 2013, Robinson et al. 2016). Another complicating factor is the response of vegetation to changing environmental conditions, including a possible, and often unknown, time-lag (e.g. Metzger et al., 2009). An integrative description for modelling biophysical interactions has been a long standing goal in soil science (Vereecken et al., 2016). We need the development of soil models that are more focused on networks, connectivity and feedbacks incorporating the most important aspects of our detailed mechanistic modelling (Paola & Leeder, 2011). Additionally, remote sensing measurement techniques facilitate non-interfering observation of biophysical interactions on a landscape scale. A joint effort to connect Earth's (sub)surface processes by a combination of innovative big data-assimilation, measurement and modelling techniques will enable the scientific community to accurately address vital issues.

The physics of life: one molecule at a time

PubMed Central

Leake, Mark C.

2013-01-01

The esteemed physicist Erwin Schrödinger, whose name is associated with the most notorious equation of quantum mechanics, also wrote a brief essay entitled ‘What is Life?’, asking: ‘How can the events in space and time which take place within the spatial boundary of a living organism be accounted for by physics and chemistry?’ The 60+ years following this seminal work have seen enormous developments in our understanding of biology on the molecular scale, with physics playing a key role in solving many central problems through the development and application of new physical science techniques, biophysical analysis and rigorous intellectual insight. The early days of single-molecule biophysics research was centred around molecular motors and biopolymers, largely divorced from a real physiological context. The new generation of single-molecule bioscience investigations has much greater scope, involving robust methods for understanding molecular-level details of the most fundamental biological processes in far more realistic, and technically challenging, physiological contexts, emerging into a new field of ‘single-molecule cellular biophysics’. Here, I outline how this new field has evolved, discuss the key active areas of current research and speculate on where this may all lead in the near future. PMID:23267186

Carrying capacity for species richness as context for conservation: a case study of North American birds

Treesearch

Andrew J. Hansen; Linda Bowers Phillips; Curtis H. Flather; Jim Robinson-Cox

2011-01-01

We evaluated the leading hypotheses on biophysical factors affecting species richness for Breeding Bird Survey routes from areas with little influence of human activities.We then derived a best model based on information theory, and used this model to extrapolate SK across North America based on the biophysical predictor variables. The predictor variables included the...

Incorporating Modeling and Simulations in Undergraduate Biophysical Chemistry Course to Promote Understanding of Structure-Dynamics-Function Relationships in Proteins

ERIC Educational Resources Information Center

Hati, Sanchita; Bhattacharyya, Sudeep

2016-01-01

A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and…

Screening of the aerodynamic and biophysical properties of barley malt

NASA Astrophysics Data System (ADS)

Ghodsvali, Alireza; Farzaneh, Vahid; Bakhshabadi, Hamid; Zare, Zahra; Karami, Zahra; Mokhtarian, Mohsen; Carvalho, Isabel. S.

2016-10-01

An understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; germination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the dependent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process.

Biophysical effects on temperature and precipitation due to land cover change

NASA Astrophysics Data System (ADS)

Perugini, Lucia; Caporaso, Luca; Marconi, Sergio; Cescatti, Alessandro; Quesada, Benjamin; de Noblet-Ducoudré, Nathalie; House, Johanna I.; Arneth, Almut

2017-05-01

Anthropogenic land cover changes (LCC) affect regional and global climate through biophysical variations of the surface energy budget mediated by albedo, evapotranspiration, and roughness. This change in surface energy budget may exacerbate or counteract biogeochemical greenhouse gas effects of LCC, with a large body of emerging assessments being produced, sometimes apparently contradictory. We reviewed the existing scientific literature with the objective to provide an overview of the state-of-the-knowledge of the biophysical LCC climate effects, in support of the assessment of mitigation/adaptation land policies. Out of the published studies that were analyzed, 28 papers fulfilled the eligibility criteria, providing surface air temperature and/or precipitation change with respect to LCC regionally and/or globally. We provide a synthesis of the signal, magnitude and uncertainty of temperature and precipitation changes in response to LCC biophysical effects by climate region (boreal/temperate/tropical) and by key land cover transitions. Model results indicate that a modification of biophysical processes at the land surface has a strong regional climate effect, and non-negligible global impact on temperature. Simulations experiments of large-scale (i.e. complete) regional deforestation lead to a mean reduction in precipitation in all regions, while air surface temperature increases in the tropics and decreases in boreal regions. The net global climate effects of regional deforestation are less certain. There is an overall consensus in the model experiments that the average global biophysical climate response to complete global deforestation is atmospheric cooling and drying. Observed estimates of temperature change following deforestation indicate a smaller effect than model-based regional estimates in boreal regions, comparable results in the tropics, and contrasting results in temperate regions. Regional/local biophysical effects following LCC are important for local climate, water cycle, ecosystems, their productivity and biodiversity, and thus important to consider in the formulation of adaptation policy. However before considering the inclusion of biophysical climate effects of LCC under the UNFCCC, science has to provide robust tools and methods for estimation of both country and global level effects.

Predicting the Presence of Scyphozoan Jellyfish in the Gulf of Mexico Using a Biophysical Model

NASA Astrophysics Data System (ADS)

Aleksa, K. T.; Nero, R. W.; Wiggert, J. D.; Graham, W. M.

2016-02-01

The study and quantification of jellyfish (cnidarian medusae and ctenophores) is difficult due to their fragile body plan and a composition similar to their environment. The development of a predictive biophysical jellyfish model would be the first of its kind for the Gulf of Mexico and could provide assistance in ecological research and human interactions. In this study, the collection data of two scyphozoan medusae, Chrysaora quinquecirrha and Aurelia spp., were extracted from SEAMAP trawling surveys and were used to determine biophysical predictors for the presence of large jellyfish medusae in the Gulf of Mexico. Both in situ and remote sensing measurements from 2003 to 2013 were obtained. Logistic regressions were then applied to 27 biophysical parameters derived from these data to explore and determine significant predictors for the presence of medusae. Significant predictors identified by this analysis included water temperature, chlorophyll a, turbidity, distance from shore, and salinity. Future application for this model include foraging assessment of gelatinous predators as well as possible near real time monitoring of the distribution and movement of these medusae in the Gulf of Mexico.

Modeling Coniferous Canopy Structure over Extensive Areas for Ray Tracing Simulations: Scaling from the Leaf to the Stand Level

NASA Astrophysics Data System (ADS)

van Aardt, J. A.; van Leeuwen, M.; Kelbe, D.; Kampe, T.; Krause, K.

2015-12-01

Remote sensing is widely accepted as a useful technology for characterizing the Earth surface in an objective, reproducible, and economically feasible manner. To date, the calibration and validation of remote sensing data sets and biophysical parameter estimates remain challenging due to the requirements to sample large areas for ground-truth data collection, and restrictions to sample these data within narrow temporal windows centered around flight campaigns or satellite overpasses. The computer graphics community have taken significant steps to ameliorate some of these challenges by providing an ability to generate synthetic images based on geometrically and optically realistic representations of complex targets and imaging instruments. These synthetic data can be used for conceptual and diagnostic tests of instrumentation prior to sensor deployment or to examine linkages between biophysical characteristics of the Earth surface and at-sensor radiance. In the last two decades, the use of image generation techniques for remote sensing of the vegetated environment has evolved from the simulation of simple homogeneous, hypothetical vegetation canopies, to advanced scenes and renderings with a high degree of photo-realism. Reported virtual scenes comprise up to 100M surface facets; however, due to the tighter coupling between hardware and software development, the full potential of image generation techniques for forestry applications yet remains to be fully explored. In this presentation, we examine the potential computer graphics techniques have for the analysis of forest structure-function relationships and demonstrate techniques that provide for the modeling of extremely high-faceted virtual forest canopies, comprising billions of scene elements. We demonstrate the use of ray tracing simulations for the analysis of gap size distributions and characterization of foliage clumping within spatial footprints that allow for a tight matching between characteristics derived from these virtual scenes and typical pixel resolutions of remote sensing imagery.

Hybrid System for Ex Vivo Hemorheological and Hemodynamic Analysis: A Feasibility Study

PubMed Central

Yeom, Eunseop; Jun Kang, Yang; Joon Lee, Sang

2015-01-01

Precise measurement of biophysical properties is important to understand the relation between these properties and the outbreak of cardiovascular diseases (CVDs). However, a systematic measurement for these biophysical parameters under in vivo conditions is nearly impossible because of complex vessel shape and limited practicality. In vitro measurements can provide more biophysical information, but in vitro exposure changes hemorheological properties. In this study, a hybrid system composed of an ultrasound system and microfluidic device is proposed for monitoring hemorheological and hemodynamic properties under more reasonable experimental conditions. Biophysical properties including RBC aggregation, viscosity, velocity, and pressure of blood flows are simultaneously measured under various conditions to demonstrate the feasibility and performance of this measurement system. The proposed technique is applied to a rat extracorporeal loop which connects the aorta and jugular vein directly. As a result, the proposed system is found to measure biophysical parameters reasonably without blood collection from the rat and provided more detailed information. This hybrid system, combining ultrasound imaging and microfluidic techniques to ex vivo animal models, would be useful for monitoring the variations of biophysical properties induced by chemical agents. It can be used to understand the relation between biophysical parameters and CVDs. PMID:26090816

Emulating short-term synaptic dynamics with memristive devices

NASA Astrophysics Data System (ADS)

Berdan, Radu; Vasilaki, Eleni; Khiat, Ali; Indiveri, Giacomo; Serb, Alexandru; Prodromakis, Themistoklis

2016-01-01

Neuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity. Here we demonstrate that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties. We show that, contrary to conventional uses of solid-state memory, the existence of rate-limiting volatility is a key feature for capturing short-term synaptic dynamics. We also show how the temporal dynamics of our prototypes can be exploited to implement spatio-temporal computation, demonstrating the memristors full potential for building biophysically realistic neural processing systems.

A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons.

PubMed

Beim Graben, Peter; Rodrigues, Serafim

2012-01-01

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

PubMed Central

beim Graben, Peter; Rodrigues, Serafim

2013-01-01

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the “open-field” configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement. PMID:23316157

A brief introduction to the model microswimmer Chlamydomonas reinhardtii

NASA Astrophysics Data System (ADS)

Jeanneret, Raphaël; Contino, Matteo; Polin, Marco

2016-11-01

The unicellular biflagellate green alga Chlamydomonas reinhardtii has been an important model system in biology for decades, and in recent years it has started to attract growing attention also within the biophysics community. Here we provide a concise review of some of the aspects of Chlamydomonas biology and biophysics most immediately relevant to physicists that might be interested in starting to work with this versatile microorganism.

Black Versus Gray T-Shirts: Comparison of Spectrophotometric and Other Biophysical Properties of Physical Fitness Uniforms and Modeled Heat Strain and Thermal Comfort

DTIC Science & Technology

2016-09-01

test method for measuring the thermal insulation of clothing using a heated manikin. 2010. 2. ASTM International. F2370-10 Standard test method for...PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT DISCLAIMER The opinions or assertions contained herein are the...SHIRTS: COMPARISON OF SPECTROPHOTOMETRIC AND OTHER BIOPHYSICAL PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT

Coupling urban growth scenarios with nearshore biophysical change models to inform coastal restoration planning in Puget Sound, Washington

NASA Astrophysics Data System (ADS)

Byrd, K. B.; Kreitler, J.; Labiosa, W.

2010-12-01

A scenario represents an account of a plausible future given logical assumptions about how conditions change over discrete bounds of space and time. Development of multiple scenarios provides a means to identify alternative directions of urban growth that account for a range of uncertainty in human behavior. Interactions between human and natural processes may be studied by coupling urban growth scenario outputs with biophysical change models; if growth scenarios encompass a sufficient range of alternative futures, scenario assumptions serve to constrain the uncertainty of biophysical models. Spatially explicit urban growth models (map-based) produce output such as distributions and densities of residential or commercial development in a GIS format that can serve as input to other models. Successful fusion of growth model outputs with other model inputs requires that both models strategically address questions of interest, incorporate ecological feedbacks, and minimize error. The U.S. Geological Survey (USGS) Puget Sound Ecosystem Portfolio Model (PSEPM) is a decision-support tool that supports land use and restoration planning in Puget Sound, Washington, a 35,500 sq. km region. The PSEPM couples future scenarios of urban growth with statistical, process-based and rule-based models of nearshore biophysical changes and ecosystem services. By using a multi-criteria approach, the PSEPM identifies cross-system and cumulative threats to the nearshore environment plus opportunities for conservation and restoration. Sub-models that predict changes in nearshore biophysical condition were developed and existing models were integrated to evaluate three growth scenarios: 1) Status Quo, 2) Managed Growth, and 3) Unconstrained Growth. These decadal scenarios were developed and projected out to 2060 at Oregon State University using the GIS-based ENVISION model. Given land management decisions and policies under each growth scenario, the sub-models predicted changes in 1) fecal coliform in shellfish growing areas, 2) sediment supply to beaches, 3) State beach recreational visits, 4) eelgrass habitat suitability, 5) forage fish habitat suitability, and 6) nutrient loadings. In some cases thousands of shoreline units were evaluated with multiple predictive models, creating a need for streamlined and consistent database development and data processing. Model development over multiple disciplines demonstrated the challenge of merging data types from multiple sources that were inconsistent in spatial and temporal resolution, classification schemes, and topology. Misalignment of data in space and time created potential for error and misinterpretation of results. This effort revealed that the fusion of growth scenarios and biophysical models requires an up-front iterative adjustment of both scenarios and models so that growth model outputs provide the needed input data in the correct format. Successful design of data flow across models that includes feedbacks between human and ecological systems was found to enhance the use of the final data product for decision making.

The Biophysics of Infection.

PubMed

Leake, Mark C

2016-01-01

Our understanding of the processes involved in infection has grown enormously in the past decade due in part to emerging methods of biophysics. This new insight has been enabled through advances in interdisciplinary experimental technologies and theoretical methods at the cutting-edge interface of the life and physical sciences. For example, this has involved several state-of-the-art biophysical tools used in conjunction with molecular and cell biology approaches, which enable investigation of infection in living cells. There are also new, emerging interfacial science tools which enable significant improvements to the resolution of quantitative measurements both in space and time. These include single-molecule biophysics methods and super-resolution microscopy approaches. These new technological tools in particular have underpinned much new understanding of dynamic processes of infection at a molecular length scale. Also, there are many valuable advances made recently in theoretical approaches of biophysics which enable advances in predictive modelling to generate new understanding of infection. Here, I discuss these advances, and take stock on our knowledge of the biophysics of infection and discuss where future advances may lead.

Biotic games and cloud experimentation as novel media for biophysics education

NASA Astrophysics Data System (ADS)

Riedel-Kruse, Ingmar; Blikstein, Paulo

2014-03-01

First-hand, open-ended experimentation is key for effective formal and informal biophysics education. We developed, tested and assessed multiple new platforms that enable students and children to directly interact with and learn about microscopic biophysical processes: (1) Biotic games that enable local and online play using galvano- and photo-tactic stimulation of micro-swimmers, illustrating concepts such as biased random walks, Low Reynolds number hydrodynamics, and Brownian motion; (2) an undergraduate course where students learn optics, electronics, micro-fluidics, real time image analysis, and instrument control by building biotic games; and (3) a graduate class on the biophysics of multi-cellular systems that contains a cloud experimentation lab enabling students to execute open-ended chemotaxis experiments on slimemolds online, analyze their data, and build biophysical models. Our work aims to generate the equivalent excitement and educational impact for biophysics as robotics and video games have had for mechatronics and computer science, respectively. We also discuss how scaled-up cloud experimentation systems can support MOOCs with true lab components and life-science research in general.

Short-Term Memory and Its Biophysical Model

NASA Astrophysics Data System (ADS)

Wang, Wei; Zhang, Kai; Tang, Xiao-wei

1996-12-01

The capacity of short-term memory has been studied using an integrate-and-fire neuronal network model. It is found that the storage of events depend on the manner of the correlation between the events, and the capacity is dominated by the value of after-depolarization potential. There is a monotonic increasing relationship between the value of after-depolarization potential and the memory numbers. The biophysics relevance of the network model is discussed and different kinds of the information processes are studied too.

BIANCA, a biophysical model of cell survival and chromosome damage by protons, C-ions and He-ions at energies and doses used in hadrontherapy

NASA Astrophysics Data System (ADS)

Pietro Carante, Mario; Aimè, Chiara; Tello Cajiao, John James; Ballarini, Francesca

2018-04-01

An upgraded version of the BIANCA II biophysical model, which describes more realistically interphase chromosome organization and the link between chromosome aberrations and cell death, was applied to V79 and AG01522 cells exposed to protons, C-ions and He-ions over a wide LET interval (0.6–502 keV µm‑1), as well as proton-irradiated U87 cells. The model assumes that (i) ionizing radiation induces DNA ‘cluster lesions’ (CLs), where by definition each CL produces two independent chromosome fragments; (ii) fragment (distance-dependent) mis-rejoining, or un-rejoining, produces chromosome aberrations; (iii) some aberrations lead to cell death. The CL yield, which mainly depends on radiation quality but is also modulated by the target cell, is an adjustable parameter. The fragment un-rejoining probability, f, is the second, and last, parameter. The value of f, which is assumed to depend on the cell type but not on radiation quality, was taken from previous studies, and only the CL yield was adjusted in the present work. Good agreement between simulations and experimental data was obtained, suggesting that BIANCA II is suitable for calculating the biological effectiveness of hadrontherapy beams. For both V79 and AG01522 cells, the mean number of CLs per micrometer was found to increase with LET in a linear-quadratic fashion before the over-killing region, where a less rapid increase, with a tendency to saturation, was observed. Although the over-killing region deserves further investigation, the possibility of fitting the CL yields is an important feature for hadrontherapy, because it allows performing predictions also at LET values where experimental data are not available. Finally, an approach was proposed to predict the ion-response of the cell line(s) of interest from the ion-response of a reference cell line and the photon response of both. A pilot study on proton-irradiated AG01522 and U87 cells, taking V79 cells as a reference, showed encouraging results.

Biophysical impacts of climate-smart agriculture in the Midwest United States.

PubMed

Bagley, Justin E; Miller, Jesse; Bernacchi, Carl J

2015-09-01

The potential impacts of climate change in the Midwest United States present unprecedented challenges to regional agriculture. In response to these challenges, a variety of climate-smart agricultural methodologies have been proposed to retain or improve crop yields, reduce agricultural greenhouse gas emissions, retain soil quality and increase climate resilience of agricultural systems. One component that is commonly neglected when assessing the environmental impacts of climate-smart agriculture is the biophysical impacts, where changes in ecosystem fluxes and storage of moisture and energy lead to perturbations in local climate and water availability. Using a combination of observational data and an agroecosystem model, a series of climate-smart agricultural scenarios were assessed to determine the biophysical impacts these techniques have in the Midwest United States. The first scenario extended the growing season for existing crops using future temperature and CO2 concentrations. The second scenario examined the biophysical impacts of no-till agriculture and the impacts of annually retaining crop debris. Finally, the third scenario evaluated the potential impacts that the adoption of perennial cultivars had on biophysical quantities. Each of these scenarios was found to have significant biophysical impacts. However, the timing and magnitude of the biophysical impacts differed between scenarios. © 2014 John Wiley & Sons Ltd.

Biophysical Discovery through the Lens of a Computational Microscope

NASA Astrophysics Data System (ADS)

Amaro, Rommie

With exascale computing power on the horizon, improvements in the underlying algorithms and available structural experimental data are enabling new paradigms for chemical discovery. My work has provided key insights for the systematic incorporation of structural information resulting from state-of-the-art biophysical simulations into protocols for inhibitor and drug discovery. We have shown that many disease targets have druggable pockets that are otherwise ``hidden'' in high resolution x-ray structures, and that this is a common theme across a wide range of targets in different disease areas. We continue to push the limits of computational biophysical modeling by expanding the time and length scales accessible to molecular simulation. My sights are set on, ultimately, the development of detailed physical models of cells, as the fundamental unit of life, and two recent achievements highlight our efforts in this arena. First is the development of a molecular and Brownian dynamics multi-scale modeling framework, which allows us to investigate drug binding kinetics in addition to thermodynamics. In parallel, we have made significant progress developing new tools to extend molecular structure to cellular environments. Collectively, these achievements are enabling the investigation of the chemical and biophysical nature of cells at unprecedented scales.

Unconstrained Structure Formation in Coarse-Grained Protein Simulations

NASA Astrophysics Data System (ADS)

Bereau, Tristan

The ability of proteins to fold into well-defined structures forms the basis of a wide variety of biochemical functions in and out of the cell membrane. Many of these processes, however, operate at time- and length-scales that are currently unattainable by all-atom computer simulations. To cope with this difficulty, increasingly more accurate and sophisticated coarse-grained models are currently being developed. In the present thesis, we introduce a solvent-free coarse-grained model for proteins. Proteins are modeled by four beads per amino acid, providing enough backbone resolution to allow for accurate sampling of local conformations. It relies on simple interactions that emphasize structure, such as hydrogen bonds and hydrophobicity. Realistic alpha/beta content is achieved by including an effective nearest-neighbor dipolar interaction. Parameters are tuned to reproduce both local conformations and tertiary structures. By studying both helical and extended conformations we make sure the force field is not biased towards any particular secondary structure. Without any further adjustments or bias a realistic oligopeptide aggregation scenario is observed. The model is subsequently applied to various biophysical problems: (i) kinetics of folding of two model peptides, (ii) large-scale amyloid-beta oligomerization, and (iii) protein folding cooperativity. The last topic---defined by the nature of the finite-size thermodynamic transition exhibited upon folding---was investigated from a microcanonical perspective: the accurate evaluation of the density of states can unambiguously characterize the nature of the transition, unlike its corresponding canonical analysis. Extending the results of lattice simulations and theoretical models, we find that it is the interplay between secondary structure and the loss of non-native tertiary contacts which determines the nature of the transition. Finally, we combine the peptide model with a high-resolution, solvent-free, lipid model. The lipid force field was systematically tuned to reproduce the structural and mechanical properties of phosphatidylcholine bilayers. The two models were cross-parametrized against atomistic potential of mean force curves for the insertion of single amino acid side chains into a bilayer. Coarse-grained transmembrane protein simulations were then compared with experiments and atomistic simulations to validate the force field. The transferability of the two models across amino acid sequences and lipid species permits the investigation of a wide variety of scenarios, while the absence of explicit solvent allows for studies of large-scale phenomena.

Increased transient Na+ conductance and action potential output in layer 2/3 prefrontal cortex neurons of the fmr1-/y mouse.

PubMed

Routh, Brandy N; Rathour, Rahul K; Baumgardner, Michael E; Kalmbach, Brian E; Johnston, Daniel; Brager, Darrin H

2017-07-01

Layer 2/3 neurons of the prefrontal cortex display higher gain of somatic excitability, responding with a higher number of action potentials for a given stimulus, in fmr1 -/y mice. In fmr1 -/y L2/3 neurons, action potentials are taller, faster and narrower. Outside-out patch clamp recordings revealed that the maximum Na + conductance density is higher in fmr1 -/y L2/3 neurons. Measurements of three biophysically distinct K + currents revealed a depolarizing shift in the activation of a rapidly inactivating (A-type) K + conductance. Realistic neuronal simulations of the biophysical observations recapitulated the elevated action potential and repetitive firing phenotype. Fragile X syndrome is the most common form of inherited mental impairment and autism. The prefrontal cortex is responsible for higher order cognitive processing, and prefrontal dysfunction is believed to underlie many of the cognitive and behavioural phenotypes associated with fragile X syndrome. We recently demonstrated that somatic and dendritic excitability of layer (L) 5 pyramidal neurons in the prefrontal cortex of the fmr1 -/y mouse is significantly altered due to changes in several voltage-gated ion channels. In addition to L5 pyramidal neurons, L2/3 pyramidal neurons play an important role in prefrontal circuitry, integrating inputs from both lower brain regions and the contralateral cortex. Using whole-cell current clamp recording, we found that L2/3 pyramidal neurons in prefrontal cortex of fmr1 -/y mouse fired more action potentials for a given stimulus compared with wild-type neurons. In addition, action potentials in fmr1 -/y neurons were significantly larger, faster and narrower. Voltage clamp of outside-out patches from L2/3 neurons revealed that the transient Na + current was significantly larger in fmr1 -/y neurons. Furthermore, the activation curve of somatic A-type K + current was depolarized. Realistic conductance-based simulations revealed that these biophysical changes in Na + and K + channel function could reliably reproduce the observed increase in action potential firing and altered action potential waveform. These results, in conjunction with our prior findings on L5 neurons, suggest that principal neurons in the circuitry of the medial prefrontal cortex are altered in distinct ways in the fmr1 -/y mouse and may contribute to dysfunctional prefrontal cortex processing in fragile X syndrome. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

AN IMPROVED STRATEGY FOR REGRESSION OF BIOPHYSICAL VARIABLES AND LANDSAT ETM+ DATA. (R828309)

EPA Science Inventory

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent wood...

Modeling global macroclimatic constraints on ectotherm energy budgets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grant, B.W.; Porter, W.P.

1992-12-31

The authors describe a mechanistic individual-based model of how global macroclimatic constraints affect the energy budgets of ectothermic animals. The model uses macroclimatic and biophysical characters of the habitat and organism and tenets of heat transfer theory to calculate hourly temperature availabilities over a year. Data on the temperature dependence of activity rate, metabolism, food consumption and food processing capacity are used to estimate the net rate of resource assimilation which is then integrated over time. They present a new test of this model in which they show that the predicted energy budget sizes for 11 populations of the lizardmore » Sceloporus undulates are in close agreement with observed results from previous field studies. This demonstrates that model tests rae feasible and the results are reasonable. Further, since the model represents an upper bound to the size of the energy budget, observed residual deviations form explicit predictions about the effects of environmental constraints on the bioenergetics of the study lizards within each site that may be tested by future field and laboratory studies. Three major new improvements to the modeling are discussed. They present a means to estimate microclimate thermal heterogeneity more realistically and include its effects on field rates of individual activity and food consumption. Second, they describe an improved model of digestive function involving batch processing of consumed food. Third, they show how optimality methods (specifically the methods of stochastic dynamic programming) may be included to model the fitness consequences of energy allocation decisions subject to food consumption and processing constraints which are predicted from the microclimate and physiological modeling.« less

Assessment of the biophysical impacts of utility-scale photovoltaics through observations and modelling

NASA Astrophysics Data System (ADS)

Broadbent, A. M.; Georgescu, M.; Krayenhoff, E. S.; Sailor, D.

2017-12-01

Utility-scale solar power plants are a rapidly growing component of the solar energy sector. Utility-scale photovoltaic (PV) solar power generation in the United States has increased by 867% since 2012 (EIA, 2016). This expansion is likely to continue as the cost PV technologies decrease. While most agree that solar power can decrease greenhouse gas emissions, the biophysical effects of PV systems on surface energy balance (SEB), and implications for surface climate, are not well understood. To our knowledge, there has never been a detailed observational study of SEB at a utility-scale solar array. This study presents data from an eddy covariance observational tower, temporarily placed above a utility-scale PV array in Southern Arizona. Comparison of PV SEB with a reference (unmodified) site, shows that solar panels can alter the SEB and near surface climate. SEB observations are used to develop and validate a new and more complete SEB PV model. In addition, the PV model is compared to simpler PV modelling methods. The simpler PV models produce differing results to our newly developed model and cannot capture the more complex processes that influence PV SEB. Finally, hypothetical scenarios of PV expansion across the continental United States (CONUS) were developed using various spatial mapping criteria. CONUS simulations of PV expansion reveal regional variability in biophysical effects of PV expansion. The study presents the first rigorous and validated simulations of the biophysical effects of utility-scale PV arrays.

Biophysical model of bacterial cell interactions with nanopatterned cicada wing surfaces.

PubMed

Pogodin, Sergey; Hasan, Jafar; Baulin, Vladimir A; Webb, Hayden K; Truong, Vi Khanh; Phong Nguyen, The Hong; Boshkovikj, Veselin; Fluke, Christopher J; Watson, Gregory S; Watson, Jolanta A; Crawford, Russell J; Ivanova, Elena P

2013-02-19

The nanopattern on the surface of Clanger cicada (Psaltoda claripennis) wings represents the first example of a new class of biomaterials that can kill bacteria on contact based solely on their physical surface structure. The wings provide a model for the development of novel functional surfaces that possess an increased resistance to bacterial contamination and infection. We propose a biophysical model of the interactions between bacterial cells and cicada wing surface structures, and show that mechanical properties, in particular cell rigidity, are key factors in determining bacterial resistance/sensitivity to the bactericidal nature of the wing surface. We confirmed this experimentally by decreasing the rigidity of surface-resistant strains through microwave irradiation of the cells, which renders them susceptible to the wing effects. Our findings demonstrate the potential benefits of incorporating cicada wing nanopatterns into the design of antibacterial nanomaterials. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Delineating Biophysical Environments of the Sunda Banda Seascape, Indonesia

PubMed Central

Wang, Mingshu; Ahmadia, Gabby N.; Chollett, Iliana; Huang, Charles; Fox, Helen; Wijonarno, Anton; Madden, Marguerite

2015-01-01

The Sunda Banda Seascape (SBS), located in the center of the Coral Triangle, is a global center of marine biodiversity and a conservation priority. We proposed the first biophysical environmental delineation of the SBS using globally available satellite remote sensing and model-assimilated data to categorize this area into unique and meaningful biophysical classes. Specifically, the SBS was partitioned into eight biophysical classes characterized by similar sea surface temperature, chlorophyll a concentration, currents, and salinity patterns. Areas within each class were expected to have similar habitat types and ecosystem functions. Our work supplemented prevailing global marine management schemes by focusing in on a regional scale with finer spatial resolution. It also provided a baseline for academic research, ecological assessments and will facilitate marine spatial planning and conservation activities in the area. In addition, the framework and methods of delineating biophysical environments we presented can be expanded throughout the whole Coral Triangle to support research and conservation activities in this important region. PMID:25648170

A dataset mapping the potential biophysical effects of vegetation cover change

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-02-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

A dataset mapping the potential biophysical effects of vegetation cover change

PubMed Central

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-01-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes. PMID:29461538

[Pulmonary surfactants: in vivo structure and in vitro biophysical models for investigation and its perspectives].

PubMed

Lalchev, Z; Khristova, E; Vasiliev, Kh; Todorov, R; Ekserova, D

2007-01-01

The metabolism, composition, structure and functions of the alveolar surfactant (AS) are described. The most adequate biophysical models for investigation of AS are considered. The principals and possibilities of three mostly used models are described in details: Monolayers, Spinning drop method and Thin liquid films. Some of the studies of Bulgarian biophysical, physicochemical, biochemical and medical groups on the structure and mechanism of action of AS in vivo using samples of amniotic fluid (AF), animal pulmonary lavages (PL) and tracheal aspirates (TA) of newborns and adults are summarized. The role of specific surfactant proteins (SP-A, SP-B, SP-C and SP-D) on the properties and function of AS is demonstrated. The opportunities of the model investigations for application in laboratory pre- and postnatal diagnosis of the respiratory distress syndrome (RDS), as well as for the efficiency of RDS therapy during exogenous surfactant therapy with ALEC (UK), Survanta (USA), Exosurf (USA), Curosurf (Italy) u Alveofact (Germany) are considered.

Astrocytes, Synapses and Brain Function: A Computational Approach

NASA Astrophysics Data System (ADS)

Nadkarni, Suhita

2006-03-01

Modulation of synaptic reliability is one of the leading mechanisms involved in long- term potentiation (LTP) and long-term depression (LTD) and therefore has implications in information processing in the brain. A recently discovered mechanism for modulating synaptic reliability critically involves recruitments of astrocytes - star- shaped cells that outnumber the neurons in most parts of the central nervous system. Astrocytes until recently were thought to be subordinate cells merely participating in supporting neuronal functions. New evidence, however, made available by advances in imaging technology has changed the way we envision the role of these cells in synaptic transmission and as modulator of neuronal excitability. We put forward a novel mathematical framework based on the biophysics of the bidirectional neuron-astrocyte interactions that quantitatively accounts for two distinct experimental manifestation of recruitment of astrocytes in synaptic transmission: a) transformation of a low fidelity synapse transforms into a high fidelity synapse and b) enhanced postsynaptic spontaneous currents when astrocytes are activated. Such a framework is not only useful for modeling neuronal dynamics in a realistic environment but also provides a conceptual basis for interpreting experiments. Based on this modeling framework, we explore the role of astrocytes for neuronal network behavior such as synchrony and correlations and compare with experimental data from cultured networks.

Estimating Temporal Causal Interaction between Spike Trains with Permutation and Transfer Entropy

PubMed Central

Li, Zhaohui; Li, Xiaoli

2013-01-01

Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performance of this method is evaluated with the spike trains generated by an Izhikevich’s neuronal model. Results show that the NPTE method can effectively estimate the causal interaction between two neurons without influence of data length. Considering both the precision of time delay estimated and the robustness of information flow estimated against neuronal firing rate, the NPTE method is superior to other information theoretic method including normalized transfer entropy, symbolic transfer entropy and permutation conditional mutual information. To test the performance of NPTE on analyzing simulated biophysically realistic synapses, an Izhikevich’s cortical network that based on the neuronal model is employed. It is found that the NPTE method is able to characterize mutual interactions and identify spurious causality in a network of three neurons exactly. We conclude that the proposed method can obtain more reliable comparison of interactions between different pairs of neurons and is a promising tool to uncover more details on the neural coding. PMID:23940662

Role of cytosolic calcium diffusion in cardiac purkinje cells.

PubMed

Limbu, Bijay; Shah, Kushal; Deo, Makarand

2016-08-01

The Cardiac Purkinje cells (PCs) exhibit distinct calcium (Ca2+) homeostasis than that in ventricular myocytes (VMs). Due to lack of t-tubules in PCs, the Ca2+ ions entering the cell have to diffuse through the cytoplasm to reach the sarcoplasmic reticulum (SR) before triggering Ca2+-induced-Ca2+-release (CICR). In recent experimental studies PCs have been shown to be more susceptible to action potential (AP) abnormalities than the VMs, however the exact mechanisms are poorly understood. In this study, we utilize morphologically realistic detailed biophysical mathematical model of a murine PC to systematically examine the role intracellular Ca2+ diffusion in the APs of PCs. A biphasic spatiotemporal Ca2+ diffusion process, as observed experimentally, was implemented in the model which includes radial Ca2+ wavelets and cell wide longitudinal Ca2+ diffusion wave (CWW). The AP morphology, specifically plateau, is affected due to changes in intracellular Ca2+ dynamics. When Ca2+ concentration in sarcolemmal region is elevated, it activated inward sodium Ca2+ exchanger (NCX) current resulting into prolongation of the plateau at faster diffusion rates. Our results demonstrate that the cytosolic Ca2+ diffusion waves play a significant role in shaping APs of PCs and could provide mechanistic insights into the increased arrhythmogeneity of PCs.

An extended model of vesicle fusion at the plasma membrane to estimate protein lateral diffusion from TIRF microscopy images.

PubMed

Basset, Antoine; Bouthemy, Patrick; Boulanger, Jérôme; Waharte, François; Salamero, Jean; Kervrann, Charles

2017-07-24

Characterizing membrane dynamics is a key issue to understand cell exchanges with the extra-cellular medium. Total internal reflection fluorescence microscopy (TIRFM) is well suited to focus on the late steps of exocytosis at the plasma membrane. However, it is still a challenging task to quantify (lateral) diffusion and estimate local dynamics of proteins. A new model was introduced to represent the behavior of cargo transmembrane proteins during the vesicle fusion to the plasma membrane at the end of the exocytosis process. Two biophysical parameters, the diffusion coefficient and the release rate parameter, are automatically estimated from TIRFM image sequences, to account for both the lateral diffusion of molecules at the membrane and the continuous release of the proteins from the vesicle to the plasma membrane. Quantitative evaluation on 300 realistic computer-generated image sequences demonstrated the efficiency and accuracy of the method. The application of our method on 16 real TIRFM image sequences additionally revealed differences in the dynamic behavior of Transferrin Receptor (TfR) and Langerin proteins. An automated method has been designed to simultaneously estimate the diffusion coefficient and the release rate for each individual vesicle fusion event at the plasma membrane in TIRFM image sequences. It can be exploited for further deciphering cell membrane dynamics.

Cooperation through Competition-Dynamics and Microeconomics of a Minimal Nutrient Trade System in Arbuscular Mycorrhizal Symbiosis.

PubMed

Schott, Stephan; Valdebenito, Braulio; Bustos, Daniel; Gomez-Porras, Judith L; Sharma, Tripti; Dreyer, Ingo

2016-01-01

In arbuscular mycorrhizal (AM) symbiosis, fungi and plants exchange nutrients (sugars and phosphate, for instance) for reciprocal benefit. Until now it is not clear how this nutrient exchange system works. Here, we used computational cell biology to simulate the dynamics of a network of proton pumps and proton-coupled transporters that are upregulated during AM formation. We show that this minimal network is sufficient to describe accurately and realistically the nutrient trade system. By applying basic principles of microeconomics, we link the biophysics of transmembrane nutrient transport with the ecology of organismic interactions and straightforwardly explain macroscopic scenarios of the relations between plant and AM fungus. This computational cell biology study allows drawing far reaching hypotheses about the mechanism and the regulation of nutrient exchange and proposes that the "cooperation" between plant and fungus can be in fact the result of a competition between both for the same resources in the tiny periarbuscular space. The minimal model presented here may serve as benchmark to evaluate in future the performance of more complex models of AM nutrient exchange. As a first step toward this goal, we included SWEET sugar transporters in the model and show that their co-occurrence with proton-coupled sugar transporters results in a futile carbon cycle at the plant plasma membrane proposing that two different pathways for the same substrate should not be active at the same time.

From Single-Cell Dynamics to Scaling Laws in Oncology

NASA Astrophysics Data System (ADS)

Chignola, Roberto; Sega, Michela; Stella, Sabrina; Vyshemirsky, Vladislav; Milotti, Edoardo

We are developing a biophysical model of tumor biology. We follow a strictly quantitative approach where each step of model development is validated by comparing simulation outputs with experimental data. While this strategy may slow down our advancements, at the same time it provides an invaluable reward: we can trust simulation outputs and use the model to explore territories of cancer biology where current experimental techniques fail. Here, we review our multi-scale biophysical modeling approach and show how a description of cancer at the cellular level has led us to general laws obeyed by both in vitro and in vivo tumors.

Mathematical and computational modelling of skin biophysics: a review

PubMed Central

2017-01-01

The objective of this paper is to provide a review on some aspects of the mathematical and computational modelling of skin biophysics, with special focus on constitutive theories based on nonlinear continuum mechanics from elasticity, through anelasticity, including growth, to thermoelasticity. Microstructural and phenomenological approaches combining imaging techniques are also discussed. Finally, recent research applications on skin wrinkles will be presented to highlight the potential of physics-based modelling of skin in tackling global challenges such as ageing of the population and the associated skin degradation, diseases and traumas. PMID:28804267

Mathematical and computational modelling of skin biophysics: a review

NASA Astrophysics Data System (ADS)

Limbert, Georges

2017-07-01

The objective of this paper is to provide a review on some aspects of the mathematical and computational modelling of skin biophysics, with special focus on constitutive theories based on nonlinear continuum mechanics from elasticity, through anelasticity, including growth, to thermoelasticity. Microstructural and phenomenological approaches combining imaging techniques are also discussed. Finally, recent research applications on skin wrinkles will be presented to highlight the potential of physics-based modelling of skin in tackling global challenges such as ageing of the population and the associated skin degradation, diseases and traumas.

A Case Study of Cognitive and Biophysical Models of Education as Linked to Anxiety and Obsessive Compulsive Disorders

ERIC Educational Resources Information Center

Maye, Kelly M.

2012-01-01

Cognitive and biophysical factors have been considered contributors linked to identifiable markers of obsessive compulsive and anxiety disorders. Research demonstrates multiple causes and mixed results for the short-term success of educational programs designed to ameliorate problems that children with obsessive compulsive and anxiety disorders…

Decompression models: review, relevance and validation capabilities.

PubMed

Hugon, J

2014-01-01

For more than a century, several types of mathematical models have been proposed to describe tissue desaturation mechanisms in order to limit decompression sickness. These models are statistically assessed by DCS cases, and, over time, have gradually included bubble formation biophysics. This paper proposes to review this evolution and discuss its limitations. This review is organized around the comparison of decompression model biophysical criteria and theoretical foundations. Then, the DCS-predictive capability was analyzed to assess whether it could be improved by combining different approaches. Most of the operational decompression models have a neo-Haldanian form. Nevertheless, bubble modeling has been gaining popularity, and the circulating bubble amount has become a major output. By merging both views, it seems possible to build a relevant global decompression model that intends to simulate bubble production while predicting DCS risks for all types of exposures and decompression profiles. A statistical approach combining both DCS and bubble detection databases has to be developed to calibrate a global decompression model. Doppler ultrasound and DCS data are essential: i. to make correlation and validation phases reliable; ii. to adjust biophysical criteria to fit at best the observed bubble kinetics; and iii. to build a relevant risk function.

An ethnographic study: Becoming a physics expert in a biophysics research group

NASA Astrophysics Data System (ADS)

Rodriguez, Idaykis

Expertise in physics has been traditionally studied in cognitive science, where physics expertise is understood through the difference between novice and expert problem solving skills. The cognitive perspective of physics experts only create a partial model of physics expertise and does not take into account the development of physics experts in the natural context of research. This dissertation takes a social and cultural perspective of learning through apprenticeship to model the development of physics expertise of physics graduate students in a research group. I use a qualitative methodological approach of an ethnographic case study to observe and video record the common practices of graduate students in their biophysics weekly research group meetings. I recorded notes on observations and conduct interviews with all participants of the biophysics research group for a period of eight months. I apply the theoretical framework of Communities of Practice to distinguish the cultural norms of the group that cultivate physics expert practices. Results indicate that physics expertise is specific to a topic or subfield and it is established through effectively publishing research in the larger biophysics research community. The participant biophysics research group follows a learning trajectory for its students to contribute to research and learn to communicate their research in the larger biophysics community. In this learning trajectory students develop expert member competencies to learn to communicate their research and to learn the standards and trends of research in the larger research community. Findings from this dissertation expand the model of physics expertise beyond the cognitive realm and add the social and cultural nature of physics expertise development. This research also addresses ways to increase physics graduate student success towards their PhD. and decrease the 48% attrition rate of physics graduate students. Cultivating effective research experiences that give graduate students agency and autonomy beyond their research groups gives students the motivation to finish graduate school and establish their physics expertise.

Dynamic causal modelling: a critical review of the biophysical and statistical foundations.

PubMed

Daunizeau, J; David, O; Stephan, K E

2011-09-15

The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs. Copyright © 2009 Elsevier Inc. All rights reserved.

Preface: Special Topic on Single-Molecule Biophysics

NASA Astrophysics Data System (ADS)

Makarov, Dmitrii E.; Schuler, Benjamin

2018-03-01

Single-molecule measurements are now almost routinely used to study biological systems and processes. The scope of this special topic emphasizes the physics side of single-molecule observations, with the goal of highlighting new developments in physical techniques as well as conceptual insights that single-molecule measurements bring to biophysics. This issue also comprises recent advances in theoretical physical models of single-molecule phenomena, interpretation of single-molecule signals, and fundamental areas of statistical mechanics that are related to single-molecule observations. A particular goal is to illustrate the increasing synergy between theory, simulation, and experiment in single-molecule biophysics.

Valuing investments in sustainable land management in the Upper Tana River basin, Kenya.

PubMed

Vogl, Adrian L; Bryant, Benjamin P; Hunink, Johannes E; Wolny, Stacie; Apse, Colin; Droogers, Peter

2017-06-15

We analyze the impacts of investments in sustainable land use practices on ecosystem services in the Upper Tana basin, Kenya. This work supports implementation of the Upper Tana-Nairobi Water Fund, a public-private partnership to safeguard ecosystem service provision and food security. We apply an integrated modelling framework, building on local knowledge and previous field- and model-based studies, to link biophysical landscape changes at high temporal and spatial resolution to economic benefits for key actors in the basin. The primary contribution of this study is that it a) presents a comprehensive analysis for targeting interventions that takes into account stakeholder preferences, local environmental and socio-economic conditions, b) relies on detailed, process-based, biophysical models to demonstrate the biophysical return on those investments for a practical, decision-driven case, and c) in close collaboration with downstream water users, links those biophysical outputs to monetary metrics, including: reduced water treatment costs, increased hydropower production, and crop yield benefits for agricultural producers in the conservation area. This study highlights the benefits and trade-offs that come with conducting participatory research as part of a stakeholder engagement process: while results are more likely to be decision-relevant within the local context, navigating stakeholder expectations and data limitations present ongoing challenges. Copyright © 2016 Elsevier Ltd. All rights reserved.

Winnowing and Flocculation in Bio-physical Cohesive Substrate: A Flume Experimental and Estuarine Study

NASA Astrophysics Data System (ADS)

Ye, L.; Parsons, D. R.; Manning, A. J.

2016-12-01

Cohesive sediment, or mud, is ubiquitously found in most aqueous environments, such as coasts and estuaries. The study of cohesive sediment behaviors requires the synchronous description of mutual interactions of grains (e.g., winnowing and flocculation), their physical properties (e.g., grain size) and also the ambient water. Herein, a series of flume experiments (14 runs) with different substrate mixtures of sand-clay-EPS (Extracellular Polymeric Substrates: secreted by aquatic microorganisms) are combined with an estuarine field survey (Dee estuary, NW England) to investigate the behavior of suspensions over bio-physical cohesive substrates. The experimental results indicate that winnowing and flocculation occur pervasively in bio-physical cohesive flow systems. Importantly however, the evolution of the bed and bedform dynamics and hence turbulence production can be lower when cohesivity is high. The estuarine survey also revealed that the bio-physical cohesion provided by both the clay and microorganism fractions in the bed, that pervasively exists in many natural estuarine systems, plays a significant role in controlling the interactions between bed substrate and sediment suspension and deposition, including controlling processes such as sediment winnowing, flocculation and re-deposition. Full understanding of these processes are essential in advancing sediment transport modelling and prediction studies across natural estuarine systems and the work will report on an improved conceptual model for sediment sorting deposition in bio-physical cohesive substrates.

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

NASA Technical Reports Server (NTRS)

Thenkabail, Prasad S.; Mariotto, Isabella; Gumma, Murali Krishna; Middleton, Elizabeth M.; Landis, David R.; Huemmrich, K. Fred

2013-01-01

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

NASA Astrophysics Data System (ADS)

Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

2015-04-01

Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

A neuro-computational model of economic decisions.

PubMed

Rustichini, Aldo; Padoa-Schioppa, Camillo

2015-09-01

Neuronal recordings and lesion studies indicate that key aspects of economic decisions take place in the orbitofrontal cortex (OFC). Previous work identified in this area three groups of neurons encoding the offer value, the chosen value, and the identity of the chosen good. An important and open question is whether and how decisions could emerge from a neural circuit formed by these three populations. Here we adapted a biophysically realistic neural network previously proposed for perceptual decisions (Wang XJ. Neuron 36: 955-968, 2002; Wong KF, Wang XJ. J Neurosci 26: 1314-1328, 2006). The domain of economic decisions is significantly broader than that for which the model was originally designed, yet the model performed remarkably well. The input and output nodes of the network were naturally mapped onto two groups of cells in OFC. Surprisingly, the activity of interneurons in the network closely resembled that of the third group of cells, namely, chosen value cells. The model reproduced several phenomena related to the neuronal origins of choice variability. It also generated testable predictions on the excitatory/inhibitory nature of different neuronal populations and on their connectivity. Some aspects of the empirical data were not reproduced, but simple extensions of the model could overcome these limitations. These results render a biologically credible model for the neuronal mechanisms of economic decisions. They demonstrate that choices could emerge from the activity of cells in the OFC, suggesting that chosen value cells directly participate in the decision process. Importantly, Wang's model provides a platform to investigate the implications of neuroscience results for economic theory. Copyright © 2015 the American Physiological Society.

CellML metadata standards, associated tools and repositories

PubMed Central

Beard, Daniel A.; Britten, Randall; Cooling, Mike T.; Garny, Alan; Halstead, Matt D.B.; Hunter, Peter J.; Lawson, James; Lloyd, Catherine M.; Marsh, Justin; Miller, Andrew; Nickerson, David P.; Nielsen, Poul M.F.; Nomura, Taishin; Subramanium, Shankar; Wimalaratne, Sarala M.; Yu, Tommy

2009-01-01

The development of standards for encoding mathematical models is an important component of model building and model sharing among scientists interested in understanding multi-scale physiological processes. CellML provides such a standard, particularly for models based on biophysical mechanisms, and a substantial number of models are now available in the CellML Model Repository. However, there is an urgent need to extend the current CellML metadata standard to provide biological and biophysical annotation of the models in order to facilitate model sharing, automated model reduction and connection to biological databases. This paper gives a broad overview of a number of new developments on CellML metadata and provides links to further methodological details available from the CellML website. PMID:19380315

A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

Treesearch

Rey S. Ofren; Edward Harvey

2000-01-01

A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

Chapter 6 - Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Treesearch

Jennifer L. Long; Melanie Miller; James P. Menakis; Robert E. Keane

2006-01-01

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype Project, required a system for classifying vegetation composition, biophysical settings, and vegetation structure to facilitate the mapping of vegetation and wildland fuel characteristics and the simulation of vegetation dynamics using landscape modeling. We developed...

An improved strategy for regression of biophysical variables and Landsat ETM+ data.

Treesearch

Warren B. Cohen; Thomas K. Maiersperger; Stith T. Gower; David P. Turner

2003-01-01

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent woody canopy cover, and leaf area index (LAI). Traditional methods of regression are not...

Places where wildfire potential and social vulnerability coincide in the coterminous United States

Treesearch

Gabriel Wigtil; Roger B. Hammer; Jeffrey D. Kline; Miranda H. Mockrin; Susan I. Stewart; Daniel Roper; Volker C. Radeloff

2016-01-01

The hazards-of-place model posits that vulnerability to environmental hazards depends on both biophysical and social factors. Biophysical factors determine where wildfire potential is elevated, whereas social factors determine where and how people are affected by wildfire. We evaluated place vulnerability to wildfire hazards in the coterminous US. We developed...

Fuel moisture content estimation: a land-surface modelling approach applied to African savannas

NASA Astrophysics Data System (ADS)

Ghent, D.; Spessa, A.; Kaduk, J.; Balzter, H.

2009-04-01

Despite the importance of fire to the global climate system, in terms of emissions from biomass burning, ecosystem structure and function, and changes to surface albedo, current land-surface models do not adequately estimate key variables affecting fire ignition and propagation. Fuel moisture content (FMC) is considered one of the most important of these variables (Chuvieco et al., 2004). Biophysical models, with appropriate plant functional type parameterisations, are the most viable option to adequately predict FMC over continental scales at high temporal resolution. However, the complexity of plant-water interactions, and the variability associated with short-term climate changes, means it is one of the most difficult fire variables to quantify and predict. Our work attempts to resolve this issue using a combination of satellite data and biophysical modelling applied to Africa. The approach we take is to represent live FMC as a surface dryness index; expressed as the ratio between the Normalised Difference Vegetation Index (NDVI) and land-surface temperature (LST). It has been argued in previous studies (Sandholt et al., 2002; Snyder et al., 2006), that this ratio displays a statistically stronger correlation to FMC than either of the variables, considered separately. In this study, simulated FMC is constrained through the assimilation of remotely sensed LST and NDVI data into the land-surface model JULES (Joint-UK Land Environment Simulator). Previous modelling studies of fire activity in Africa savannas, such as Lehsten et al. (2008), have reported significant levels of uncertainty associated with the simulations. This uncertainty is important because African savannas are among some of the most frequently burnt ecosystems and are a major source of greenhouse trace gases and aerosol emissions (Scholes et al., 1996). Furthermore, regional climate model studies indicate that many parts of the African savannas will experience drier and warmer conditions in future (IPCC 2007). The simulation of realistic fire disturbance regimes with biophysical and biogeochemical models is a prerequisite for reducing the uncertainty of the African carbon cycle, and the feedbacks associated with this cycle and the global climate system. Using multi-temporal modelling analysis techniques, we present preliminary results that provide a more robust estimation of live FMC. References Chuvieco, E., Aguado, I. and Dimitrakopoulos, A. P. (2004) Conversion of fuel moisture content values to ignition potential for integrated fire danger assessment. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 34(11): 2284-2293. IPCC (2007) 'Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)].' IPCC, (Geneva, Switzerland). Lehsten, V., Tansey, K. J., Balzter, H, Thonicke, K., Spessa, A., Weber, U., Smith, B., and Arneth, A. (2008). Estimating carbon emissions from African wildfires. Accepted Biogeosciences. Sandholt, I., Rasmussen, K. & Andersen, J. (2002) A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79(2-3): 213-224. Scholes, R. J., Ward, D. E. and Justice, C. O. (1996) Emissions of trace gases and aerosol particles due to vegetation burning in southern hemisphere Africa. Journal of Geophysical Research-Atmospheres 101(D19): 23677-23682. Snyder, R. L., Spano, D., Duce, P., Baldocchi, D., Xu, L. K. & Kyaw, T. P. U. (2006) A fuel dryness index for grassland fire-danger assessment. Agricultural and Forest Meteorology 139(1-2): 1-11.

Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery

PubMed Central

Malerba, Paola; Straudi, Sofia; Fregni, Felipe; Bazhenov, Maxim; Basaglia, Nino

2017-01-01

Stroke is a leading cause of worldwide disability, and up to 75% of survivors suffer from some degree of arm paresis. Recently, rehabilitation of stroke patients has focused on recovering motor skills by taking advantage of use-dependent neuroplasticity, where high-repetition of goal-oriented movement is at times combined with non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS). Merging the two approaches is thought to provide outlasting clinical gains, by enhancing synaptic plasticity and motor relearning in the motor cortex primary area. However, this general approach has shown mixed results across the stroke population. In particular, stroke location has been found to correlate with the likelihood of success, which suggests that different patients might require different protocols. Understanding how motor rehabilitation and stimulation interact with ongoing neural dynamics is crucial to optimize rehabilitation strategies, but it requires theoretical and computational models to consider the multiple levels at which this complex phenomenon operate. In this work, we argue that biophysical models of cortical dynamics are uniquely suited to address this problem. Specifically, biophysical models can predict treatment efficacy by introducing explicit variables and dynamics for damaged connections, changes in neural excitability, neurotransmitters, neuromodulators, plasticity mechanisms, and repetitive movement, which together can represent brain state, effect of incoming stimulus, and movement-induced activity. In this work, we hypothesize that effects of tDCS depend on ongoing neural activity and that tDCS effects on plasticity may be also related to enhancing inhibitory processes. We propose a model design for each step of this complex system, and highlight strengths and limitations of the different modeling choices within our approach. Our theoretical framework proposes a change in paradigm, where biophysical models can contribute to the future design of novel protocols, in which combined tDCS and motor rehabilitation strategies are tailored to the ongoing dynamics that they interact with, by considering the known biophysical factors recruited by such protocols and their interaction. PMID:28280482

Accelerating cardiac bidomain simulations using graphics processing units.

PubMed

Neic, A; Liebmann, M; Hoetzl, E; Mitchell, L; Vigmond, E J; Haase, G; Plank, G

2012-08-01

Anatomically realistic and biophysically detailed multiscale computer models of the heart are playing an increasingly important role in advancing our understanding of integrated cardiac function in health and disease. Such detailed simulations, however, are computationally vastly demanding, which is a limiting factor for a wider adoption of in-silico modeling. While current trends in high-performance computing (HPC) hardware promise to alleviate this problem, exploiting the potential of such architectures remains challenging since strongly scalable algorithms are necessitated to reduce execution times. Alternatively, acceleration technologies such as graphics processing units (GPUs) are being considered. While the potential of GPUs has been demonstrated in various applications, benefits in the context of bidomain simulations where large sparse linear systems have to be solved in parallel with advanced numerical techniques are less clear. In this study, the feasibility of multi-GPU bidomain simulations is demonstrated by running strong scalability benchmarks using a state-of-the-art model of rabbit ventricles. The model is spatially discretized using the finite element methods (FEM) on fully unstructured grids. The GPU code is directly derived from a large pre-existing code, the Cardiac Arrhythmia Research Package (CARP), with very minor perturbation of the code base. Overall, bidomain simulations were sped up by a factor of 11.8 to 16.3 in benchmarks running on 6-20 GPUs compared to the same number of CPU cores. To match the fastest GPU simulation which engaged 20 GPUs, 476 CPU cores were required on a national supercomputing facility.

Accelerating Cardiac Bidomain Simulations Using Graphics Processing Units

PubMed Central

Neic, Aurel; Liebmann, Manfred; Hoetzl, Elena; Mitchell, Lawrence; Vigmond, Edward J.; Haase, Gundolf

2013-01-01

Anatomically realistic and biophysically detailed multiscale computer models of the heart are playing an increasingly important role in advancing our understanding of integrated cardiac function in health and disease. Such detailed simulations, however, are computationally vastly demanding, which is a limiting factor for a wider adoption of in-silico modeling. While current trends in high-performance computing (HPC) hardware promise to alleviate this problem, exploiting the potential of such architectures remains challenging since strongly scalable algorithms are necessitated to reduce execution times. Alternatively, acceleration technologies such as graphics processing units (GPUs) are being considered. While the potential of GPUs has been demonstrated in various applications, benefits in the context of bidomain simulations where large sparse linear systems have to be solved in parallel with advanced numerical techniques are less clear. In this study, the feasibility of multi-GPU bidomain simulations is demonstrated by running strong scalability benchmarks using a state-of-the-art model of rabbit ventricles. The model is spatially discretized using the finite element methods (FEM) on fully unstructured grids. The GPU code is directly derived from a large pre-existing code, the Cardiac Arrhythmia Research Package (CARP), with very minor perturbation of the code base. Overall, bidomain simulations were sped up by a factor of 11.8 to 16.3 in benchmarks running on 6–20 GPUs compared to the same number of CPU cores. To match the fastest GPU simulation which engaged 20GPUs, 476 CPU cores were required on a national supercomputing facility. PMID:22692867

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

Effective connectivity: Influence, causality and biophysical modeling

PubMed Central

Valdes-Sosa, Pedro A.; Roebroeck, Alard; Daunizeau, Jean; Friston, Karl

2011-01-01

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments. PMID:21477655

PyFolding: Open-Source Graphing, Simulation, and Analysis of the Biophysical Properties of Proteins.

PubMed

Lowe, Alan R; Perez-Riba, Albert; Itzhaki, Laura S; Main, Ewan R G

2018-02-06

For many years, curve-fitting software has been heavily utilized to fit simple models to various types of biophysical data. Although such software packages are easy to use for simple functions, they are often expensive and present substantial impediments to applying more complex models or for the analysis of large data sets. One field that is reliant on such data analysis is the thermodynamics and kinetics of protein folding. Over the past decade, increasingly sophisticated analytical models have been generated, but without simple tools to enable routine analysis. Consequently, users have needed to generate their own tools or otherwise find willing collaborators. Here we present PyFolding, a free, open-source, and extensible Python framework for graphing, analysis, and simulation of the biophysical properties of proteins. To demonstrate the utility of PyFolding, we have used it to analyze and model experimental protein folding and thermodynamic data. Examples include: 1) multiphase kinetic folding fitted to linked equations, 2) global fitting of multiple data sets, and 3) analysis of repeat protein thermodynamics with Ising model variants. Moreover, we demonstrate how PyFolding is easily extensible to novel functionality beyond applications in protein folding via the addition of new models. Example scripts to perform these and other operations are supplied with the software, and we encourage users to contribute notebooks and models to create a community resource. Finally, we show that PyFolding can be used in conjunction with Jupyter notebooks as an easy way to share methods and analysis for publication and among research teams. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart.

PubMed

Chapelle, D; Fragu, M; Mallet, V; Moireau, P

2013-11-01

We present the fundamental principles of data assimilation underlying the Verdandi library, and how they are articulated with the modular architecture of the library. This translates--in particular--into the definition of standardized interfaces through which the data assimilation library interoperates with the model simulation software and the so-called observation manager. We also survey various examples of data assimilation applied to the personalization of biophysical models, in particular, for cardiac modeling applications within the euHeart European project. This illustrates the power of data assimilation concepts in such novel applications, with tremendous potential in clinical diagnosis assistance.

Network model of top-down influences on local gain and contextual interactions in visual cortex.

PubMed

Piëch, Valentin; Li, Wu; Reeke, George N; Gilbert, Charles D

2013-10-22

The visual system uses continuity as a cue for grouping oriented line segments that define object boundaries in complex visual scenes. Many studies support the idea that long-range intrinsic horizontal connections in early visual cortex contribute to this grouping. Top-down influences in primary visual cortex (V1) play an important role in the processes of contour integration and perceptual saliency, with contour-related responses being task dependent. This suggests an interaction between recurrent inputs to V1 and intrinsic connections within V1 that enables V1 neurons to respond differently under different conditions. We created a network model that simulates parametrically the control of local gain by hypothetical top-down modification of local recurrence. These local gain changes, as a consequence of network dynamics in our model, enable modulation of contextual interactions in a task-dependent manner. Our model displays contour-related facilitation of neuronal responses and differential foreground vs. background responses over the neuronal ensemble, accounting for the perceptual pop-out of salient contours. It quantitatively reproduces the results of single-unit recording experiments in V1, highlighting salient contours and replicating the time course of contextual influences. We show by means of phase-plane analysis that the model operates stably even in the presence of large inputs. Our model shows how a simple form of top-down modulation of the effective connectivity of intrinsic cortical connections among biophysically realistic neurons can account for some of the response changes seen in perceptual learning and task switching.

Forest Management in Earth System Modelling: a Vertically Discretised Canopy Description for ORCHIDEE and Effects on European Climate Since 1750

NASA Astrophysics Data System (ADS)

McGrath, M.; Luyssaert, S.; Naudts, K.; Chen, Y.; Ryder, J.; Otto, J.; Valade, A.

2015-12-01

Forest management has the potential to impact surface physical characteristics to the same degree that changes in land cover do. The impacts of land cover changes on the global climate are well-known. Despite an increasingly detailed understanding of the potential for forest management to affect climate, none of the current generation of Earth system models account for forest management through their land surface modules. We addressed this gap by developing and reparameterizing the ORCHIDEE land surface model to simulate the biogeochemical and biophysical effects of forest management. Through vertical discretization of the forest canopy and corresponding modifications to the energy budget, radiation transfer, and carbon allocation, forest management can now be simulated much more realistically on the global scale. This model was used to explore the effect of forest management on European climate since 1750. Reparameterization was carried out to replace generic forest plant functional types with real tree species, covering the most dominant species across the continent. Historical forest management and land cover maps were created to run the simulations from 1600 until the present day. The model was coupled to the atmospheric model LMDz to explore differences in climate between 1750 and 2010 and attribute those differences to changes in atmospheric carbon dioxide concentrations and concurrent warming, land cover, species composition, and wood extraction. Although Europe's forest are considered a carbon sink in this century, our simulations show the modern forests are still experiencing carbon debt compared to their historical values.

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

PubMed

Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

2016-05-30

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R² = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m²), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

Biophysical connectivity explains population genetic structure in a highly dispersive marine species

NASA Astrophysics Data System (ADS)

Truelove, Nathan K.; Kough, Andrew S.; Behringer, Donald C.; Paris, Claire B.; Box, Stephen J.; Preziosi, Richard F.; Butler, Mark J.

2017-03-01

Connectivity, the exchange of individuals among locations, is a fundamental ecological process that explains how otherwise disparate populations interact. For most marine organisms, dispersal occurs primarily during a pelagic larval phase that connects populations. We paired population structure from comprehensive genetic sampling and biophysical larval transport modeling to describe how spiny lobster ( Panulirus argus) population differentiation is related to biological oceanography. A total of 581 lobsters were genotyped with 11 microsatellites from ten locations around the greater Caribbean. The overall F ST of 0.0016 ( P = 0.005) suggested low yet significant levels of structuring among sites. An isolation by geographic distance model did not explain spatial patterns of genetic differentiation in P. argus ( P = 0.19; Mantel r = 0.18), whereas a biophysical connectivity model provided a significant explanation of population differentiation ( P = 0.04; Mantel r = 0.47). Thus, even for a widely dispersing species, dispersal occurs over a continuum where basin-wide larval retention creates genetic structure. Our study provides a framework for future explorations of wide-scale larval dispersal and marine connectivity by integrating empirical genetic research and probabilistic modeling.

An Analytical Model for Determining Two-Dimensional Receptor-Ligand Kinetics

PubMed Central

Cheung, Luthur Siu-Lun; Konstantopoulos, Konstantinos

2011-01-01

Cell-cell adhesive interactions play a pivotal role in major pathophysiological vascular processes, such as inflammation, infection, thrombosis, and cancer metastasis, and are regulated by hemodynamic forces generated by blood flow. Cell adhesion is mediated by the binding of receptors to ligands, which are both anchored on two-dimensional (2-D) membranes of apposing cells. Biophysical assays have been developed to determine the unstressed (no-force) 2-D affinity but fail to disclose its dependence on force. Here we develop an analytical model to estimate the 2-D kinetics of diverse receptor-ligand pairs as a function of force, including antibody-antigen, vascular selectin-ligand, and bacterial adhesin-ligand interactions. The model can account for multiple bond interactions necessary to mediate adhesion and resist detachment amid high hemodynamic forces. Using this model, we provide a generalized biophysical interpretation of the counterintuitive force-induced stabilization of cell rolling observed by a select subset of receptor-ligand pairs with specific intrinsic kinetic properties. This study enables us to understand how single-molecule and multibond biophysics modulate the macroscopic cell behavior in diverse pathophysiological processes. PMID:21575567

A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development

PubMed Central

Harris, Kristen M.; Spacek, Josef; Bell, Maria Elizabeth; Parker, Patrick H.; Lindsey, Laurence F.; Baden, Alexander D.; Vogelstein, Joshua T.; Burns, Randal

2015-01-01

Resurgent interest in synaptic circuitry and plasticity has emphasized the importance of 3D reconstruction from serial section electron microscopy (3DEM). Three volumes of hippocampal CA1 neuropil from adult rat were imaged at X-Y resolution of ~2 nm on serial sections of ~50–60 nm thickness. These are the first densely reconstructed hippocampal volumes. All axons, dendrites, glia, and synapses were reconstructed in a cube (~10 μm3) surrounding a large dendritic spine, a cylinder (~43 μm3) surrounding an oblique dendritic segment (3.4 μm long), and a parallelepiped (~178 μm3) surrounding an apical dendritic segment (4.9 μm long). The data provide standards for identifying ultrastructural objects in 3DEM, realistic reconstructions for modeling biophysical properties of synaptic transmission, and a test bed for enhancing reconstruction tools. Representative synapses are quantified from varying section planes, and microtubules, polyribosomes, smooth endoplasmic reticulum, and endosomes are identified and reconstructed in a subset of dendrites. The original images, traces, and Reconstruct software and files are freely available and visualized at the Open Connectome Project (Data Citation 1). PMID:26347348

Overview of the Graphical User Interface for the GERM Code (GCR Event-Based Risk Model

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee; Cucinotta, Francis A.

2010-01-01

The descriptions of biophysical events from heavy ions are of interest in radiobiology, cancer therapy, and space exploration. The biophysical description of the passage of heavy ions in tissue and shielding materials is best described by a stochastic approach that includes both ion track structure and nuclear interactions. A new computer model called the GCR Event-based Risk Model (GERM) code was developed for the description of biophysical events from heavy ion beams at the NASA Space Radiation Laboratory (NSRL). The GERM code calculates basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at NSRL for the purpose of simulating space radiobiological effects. For mono-energetic beams, the code evaluates the linear-energy transfer (LET), range (R), and absorption in tissue equivalent material for a given Charge (Z), Mass Number (A) and kinetic energy (E) of an ion. In addition, a set of biophysical properties are evaluated such as the Poisson distribution of ion or delta-ray hits for a specified cellular area, cell survival curves, and mutation and tumor probabilities. The GERM code also calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle. The contributions from primary ion and nuclear secondaries are evaluated. The GERM code accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections, and has been used by the GERM code for application to thick target experiments. The GERM code provides scientists participating in NSRL experiments with the data needed for the interpretation of their experiments, including the ability to model the beam line, the shielding of samples and sample holders, and the estimates of basic physical and biological outputs of the designed experiments. We present an overview of the GERM code GUI, as well as providing training applications.

Overview of the Graphical User Interface for the GERMcode (GCR Event-Based Risk Model)

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee Y.; Cucinotta, Francis A.

2010-01-01

The descriptions of biophysical events from heavy ions are of interest in radiobiology, cancer therapy, and space exploration. The biophysical description of the passage of heavy ions in tissue and shielding materials is best described by a stochastic approach that includes both ion track structure and nuclear interactions. A new computer model called the GCR Event-based Risk Model (GERM) code was developed for the description of biophysical events from heavy ion beams at the NASA Space Radiation Laboratory (NSRL). The GERMcode calculates basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at NSRL for the purpose of simulating space radiobiological effects. For mono-energetic beams, the code evaluates the linear-energy transfer (LET), range (R), and absorption in tissue equivalent material for a given Charge (Z), Mass Number (A) and kinetic energy (E) of an ion. In addition, a set of biophysical properties are evaluated such as the Poisson distribution of ion or delta-ray hits for a specified cellular area, cell survival curves, and mutation and tumor probabilities. The GERMcode also calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle. The contributions from primary ion and nuclear secondaries are evaluated. The GERMcode accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections, and has been used by the GERMcode for application to thick target experiments. The GERMcode provides scientists participating in NSRL experiments with the data needed for the interpretation of their experiments, including the ability to model the beam line, the shielding of samples and sample holders, and the estimates of basic physical and biological outputs of the designed experiments. We present an overview of the GERMcode GUI, as well as providing training applications.

Validation of a T1 and T2* leakage correction method based on multi-echo DSC-MRI using MION as a reference standard

PubMed Central

Stokes, Ashley M.; Semmineh, Natenael; Quarles, C. Chad

2015-01-01

Purpose A combined biophysical- and pharmacokinetic-based method is proposed to separate, quantify, and correct for both T1 and T2* leakage effects using dual-echo DSC acquisitions to provide more accurate hemodynamic measures, as validated by a reference intravascular contrast agent (CA). Methods Dual-echo DSC-MRI data were acquired in two rodent glioma models. The T1 leakage effects were removed and also quantified in order to subsequently correct for the remaining T2* leakage effects. Pharmacokinetic, biophysical, and combined biophysical and pharmacokinetic models were used to obtain corrected cerebral blood volume (CBV) and cerebral blood flow (CBF), and these were compared with CBV and CBF from an intravascular CA. Results T1-corrected CBV was significantly overestimated compared to MION CBV, while T1+T2*-correction yielded CBV values closer to the reference values. The pharmacokinetic and simplified biophysical methods showed similar results and underestimated CBV in tumors exhibiting strong T2* leakage effects. The combined method was effective for correcting T1 and T2* leakage effects across tumor types. Conclusions Correcting for both T1 and T2* leakage effects yielded more accurate measures of CBV. The combined correction method yields more reliable CBV measures than either correction method alone, but for certain brain tumor types (e.g., gliomas) the simplified biophysical method may provide a robust and computationally efficient alternative. PMID:26362714

Anti-pulmonary fibrotic activity of salvianolic acid B was screened by a novel method based on the cyto-biophysical properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Miao; Zheng, Mingjing; Xu, Hanying

Various methods have been used to evaluate anti-fibrotic activity of drugs. However, most of them are complicated, labor-intensive and lack of efficiency. This study was intended to develop a rapid method for anti-fibrotic drugs screening based on biophysical properties. A549 cells in vitro were stimulated with transforming growth factor-β1 (TGF-β1), and fibrogenesis was confirmed by conventional immunological assays. Meanwhile, the alterations of cyto-biophysical properties including morphology, roughness and stiffness were measured utilizing atomic force microscopy (AFM). It was found that fibrogenesis was accompanied with changes of cellular biophysical properties. TGF-β1-stimulated A549 cells became remarkably longer, rougher and stiffer than the control.more » Then, the effect of N-acetyl-L-cysteine (NAC) as a positive drug on ameliorating fibrogenesis in TGF-β1-stimulated A549 cells was verified respectively by immunological and biophysical markers. The result of Principal Component Analysis showed that stiffness was a leading index among all biophysical markers during fibrogenesis. Salvianolic acid B (SalB), a natural anti-oxidant, was detected by AFM to protect TGF-β1-stimulated A549 cells against stiffening. Then, SalB treatment was provided in preventive mode on a rat model of bleomycin (BLM) -induced pulmonary fibrosis. The results showed that SalB treatment significantly ameliorated BLM-induced histological alterations, blocked collagen accumulations and reduced α-SMA expression in lung tissues. All these results revealed the anti-pulmonary fibrotic activity of SalB. Detection of cyto-biophysical properties were therefore recommended as a rapid method for anti-pulmonary fibrotic drugs screening. - Highlights: • Fibrogenesis was accompanied with the changes of cyto-biophysical properties. • Cyto-biophysical properties could be markers for anti-fibrotic drugs screening. • Stiffness is a leading index among all biophysical markers. • SalB was detected to protect TGF-β1-stimulated A549 cells against stiffening. • SalB treatment ameliorated pulmonary fibrosis induced by BLM in rats.« less

Biophysical model of ion transport across human respiratory epithelia allows quantification of ion permeabilities.

PubMed

Garcia, Guilherme J M; Boucher, Richard C; Elston, Timothy C

2013-02-05

Lung health and normal mucus clearance depend on adequate hydration of airway surfaces. Because transepithelial osmotic gradients drive water flows, sufficient hydration of the airway surface liquid depends on a balance between ion secretion and absorption by respiratory epithelia. In vitro experiments using cultures of primary human nasal epithelia and human bronchial epithelia have established many of the biophysical processes involved in airway surface liquid homeostasis. Most experimental studies, however, have focused on the apical membrane, despite the fact that ion transport across respiratory epithelia involves both cellular and paracellular pathways. In fact, the ion permeabilities of the basolateral membrane and paracellular pathway remain largely unknown. Here we use a biophysical model for water and ion transport to quantify ion permeabilities of all pathways (apical, basolateral, paracellular) in human nasal epithelia cultures using experimental (Ussing Chamber and microelectrode) data reported in the literature. We derive analytical formulas for the steady-state short-circuit current and membrane potential, which are for polarized epithelia the equivalent of the Goldman-Hodgkin-Katz equation for single isolated cells. These relations allow parameter estimation to be performed efficiently. By providing a method to quantify all the ion permeabilities of respiratory epithelia, the model may aid us in understanding the physiology that regulates normal airway surface hydration. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Quality Saving Mechanisms of Mitochondria during Aging in a Fully Time-Dependent Computational Biophysical Model

PubMed Central

Mellem, Daniel; Fischer, Frank; Jaspers, Sören; Wenck, Horst; Rübhausen, Michael

2016-01-01

Mitochondria are essential for the energy production of eukaryotic cells. During aging mitochondria run through various processes which change their quality in terms of activity, health and metabolic supply. In recent years, many of these processes such as fission and fusion of mitochondria, mitophagy, mitochondrial biogenesis and energy consumption have been subject of research. Based on numerous experimental insights, it was possible to qualify mitochondrial behaviour in computational simulations. Here, we present a new biophysical model based on the approach of Figge et al. in 2012. We introduce exponential decay and growth laws for each mitochondrial process to derive its time-dependent probability during the aging of cells. All mitochondrial processes of the original model are mathematically and biophysically redefined and additional processes are implemented: Mitochondrial fission and fusion is separated into a metabolic outer-membrane part and a protein-related inner-membrane part, a quality-dependent threshold for mitophagy and mitochondrial biogenesis is introduced and processes for activity-dependent internal oxidative stress as well as mitochondrial repair mechanisms are newly included. Our findings reveal a decrease of mitochondrial quality and a fragmentation of the mitochondrial network during aging. Additionally, the model discloses a quality increasing mechanism due to the interplay of the mitophagy and biogenesis cycle and the fission and fusion cycle of mitochondria. It is revealed that decreased mitochondrial repair can be a quality saving process in aged cells. Furthermore, the model finds strategies to sustain the quality of the mitochondrial network in cells with high production rates of reactive oxygen species due to large energy demands. Hence, the model adds new insights to biophysical mechanisms of mitochondrial aging and provides novel understandings of the interdependency of mitochondrial processes. PMID:26771181

Local cooling and warming effects of forests based on satellite observations.

PubMed

Li, Yan; Zhao, Maosheng; Motesharrei, Safa; Mu, Qiaozhen; Kalnay, Eugenia; Li, Shuangcheng

2015-03-31

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies.

Local cooling and warming effects of forests based on satellite observations

PubMed Central

Li, Yan; Zhao, Maosheng; Motesharrei, Safa; Mu, Qiaozhen; Kalnay, Eugenia; Li, Shuangcheng

2015-01-01

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies. PMID:25824529

Modeling disordered protein interactions from biophysical principles

PubMed Central

Christoffer, Charles; Terashi, Genki

2017-01-01

Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs. PMID:28394890

Is realistic neuronal modeling realistic?

PubMed Central

Almog, Mara

2016-01-01

Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models. PMID:27535372

Biophysical model of the role of actin remodeling on dendritic spine morphology

PubMed Central

Miermans, C. A.; Kusters, R. P. T.; Hoogenraad, C. C.; Storm, C.

2017-01-01

Dendritic spines are small membranous structures that protrude from the neuronal dendrite. Each spine contains a synaptic contact site that may connect its parent dendrite to the axons of neighboring neurons. Dendritic spines are markedly distinct in shape and size, and certain types of stimulation prompt spines to evolve, in fairly predictable fashion, from thin nascent morphologies to the mushroom-like shapes associated with mature spines. It is well established that the remodeling of spines is strongly dependent upon the actin cytoskeleton inside the spine. A general framework that details the precise role of actin in directing the transitions between the various spine shapes is lacking. We address this issue, and present a quantitative, model-based scenario for spine plasticity validated using realistic and physiologically relevant parameters. Our model points to a crucial role for the actin cytoskeleton. In the early stages of spine formation, the interplay between the elastic properties of the spine membrane and the protrusive forces generated in the actin cytoskeleton propels the incipient spine. In the maturation stage, actin remodeling in the form of the combined dynamics of branched and bundled actin is required to form mature, mushroom-like spines. Importantly, our model shows that constricting the spine-neck aids in the stabilization of mature spines, thus pointing to a role in stabilization and maintenance for additional factors such as ring-like F-actin structures. Taken together, our model provides unique insights into the fundamental role of actin remodeling and polymerization forces during spine formation and maturation. PMID:28158194

Order Matters: Sequencing Scale-Realistic Versus Simplified Models to Improve Science Learning

NASA Astrophysics Data System (ADS)

Chen, Chen; Schneps, Matthew H.; Sonnert, Gerhard

2016-10-01

Teachers choosing between different models to facilitate students' understanding of an abstract system must decide whether to adopt a model that is simplified and striking or one that is realistic and complex. Only recently have instructional technologies enabled teachers and learners to change presentations swiftly and to provide for learning based on multiple models, thus giving rise to questions about the order of presentation. Using disjoint individual growth modeling to examine the learning of astronomical concepts using a simulation of the solar system on tablets for 152 high school students (age 15), the authors detect both a model effect and an order effect in the use of the Orrery, a simplified model that exaggerates the scale relationships, and the True-to-scale, a proportional model that more accurately represents the realistic scale relationships. Specifically, earlier exposure to the simplified model resulted in diminution of the conceptual gain from the subsequent realistic model, but the realistic model did not impede learning from the following simplified model.

Hierarchy and Interactions in Environmental Interfaces Regarded as Biophysical Complex Systems

NASA Astrophysics Data System (ADS)

Mihailovic, Dragutin T.; Balaz, Igor

The field of environmental sciences is abundant with various interfaces and is the right place for the application of new fundamental approaches leading towards a better understanding of environmental phenomena. For example, following the definition of environmental interface by Mihailovic and Balaž [23], such interface can be placed between: human or animal bodies and surrounding air, aquatic species and water and air around them, and natural or artificially built surfaces (vegetation, ice, snow, barren soil, water, urban communities) and the atmosphere. Complex environmental interface systems are open and hierarchically organised, interactions between their constituent parts are nonlinear, and the interaction with the surrounding environment is noisy. These systems are therefore very sensitive to initial conditions, deterministic external perturbations and random fluctuations always present in nature. The study of noisy non-equilibrium processes is fundamental for modelling the dynamics of environmental interface systems and for understanding the mechanisms of spatio-temporal pattern formation in contemporary environmental sciences, particularly in environmental fluid mechanics. In modelling complex biophysical systems one of the main tasks is to successfully create an operative interface with the external environment. It should provide a robust and prompt translation of the vast diversity of external physical and/or chemical changes into a set of signals, which are "understandable" for an organism. Although the establishment of organisation in any system is of crucial importance for its functioning, it should not be forgotten that in biophysical systems we deal with real-life problems where a number of other conditions should be reached in order to put the system to work. One of them is the proper supply of the system by the energy. Therefore, we will investigate an aspect of dynamics of energy flow based on the energy balance equation. The energy as well as the exchange of biological, chemical and other physical quantities between interacting environmental interfaces can be represented by coupled maps. In this chapter we will address only two illustrative issues important for the modelling of interacting environmental interfaces regarded as complex systems. These are (i) use of algebra for modelling the autonomous establishment of local hierarchies in biophysical systems and (ii) numerical investigation of coupled maps representing exchange of energy, chemical and other relevant biophysical quantities between biophysical entities in their surrounding environment.

Effect of Anatomically Realistic Full-Head Model on Activation of Cortical Neurons in Subdural Cortical Stimulation—A Computational Study

NASA Astrophysics Data System (ADS)

Seo, Hyeon; Kim, Donghyeon; Jun, Sung Chan

2016-06-01

Electrical brain stimulation (EBS) is an emerging therapy for the treatment of neurological disorders, and computational modeling studies of EBS have been used to determine the optimal parameters for highly cost-effective electrotherapy. Recent notable growth in computing capability has enabled researchers to consider an anatomically realistic head model that represents the full head and complex geometry of the brain rather than the previous simplified partial head model (extruded slab) that represents only the precentral gyrus. In this work, subdural cortical stimulation (SuCS) was found to offer a better understanding of the differential activation of cortical neurons in the anatomically realistic full-head model than in the simplified partial-head models. We observed that layer 3 pyramidal neurons had comparable stimulation thresholds in both head models, while layer 5 pyramidal neurons showed a notable discrepancy between the models; in particular, layer 5 pyramidal neurons demonstrated asymmetry in the thresholds and action potential initiation sites in the anatomically realistic full-head model. Overall, the anatomically realistic full-head model may offer a better understanding of layer 5 pyramidal neuronal responses. Accordingly, the effects of using the realistic full-head model in SuCS are compelling in computational modeling studies, even though this modeling requires substantially more effort.

Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone.

PubMed

Geerts, Hugo; Roberts, Patrick; Spiros, Athan; Potkin, Steven

2015-04-01

The concept of targeted therapies remains a holy grail for the pharmaceutical drug industry for identifying responder populations or new drug targets. Here we provide quantitative systems pharmacology as an alternative to the more traditional approach of retrospective responder pharmacogenomics analysis and applied this to the case of iloperidone in schizophrenia. This approach implements the actual neurophysiological effect of genotypes in a computer-based biophysically realistic model of human neuronal circuits, is parameterized with human imaging and pathology, and is calibrated by clinical data. We keep the drug pharmacology constant, but allowed the biological model coupling values to fluctuate in a restricted range around their calibrated values, thereby simulating random genetic mutations and representing variability in patient response. Using hypothesis-free Design of Experiments methods the dopamine D4 R-AMPA (receptor-alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor coupling in cortical neurons was found to drive the beneficial effect of iloperidone, likely corresponding to the rs2513265 upstream of the GRIA4 gene identified in a traditional pharmacogenomics analysis. The serotonin 5-HT3 receptor-mediated effect on interneuron gamma-aminobutyric acid conductance was identified as the process that moderately drove the differentiation of iloperidone versus ziprasidone. This paper suggests that reverse-engineered quantitative systems pharmacology is a powerful alternative tool to characterize the underlying neurobiology of a responder population and possibly identifying new targets. © The Author(s) 2015.

Cooperation through Competition—Dynamics and Microeconomics of a Minimal Nutrient Trade System in Arbuscular Mycorrhizal Symbiosis

PubMed Central

Schott, Stephan; Valdebenito, Braulio; Bustos, Daniel; Gomez-Porras, Judith L.; Sharma, Tripti; Dreyer, Ingo

2016-01-01

In arbuscular mycorrhizal (AM) symbiosis, fungi and plants exchange nutrients (sugars and phosphate, for instance) for reciprocal benefit. Until now it is not clear how this nutrient exchange system works. Here, we used computational cell biology to simulate the dynamics of a network of proton pumps and proton-coupled transporters that are upregulated during AM formation. We show that this minimal network is sufficient to describe accurately and realistically the nutrient trade system. By applying basic principles of microeconomics, we link the biophysics of transmembrane nutrient transport with the ecology of organismic interactions and straightforwardly explain macroscopic scenarios of the relations between plant and AM fungus. This computational cell biology study allows drawing far reaching hypotheses about the mechanism and the regulation of nutrient exchange and proposes that the “cooperation” between plant and fungus can be in fact the result of a competition between both for the same resources in the tiny periarbuscular space. The minimal model presented here may serve as benchmark to evaluate in future the performance of more complex models of AM nutrient exchange. As a first step toward this goal, we included SWEET sugar transporters in the model and show that their co-occurrence with proton-coupled sugar transporters results in a futile carbon cycle at the plant plasma membrane proposing that two different pathways for the same substrate should not be active at the same time. PMID:27446142

On the biophysics and kinetics of toehold-mediated DNA strand displacement

PubMed Central

Srinivas, Niranjan; Ouldridge, Thomas E.; Šulc, Petr; Schaeffer, Joseph M.; Yurke, Bernard; Louis, Ard A.; Doye, Jonathan P. K.; Winfree, Erik

2013-01-01

Dynamic DNA nanotechnology often uses toehold-mediated strand displacement for controlling reaction kinetics. Although the dependence of strand displacement kinetics on toehold length has been experimentally characterized and phenomenologically modeled, detailed biophysical understanding has remained elusive. Here, we study strand displacement at multiple levels of detail, using an intuitive model of a random walk on a 1D energy landscape, a secondary structure kinetics model with single base-pair steps and a coarse-grained molecular model that incorporates 3D geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Two factors explain the dependence of strand displacement kinetics on toehold length: (i) the physical process by which a single step of branch migration occurs is significantly slower than the fraying of a single base pair and (ii) initiating branch migration incurs a thermodynamic penalty, not captured by state-of-the-art nearest neighbor models of DNA, due to the additional overhang it engenders at the junction. Our findings are consistent with previously measured or inferred rates for hybridization, fraying and branch migration, and they provide a biophysical explanation of strand displacement kinetics. Our work paves the way for accurate modeling of strand displacement cascades, which would facilitate the simulation and construction of more complex molecular systems. PMID:24019238

On the biophysics and kinetics of toehold-mediated DNA strand displacement.

PubMed

Srinivas, Niranjan; Ouldridge, Thomas E; Sulc, Petr; Schaeffer, Joseph M; Yurke, Bernard; Louis, Ard A; Doye, Jonathan P K; Winfree, Erik

2013-12-01

Dynamic DNA nanotechnology often uses toehold-mediated strand displacement for controlling reaction kinetics. Although the dependence of strand displacement kinetics on toehold length has been experimentally characterized and phenomenologically modeled, detailed biophysical understanding has remained elusive. Here, we study strand displacement at multiple levels of detail, using an intuitive model of a random walk on a 1D energy landscape, a secondary structure kinetics model with single base-pair steps and a coarse-grained molecular model that incorporates 3D geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Two factors explain the dependence of strand displacement kinetics on toehold length: (i) the physical process by which a single step of branch migration occurs is significantly slower than the fraying of a single base pair and (ii) initiating branch migration incurs a thermodynamic penalty, not captured by state-of-the-art nearest neighbor models of DNA, due to the additional overhang it engenders at the junction. Our findings are consistent with previously measured or inferred rates for hybridization, fraying and branch migration, and they provide a biophysical explanation of strand displacement kinetics. Our work paves the way for accurate modeling of strand displacement cascades, which would facilitate the simulation and construction of more complex molecular systems.

Biophysical interactions with model lipid membranes: applications in drug discovery and drug delivery

PubMed Central

Peetla, Chiranjeevi; Stine, Andrew; Labhasetwar, Vinod

2009-01-01

The transport of drugs or drug delivery systems across the cell membrane is a complex biological process, often difficult to understand because of its dynamic nature. In this regard, model lipid membranes, which mimic many aspects of cell-membrane lipids, have been very useful in helping investigators to discern the roles of lipids in cellular interactions. One can use drug-lipid interactions to predict pharmacokinetic properties of drugs, such as their transport, biodistribution, accumulation, and hence efficacy. These interactions can also be used to study the mechanisms of transport, based on the structure and hydrophilicity/hydrophobicity of drug molecules. In recent years, model lipid membranes have also been explored to understand their mechanisms of interactions with peptides, polymers, and nanocarriers. These interaction studies can be used to design and develop efficient drug delivery systems. Changes in the lipid composition of cells and tissue in certain disease conditions may alter biophysical interactions, which could be explored to develop target-specific drugs and drug delivery systems. In this review, we discuss different model membranes, drug-lipid interactions and their significance, studies of model membrane interactions with nanocarriers, and how biophysical interaction studies with lipid model membranes could play an important role in drug discovery and drug delivery. PMID:19432455

Remote sensing of the Canadian Arctic: Modelling biophysical variables

NASA Astrophysics Data System (ADS)

Liu, Nanfeng

It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic overestimation of 0.08, which was attributed to PAR absorption by soil that could not be excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial resolution remote sensing VIs can be used to successfully model Arctic biophysical variables. The methods and results presented in this research provided a guide for future studies aiming to model other Arctic biophysical variables through remote sensing data.

Effect of Molecular Structure of Cationic Surfactants on Biophysical Interactions of the Surfactant-modified Nanoparticles with a Model Membrane and Cellular Uptake

PubMed Central

Peetla, Chiranjeevi; Labhasetwar, Vinod

2009-01-01

The aim of this study was to test the hypothesis that the molecular structure of cationic surfactants at the nanoparticle (NP)-interface influences the biophysical interactions of NPs with a model membrane and cellular uptake of NPs. Polystyrene NPs (surfactant free, 130 nm) were modified with cationic surfactants. These surfactants were of either dichained (didodecyldimethylammonium bromide [DMAB]) or single chained (cetyltrimethylammonium bromide [CTAB] and dodecyltrimethylammonium bromide [DTAB]) forms, the latter two with different hydrophobic chain lengths. Biophysical interactions of these surfactant-modified NPs with an endothelial cell model membrane (EMM) were studied using a Langmuir film balance. Changes in surface pressure (SP) of EMM as a function of time following interaction with NPs and in the compression isotherm (π - A) of the lipid mixture of EMM in the presence of NPs were analyzed. Langmuir-Schaeffer (LS) films, which are EMMs that have been transferred onto a suitable substrate, were imaged by atomic force microscopy (AFM), and the images were analyzed to determine the mechanisms of the NP-EMM interaction. DMAB-modified NPs showed a greater increase in SP and a shift towards higher mean molecular area (mmA) than CTAB- and DTAB-modified NPs, indicating stronger interactions of DMAB-modified NPs with the EMM. However, analysis of the AFM phase and height images of the LS films revealed that both DMAB- and CTAB-modified NPs interacted with the EMM but via different mechanisms: DMAB-modified NPs penetrated the EMM, thus explaining the increase in SP, whereas CTAB-modified NPs anchored onto the EMM's condensed lipid domains, and hence did not cause any significant change in SP. Human umbilical vein endothelial cells showed greater uptake of DMAB- and CTAB-modified NPs than of DTAB-modified or unmodified NPs. We conclude that (i) the dichained and single-chained cationic surfactants on NPs have different mechanisms of interaction with the model membrane and (ii) NPs that demonstrate greater biophysical interactions with the membrane also show greater cellular uptake. Biophysical interactions of NPs with a model membrane thus could be effectively used for developing nanocarriers with optimized surface properties for drug delivery and imaging applications. PMID:19161268

SMART-DS: Synthetic Models for Advanced, Realistic Testing: Distribution

Science.gov Websites

statistical summary of the U.S. distribution systems World-class, high spatial/temporal resolution of solar Systems and Scenarios | Grid Modernization | NREL SMART-DS: Synthetic Models for Advanced , Realistic Testing: Distribution Systems and Scenarios SMART-DS: Synthetic Models for Advanced, Realistic

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

PubMed

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

Raman spectroscopy reveals biophysical markers in skin cancer surgical margins

NASA Astrophysics Data System (ADS)

Feng, Xu; Moy, Austin J.; Nguyen, Hieu T. M.; Zhang, Yao; Fox, Matthew C.; Sebastian, Katherine R.; Reichenberg, Jason S.; Markey, Mia K.; Tunnell, James W.

2018-02-01

The recurrence rate of nonmelanoma skin cancer is highly related to the residual tumor after surgery. Although tissueconserving surgery, such as Mohs surgery, is a standard method for the treatment of nonmelanoma skin cancer, they are limited by lengthy and costly frozen-section histopathology. Raman spectroscopy (RS) is proving to be an objective, sensitive, and non-destructive tool for detecting skin cancer. Previous studies demonstrated the high sensitivity of RS in detecting tumor margins of basal cell carcinoma (BCC). However, those studies rely on statistical classification models and do not elucidate the skin biophysical composition. As a result, we aim to discover the biophysical differences between BCC and primary normal skin structures (including epidermis, dermis, hair follicle, sebaceous gland and fat). We obtained freshly resected ex vivo skin samples from fresh resection specimens from 14 patients undergoing Mohs surgery. Raman images were acquired from regions containing one or more structures using a custom built 830nm confocal Raman microscope. The spectra were grouped using K-means clustering analysis and annotated as either BCC or each of the five normal structures by comparing with the histopathology image of the serial section. The spectral data were then fit by a previously established biophysical model with eight primary skin constituents. Our results show that BCC has significant differences in the fit coefficients of nucleus, collagen, triolein, keratin and elastin compared with normal structures. Our study reveals RS has the potential to detect biophysical changes in resection margins, and supports the development of diagnostic algorithms for future intraoperative implementation of RS during Mohs surgery.

Importance of biophysical effects on climate warming mitigation potential of biofuel crops over the conterminous United States

DOE PAGES

Zhu, Peng; Zhuang, Qianlai; Eva, Joo; ...

2016-06-21

Current quantification of climate warming mitigation potential (CWMP) of biomass-derived energy has focused primarily on its biogeochemical effects. This study used site-level observations of carbon, water, and energy fluxes of biofuel crops to parameterize and evaluate the community land model (CLM) and estimate CO 2 fluxes, surface energy balance, soil carbon dynamics of corn (Zea mays), switchgrass (Panicum virgatum), and miscanthus (Miscanthus × giganteus) ecosystems across the conterminous United States considering different agricultural management practices and land-use scenarios. Here, we find that neglecting biophysical effects underestimates the CWMP of transitioning from croplands and marginal lands to energy crops. Biogeochemical effectsmore » alone result in changes in carbon storage of -1.9, 49.1, and 69.3 g C m -2 y -1 compared to 20.5, 78.5, and 96.2 g C m -2 y -1 when considering both biophysical and biogeochemical effects for corn, switchgrass, and miscanthus, respectively. The biophysical contribution to CWMP is dominated by changes in latent heat fluxes. Using the model to optimize growth conditions through fertilization and irrigation increases the CWMP further to 79.6, 98.3, and 118.8 g C m -2 y -1, respectively, representing the upper threshold for CWMP. Results also show that the CWMP over marginal lands is lower than that over croplands. Our study highlights that neglecting the biophysical effects of altered surface energy and water balance underestimates the CWMP of transitioning to bioenergy crops at regional scales.« less

Carbon farming economics: What have we learned?

PubMed

Tang, Kai; Kragt, Marit E; Hailu, Atakelty; Ma, Chunbo

2016-05-01

This study reviewed 62 economic analyses published between 1995 and 2014 on the economic impacts of policies that incentivise agricultural greenhouse (GHG) mitigation. Typically, biophysical models are used to evaluate the changes in GHG mitigation that result from landholders changing their farm and land management practices. The estimated results of biophysical models are then integrated with economic models to simulate the costs of different policy scenarios to production systems. The cost estimates vary between $3 and $130/t CO2 equivalent in 2012 US dollars, depending on the mitigation strategies, spatial locations, and policy scenarios considered. Most studies assessed the consequences of a single, rather than multiple, mitigation strategies, and few considered the co-benefits of carbon farming. These omissions could challenge the reality and robustness of the studies' results. One of the biggest challenges facing agricultural economists is to assess the full extent of the trade-offs involved in carbon farming. We need to improve our biophysical knowledge about carbon farming co-benefits, predict the economic impacts of employing multiple strategies and policy incentives, and develop the associated integrated models, to estimate the full costs and benefits of agricultural GHG mitigation to farmers and the rest of society. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-scale modelling of the dynamics of cell colonies: insights into cell-adhesion forces and cancer invasion from in silico simulations.

PubMed

Schlüter, Daniela K; Ramis-Conde, Ignacio; Chaplain, Mark A J

2015-02-06

Studying the biophysical interactions between cells is crucial to understanding how normal tissue develops, how it is structured and also when malfunctions occur. Traditional experiments try to infer events at the tissue level after observing the behaviour of and interactions between individual cells. This approach assumes that cells behave in the same biophysical manner in isolated experiments as they do within colonies and tissues. In this paper, we develop a multi-scale multi-compartment mathematical model that accounts for the principal biophysical interactions and adhesion pathways not only at a cell-cell level but also at the level of cell colonies (in contrast to the traditional approach). Our results suggest that adhesion/separation forces between cells may be lower in cell colonies than traditional isolated single-cell experiments infer. As a consequence, isolated single-cell experiments may be insufficient to deduce important biological processes such as single-cell invasion after detachment from a solid tumour. The simulations further show that kinetic rates and cell biophysical characteristics such as pressure-related cell-cycle arrest have a major influence on cell colony patterns and can allow for the development of protrusive cellular structures as seen in invasive cancer cell lines independent of expression levels of pro-invasion molecules.

Multi-scale modelling of the dynamics of cell colonies: insights into cell-adhesion forces and cancer invasion from in silico simulations

PubMed Central

Schlüter, Daniela K.; Ramis-Conde, Ignacio; Chaplain, Mark A. J.

2015-01-01

Studying the biophysical interactions between cells is crucial to understanding how normal tissue develops, how it is structured and also when malfunctions occur. Traditional experiments try to infer events at the tissue level after observing the behaviour of and interactions between individual cells. This approach assumes that cells behave in the same biophysical manner in isolated experiments as they do within colonies and tissues. In this paper, we develop a multi-scale multi-compartment mathematical model that accounts for the principal biophysical interactions and adhesion pathways not only at a cell–cell level but also at the level of cell colonies (in contrast to the traditional approach). Our results suggest that adhesion/separation forces between cells may be lower in cell colonies than traditional isolated single-cell experiments infer. As a consequence, isolated single-cell experiments may be insufficient to deduce important biological processes such as single-cell invasion after detachment from a solid tumour. The simulations further show that kinetic rates and cell biophysical characteristics such as pressure-related cell-cycle arrest have a major influence on cell colony patterns and can allow for the development of protrusive cellular structures as seen in invasive cancer cell lines independent of expression levels of pro-invasion molecules. PMID:25519994

Biophysical parameters in a wheat producer region in southern Brazil

NASA Astrophysics Data System (ADS)

Leivas, Janice F.; de C. Teixeira, Antonio Heriberto; Andrade, Ricardo G.; de C. Victoria, Daniel; Bolfe, Edson L.; Cruz, Caroline R.

2014-10-01

Wheat (Triticum aestivum) is the second most produced cereal in the world, and has major importance in the global agricultural economy. Brazil is a large producer of wheat, especially the Rio Grande do Sul state, located in the south of the country. The purpose of this study was to analyze the estimation of biophysical parameters - evapotranspiration (ET), biomass (BIO) and water productivity (WP) - from satellite images of the municipalities with large areas planted with wheat in Rio Grande do Sul (RS). The evapotranspiration rate was obtained using the SAFER Model (Simple Algorithm for Retrieving Evapotranspiration) on MODIS (Moderate Resolution Imaging Spectroradiometer) images taken in the agricultural year 2012. In order to obtain biomass and water productivity rates we applied the Monteith model and the ratio between BIO and ET. In the beginning of the cycle (the planting period) we observed low values for ET, BIO and WP. During the development period, we observed an increase in the values of the parameters and decline at the end of the cycle, for the period of the wheat harvest. The SAFER model proved effective for estimating the biophysical parameters evapotranspiration, biomass production and water productivity in areas planted with wheat in Brazilian Southern. The methodology can be used for monitoring the crops' water conditions and biomass using satellite images, assisting in estimates of productivity and crop yield. The results may assist the understanding of biophysical properties of important agro-ecosystems, like wheat crop, and are important to improve the rational use of water resources.

A Comparative Study of Spatial Aggregation Methodologies under the BioEarth Framework

NASA Astrophysics Data System (ADS)

Chandrasekharan, B.; Rajagopalan, K.; Malek, K.; Stockle, C. O.; Adam, J. C.; Brady, M.

2014-12-01

The increasing probability of water resource scarcity due to climate change has highlighted the need for adopting an economic focus in modelling water resource uses. Hydro-economic models, developed by integrating economic optimization with biophysical crop models, are driven by the economic value of water, revealing it's most efficient uses and helping policymakers evaluate different water management strategies. One of the challenges in integrating biophysical models with economic models is the difference in the spatial scales in which they operate. Biophysical models that provide crop production functions typically run at smaller scale than economic models, and substantial spatial aggregation is required. However, any aggregation introduces a bias, i.e., a discrepancy between the functional value at the higher spatial scale and the value at the spatial scale of the aggregated units. The objective of this work is to study the sensitivity of net economic benefits in the Yakima River basin (YRB) to different spatial aggregation methods for crop production functions. The spatial aggregation methodologies that we compare involve agro-ecological zones (AEZs) and aggregation levels that reflect water management regimes (e.g. irrigation districts). Aggregation bias can distort the underlying data and result in extreme solutions. In order to avoid this we use an economic optimization model that incorporates the synthetic and historical crop mixes approach (Onal & Chen, 2012). This restricts the solutions between the weighted averages of historical and simulated feasible planting decisions, with the weights associated with crop mixes being treated as endogenous variables. This study is focused on 5 major irrigation districts of the YRB in the Pacific Northwest US. The biophysical modeling framework we use, BioEarth, includes the coupled hydrology and crop growth model, VIC-Cropsyst and an economic optimization model. Preliminary findings indicate that the standard approach of developing AEZs does not perform well when overlaid with irrigation districts. Moreover, net economic benefits were significantly different between the two aggregation methodologies. Therefore, while developing hydro-economic models, significant consideration should be placed on the aggregation methodology.

Climate Regulation Services of Natural and Managed Ecosystems of the Americas

NASA Astrophysics Data System (ADS)

Anderson-Teixeira, K. J.; Snyder, P. K.; Twine, T. E.; Costa, M. H.; Cuadra, S.; DeLucia, E. H.

2011-12-01

Terrestrial ecosystems regulate climate through both biogeochemical mechanisms (greenhouse gas regulation) and biophysical mechanisms (regulation of water and energy). Land management therefore provides some of the most effective strategies for climate change mitigation. However, most policies aimed at climate protection through land management, including UNFCCC mechanisms and bioenergy sustainability standards, account only for biogeochemical climate services. By ignoring biophysical climate regulation services that in some cases offset the biogeochemical regulation services, these policies run the risk of failing to advance the best climate solutions. Quantifying the combined value of biogeochemical and biophysical climate regulation services remains an important challenge. Here, we use a combination of data synthesis and modeling to quantify how biogeochemical and biophysical effects combine to shape the climate regulation value (CRV) of 18 natural and managed ecosystem types across the Western Hemisphere. Natural ecosystems generally had higher CRVs than agroecosystems, largely driven by differences in biogeochemical services. Biophysical contributions ranged from minimal to dominant. They were highly variable in space and across ecosystem types, and their relative importance varied strongly with the spatio-temporal scale of analysis. Our findings pertain to current efforts to protect climate through land management. Specifically, they reinforce the importance of protecting tropical forests and recent findings that the climatic effects of bioenergy production may be somewhat more positive than previously estimated. Given that biophysical effects in some cases dominate, ensuring effective climate protection through land management requires consideration of combined biogeochemical and biophysical climate regulation services. While quantification of ecosystem climate services is necessarily complex, our CRV index serves as one potential approach to measure the full climate services of terrestrial ecosystems.

Random Photon Absorption Model Elucidates How Early Gain Control in Fly Photoreceptors Arises from Quantal Sampling

PubMed Central

Song, Zhuoyi; Zhou, Yu; Juusola, Mikko

2016-01-01

Many diurnal photoreceptors encode vast real-world light changes effectively, but how this performance originates from photon sampling is unclear. A 4-module biophysically-realistic fly photoreceptor model, in which information capture is limited by the number of its sampling units (microvilli) and their photon-hit recovery time (refractoriness), can accurately simulate real recordings and their information content. However, sublinear summation in quantum bump production (quantum-gain-nonlinearity) may also cause adaptation by reducing the bump/photon gain when multiple photons hit the same microvillus simultaneously. Here, we use a Random Photon Absorption Model (RandPAM), which is the 1st module of the 4-module fly photoreceptor model, to quantify the contribution of quantum-gain-nonlinearity in light adaptation. We show how quantum-gain-nonlinearity already results from photon sampling alone. In the extreme case, when two or more simultaneous photon-hits reduce to a single sublinear value, quantum-gain-nonlinearity is preset before the phototransduction reactions adapt the quantum bump waveform. However, the contribution of quantum-gain-nonlinearity in light adaptation depends upon the likelihood of multi-photon-hits, which is strictly determined by the number of microvilli and light intensity. Specifically, its contribution to light-adaptation is marginal (≤ 1%) in fly photoreceptors with many thousands of microvilli, because the probability of simultaneous multi-photon-hits on any one microvillus is low even during daylight conditions. However, in cells with fewer sampling units, the impact of quantum-gain-nonlinearity increases with brightening light. PMID:27445779

The Arctic Marine Pulses Model: Linking Contiguous Domains in the Pacific Arctic Region

NASA Astrophysics Data System (ADS)

Moore, S. E.; Stabeno, P. J.

2016-02-01

The Pacific Arctic marine ecosystem extends from the northern Bering Sea, across the Chukchi and into the East Siberian and Beaufort seas. Food webs in this domain are short, a simplicity that belies the biophysical complexity underlying trophic linkages from primary production to humans. Existing biophysical models, such as pelagic-benthic coupling and advective processes, provide frameworks for connecting certain aspects of the marine food web, but do not offer a full accounting of events that occur seasonally across the Pacific Arctic. In the course of the Synthesis of Arctic Research (SOAR) project, a holistic Arctic Marine Pulses (AMP) model was developed that depicts seasonal biophysical `pulses' across a latitudinal gradient, and linking four previously-described contiguous domains, including the: (i) Pacific-Arctic domain = the focal region; (ii) seasonal ice zone domain; (iii) Pacific marginal domain; and (iv) riverine coastal domain. The AMP model provides a spatial-temporal framework to guide research on dynamic ecosystem processes during this period of rapid biophysical changes in the Pacific Arctic. Some of the processes included in the model, such as pelagic-benthic coupling in the Northern Bering and Chukchi seas, and advection and upwelling along the Beaufort shelf, are already the focus of sampling via the Distributed Biological Observatory (DBO) and other research programs. Other aspects such as biological processes associated with the seasonal ice zone and trophic responses to riverine outflow have received less attention. The AMP model could be enhanced by the application of visualization tools to provide a means to watch a season unfold in space and time. The capability to track sea ice dynamics and water masses and to move nutrients, prey and upper-trophic predators in space and time would provide a strong foundation for the development of predictive human-inclusive ecosystem models for the Pacific Arctic.

On The Development of Biophysical Models for Space Radiation Risk Assessment

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Dicello, J. F.

1999-01-01

Experimental techniques in molecular biology are being applied to study biological risks from space radiation. The use of molecular assays presents a challenge to biophysical models which in the past have relied on descriptions of energy deposition and phenomenological treatments of repair. We describe a biochemical kinetics model of cell cycle control and DNA damage response proteins in order to model cellular responses to radiation exposures. Using models of cyclin-cdk, pRB, E2F's, p53, and GI inhibitors we show that simulations of cell cycle populations and GI arrest can be described by our biochemical approach. We consider radiation damaged DNA as a substrate for signal transduction processes and consider a dose and dose-rate reduction effectiveness factor (DDREF) for protein expression.

How well do we characterize the biophysical effects of vegetation cover change? Benchmarking land surface models against satellite observations.

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Forzieri, Giovanni; Robertson, Eddy; Georgievski, Goran; Li, Wei; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

2017-04-01

Changes in vegetation cover can affect the climate by altering the carbon, water and energy cycles. The main tools to characterize such land-climate interactions for both the past and future are land surface models (LSMs) that can be embedded in larger Earth System models (ESMs). While such models have long been used to characterize the biogeochemical effects of vegetation cover change, their capacity to model biophysical effects accurately across the globe remains unclear due to the complexity of the phenomena. The result of competing biophysical processes on the surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and on the background climate (e.g. presence of snow or soil moisture). Here we present a global scale benchmarking exercise of four of the most commonly used LSMs (JULES, ORCHIDEE, JSBACH and CLM) against a dedicated dataset of satellite observations. To facilitate the understanding of the causes that lead to discrepancies between simulated and observed data, we focus on pure transitions amongst major plant functional types (PFTs): from different tree types (evergreen broadleaf trees, deciduous broadleaf trees and needleleaf trees) to either grasslands or crops. From the modelling perspective, this entails generating a separate simulation for each PFT in which all 1° by 1° grid cells are uniformly covered with that PFT, and then analysing the differences amongst them in terms of resulting biophysical variables (e.g net radiation, latent and sensible heat). From the satellite perspective, the effect of pure transitions is obtained by unmixing the signal of different 0.05° spatial resolution MODIS products (albedo, latent heat, upwelling longwave radiation) over a local moving window using PFT maps derived from the ESA Climate Change Initiative land cover map. After aggregating to a common spatial support, the observation and model-driven datasets are confronted and analysed across different climate zones. Results indicate that models tend to catch better radiative than non-radiative energy fluxes. However, for various vegetation transitions, models do not agree amongst themselves on the magnitude nor the sign of the change. In particular, predicting the impact of land cover change on the partitioning of the available energy between latent and sensible heat proves to be a challenging task for vegetation models. We expect that this benchmarking exercise will shed a light on where to prioritize the efforts in model development as well as inform where consensus between model and observations is already met. Improving the robustness and consistency of land-model is essential to develop and inform land-based mitigation and adaptation policies that account for both biogeochemical and biophysical vegetation impacts on climate.

Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

PubMed

Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

2010-01-01

In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

Estimation of biophysical properties of upland Sitka spruce (Picea sitchensis) plantations

NASA Technical Reports Server (NTRS)

Green, Robert M.

1993-01-01

It is widely accepted that estimates of forest above-ground biomass are required as inputs to forest ecosystem models, and that SAR data have the potential to provide such information. This study describes relationships between polarimetric radar backscatter and key biophysical properties of a coniferous plantation in upland central Wales, U.K. Over the test site, topography was relatively complex and was expected to influence the amount of radar backscatter.

A review on vegetation models and applicability to climate simulations at regional scale

NASA Astrophysics Data System (ADS)

Myoung, Boksoon; Choi, Yong-Sang; Park, Seon Ki

2011-11-01

The lack of accurate representations of biospheric components and their biophysical and biogeochemical processes is a great source of uncertainty in current climate models. The interactions between terrestrial ecosystems and the climate include exchanges not only of energy, water and momentum, but also of carbon and nitrogen. Reliable simulations of these interactions are crucial for predicting the potential impacts of future climate change and anthropogenic intervention on terrestrial ecosystems. In this paper, two biogeographical (Neilson's rule-based model and BIOME), two biogeochemical (BIOME-BGC and PnET-BGC), and three dynamic global vegetation models (Hybrid, LPJ, and MC1) were reviewed and compared in terms of their biophysical and physiological processes. The advantages and limitations of the models were also addressed. Lastly, the applications of the dynamic global vegetation models to regional climate simulations have been discussed.

A comparison of different radiative transfer model inversion methods for canopy water content retrieval

NASA Astrophysics Data System (ADS)

Boren, E. J.; Boschetti, L.; Johnson, D.

2016-12-01

With near-future droughts predicted to become both more frequent and more intense (Allen et al. 2015, Diffenbaugh et al. 2015), the estimation of satellite-derived vegetation water content would benefit a wide range of environmental applications including agricultural, vegetation, and fire risk monitoring. No vegetation water content thematic product is currently available (Yebra et al. 2013), but the successful launch of the Landsat 8 OLI and Sentinel 2A satellites, and the forthcoming Sentinel 2B, provide the opportunity for monitoring biophysical variables at a scale (10-30m) and temporal resolution (5 days) needed by most applications. Radiative transfer models (RTM) use a set of biophysical parameters to produce an estimated spectral response and - when used in inverse mode - provide a way to use satellite spectral data to estimate vegetation biophysical parameters, including water content (Zarco-Tejada et al. 2003). Using the coupled leaf and canopy level model PROSAIL5, and Landsat 8 OLI and Sentinel 2A MSI optical satellite data, the present research compares the results of three model inversion techniques: iterative optimization (OPT), look-up table (LUT), and artificial neural network (ANN) training. Ancillary biophysical data, needed for constraining the inversion process, were collected from various crop species grown in a controlled setting and under different water stress conditions. The measurements included fresh weight, dry weight, leaf area, and spectral leaf transmittance and reflectance in the 350-2500 nm range. Plot-level data, collected coincidently with satellite overpasses during three summer field campaigns in northern Idaho (2014 to 2016), are used to evaluate the results of the model inversion. Field measurements included fresh weight, dry weight, leaf area index, plant height, and top of canopy reflectance in the 350-2500 nm range. The results of the model inversion intercomparison exercised are used to characterize the uncertainties of vegetation water content estimation from Landsat 8 OLI and Sentinel 2A data.

Bio-physical modeling of time-resolved forward scattering by Listeria colonies

NASA Astrophysics Data System (ADS)

Bae, Euiwon; Banada, Padmapriya P.; Bhunia, Arun K.; Hirleman, E. Daniel

2006-10-01

We have developed a detection system and associated protocol based on optical forward scattering where the bacterial colonies of various species and strains growing on solid nutrient surfaces produced unique scatter signatures. The aim of the present investigation was to develop a bio-physical model for the relevant phenomena. In particular, we considered time-varying macroscopic morphological properties of the growing colonies and modeled the scattering using scalar diffraction theory. For the present work we performed detailed studies with three species of Listeria; L. innocua, L. monocytogenes, and L. ivanovii. The baseline experiments involved cultures grown on brain heart infusion (BHI) agar and the scatter images were captured every six hours for an incubation period of 42 hours. The morphologies of the colonies were studied by phase contrast microscopy, including measurement of the diameter of the colony. Growth curves, represented by colony diameter as a function of time, were compared with the time-evolution of scattering signatures. Similar studies were carried out with L. monocytogenes grown on different substrates. Non-dimensionalizing incubation time in terms of the time to reach stationary phase was effective in reducing the dimensionality of the model. Bio-physical properties of the colony such as diameter, bacteria density variation, surface curvature/profile, and transmission coefficient are important parameters in predicting the features of the forward scattering signatures. These parameters are included in a baseline model that treats the colony as a concentric structure with radial variations in phase modulation. In some cases azimuthal variations and random phase inclusions were included as well. The end result is a protocol (growth media, incubation time and conditions) that produces reproducible and distinguishable scatter patterns for a variety of harmful food borne pathogens in a short period of time. Further, the bio-physical model we developed is very effective in predicting the dominant features of the scattering signatures required by the identification process and will be effective for informing further improvements in the instrumentation.

Severity of climate change dictates the direction of biophysical feedbacks of vegetation change to Arctic climate

NASA Astrophysics Data System (ADS)

Zhang, Wenxin; Jansson, Christer; Miller, Paul; Smith, Ben; Samuelsson, Patrick

2014-05-01

Vegetation-climate feedbacks induced by vegetation dynamics under climate change alter biophysical properties of the land surface that regulate energy and water exchange with the atmosphere. Simulations with Earth System Models applied at global scale suggest that the current warming in the Arctic has been amplified, with large contributions from positive feedbacks, dominated by the effect of reduced surface albedo as an increased distribution, cover and taller stature of trees and shrubs mask underlying snow, darkening the surface. However, these models generally employ simplified representation of vegetation dynamics and structure and a coarse grid resolution, overlooking local or regional scale details determined by diverse vegetation composition and landscape heterogeneity. In this study, we perform simulations using an advanced regional coupled vegetation-climate model (RCA-GUESS) applied at high resolution (0.44×0.44° ) over the Arctic Coordinated Regional Climate Downscaling Experiment (CORDEX-Arctic) domain. The climate component (RCA4) is forced with lateral boundary conditions from EC-EARTH CMIP5 simulations for three representative concentration pathways (RCP 2.6, 4.5, 8.5). Vegetation-climate response is simulated by the individual-based dynamic vegetation model (LPJ-GUESS), accounting for phenology, physiology, demography and resource competition of individual-based vegetation, and feeding variations of leaf area index and vegetative cover fraction back to the climate component, thereby adjusting surface properties and surface energy fluxes. The simulated 2m air temperature, precipitation, vegetation distribution and carbon budget for the present period has been evaluated in another paper. The purpose of this study is to elucidate the spatial and temporal characteristics of the biophysical feedbacks arising from vegetation shifts in response to different CO2 concentration pathways and their associated climate change. Our results indicate that the albedo feedback dominates simulated warming in spring in all three scenarios, while in summer, evapotranspiration feedback, governing the partitioning of the return energy flux from the surface to the atmosphere into latent and sensible heat, exerts evaporative cooling effects, the magnitude of which depends on the severity of climate change, in turn driven by the underlying GHG emissions pathway, resulting in shift in the sign of net biophysical at higher levels of warming. Spatially, western Siberia is identified as the most susceptible location, experiencing the potential to reverse biophysical feedbacks in all seasons. We further analyze how the pattern of vegetation shifts triggers different signs of net effects of biophysical feedbacks.

Biophysical and Economic Uncertainty in the Analysis of Poverty Impacts of Climate Change

NASA Astrophysics Data System (ADS)

Hertel, T. W.; Lobell, D. B.; Verma, M.

2011-12-01

This paper seeks to understand the main sources of uncertainty in assessing the impacts of climate change on agricultural output, international trade, and poverty. We incorporate biophysical uncertainty by sampling from a distribution of global climate model predictions for temperature and precipitation for 2050. The implications of these realizations for crop yields around the globe are estimated using the recently published statistical crop yield functions provided by Lobell, Schlenker and Costa-Roberts (2011). By comparing these yields to those predicted under current climate, we obtain the likely change in crop yields owing to climate change. The economic uncertainty in our analysis relates to the response of the global economic system to these biophysical shocks. We use a modified version of the GTAP model to elicit the impact of the biophysical shocks on global patterns of production, consumption, trade and poverty. Uncertainty in these responses is reflected in the econometrically estimated parameters governing the responsiveness of international trade, consumption, production (and hence the intensive margin of supply response), and factor supplies (which govern the extensive margin of supply response). We sample from the distributions of these parameters as specified by Hertel et al. (2007) and Keeney and Hertel (2009). We find that, even though it is difficult to predict where in the world agricultural crops will be favorably affected by climate change, the responses of economic variables, including output and exports can be far more robust (Table 1). This is due to the fact that supply and demand decisions depend on relative prices, and relative prices depend on productivity changes relative to other crops in a given region, or relative to similar crops in other parts of the world. We also find that uncertainty in poverty impacts of climate change appears to be almost entirely driven by biophysical uncertainty.

The Conformational Stability and Biophysical Properties of the Eukaryotic Thioredoxins of Pisum Sativum Are Not Family-Conserved

PubMed Central

Aguado-Llera, David; Martínez-Gómez, Ana Isabel; Prieto, Jesús; Marenchino, Marco; Traverso, José Angel; Gómez, Javier; Chueca, Ana; Neira, José L.

2011-01-01

Thioredoxins (TRXs) are ubiquitous proteins involved in redox processes. About forty genes encode TRX or TRX-related proteins in plants, grouped in different families according to their subcellular localization. For instance, the h-type TRXs are located in cytoplasm or mitochondria, whereas f-type TRXs have a plastidial origin, although both types of proteins have an eukaryotic origin as opposed to other TRXs. Herein, we study the conformational and the biophysical features of TRXh1, TRXh2 and TRXf from Pisum sativum. The modelled structures of the three proteins show the well-known TRX fold. While sharing similar pH-denaturations features, the chemical and thermal stabilities are different, being PsTRXh1 (Pisum sativum thioredoxin h1) the most stable isoform; moreover, the three proteins follow a three-state denaturation model, during the chemical-denaturations. These differences in the thermal- and chemical-denaturations result from changes, in a broad sense, of the several ASAs (accessible surface areas) of the proteins. Thus, although a strong relationship can be found between the primary amino acid sequence and the structure among TRXs, that between the residue sequence and the conformational stability and biophysical properties is not. We discuss how these differences in the biophysical properties of TRXs determine their unique functions in pea, and we show how residues involved in the biophysical features described (pH-titrations, dimerizations and chemical-denaturations) belong to regions involved in interaction with other proteins. Our results suggest that the sequence demands of protein-protein function are relatively rigid, with different protein-binding pockets (some in common) for each of the three proteins, but the demands of structure and conformational stability per se (as long as there is a maintained core), are less so. PMID:21364950

Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks

NASA Technical Reports Server (NTRS)

Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D.; Havlik, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina

2014-01-01

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(sup 2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Climate change effects on agriculture: Economic responses to biophysical shocks

PubMed Central

Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D.; Havlík, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina; Kyle, Page; Von Lampe, Martin; Lotze-Campen, Hermann; Mason d’Croz, Daniel; van Meijl, Hans; van der Mensbrugghe, Dominique; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; Schmid, Erwin; Schmitz, Christoph; Tabeau, Andrzej; Willenbockel, Dirk

2014-01-01

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change. PMID:24344285

Climate change effects on agriculture: economic responses to biophysical shocks.

PubMed

Nelson, Gerald C; Valin, Hugo; Sands, Ronald D; Havlík, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina; Kyle, Page; Von Lampe, Martin; Lotze-Campen, Hermann; Mason d'Croz, Daniel; van Meijl, Hans; van der Mensbrugghe, Dominique; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; Schmid, Erwin; Schmitz, Christoph; Tabeau, Andrzej; Willenbockel, Dirk

2014-03-04

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Modeling carbon dynamics and social drivers of bioenergy agroecosystems

NASA Astrophysics Data System (ADS)

Hunt, Natalie D.

Meeting society's energy needs through bioenergy feedstock production presents a significant and urgent challenge, as it can aid in achieving energy independence goals and mitigating climate change. With federal biofuel production standards to be met within the next decade, and with no commercial scale production or markets currently in place, many questions regarding the sustainability and social feasibility of bioenergy still persist. Clarifying these uncertainties requires the incorporation of biogeochemical, biophysical, and socioeconomic modeling tools. Chapter 2 validated the biogeochemical cycling model AGRO-BGC by comparing model estimates with empirical observations from corn and perennial C4 grass systems across Wisconsin and Illinois. AGRO-BGC, in its first application to an annual cropping system, was found to be a robust model for simulating carbon dynamics of an annual cropping system. Chapter 3 investigated the long-term implications of bioenergy feedstock harvest on soil productivity and erosion in annual corn and perennial switchgrass agroecosystems using AGRO-BGC and the soil erosion model RUSLE2. Modeling environments included biophysical landscape characteristics and management practices of bioenergy feedstock production systems. This study found that intensifying aboveground residue harvest reduces soil productivity over time, and the magnitude of these losses is greater in corn than in switchgrass systems. Results of this study will aid in the design of sustainable bioenergy feedstock management practices. Chapter 4 provided evidence that combining biophysical crop canopy characteristics with satellite-derived vegetation indices offers suitable estimates of crop canopy phenology for corn and soybeans in Southwest Wisconsin farms. LANDSAT based vegetation indices, when combined with a light use efficiency model, provide yield estimates in agreement with farmer reports, providing an efficient and accurate means of estimating crop yields from satellite data. The most important stakeholder in bioenergy sustainability and feasibility research is the farmer. Chapter 5 identified and measured the influence of bioenergy feedstock choice drivers using logistic regression choice models constructed from survey and geospatial data. The strongest choice drivers among farmers willing to participate in a proposed bioenergy feedstock production program included socioeconomic, biophysical, and environmental attitudes. Outcomes of this research will be useful in designing further bioenergy policy and economic incentives.

Biophysical and structural considerations for protein sequence evolution

PubMed Central

2011-01-01

Background Protein sequence evolution is constrained by the biophysics of folding and function, causing interdependence between interacting sites in the sequence. However, current site-independent models of sequence evolutions do not take this into account. Recent attempts to integrate the influence of structure and biophysics into phylogenetic models via statistical/informational approaches have not resulted in expected improvements in model performance. This suggests that further innovations are needed for progress in this field. Results Here we develop a coarse-grained physics-based model of protein folding and binding function, and compare it to a popular informational model. We find that both models violate the assumption of the native sequence being close to a thermodynamic optimum, causing directional selection away from the native state. Sampling and simulation show that the physics-based model is more specific for fold-defining interactions that vary less among residue type. The informational model diffuses further in sequence space with fewer barriers and tends to provide less support for an invariant sites model, although amino acid substitutions are generally conservative. Both approaches produce sequences with natural features like dN/dS < 1 and gamma-distributed rates across sites. Conclusions Simple coarse-grained models of protein folding can describe some natural features of evolving proteins but are currently not accurate enough to use in evolutionary inference. This is partly due to improper packing of the hydrophobic core. We suggest possible improvements on the representation of structure, folding energy, and binding function, as regards both native and non-native conformations, and describe a large number of possible applications for such a model. PMID:22171550

Why noise is useful in functional and neural mechanisms of interval timing?

PubMed Central

2013-01-01

Background The ability to estimate durations in the seconds-to-minutes range - interval timing - is essential for survival, adaptation and its impairment leads to severe cognitive and/or motor dysfunctions. The response rate near a memorized duration has a Gaussian shape centered on the to-be-timed interval (criterion time). The width of the Gaussian-like distribution of responses increases linearly with the criterion time, i.e., interval timing obeys the scalar property. Results We presented analytical and numerical results based on the striatal beat frequency (SBF) model showing that parameter variability (noise) mimics behavioral data. A key functional block of the SBF model is the set of oscillators that provide the time base for the entire timing network. The implementation of the oscillators block as simplified phase (cosine) oscillators has the additional advantage that is analytically tractable. We also checked numerically that the scalar property emerges in the presence of memory variability by using biophysically realistic Morris-Lecar oscillators. First, we predicted analytically and tested numerically that in a noise-free SBF model the output function could be approximated by a Gaussian. However, in a noise-free SBF model the width of the Gaussian envelope is independent of the criterion time, which violates the scalar property. We showed analytically and verified numerically that small fluctuations of the memorized criterion time leads to scalar property of interval timing. Conclusions Noise is ubiquitous in the form of small fluctuations of intrinsic frequencies of the neural oscillators, the errors in recording/retrieving stored information related to criterion time, fluctuation in neurotransmitters’ concentration, etc. Our model suggests that the biological noise plays an essential functional role in the SBF interval timing. PMID:23924391

Breaking conceptual locks in modelling root absorption of nutrients: reopening the thermodynamic viewpoint of ion transport across the root

PubMed Central

Le Deunff, Erwan; Malagoli, Philippe

2014-01-01

Background The top-down analysis of nitrate influx isotherms through the Enzyme-Substrate interpretation has not withstood recent molecular and histochemical analyses of nitrate transporters. Indeed, at least four families of nitrate transporters operating at both high and/or low external nitrate concentrations, and which are located in series and/or parallel in the different cellular layers of the mature root, are involved in nitrate uptake. Accordingly, the top-down analysis of the root catalytic structure for ion transport from the Enzyme-Substrate interpretation of nitrate influx isotherms is inadequate. Moreover, the use of the Enzyme-Substrate velocity equation as a single reference in agronomic models is not suitable in its formalism to account for variations in N uptake under fluctuating environmental conditions. Therefore, a conceptual paradigm shift is required to improve the mechanistic modelling of N uptake in agronomic models. Scope An alternative formalism, the Flow-Force theory, was proposed in the 1970s to describe ion isotherms based upon biophysical ‘flows and forces’ relationships of non-equilibrium thermodynamics. This interpretation describes, with macroscopic parameters, the patterns of N uptake provided by a biological system such as roots. In contrast to the Enzyme-Substrate interpretation, this approach does not claim to represent molecular characteristics. Here it is shown that it is possible to combine the Flow-Force formalism with polynomial responses of nitrate influx rate induced by climatic and in planta factors in relation to nitrate availability. Conclusions Application of the Flow-Force formalism allows nitrate uptake to be modelled in a more realistic manner, and allows scaling-up in time and space of the regulation of nitrate uptake across the plant growth cycle. PMID:25425406

A Stochastic Model of Space Radiation Transport as a Tool in the Development of Time-Dependent Risk Assessment

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee Y.; Nounu, Hatem N.; Ponomarev, Artem L.; Cucinotta, Francis A.

2011-01-01

A new computer model, the GCR Event-based Risk Model code (GERMcode), was developed to describe biophysical events from high-energy protons and heavy ions that have been studied at the NASA Space Radiation Laboratory (NSRL) [1] for the purpose of simulating space radiation biological effects. In the GERMcode, the biophysical description of the passage of heavy ions in tissue and shielding materials is made with a stochastic approach that includes both ion track structure and nuclear interactions. The GERMcode accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model [2]. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections

Uncertainty Analysis of Coupled Socioeconomic-Cropping Models: Building Confidence in Climate Change Decision-Support Tools for Local Stakeholders

NASA Astrophysics Data System (ADS)

Malard, J. J.; Rojas, M.; Adamowski, J. F.; Gálvez, J.; Tuy, H. A.; Melgar-Quiñonez, H.

2015-12-01

While cropping models represent the biophysical aspects of agricultural systems, system dynamics modelling offers the possibility of representing the socioeconomic (including social and cultural) aspects of these systems. The two types of models can then be coupled in order to include the socioeconomic dimensions of climate change adaptation in the predictions of cropping models.We develop a dynamically coupled socioeconomic-biophysical model of agricultural production and its repercussions on food security in two case studies from Guatemala (a market-based, intensive agricultural system and a low-input, subsistence crop-based system). Through the specification of the climate inputs to the cropping model, the impacts of climate change on the entire system can be analysed, and the participatory nature of the system dynamics model-building process, in which stakeholders from NGOs to local governmental extension workers were included, helps ensure local trust in and use of the model.However, the analysis of climate variability's impacts on agroecosystems includes uncertainty, especially in the case of joint physical-socioeconomic modelling, and the explicit representation of this uncertainty in the participatory development of the models is important to ensure appropriate use of the models by the end users. In addition, standard model calibration, validation, and uncertainty interval estimation techniques used for physically-based models are impractical in the case of socioeconomic modelling. We present a methodology for the calibration and uncertainty analysis of coupled biophysical (cropping) and system dynamics (socioeconomic) agricultural models, using survey data and expert input to calibrate and evaluate the uncertainty of the system dynamics as well as of the overall coupled model. This approach offers an important tool for local decision makers to evaluate the potential impacts of climate change and their feedbacks through the associated socioeconomic system.

Incorporating modeling and simulations in undergraduate biophysical chemistry course to promote understanding of structure-dynamics-function relationships in proteins.

PubMed

Hati, Sanchita; Bhattacharyya, Sudeep

2016-01-01

A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and simulations. In particular, modern computational tools are employed to elucidate the relationship between structure, dynamics, and function in proteins. Computer-based laboratory protocols that we introduced in three modules allow students to visualize the secondary, super-secondary, and tertiary structures of proteins, analyze non-covalent interactions in protein-ligand complexes, develop three-dimensional structural models (homology model) for new protein sequences and evaluate their structural qualities, and study proteins' intrinsic dynamics to understand their functions. In the fourth module, students are assigned to an authentic research problem, where they apply their laboratory skills (acquired in modules 1-3) to answer conceptual biophysical questions. Through this process, students gain in-depth understanding of protein dynamics-the missing link between structure and function. Additionally, the requirement of term papers sharpens students' writing and communication skills. Finally, these projects result in new findings that are communicated in peer-reviewed journals. © 2016 The International Union of Biochemistry and Molecular Biology.

Geostatistical regularization of inverse models for the retrieval of vegetation biophysical variables

NASA Astrophysics Data System (ADS)

Atzberger, C.; Richter, K.

2009-09-01

The robust and accurate retrieval of vegetation biophysical variables using radiative transfer models (RTM) is seriously hampered by the ill-posedness of the inverse problem. With this research we further develop our previously published (object-based) inversion approach [Atzberger (2004)]. The object-based RTM inversion takes advantage of the geostatistical fact that the biophysical characteristics of nearby pixel are generally more similar than those at a larger distance. A two-step inversion based on PROSPECT+SAIL generated look-up-tables is presented that can be easily implemented and adapted to other radiative transfer models. The approach takes into account the spectral signatures of neighboring pixel and optimizes a common value of the average leaf angle (ALA) for all pixel of a given image object, such as an agricultural field. Using a large set of leaf area index (LAI) measurements (n = 58) acquired over six different crops of the Barrax test site, Spain), we demonstrate that the proposed geostatistical regularization yields in most cases more accurate and spatially consistent results compared to the traditional (pixel-based) inversion. Pros and cons of the approach are discussed and possible future extensions presented.

How trees uptake carbon, release water and cool themselves in air: a marriage between biophysics and turbulent fluid dynamics

NASA Astrophysics Data System (ADS)

Banerjee, Tirtha; Linn, Rodman

2017-11-01

Resolving the role of the biosphere as a terrestrial carbon sink and the nature of nonlinear couplings between carbon and water cycles across a very wide range of spatiotemporal scales constitute the scope of this work. To achieve this goal, plant physiology models are coupled with atmospheric turbulence simulations. The plant biophysics code is based on the following principles: (1) a model for photosynthesis; (2) a mass transfer model through the laminar boundary layer on leaves; (3) an optimal leaf water use strategy regulated by stomatal aperture variation; (4) a leaf-level energy balance to accommodate evaporative cooling. Leaf-level outputs are upscaled to plant, canopy and landscape scales using HIGRAD/FIRETEC, a high fidelity large eddy simulation (LES) framework developed at LANL. The coupled biophysics-CFD code can take inputs such as wind speed, light availability, ambient CO2 concentration, air temperature, site characteristics etc. and can deliver predictions for leaf temperature, transpiration, carbon assimilation, sensible and latent heat flux, which is used to illustrate the complex the complex interaction between trees and their surrounding environments. These simulation capabilities are being used to study climate feedbacks of forests and agroecosystems.

The effect of collagen fibril orientation on the biphasic mechanics of articular cartilage.

PubMed

Meng, Qingen; An, Shuqiang; Damion, Robin A; Jin, Zhongmin; Wilcox, Ruth; Fisher, John; Jones, Alison

2017-01-01

The highly inhomogeneous distribution of collagen fibrils may have important effects on the biphasic mechanics of articular cartilage. However, the effect of the inhomogeneity of collagen fibrils has mainly been investigated using simplified three-layered models, which may have underestimated the effect of collagen fibrils by neglecting their realistic orientation. The aim of this study was to investigate the effect of the realistic orientation of collagen fibrils on the biphasic mechanics of articular cartilage. Five biphasic material models, each of which included a different level of complexity of fibril reinforcement, were solved using two different finite element software packages (Abaqus and FEBio). Model 1 considered the realistic orientation of fibrils, which was derived from diffusion tensor magnetic resonance images. The simplified three-layered orientation was used for Model 2. Models 3-5 were three control models. The realistic collagen orientations obtained in this study were consistent with the literature. Results from the two finite element implementations were in agreement for each of the conditions modelled. The comparison between the control models confirmed some functions of collagen fibrils. The comparison between Models 1 and 2 showed that the widely-used three-layered inhomogeneous model can produce similar fluid load support to the model including the realistic fibril orientation; however, an accurate prediction of the other mechanical parameters requires the inclusion of the realistic orientation of collagen fibrils. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Testing the Applicability of Nernst-Planck Theory in Ion Channels: Comparisons with Brownian Dynamics Simulations

PubMed Central

Song, Chen; Corry, Ben

2011-01-01

The macroscopic Nernst-Planck (NP) theory has often been used for predicting ion channel currents in recent years, but the validity of this theory at the microscopic scale has not been tested. In this study we systematically tested the ability of the NP theory to accurately predict channel currents by combining and comparing the results with those of Brownian dynamics (BD) simulations. To thoroughly test the theory in a range of situations, calculations were made in a series of simplified cylindrical channels with radii ranging from 3 to 15 Å, in a more complex ‘catenary’ channel, and in a realistic model of the mechanosensitive channel MscS. The extensive tests indicate that the NP equation is applicable in narrow ion channels provided that accurate concentrations and potentials can be input as the currents obtained from the combination of BD and NP match well with those obtained directly from BD simulations, although some discrepancies are seen when the ion concentrations are not radially uniform. This finding opens a door to utilising the results of microscopic simulations in continuum theory, something that is likely to be useful in the investigation of a range of biophysical and nano-scale applications and should stimulate further studies in this direction. PMID:21731672

Testing the applicability of Nernst-Planck theory in ion channels: comparisons with Brownian dynamics simulations.

PubMed

Song, Chen; Corry, Ben

2011-01-01

The macroscopic Nernst-Planck (NP) theory has often been used for predicting ion channel currents in recent years, but the validity of this theory at the microscopic scale has not been tested. In this study we systematically tested the ability of the NP theory to accurately predict channel currents by combining and comparing the results with those of Brownian dynamics (BD) simulations. To thoroughly test the theory in a range of situations, calculations were made in a series of simplified cylindrical channels with radii ranging from 3 to 15 Å, in a more complex 'catenary' channel, and in a realistic model of the mechanosensitive channel MscS. The extensive tests indicate that the NP equation is applicable in narrow ion channels provided that accurate concentrations and potentials can be input as the currents obtained from the combination of BD and NP match well with those obtained directly from BD simulations, although some discrepancies are seen when the ion concentrations are not radially uniform. This finding opens a door to utilising the results of microscopic simulations in continuum theory, something that is likely to be useful in the investigation of a range of biophysical and nano-scale applications and should stimulate further studies in this direction.

Refractory Sampling Links Efficiency and Costs of Sensory Encoding to Stimulus Statistics

PubMed Central

Song, Zhuoyi

2014-01-01

Sensory neurons integrate information about the world, adapting their sampling to its changes. However, little is understood mechanistically how this primary encoding process, which ultimately limits perception, depends upon stimulus statistics. Here, we analyze this open question systematically by using intracellular recordings from fly (Drosophila melanogaster and Coenosia attenuata) photoreceptors and corresponding stochastic simulations from biophysically realistic photoreceptor models. Recordings show that photoreceptors can sample more information from naturalistic light intensity time series (NS) than from Gaussian white-noise (GWN), shuffled-NS or Gaussian-1/f stimuli; integrating larger responses with higher signal-to-noise ratio and encoding efficiency to large bursty contrast changes. Simulations reveal how a photoreceptor's information capture depends critically upon the stochastic refractoriness of its 30,000 sampling units (microvilli). In daylight, refractoriness sacrifices sensitivity to enhance intensity changes in neural image representations, with more and faster microvilli improving encoding. But for GWN and other stimuli, which lack longer dark contrasts of real-world intensity changes that reduce microvilli refractoriness, these performance gains are submaximal and energetically costly. These results provide mechanistic reasons why information sampling is more efficient for natural/naturalistic stimulation and novel insight into the operation, design, and evolution of signaling and code in sensory neurons. PMID:24849356

Reconstructed historical land cover and biophysical parameters for studies of land-atmosphere interactions within the eastern United States

USGS Publications Warehouse

Steyaert, Louis T.; Knox, R.G.

2008-01-01

Over the past 350 years, the eastern half of the United States experienced extensive land cover changes. These began with land clearing in the 1600s, continued with widespread deforestation, wetland drainage, and intensive land use by 1920, and then evolved to the present-day landscape of forest regrowth, intensive agriculture, urban expansion, and landscape fragmentation. Such changes alter biophysical properties that are key determinants of land-atmosphere interactions (water, energy, and carbon exchanges). To understand the potential implications of these land use transformations, we developed and analyzed 20-km land cover and biophysical parameter data sets for the eastern United States at 1650, 1850, 1920, and 1992 time slices. Our approach combined potential vegetation, county-level census data, soils data, resource statistics, a Landsat-derived land cover classification, and published historical information on land cover and land use. We reconstructed land use intensity maps for each time slice and characterized the land cover condition. We combined these land use data with a mutually consistent set of biophysical parameter classes, to characterize the historical diversity and distribution of land surface properties. Time series maps of land surface albedo, leaf area index, a deciduousness index, canopy height, surface roughness, and potential saturated soils in 1650, 1850, 1920, and 1992 illustrate the profound effects of land use change on biophysical properties of the land surface. Although much of the eastern forest has returned, the average biophysical parameters for recent landscapes remain markedly different from those of earlier periods. Understanding the consequences of these historical changes will require land-atmosphere interactions modeling experiments.

Quantifying the Climate Impacts of Land Use Change (Invited)

NASA Astrophysics Data System (ADS)

Anderson-Teixeira, K. J.; Snyder, P. K.; Twine, T. E.

2010-12-01

Climate change mitigation efforts that involve land use decisions call for comprehensive quantification of the climate services of terrestrial ecosystems. This is particularly imperative for analyses of the climate impact of bioenergy production, as land use change is often the single most important factor in determining bioenergy’s sustainability. However, current metrics of the climate services of terrestrial ecosystems used for policy applications—including biofuels life cycle analyses—account only for biogeochemical climate services (greenhouse gas regulation), ignoring biophysical climate regulation services (regulation of water and energy balances). Policies thereby run the risk of failing to advance the best climate solutions. Here, we present a quantitative metric that combines biogeochemical and biophysical climate services of terrestrial ecosystems, the ‘climate regulation value’ (CRV), which characterizes the climate benefit of maintaining an ecosystem over a multiple-year time frame. Using a combination of data synthesis and modeling, we calculate the CRV for a variety of natural and managed ecosystem types within the western hemisphere. Biogeochemical climate services are generally positive in unmanaged ecosystems (clearing the ecosystem has a warming effect), and may be positive or negative (clearing the ecosystem has a cooling effect) for managed ecosystems. Biophysical climate services may be either positive (e.g., tropical forests) or negative (e.g., high latitude forests). When averaged on a global scale, biogeochemical services usually outweigh biophysical services; however, biophysical climate services are not negligible. This implies that effective analysis of the climate impacts of bioenergy production must consider the integrated effects of biogeochemical and biophysical ecosystem climate services.

Characterizing Woody Vegetation Spectral and Structural Parameters with a 3-D Scene Model

NASA Astrophysics Data System (ADS)

Qin, W.; Yang, L.

2004-05-01

Quantification of structural and biophysical parameters of woody vegetation is of great significance in understanding vegetation condition, dynamics and functionality. Such information over a landscape scale is crucial for global and regional land cover characterization, global carbon-cycle research, forest resource inventories, and fire fuel estimation. While great efforts and progress have been made in mapping general land cover types over large area, at present, the ability to quantify regional woody vegetation structural and biophysical parameters is limited. One approach to address this research issue is through an integration of physically based 3-D scene model with multiangle and multispectral remote sensing data and in-situ measurements. The first step of this work is to model woody vegetation structure and its radiation regime using a physically based 3-D scene model and field data, before a robust operational algorithm can be developed for retrieval of important woody vegetation structural/biophysical parameters. In this study, we use an advanced 3-D scene model recently developed by Qin and Gerstl (2000), based on L-systems and radiosity theories. This 3-D scene model has been successfully applied to semi-arid shrubland to study structure and radiation regime at a regional scale. We apply this 3-D scene model to a more complicated and heterogeneous forest environment dominated by deciduous and coniferous trees. The data used in this study are from a field campaign conducted by NASA in a portion of the Superior National Forest (SNF) near Ely, Minnesota during the summers of 1983 and 1984, and supplement data collected during our revisit to the same area of SNF in summer of 2003. The model is first validated with reflectance measurements at different scales (ground observations, helicopter, aircraft, and satellite). Then its ability to characterize the structural and spectral parameters of the forest scene is evaluated. Based on the results from this study and the current multi-spectral and multi-angular satellite data (MODIS, MISR), a robust retrieval system to estimate woody vegetation structural/biophysical parameters is proposed.

Sparse short-distance connections enhance calcium wave propagation in a 3D model of astrocyte networks

PubMed Central

Lallouette, Jules; De Pittà, Maurizio; Ben-Jacob, Eshel; Berry, Hugues

2014-01-01

Traditionally, astrocytes have been considered to couple via gap-junctions into a syncytium with only rudimentary spatial organization. However, this view is challenged by growing experimental evidence that astrocytes organize as a proper gap-junction mediated network with more complex region-dependent properties. On the other hand, the propagation range of intercellular calcium waves (ICW) within astrocyte populations is as well highly variable, depending on the brain region considered. This suggests that the variability of the topology of gap-junction couplings could play a role in the variability of the ICW propagation range. Since this hypothesis is very difficult to investigate with current experimental approaches, we explore it here using a biophysically realistic model of three-dimensional astrocyte networks in which we varied the topology of the astrocyte network, while keeping intracellular properties and spatial cell distribution and density constant. Computer simulations of the model suggest that changing the topology of the network is indeed sufficient to reproduce the distinct ranges of ICW propagation reported experimentally. Unexpectedly, our simulations also predict that sparse connectivity and restriction of gap-junction couplings to short distances should favor propagation while long–distance or dense connectivity should impair it. Altogether, our results provide support to recent experimental findings that point toward a significant functional role of the organization of gap-junction couplings into proper astroglial networks. Dynamic control of this topology by neurons and signaling molecules could thus constitute a new type of regulation of neuron-glia and glia-glia interactions. PMID:24795613

[L.A. Blumenfeld and study of photosynthesis by spectroscopy methods at Chair of Biophysics, Faculty of Physics, Moscow State University].

PubMed

Kukushkin, A K

2013-01-01

Nowadays spectroscopy methods are widely employed to study photosynthesis. For instance, fluorescence methods are often in use to study virtually all steps of photosynthesis process. Theoretical models of phenomena under study are of importance for interpretation of experimental data. A decisive role of L.A. Blumenfeld, the former head of the Chair of Biophysics, Faculty of Physics, Moscow State University, in the study of photosynthesis process is shown in this work.

Effects of channel noise on firing coherence of small-world Hodgkin-Huxley neuronal networks

NASA Astrophysics Data System (ADS)

Sun, X. J.; Lei, J. Z.; Perc, M.; Lu, Q. S.; Lv, S. J.

2011-01-01

We investigate the effects of channel noise on firing coherence of Watts-Strogatz small-world networks consisting of biophysically realistic HH neurons having a fraction of blocked voltage-gated sodium and potassium ion channels embedded in their neuronal membranes. The intensity of channel noise is determined by the number of non-blocked ion channels, which depends on the fraction of working ion channels and the membrane patch size with the assumption of homogeneous ion channel density. We find that firing coherence of the neuronal network can be either enhanced or reduced depending on the source of channel noise. As shown in this paper, sodium channel noise reduces firing coherence of neuronal networks; in contrast, potassium channel noise enhances it. Furthermore, compared with potassium channel noise, sodium channel noise plays a dominant role in affecting firing coherence of the neuronal network. Moreover, we declare that the observed phenomena are independent of the rewiring probability.

Dynamic modulation of spike timing-dependent calcium influx during corticostriatal upstates

PubMed Central

Evans, R. C.; Maniar, Y. M.

2013-01-01

The striatum of the basal ganglia demonstrates distinctive upstate and downstate membrane potential oscillations during slow-wave sleep and under anesthetic. The upstates generate calcium transients in the dendrites, and the amplitude of these calcium transients depends strongly on the timing of the action potential (AP) within the upstate. Calcium is essential for synaptic plasticity in the striatum, and these large calcium transients during the upstates may control which synapses undergo plastic changes. To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). We have implemented sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion, which accurately replicate published data. Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. These findings have implications for synaptic plasticity in vivo during sleep when the upstate-downstate pattern is prominent in the striatum. PMID:23843436

The possible consequences for cognitive functions of external electric fields at power line frequency on hippocampal CA1 pyramidal neurons.

PubMed

Migliore, Rosanna; De Simone, Giada; Leinekugel, Xavier; Migliore, Michele

2017-04-01

The possible effects on cognitive processes of external electric fields, such as those generated by power line pillars and household appliances are of increasing public concern. They are difficult to study experimentally, and the relatively scarce and contradictory evidence make it difficult to clearly assess these effects. In this study, we investigate how, why and to what extent external perturbations of the intrinsic neuronal activity, such as those that can be caused by generation, transmission and use of electrical energy can affect neuronal activity during cognitive processes. For this purpose, we used a morphologically and biophysically realistic three-dimensional model of CA1 pyramidal neurons. The simulation findings suggest that an electric field oscillating at power lines frequency, and environmentally measured strength, can significantly alter both the average firing rate and temporal spike distribution properties of a hippocampal CA1 pyramidal neuron. This effect strongly depends on the specific and instantaneous relative spatial location of the neuron with respect to the field, and on the synaptic input properties. The model makes experimentally testable predictions on the possible functional consequences for normal hippocampal functions such as object recognition and spatial navigation. The results suggest that, although EF effects on cognitive processes may be difficult to occur in everyday life, their functional consequences deserve some consideration, especially when they constitute a systematic presence in living environments. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

High Order Accurate Finite Difference Modeling of Seismo-Acoustic Wave Propagation in a Moving Atmosphere and a Heterogeneous Earth Model Coupled Across a Realistic Topography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petersson, N. Anders; Sjogreen, Bjorn

Here, we develop a numerical method for simultaneously simulating acoustic waves in a realistic moving atmosphere and seismic waves in a heterogeneous earth model, where the motions are coupled across a realistic topography. We model acoustic wave propagation by solving the linearized Euler equations of compressible fluid mechanics. The seismic waves are modeled by the elastic wave equation in a heterogeneous anisotropic material. The motion is coupled by imposing continuity of normal velocity and normal stresses across the topographic interface. Realistic topography is resolved on a curvilinear grid that follows the interface. The governing equations are discretized using high ordermore » accurate finite difference methods that satisfy the principle of summation by parts. We apply the energy method to derive the discrete interface conditions and to show that the coupled discretization is stable. The implementation is verified by numerical experiments, and we demonstrate a simulation of coupled wave propagation in a windy atmosphere and a realistic earth model with non-planar topography.« less

High Order Accurate Finite Difference Modeling of Seismo-Acoustic Wave Propagation in a Moving Atmosphere and a Heterogeneous Earth Model Coupled Across a Realistic Topography

DOE PAGES

Petersson, N. Anders; Sjogreen, Bjorn

2017-04-18

Here, we develop a numerical method for simultaneously simulating acoustic waves in a realistic moving atmosphere and seismic waves in a heterogeneous earth model, where the motions are coupled across a realistic topography. We model acoustic wave propagation by solving the linearized Euler equations of compressible fluid mechanics. The seismic waves are modeled by the elastic wave equation in a heterogeneous anisotropic material. The motion is coupled by imposing continuity of normal velocity and normal stresses across the topographic interface. Realistic topography is resolved on a curvilinear grid that follows the interface. The governing equations are discretized using high ordermore » accurate finite difference methods that satisfy the principle of summation by parts. We apply the energy method to derive the discrete interface conditions and to show that the coupled discretization is stable. The implementation is verified by numerical experiments, and we demonstrate a simulation of coupled wave propagation in a windy atmosphere and a realistic earth model with non-planar topography.« less

Statistical multi-path exposure method for assessing the whole-body SAR in a heterogeneous human body model in a realistic environment.

PubMed

Vermeeren, Günter; Joseph, Wout; Martens, Luc

2013-04-01

Assessing the whole-body absorption in a human in a realistic environment requires a statistical approach covering all possible exposure situations. This article describes the development of a statistical multi-path exposure method for heterogeneous realistic human body models. The method is applied for the 6-year-old Virtual Family boy (VFB) exposed to the GSM downlink at 950 MHz. It is shown that the whole-body SAR does not differ significantly over the different environments at an operating frequency of 950 MHz. Furthermore, the whole-body SAR in the VFB for multi-path exposure exceeds the whole-body SAR for worst-case single-incident plane wave exposure by 3.6%. Moreover, the ICNIRP reference levels are not conservative with the basic restrictions in 0.3% of the exposure samples for the VFB at the GSM downlink of 950 MHz. The homogeneous spheroid with the dielectric properties of the head suggested by the IEC underestimates the absorption compared to realistic human body models. Moreover, the variation in the whole-body SAR for realistic human body models is larger than for homogeneous spheroid models. This is mainly due to the heterogeneity of the tissues and the irregular shape of the realistic human body model compared to homogeneous spheroid human body models. Copyright © 2012 Wiley Periodicals, Inc.

Axon tension regulates fasciculation/defasciculation through the control of axon shaft zippering

PubMed Central

Šmít, Daniel; Fouquet, Coralie; Pincet, Frédéric; Zapotocky, Martin; Trembleau, Alain

2017-01-01

While axon fasciculation plays a key role in the development of neural networks, very little is known about its dynamics and the underlying biophysical mechanisms. In a model system composed of neurons grown ex vivo from explants of embryonic mouse olfactory epithelia, we observed that axons dynamically interact with each other through their shafts, leading to zippering and unzippering behavior that regulates their fasciculation. Taking advantage of this new preparation suitable for studying such interactions, we carried out a detailed biophysical analysis of zippering, occurring either spontaneously or induced by micromanipulations and pharmacological treatments. We show that zippering arises from the competition of axon-axon adhesion and mechanical tension in the axons, and provide the first quantification of the force of axon-axon adhesion. Furthermore, we introduce a biophysical model of the zippering dynamics, and we quantitatively relate the individual zipper properties to global characteristics of the developing axon network. Our study uncovers a new role of mechanical tension in neural development: the regulation of axon fasciculation. DOI: http://dx.doi.org/10.7554/eLife.19907.001 PMID:28422009

Biophysical synaptic dynamics in an analog VLSI network of Hodgkin-Huxley neurons.

PubMed

Yu, Theodore; Cauwenberghs, Gert

2009-01-01

We study synaptic dynamics in a biophysical network of four coupled spiking neurons implemented in an analog VLSI silicon microchip. The four neurons implement a generalized Hodgkin-Huxley model with individually configurable rate-based kinetics of opening and closing of Na+ and K+ ion channels. The twelve synapses implement a rate-based first-order kinetic model of neurotransmitter and receptor dynamics, accounting for NMDA and non-NMDA type chemical synapses. The implemented models on the chip are fully configurable by 384 parameters accounting for conductances, reversal potentials, and pre/post-synaptic voltage-dependence of the channel kinetics. We describe the models and present experimental results from the chip characterizing single neuron dynamics, single synapse dynamics, and multi-neuron network dynamics showing phase-locking behavior as a function of synaptic coupling strength. The 3mm x 3mm microchip consumes 1.29 mW power making it promising for applications including neuromorphic modeling and neural prostheses.

Climate change effects on agriculture: Economic responses to biophysical shocks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, Gerald; Valin, Hugo; Sands, Ronald

Agricultural production is sensitive to weather and will thus be directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments inmore » yields, area, consumption, and international trade. We apply biophysical shocks derived from the IPCC’s Representative Concentration Pathway that result in end-of-century radiative forcing of 8.5 watts per square meter. The mean biophysical impact on crop yield with no incremental CO2 fertilization is a 17 percent reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11 percent, increase area of major crops by 12 percent, and reduce consumption by 2 percent. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences includes model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.« less

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations

NASA Astrophysics Data System (ADS)

Smaragdos, Georgios; Chatzikonstantis, Georgios; Kukreja, Rahul; Sidiropoulos, Harry; Rodopoulos, Dimitrios; Sourdis, Ioannis; Al-Ars, Zaid; Kachris, Christoforos; Soudris, Dimitrios; De Zeeuw, Chris I.; Strydis, Christos

2017-12-01

Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload’s performance characteristics. Main results. The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance. The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations.

PubMed

Smaragdos, Georgios; Chatzikonstantis, Georgios; Kukreja, Rahul; Sidiropoulos, Harry; Rodopoulos, Dimitrios; Sourdis, Ioannis; Al-Ars, Zaid; Kachris, Christoforos; Soudris, Dimitrios; De Zeeuw, Chris I; Strydis, Christos

2017-12-01

The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

Native Contact Density and Nonnative Hydrophobic Effects in the Folding of Bacterial Immunity Proteins

PubMed Central

Chen, Tao; Chan, Hue Sun

2015-01-01

The bacterial colicin-immunity proteins Im7 and Im9 fold by different mechanisms. Experimentally, at pH 7.0 and 10°C, Im7 folds in a three-state manner via an intermediate but Im9 folding is two-state-like. Accordingly, Im7 exhibits a chevron rollover, whereas the chevron arm for Im9 folding is linear. Here we address the biophysical basis of their different behaviors by using native-centric models with and without additional transferrable, sequence-dependent energies. The Im7 chevron rollover is not captured by either a pure native-centric model or a model augmented by nonnative hydrophobic interactions with a uniform strength irrespective of residue type. By contrast, a more realistic nonnative interaction scheme that accounts for the difference in hydrophobicity among residues leads simultaneously to a chevron rollover for Im7 and an essentially linear folding chevron arm for Im9. Hydrophobic residues identified by published experiments to be involved in nonnative interactions during Im7 folding are found to participate in the strongest nonnative contacts in this model. Thus our observations support the experimental perspective that the Im7 folding intermediate is largely underpinned by nonnative interactions involving large hydrophobics. Our simulation suggests further that nonnative effects in Im7 are facilitated by a lower local native contact density relative to that of Im9. In a one-dimensional diffusion picture of Im7 folding with a coordinate- and stability-dependent diffusion coefficient, a significant chevron rollover is consistent with a diffusion coefficient that depends strongly on native stability at the conformational position of the folding intermediate. PMID:26016652

Estimation of Some Bio-Physical Indicators for Sustainable Crop Production in the Eastern Nile Basin of Sudan Using Landsat-8 Imagery and SEBAL Model

NASA Astrophysics Data System (ADS)

Guma Biro Turk, Khalid

2016-07-01

Crop production under modern irrigation systems require unique management at field level and hence better utilization of agricultural inputs and water resources. This study aims to make use of remote sensing (RS) data and the surface energy balance algorithm for land (SEBAL) to improve the on-farm management. The study area is located in the Eastern part of the Blue Nile River about 60 km south of Khartoum, Sudan. Landsat-8 data were used to estimate a number of bio-physical indicators during the growing season of the year 2014/2015. Accordingly, in-situ weather data and SEBAL model were applied to calculate: the reference (ET0), actual (ETa) and potential (ETp) evapotranspiration, soil moisture (SM), crop factor (kc), nitrogen (N), biomass production (BP) and crop water productivity (CWP). Results revealed that ET0 showed steady variation throughout the year, varying from 5 to 7 mm/day. However, ETa and ETp showed clear temporal variation attributed to frequent cutting of the alfalfa, almost monthly. The BP of the alfalfa was observed to be high when there is no cutting activates were made before the image acquisition date. Nevertheless the CWP trends are following the biomass production ones, low when there is no biomass and high when the biomass is high. The application of SEBAL model within the study area using the Landsat-8 imagery indicates that it's possible to produce field-based bio-physical indicators, which can be useful in monitoring and managing the field during the growing season. However, a cross-calibration with the in-situ data should be considered in order to maintain the spatial variability within the field. Keywords: Bio-physical Indicators; Remote Sensing; SEBAL; Landsat-8; Eastern Nile Basin

The value of mechanistic biophysical information for systems-level understanding of complex biological processes such as cytokinesis.

PubMed

Pollard, Thomas D

2014-12-02

This review illustrates the value of quantitative information including concentrations, kinetic constants and equilibrium constants in modeling and simulating complex biological processes. Although much has been learned about some biological systems without these parameter values, they greatly strengthen mechanistic accounts of dynamical systems. The analysis of muscle contraction is a classic example of the value of combining an inventory of the molecules, atomic structures of the molecules, kinetic constants for the reactions, reconstitutions with purified proteins and theoretical modeling to account for the contraction of whole muscles. A similar strategy is now being used to understand the mechanism of cytokinesis using fission yeast as a favorable model system. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Quantify the Biophysical and Socioeconomic Drivers of Changes in Forest and Agricultural Land in South and Southeast Asia

NASA Astrophysics Data System (ADS)

Xu, X.; Jain, A. K.; Calvin, K. V.

2017-12-01

Due to the rapid socioeconomic development and biophysical factors, South and Southeast Asia (SSEA) has become a hotspot region of land use and land cover changes (LULCCs) in past few decades. Uncovering the drivers of LULCC is crucial for improving the understanding of LULCC processes. Due to the differences from spatiotemporal scales, methods and data sources in previous studies, the quantitative relationships between the LULCC activities and biophysical and socioeconomic drivers at the regional scale of SSEA have not been established. Here we present a comprehensive estimation of the biophysical and socioeconomic drivers of the major LULCC activities in SSEA: changes in forest and agricultural land. We used the Climate Change Initiative land cover data developed by European Space Agency to reveal the dynamics of forest and agricultural land from 1992 to 2015. Then we synthesized 200 publications about LULCC drivers at different spatial scales in SSEA to identify the major drivers of these LULCC activities. Corresponding representative variables of the major drivers were collected. The geographically weighted regression was employed to assess the spatiotemporally heterogeneous drivers of LULCC. Moreover, we validated our results with some national level case studies in SSEA. The results showed that both biophysical conditions such as terrain, soil, and climate, and socioeconomic factors such as migration, poverty, and economy played important roles in driving the changes of forest and agricultural land. The major drivers varied in different locations and periods. Our study integrated the bottom-up knowledge from local scale case studies with the top-down estimation of LULCC drivers, therefore generated more accurate and credible results. The identified biophysical and socioeconomic components could be used to improve the LULCC modelling and projection.

Inferior olive mirrors joint dynamics to implement an inverse controller.

PubMed

Alvarez-Icaza, Rodrigo; Boahen, Kwabena

2012-10-01

To produce smooth and coordinated motion, our nervous systems need to generate precisely timed muscle activation patterns that, due to axonal conduction delay, must be generated in a predictive and feedforward manner. Kawato proposed that the cerebellum accomplishes this by acting as an inverse controller that modulates descending motor commands to predictively drive the spinal cord such that the musculoskeletal dynamics are canceled out. This and other cerebellar theories do not, however, account for the rich biophysical properties expressed by the olivocerebellar complex's various cell types, making these theories difficult to verify experimentally. Here we propose that a multizonal microcomplex's (MZMC) inferior olivary neurons use their subthreshold oscillations to mirror a musculoskeletal joint's underdamped dynamics, thereby achieving inverse control. We used control theory to map a joint's inverse model onto an MZMC's biophysics, and we used biophysical modeling to confirm that inferior olivary neurons can express the dynamics required to mirror biomechanical joints. We then combined both techniques to predict how experimentally injecting current into the inferior olive would affect overall motor output performance. We found that this experimental manipulation unmasked a joint's natural dynamics, as observed by motor output ringing at the joint's natural frequency, with amplitude proportional to the amount of current. These results support the proposal that the cerebellum-in particular an MZMC-is an inverse controller; the results also provide a biophysical implementation for this controller and allow one to make an experimentally testable prediction.

Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction.

PubMed

Epstein, Michael; Calderhead, Ben; Girolami, Mark A; Sivilotti, Lucia G

2016-07-26

The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME", which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel models is publicly available. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Applications of the BIOPHYS Algorithm for Physically-Based Retrieval of Biophysical, Structural and Forest Disturbance Information

NASA Technical Reports Server (NTRS)

Peddle, Derek R.; Huemmrich, K. Fred; Hall, Forrest G.; Masek, Jeffrey G.; Soenen, Scott A.; Jackson, Chris D.

2011-01-01

Canopy reflectance model inversion using look-up table approaches provides powerful and flexible options for deriving improved forest biophysical structural information (BSI) compared with traditional statistical empirical methods. The BIOPHYS algorithm is an improved, physically-based inversion approach for deriving BSI for independent use and validation and for monitoring, inventory and quantifying forest disturbance as well as input to ecosystem, climate and carbon models. Based on the multiple-forward mode (MFM) inversion approach, BIOPHYS results were summarized from different studies (Minnesota/NASA COVER; Virginia/LEDAPS; Saskatchewan/BOREAS), sensors (airborne MMR; Landsat; MODIS) and models (GeoSail; GOMS). Applications output included forest density, height, crown dimension, branch and green leaf area, canopy cover, disturbance estimates based on multi-temporal chronosequences, and structural change following recovery from forest fires over the last century. Good correspondences with validation field data were obtained. Integrated analyses of multiple solar and view angle imagery further improved retrievals compared with single pass data. Quantifying ecosystem dynamics such as the area and percent of forest disturbance, early regrowth and succession provide essential inputs to process-driven models of carbon flux. BIOPHYS is well suited for large-area, multi-temporal applications involving multiple image sets and mosaics for assessing vegetation disturbance and quantifying biophysical structural dynamics and change. It is also suitable for integration with forest inventory, monitoring, updating, and other programs.

Modeling Endoplasmic Reticulum Network Maintenance in a Plant Cell.

PubMed

Lin, Congping; White, Rhiannon R; Sparkes, Imogen; Ashwin, Peter

2017-07-11

The endoplasmic reticulum (ER) in plant cells forms a highly dynamic network of complex geometry. ER network morphology and dynamics are influenced by a number of biophysical processes, including filament/tubule tension, viscous forces, Brownian diffusion, and interactions with many other organelles and cytoskeletal elements. Previous studies have indicated that ER networks can be thought of as constrained minimal-length networks acted on by a variety of forces that perturb and/or remodel the network. Here, we study two specific biophysical processes involved in remodeling. One is the dynamic relaxation process involving a combination of tubule tension and viscous forces. The other is the rapid creation of cross-connection tubules by direct or indirect interactions with cytoskeletal elements. These processes are able to remodel the ER network: the first reduces network length and complexity whereas the second increases both. Using live cell imaging of ER network dynamics in tobacco leaf epidermal cells, we examine these processes on ER network dynamics. Away from regions of cytoplasmic streaming, we suggest that the dynamic network structure is a balance between the two processes, and we build an integrative model of the two processes for network remodeling. This model produces quantitatively similar ER networks to those observed in experiments. We use the model to explore the effect of parameter variation on statistical properties of the ER network. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Comparison of Deterministic and Probabilistic Radial Distribution Systems Load Flow

NASA Astrophysics Data System (ADS)

Gupta, Atma Ram; Kumar, Ashwani

2017-12-01

Distribution system network today is facing the challenge of meeting increased load demands from the industrial, commercial and residential sectors. The pattern of load is highly dependent on consumer behavior and temporal factors such as season of the year, day of the week or time of the day. For deterministic radial distribution load flow studies load is taken as constant. But, load varies continually with a high degree of uncertainty. So, there is a need to model probable realistic load. Monte-Carlo Simulation is used to model the probable realistic load by generating random values of active and reactive power load from the mean and standard deviation of the load and for solving a Deterministic Radial Load Flow with these values. The probabilistic solution is reconstructed from deterministic data obtained for each simulation. The main contribution of the work is: Finding impact of probable realistic ZIP load modeling on balanced radial distribution load flow. Finding impact of probable realistic ZIP load modeling on unbalanced radial distribution load flow. Compare the voltage profile and losses with probable realistic ZIP load modeling for balanced and unbalanced radial distribution load flow.

Extended ecosystem signatures with application to Eos synergism requirements

NASA Technical Reports Server (NTRS)

Ulaby, Fawwaz T.; Dobson, M. Craig; Sarabandi, Kamal

1993-01-01

The primary objective is to define the advantages of synergistically combining optical and microwave remote sensing measurements for the determination of biophysical properties important in ecosystem modeling. This objective was approached in a stepwise fashion starting with ground-based observations of controlled agricultural and orchard canopies and progressing to airborne observations of more natural forest ecosystems. This observational program is complemented by a parallel effort to model the visible reflectance and microwave scattering properties of composite vegetation canopies. The goals of the modeling studies are to verify our basic understanding of the sensor-scene interaction physics and to provide the basis for development of inverse models optimized for retrieval of key biophysical properties. These retrieval algorithms can then be used to simulate the expected performance of various aspects of Eos including the need for simultaneous SAR and HIRIS observations or justification for other (non-synchronous) relative timing constraints and the frequency, polarization, and angle of incidence requirements for accurate biophysical parameter extractions. This program completed a very successful series of truck-mounted experiments, made remarkable progress in development and validation of optical reflectance and microwave scattering models for vegetation, extended the scattering models to accommodate discontinuous and periodic canopies, developed inversion approaches for surface and canopy properties, and disseminated these results widely through symposia and journal publications. In addition, the third generation of the computer code for the microwave scattering models was provided to a number of other US, Canadian, Australian, and European investigators who are currently presenting and publishing results using the MIMICS research code.

Determining Biophysical Controls on Forest Structure using Hyperspatial Satellite Imagery and Ecological Gradient Modeling

NASA Astrophysics Data System (ADS)

Dobrowski, S. Z.; Greenberg, J. A.; Schladow, G.

2006-12-01

There is evidence from the Sierra Nevada that sub-alpine and alpine environments are currently experiencing landscape-mediated changes in growth and recruitment due to recent climate change. Understanding the biophysical controls of forest structure, growth, and recruitment in these environments is critical for interpreting and predicting the direction and magnitude of biotic responses to climate shift. We examined the abiotic controls of forest biomass within a 305 km2 region of the Carson Range on the eastern shore of Lake Tahoe, CA USA using estimates of forest structure and biophysical drivers developed continuously over the landscape. The study area ranged from 1900 m to 3400 m a.s.l. and encompassed montane, sub-alpine, and alpine environments. From hyperspatial optical imagery (IKONOS), we derived per-tree positions and crown sizes using a template matching approach applied to a pre-classified image of sunlit and shadowed vegetation pixels. From this remote sensing derived stem map, we calculated plot-level estimates of stem density, tree cover and average crown size. Additionally, we developed high resolution (30 m) estimates of climate variables within the study area using meteorological station data, topographic data, and a combination of empirical and mechanistic modeling approaches. From these climate surfaces, digital elevation data, and soil survey data, we derived estimates of direct and indirect biophysical drivers including heat loading, reference evapotranspiration, water deficit, solar radiation, topographic convergence, soil depth, and soil water holding capacity. Using these data sets, we conducted a regression tree analysis with stem density, tree cover, and average tree size as response and biophysical drivers as predictors. Trees were fit using half of the dataset randomly sampled (168,000 samples) and pruned using cost-complexity pruning based on 10-fold cross- validation. Predictions from pruned trees were then assessed against the hold-out data. Preliminary results from this analysis suggest that: 1) the relative importance and dependencies of biophysical drivers on forest structure are contingent upon the position of these forests along gradients of a limiting resource, 2) stem density shows a stronger dependence on water availability than tree size and 3) that the predictive power of abiotic variables are limited with our best models accounting for only 36-40 percent of the variance in the response. These results suggest that the response of forest structure to climate change may be highly idiosyncratic and difficult to predict using abiotic drivers alone.

Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

NASA Astrophysics Data System (ADS)

Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

2017-04-01

Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates, ultimately affecting the amount of absorbed radiation. In addition patterns of simulated turbulent fluxes appear opposite to observations. Such systematic errors shed light on the current partial understanding of some of the mechanisms controlling the surface energy balance. In contrast forests appear reasonably well represented with respect to the interactions between LAI and turbulent fluxes across most climatological gradients, while for net radiation this is only true for warm climates. These proven strengths increase the confidence on how certain processes are simulated in LSMs. The model capacity to mimic the vegetation-biophysics interplay has been tested over the real scenario of greening that occurred in the last 30 years. We found that the modeled trends in surface heat fluxes associated with the long-term changes in leaf area could vary largely from those observed, with different discrepancies across models and climate zones. Our findings help to identify knowledge gaps and improve model representation of the sensitivity of biophysical processes to changes in leaf area density. In particular, comparing models and observations over a wide range of climate and vegetation conditions, as analyzed here, allowed capturing non-linearity of system responses that may emerge more frequently in future climate scenarios.

Bidirectional Reflectance Modeling of Non-homogeneous Plant Canopies

NASA Technical Reports Server (NTRS)

Norman, J. M. (Principal Investigator)

1985-01-01

The objective of this research is to develop a 3-dimensional radiative transfer model for predicting the bidirectional reflectance distribution function (BRDF) for heterogeneous vegetation canopies. The model (named BIGAR) considers the angular distribution of leaves, leaf area index, the location and size of individual subcanopies such as widely spaced rows or trees, spectral and directional properties of leaves, multiple scattering, solar position and sky condition, and characteristics of the soil. The model relates canopy biophysical attributes to down-looking radiation measurements for nadir and off-nadir viewing angles. Therefore, inversion of this model, which is difficult but practical should provide surface biophysical pattern; a fundamental goal of remote sensing. Such a model also will help to evaluate atmospheric limitations to satellite remote sensing by providing a good surface boundary condition for many different kinds of canopies. Furthermore, this model can relate estimates of nadir reflectance, which is approximated by most satellites, to hemispherical reflectance, which is necessary in the energy budget of vegetated surfaces.

Biophysical Model of Ion Transport across Human Respiratory Epithelia Allows Quantification of Ion Permeabilities

PubMed Central

Garcia, Guilherme J.M.; Boucher, Richard C.; Elston, Timothy C.

2013-01-01

Lung health and normal mucus clearance depend on adequate hydration of airway surfaces. Because transepithelial osmotic gradients drive water flows, sufficient hydration of the airway surface liquid depends on a balance between ion secretion and absorption by respiratory epithelia. In vitro experiments using cultures of primary human nasal epithelia and human bronchial epithelia have established many of the biophysical processes involved in airway surface liquid homeostasis. Most experimental studies, however, have focused on the apical membrane, despite the fact that ion transport across respiratory epithelia involves both cellular and paracellular pathways. In fact, the ion permeabilities of the basolateral membrane and paracellular pathway remain largely unknown. Here we use a biophysical model for water and ion transport to quantify ion permeabilities of all pathways (apical, basolateral, paracellular) in human nasal epithelia cultures using experimental (Ussing Chamber and microelectrode) data reported in the literature. We derive analytical formulas for the steady-state short-circuit current and membrane potential, which are for polarized epithelia the equivalent of the Goldman-Hodgkin-Katz equation for single isolated cells. These relations allow parameter estimation to be performed efficiently. By providing a method to quantify all the ion permeabilities of respiratory epithelia, the model may aid us in understanding the physiology that regulates normal airway surface hydration. PMID:23442922

Potential and limits for rapid genetic adaptation to warming in a Great Barrier Reef coral.

PubMed

Matz, Mikhail V; Treml, Eric A; Aglyamova, Galina V; Bay, Line K

2018-04-01

Can genetic adaptation in reef-building corals keep pace with the current rate of sea surface warming? Here we combine population genomics, biophysical modeling, and evolutionary simulations to predict future adaptation of the common coral Acropora millepora on the Great Barrier Reef (GBR). Genomics-derived migration rates were high (0.1-1% of immigrants per generation across half the latitudinal range of the GBR) and closely matched the biophysical model of larval dispersal. Both genetic and biophysical models indicated the prevalence of southward migration along the GBR that would facilitate the spread of heat-tolerant alleles to higher latitudes as the climate warms. We developed an individual-based metapopulation model of polygenic adaptation and parameterized it with population sizes and migration rates derived from the genomic analysis. We find that high migration rates do not disrupt local thermal adaptation, and that the resulting standing genetic variation should be sufficient to fuel rapid region-wide adaptation of A. millepora populations to gradual warming over the next 20-50 coral generations (100-250 years). Further adaptation based on novel mutations might also be possible, but this depends on the currently unknown genetic parameters underlying coral thermal tolerance and the rate of warming realized. Despite this capacity for adaptation, our model predicts that coral populations would become increasingly sensitive to random thermal fluctuations such as ENSO cycles or heat waves, which corresponds well with the recent increase in frequency of catastrophic coral bleaching events.

Landscape Variation in Tree Species Richness in Northern Iran Forests

PubMed Central

Bourque, Charles P.-A.; Bayat, Mahmoud

2015-01-01

Mapping landscape variation in tree species richness (SR) is essential to the long term management and conservation of forest ecosystems. The current study examines the prospect of mapping field assessments of SR in a high-elevation, deciduous forest in northern Iran as a function of 16 biophysical variables representative of the area’s unique physiography, including topography and coastal placement, biophysical environment, and forests. Basic to this study is the development of moderate-resolution biophysical surfaces and associated plot-estimates for 202 permanent sampling plots. The biophysical variables include: (i) three topographic variables generated directly from the area’s digital terrain model; (ii) four ecophysiologically-relevant variables derived from process models or from first principles; and (iii) seven variables of Landsat-8-acquired surface reflectance and two, of surface radiance. With symbolic regression, it was shown that only four of the 16 variables were needed to explain 85% of observed plot-level variation in SR (i.e., wind velocity, surface reflectance of blue light, and topographic wetness indices representative of soil water content), yielding mean-absolute and root-mean-squared error of 0.50 and 0.78, respectively. Overall, localised calculations of wind velocity and surface reflectance of blue light explained about 63% of observed variation in SR, with wind velocity accounting for 51% of that variation. The remaining 22% was explained by linear combinations of soil-water-related topographic indices and associated thresholds. In general, SR and diversity tended to be greatest for plots dominated by Carpinus betulus (involving ≥ 33% of all trees in a plot), than by Fagus orientalis (median difference of one species). This study provides a significant step towards describing landscape variation in SR as a function of modelled and satellite-based information and symbolic regression. Methods in this study are sufficiently general to be applicable to the characterisation of SR in other forested regions of the world, providing plot-scale data are available for model generation. PMID:25849029

Landscape variation in tree species richness in northern Iran forests.

PubMed

Bourque, Charles P-A; Bayat, Mahmoud

2015-01-01

Mapping landscape variation in tree species richness (SR) is essential to the long term management and conservation of forest ecosystems. The current study examines the prospect of mapping field assessments of SR in a high-elevation, deciduous forest in northern Iran as a function of 16 biophysical variables representative of the area's unique physiography, including topography and coastal placement, biophysical environment, and forests. Basic to this study is the development of moderate-resolution biophysical surfaces and associated plot-estimates for 202 permanent sampling plots. The biophysical variables include: (i) three topographic variables generated directly from the area's digital terrain model; (ii) four ecophysiologically-relevant variables derived from process models or from first principles; and (iii) seven variables of Landsat-8-acquired surface reflectance and two, of surface radiance. With symbolic regression, it was shown that only four of the 16 variables were needed to explain 85% of observed plot-level variation in SR (i.e., wind velocity, surface reflectance of blue light, and topographic wetness indices representative of soil water content), yielding mean-absolute and root-mean-squared error of 0.50 and 0.78, respectively. Overall, localised calculations of wind velocity and surface reflectance of blue light explained about 63% of observed variation in SR, with wind velocity accounting for 51% of that variation. The remaining 22% was explained by linear combinations of soil-water-related topographic indices and associated thresholds. In general, SR and diversity tended to be greatest for plots dominated by Carpinus betulus (involving ≥ 33% of all trees in a plot), than by Fagus orientalis (median difference of one species). This study provides a significant step towards describing landscape variation in SR as a function of modelled and satellite-based information and symbolic regression. Methods in this study are sufficiently general to be applicable to the characterisation of SR in other forested regions of the world, providing plot-scale data are available for model generation.

Irrigation Requirement Estimation Using Vegetation Indices and Inverse Biophysical Modeling

NASA Technical Reports Server (NTRS)

Bounoua, Lahouari; Imhoff, Marc L.; Franks, Shannon

2010-01-01

We explore an inverse biophysical modeling process forced by satellite and climatological data to quantify irrigation requirements in semi-arid agricultural areas. We constrain the carbon and water cycles modeled under both equilibrium, balance between vegetation and climate, and non-equilibrium, water added through irrigation. We postulate that the degree to which irrigated dry lands vary from equilibrium climate conditions is related to the amount of irrigation. The amount of water required over and above precipitation is considered as an irrigation requirement. For July, results show that spray irrigation resulted in an additional amount of water of 1.3 mm per occurrence with a frequency of 24.6 hours. In contrast, the drip irrigation required only 0.6 mm every 45.6 hours or 46% of that simulated by the spray irrigation. The modeled estimates account for 87% of the total reported irrigation water use, when soil salinity is not important and 66% in saline lands.

Phenemenological vs. biophysical models of thermal stress in aquatic eggs

NASA Astrophysics Data System (ADS)

Martin, B.

2016-12-01

Predicting species responses to climate change is a central challenge in ecology, with most efforts relying on lab derived phenomenological relationships between temperature and fitness metrics. We tested one of these models using the embryonic stage of a Chinook salmon population. We parameterized the model with laboratory data, applied it to predict survival in the field, and found that it significantly underestimated field-derived estimates of thermal mortality. We used a biophysical model based on mass-transfer theory to show that the discrepancy was due to the differences in water flow velocities between the lab and the field. This mechanistic approach provides testable predictions for how the thermal tolerance of embryos depends on egg size and flow velocity of the surrounding water. We found support for these predictions across more than 180 fish species, suggesting that flow and temperature mediated oxygen limitation is a general mechanism underlying the thermal tolerance of embryos.

Neuroprotective Role of Gap Junctions in a Neuron Astrocyte Network Model.

PubMed

Huguet, Gemma; Joglekar, Anoushka; Messi, Leopold Matamba; Buckalew, Richard; Wong, Sarah; Terman, David

2016-07-26

A detailed biophysical model for a neuron/astrocyte network is developed to explore mechanisms responsible for the initiation and propagation of cortical spreading depolarizations and the role of astrocytes in maintaining ion homeostasis, thereby preventing these pathological waves. Simulations of the model illustrate how properties of spreading depolarizations, such as wave speed and duration of depolarization, depend on several factors, including the neuron and astrocyte Na(+)-K(+) ATPase pump strengths. In particular, we consider the neuroprotective role of astrocyte gap junction coupling. The model demonstrates that a syncytium of electrically coupled astrocytes can maintain a physiological membrane potential in the presence of an elevated extracellular K(+) concentration and efficiently distribute the excess K(+) across the syncytium. This provides an effective neuroprotective mechanism for delaying or preventing the initiation of spreading depolarizations. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Thalamic neuron models encode stimulus information by burst-size modulation

PubMed Central

Elijah, Daniel H.; Samengo, Inés; Montemurro, Marcelo A.

2015-01-01

Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons. PMID:26441623

Thalamic neuron models encode stimulus information by burst-size modulation.

PubMed

Elijah, Daniel H; Samengo, Inés; Montemurro, Marcelo A

2015-01-01

Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

Problem Posing with Realistic Mathematics Education Approach in Geometry Learning

NASA Astrophysics Data System (ADS)

Mahendra, R.; Slamet, I.; Budiyono

2017-09-01

One of the difficulties of students in the learning of geometry is on the subject of plane that requires students to understand the abstract matter. The aim of this research is to determine the effect of Problem Posing learning model with Realistic Mathematics Education Approach in geometry learning. This quasi experimental research was conducted in one of the junior high schools in Karanganyar, Indonesia. The sample was taken using stratified cluster random sampling technique. The results of this research indicate that the model of Problem Posing learning with Realistic Mathematics Education Approach can improve students’ conceptual understanding significantly in geometry learning especially on plane topics. It is because students on the application of Problem Posing with Realistic Mathematics Education Approach are become to be active in constructing their knowledge, proposing, and problem solving in realistic, so it easier for students to understand concepts and solve the problems. Therefore, the model of Problem Posing learning with Realistic Mathematics Education Approach is appropriately applied in mathematics learning especially on geometry material. Furthermore, the impact can improve student achievement.

Italian biophysics and SIBPA speed-up the pace towards the long and winding road of the interdisciplinary science.

PubMed

Giacomazza, Daniela; Musio, Carlo

2016-01-01

This Special Issue of Biophysical Chemistry presents a selection of the contributions presented at the XXII National Congress of the Italian Society of Pure and Applied Biophysics (i.e., SIBPA, Società Italiana di Biofisica Pura ed Applicata) held on September 2014 in Palermo, Italy. Topics cover all biophysical disciplines, from molecular to cellular, to integrative biophysics giving a comprehensive view of the inter- and multi-disciplinary approach of modern biophysics. SIBPA, which turned 40 in 2013, continues to grow and attract interest.

A method of online quantitative interpretation of diffuse reflection profiles of biological tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-02-01

We have developed a method of combined interpretation of spectral and spatial characteristics of diffuse reflection of biological tissues, which makes it possible to determine biophysical parameters of the tissue with a high accuracy in real time under conditions of their general variability. Using the Monte Carlo method, we have modeled a statistical ensemble of profiles of diffuse reflection coefficients of skin, which corresponds to a wave variation of its biophysical parameters. On its basis, we have estimated the retrieval accuracy of biophysical parameters using the developed method and investigated the stability of the method to errors of optical measurements. We have showed that it is possible to determine online the concentrations of melanin, hemoglobin, bilirubin, oxygen saturation of blood, and structural parameters of skin from measurements of its diffuse reflection in the spectral range 450-800 nm at three distances between the radiation source and detector.

Plot Scale Factor Models for Standard Orthographic Views

ERIC Educational Resources Information Center

Osakue, Edward E.

2007-01-01

Geometric modeling provides graphic representations of real or abstract objects. Realistic representation requires three dimensional (3D) attributes since natural objects have three principal dimensions. CAD software gives the user the ability to construct realistic 3D models of objects, but often prints of these models must be generated on two…

Reduced-Order Biogeochemical Flux Model for High-Resolution Multi-Scale Biophysical Simulations

NASA Astrophysics Data System (ADS)

Smith, Katherine; Hamlington, Peter; Pinardi, Nadia; Zavatarelli, Marco

2017-04-01

Biogeochemical tracers and their interactions with upper ocean physical processes such as submesoscale circulations and small-scale turbulence are critical for understanding the role of the ocean in the global carbon cycle. These interactions can cause small-scale spatial and temporal heterogeneity in tracer distributions that can, in turn, greatly affect carbon exchange rates between the atmosphere and interior ocean. For this reason, it is important to take into account small-scale biophysical interactions when modeling the global carbon cycle. However, explicitly resolving these interactions in an earth system model (ESM) is currently infeasible due to the enormous associated computational cost. As a result, understanding and subsequently parameterizing how these small-scale heterogeneous distributions develop and how they relate to larger resolved scales is critical for obtaining improved predictions of carbon exchange rates in ESMs. In order to address this need, we have developed the reduced-order, 17 state variable Biogeochemical Flux Model (BFM-17) that follows the chemical functional group approach, which allows for non-Redfield stoichiometric ratios and the exchange of matter through units of carbon, nitrate, and phosphate. This model captures the behavior of open-ocean biogeochemical systems without substantially increasing computational cost, thus allowing the model to be combined with computationally-intensive, fully three-dimensional, non-hydrostatic large eddy simulations (LES). In this talk, we couple BFM-17 with the Princeton Ocean Model and show good agreement between predicted monthly-averaged results and Bermuda testbed area field data (including the Bermuda-Atlantic Time-series Study and Bermuda Testbed Mooring). Through these tests, we demonstrate the capability of BFM-17 to accurately model open-ocean biochemistry. Additionally, we discuss the use of BFM-17 within a multi-scale LES framework and outline how this will further our understanding of turbulent biophysical interactions in the upper ocean.

Reduced-Order Biogeochemical Flux Model for High-Resolution Multi-Scale Biophysical Simulations

NASA Astrophysics Data System (ADS)

Smith, K.; Hamlington, P.; Pinardi, N.; Zavatarelli, M.; Milliff, R. F.

2016-12-01

Biogeochemical tracers and their interactions with upper ocean physical processes such as submesoscale circulations and small-scale turbulence are critical for understanding the role of the ocean in the global carbon cycle. These interactions can cause small-scale spatial and temporal heterogeneity in tracer distributions which can, in turn, greatly affect carbon exchange rates between the atmosphere and interior ocean. For this reason, it is important to take into account small-scale biophysical interactions when modeling the global carbon cycle. However, explicitly resolving these interactions in an earth system model (ESM) is currently infeasible due to the enormous associated computational cost. As a result, understanding and subsequently parametrizing how these small-scale heterogeneous distributions develop and how they relate to larger resolved scales is critical for obtaining improved predictions of carbon exchange rates in ESMs. In order to address this need, we have developed the reduced-order, 17 state variable Biogeochemical Flux Model (BFM-17). This model captures the behavior of open-ocean biogeochemical systems without substantially increasing computational cost, thus allowing the model to be combined with computationally-intensive, fully three-dimensional, non-hydrostatic large eddy simulations (LES). In this talk, we couple BFM-17 with the Princeton Ocean Model and show good agreement between predicted monthly-averaged results and Bermuda testbed area field data (including the Bermuda-Atlantic Time Series and Bermuda Testbed Mooring). Through these tests, we demonstrate the capability of BFM-17 to accurately model open-ocean biochemistry. Additionally, we discuss the use of BFM-17 within a multi-scale LES framework and outline how this will further our understanding of turbulent biophysical interactions in the upper ocean.

Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest

NASA Astrophysics Data System (ADS)

Kumar, Shashi; Khati, Unmesh G.; Chandola, Shreya; Agrawal, Shefali; Kushwaha, Satya P. S.

2017-08-01

The regulation of the carbon cycle is a critical ecosystem service provided by forests globally. It is, therefore, necessary to have robust techniques for speedy assessment of forest biophysical parameters at the landscape level. It is arduous and time taking to monitor the status of vast forest landscapes using traditional field methods. Remote sensing and GIS techniques are efficient tools that can monitor the health of forests regularly. Biomass estimation is a key parameter in the assessment of forest health. Polarimetric SAR (PolSAR) remote sensing has already shown its potential for forest biophysical parameter retrieval. The current research work focuses on the retrieval of forest biophysical parameters of tropical deciduous forest, using fully polarimetric spaceborne C-band data with Polarimetric SAR Interferometry (PolInSAR) techniques. PolSAR based Interferometric Water Cloud Model (IWCM) has been used to estimate aboveground biomass (AGB). Input parameters to the IWCM have been extracted from the decomposition modeling of SAR data as well as PolInSAR coherence estimation. The technique of forest tree height retrieval utilized PolInSAR coherence based modeling approach. Two techniques - Coherence Amplitude Inversion (CAI) and Three Stage Inversion (TSI) - for forest height estimation are discussed, compared and validated. These techniques allow estimation of forest stand height and true ground topography. The accuracy of the forest height estimated is assessed using ground-based measurements. PolInSAR based forest height models showed enervation in the identification of forest vegetation and as a result height values were obtained in river channels and plain areas. Overestimation in forest height was also noticed at several patches of the forest. To overcome this problem, coherence and backscatter based threshold technique is introduced for forest area identification and accurate height estimation in non-forested regions. IWCM based modeling for forest AGB retrieval showed R2 value of 0.5, RMSE of 62.73 (t ha-1) and a percent accuracy of 51%. TSI based PolInSAR inversion modeling showed the most accurate result for forest height estimation. The correlation between the field measured forest height and the estimated tree height using TSI technique is 62% with an average accuracy of 91.56% and RMSE of 2.28 m. The study suggested that PolInSAR coherence based modeling approach has significant potential for retrieval of forest biophysical parameters.

Inter-Individual Variability in Human Response to Low-Dose Ionizing Radiation, Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rocke, David

2016-08-01

In order to investigate inter-individual variability in response to low-dose ionizing radiation, we are working with three models, 1) in-vivo irradiated human skin, for which we have a realistic model, but with few subjects, all from a previous project, 2) ex-vivo irradiated human skin, for which we also have a realistic model, though with the limitations involved in keeping skin pieces alive in media, and 3) MatTek EpiDermFT skin plugs, which provides a more realistic model than cell lines, which is more controllable than human samples.

Network-specific mechanisms may explain the paradoxical effects of carbamazepine and phenytoin.

PubMed

Thomas, Evan A; Petrou, Steven

2013-07-01

A common notion of the mechanism by which the antiepileptic drugs (AEDs) carbamazepine and phenytoin act is that they block sodium channels by binding preferentially to the inactivated state, thereby allowing normal neuronal firing while blocking ictal activity. However, these drugs have unpredictable efficacy and, in some cases, may exacerbate seizures. Previous studies have suggested that reducing sodium channel availability in the dentate gyrus (DG) paradoxically increases excitability. We used a biophysically detailed computer model of the DG to test the hypothesis that AEDs increase excitability by disproportionately reducing negative feedback mechanisms. We built a Markov model of sodium channel gating that reproduces responses to voltage clamp experiments in the presence of carbamazepine and phenytoin. We incorporated this validated Markov model into a biophysically realistic computer model of DG neurons and networks. Simulated drug concentrations were similar to those measured in cerebral spinal fluid in medicated patients. Single neuron models were stimulated with current injections, and networks were stimulated with perforant path synaptic input. In the network model, environmental effects were studied by introducing mossy fiber sprouting. As expected, drugs reduced sodium channel availability, which in turn reduced action potential amplitude. This had only a small effect on action potential (AP) firing rate during brief (100 msec) current injections. Paradoxically, long current injections (2,500 msec) increased AP firing rates. This was caused by reduced calcium entry and consequently reduced activation of calcium activated potassium channels. It is important to note that the main determinant of drug effect was resting membrane potential (RMP) and not action potential firing rate. Binding of phenytoin and carbamazepine is slow and, thus drug effects are largely determined by the long term state of the RMP. This paradoxical AP firing increase was dependent on the unusually large calcium-activated potassium conductances expressed by DG granule cells. This predicts that drug efficacy in a given network will depend on the precise makeup of conductances in the network. RMP is expected to vary with the level of activity in the network. We simulated the effects of drugs on single shot stimulus responses in networks with mossy fiber sprouting and varied the RMP in all neurons as a model for network activity. For an RMP of -50 mV, representing an active network, drugs had no effect, or in some cases, increased excitability. Drugs had an increasingly larger inhibitory effect on network responses as RMP decreased. An important prediction is that drugs will be unable to block ictal activity invading an active network. Our key findings are that drug effects depend on both intrinsic properties of the network and its behavioral state. This may explain the paradoxical and unpredictable effects of some AEDs on seizure control in some patients. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Evaluating Force-Field London Dispersion Coefficients Using the Exchange-Hole Dipole Moment Model.

PubMed

Mohebifar, Mohamad; Johnson, Erin R; Rowley, Christopher N

2017-12-12

London dispersion interactions play an integral role in materials science and biophysics. Force fields for atomistic molecular simulations typically represent dispersion interactions by the 12-6 Lennard-Jones potential using empirically determined parameters. These parameters are generally underdetermined, and there is no straightforward way to test if they are physically realistic. Alternatively, the exchange-hole dipole moment (XDM) model from density-functional theory predicts atomic and molecular London dispersion coefficients from first principles, providing an innovative strategy to validate the dispersion terms of molecular-mechanical force fields. In this work, the XDM model was used to obtain the London dispersion coefficients of 88 organic molecules relevant to biochemistry and pharmaceutical chemistry and the values compared with those derived from the Lennard-Jones parameters of the CGenFF, GAFF, OPLS, and Drude polarizable force fields. The molecular dispersion coefficients for the CGenFF, GAFF, and OPLS models are systematically higher than the XDM-calculated values by a factor of roughly 1.5, likely due to neglect of higher order dispersion terms and premature truncation of the dispersion-energy summation. The XDM dispersion coefficients span a large range for some molecular-mechanical atom types, suggesting an unrecognized source of error in force-field models, which assume that atoms of the same type have the same dispersion interactions. Agreement with the XDM dispersion coefficients is even poorer for the Drude polarizable force field. Popular water models were also examined, and TIP3P was found to have dispersion coefficients similar to the experimental and XDM references, although other models employ anomalously high values. Finally, XDM-derived dispersion coefficients were used to parametrize molecular-mechanical force fields for five liquids-benzene, toluene, cyclohexane, n-pentane, and n-hexane-which resulted in improved accuracy in the computed enthalpies of vaporization despite only having to evaluate a much smaller section of the parameter space.

Pro-arrhythmogenic effects of atrial fibrillation-induced electrical remodelling: insights from the three-dimensional virtual human atria

PubMed Central

Colman, Michael A; Aslanidi, Oleg V; Kharche, Sanjay; Boyett, Mark R; Garratt, Clifford; Hancox, Jules C; Zhang, Henggui

2013-01-01

Chronic atrial fibrillation (AF) is associated with structural and electrical remodelling in the atria, which are associated with a high recurrence of AF. Through biophysically detailed computer modelling, this study investigated mechanisms by which AF-induced electrical remodelling promotes and perpetuates AF. A family of Courtemanche–Ramirez–Nattel variant models of human atrial cell action potentials (APs), taking into account of intrinsic atrial electrophysiological properties, was modified to incorporate various experimental data sets on AF-induced changes of major ionic channel currents (ICaL, IKur, Ito, IK1, IKs, INaCa) and on intracellular Ca2+ handling. The single cell models for control and AF-remodelled conditions were incorporated into multicellular three-dimensional (3D) atrial tissue models. Effects of the AF-induced electrical remodelling were quantified as the changes of AP profile, AP duration (APD) and its dispersion across the atria, and the vulnerability of atrial tissue to the initiation of re-entry. The dynamic behaviour of re-entrant excitation waves in the 3D models was characterised. In our simulations, AF-induced electrical remodelling abbreviated atrial APD non-uniformly across the atria; this resulted in relatively short APDs co-existing with marked regional differences in the APD at junctions of the crista terminalis/pectinate muscle, pulmonary veins/left atrium. As a result, the measured tissue vulnerability to re-entry initiation at these tissue junctions was increased. The AF-induced electrical remodelling also stabilized and accelerated re-entrant excitation waves, leading to rapid and sustained re-entry. Under the AF-remodelled condition, re-entrant scroll waves in the 3D model degenerated into persistent and erratic wavelets, leading to fibrillation. In conclusion, realistic 3D atrial tissue models indicate that AF-induced electrical remodelling produces regionally heterogeneous and shortened APD; these respectively facilitate initiation and maintenance of re-entrant excitation waves. PMID:23732649

Pro-arrhythmogenic effects of atrial fibrillation-induced electrical remodelling: insights from the three-dimensional virtual human atria.

PubMed

Colman, Michael A; Aslanidi, Oleg V; Kharche, Sanjay; Boyett, Mark R; Garratt, Clifford; Hancox, Jules C; Zhang, Henggui

2013-09-01

Chronic atrial fibrillation (AF) is associated with structural and electrical remodelling in the atria, which are associated with a high recurrence of AF. Through biophysically detailed computer modelling, this study investigated mechanisms by which AF-induced electrical remodelling promotes and perpetuates AF. A family of Courtemanche-Ramirez-Nattel variant models of human atrial cell action potentials (APs), taking into account of intrinsic atrial electrophysiological properties, was modified to incorporate various experimental data sets on AF-induced changes of major ionic channel currents (ICaL, IKur, Ito, IK1, IKs, INaCa) and on intracellular Ca(2+) handling. The single cell models for control and AF-remodelled conditions were incorporated into multicellular three-dimensional (3D) atrial tissue models. Effects of the AF-induced electrical remodelling were quantified as the changes of AP profile, AP duration (APD) and its dispersion across the atria, and the vulnerability of atrial tissue to the initiation of re-entry. The dynamic behaviour of re-entrant excitation waves in the 3D models was characterised. In our simulations, AF-induced electrical remodelling abbreviated atrial APD non-uniformly across the atria; this resulted in relatively short APDs co-existing with marked regional differences in the APD at junctions of the crista terminalis/pectinate muscle, pulmonary veins/left atrium. As a result, the measured tissue vulnerability to re-entry initiation at these tissue junctions was increased. The AF-induced electrical remodelling also stabilized and accelerated re-entrant excitation waves, leading to rapid and sustained re-entry. Under the AF-remodelled condition, re-entrant scroll waves in the 3D model degenerated into persistent and erratic wavelets, leading to fibrillation. In conclusion, realistic 3D atrial tissue models indicate that AF-induced electrical remodelling produces regionally heterogeneous and shortened APD; these respectively facilitate initiation and maintenance of re-entrant excitation waves.

GUI to Facilitate Research on Biological Damage from Radiation

NASA Technical Reports Server (NTRS)

Cucinotta, Frances A.; Ponomarev, Artem Lvovich

2010-01-01

A graphical-user-interface (GUI) computer program has been developed to facilitate research on the damage caused by highly energetic particles and photons impinging on living organisms. The program brings together, into one computational workspace, computer codes that have been developed over the years, plus codes that will be developed during the foreseeable future, to address diverse aspects of radiation damage. These include codes that implement radiation-track models, codes for biophysical models of breakage of deoxyribonucleic acid (DNA) by radiation, pattern-recognition programs for extracting quantitative information from biological assays, and image-processing programs that aid visualization of DNA breaks. The radiation-track models are based on transport models of interactions of radiation with matter and solution of the Boltzmann transport equation by use of both theoretical and numerical models. The biophysical models of breakage of DNA by radiation include biopolymer coarse-grained and atomistic models of DNA, stochastic- process models of deposition of energy, and Markov-based probabilistic models of placement of double-strand breaks in DNA. The program is designed for use in the NT, 95, 98, 2000, ME, and XP variants of the Windows operating system.

Commentary on “Biophysical Economics” and Evolving Areas

NASA Astrophysics Data System (ADS)

Flomenbom, Ophir; Coban, Gul Unal; Adigüzel, Yekbun

2016-07-01

In this Issue, papers in the area of socio-econo-physics and biophysical economics are presented. We have recently introduced socio-econo-physics and biophysical economics in Biophysical Reviews and Letters (BRL), yet saw 3 to 4 relevant papers just in these most recent three quarters. In this commentary, we therefore would like to elaborate on the topics of socio-econo-physics and biophysical economics and to introduce these concepts to the readers of BRL and the biophysical community of science, with the purpose of supporting many more publications here in BRL, in this evolving area.

Conventions and workflows for using Situs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wriggers, Willy, E-mail: wriggers@biomachina.org

2012-04-01

Recent developments of the Situs software suite for multi-scale modeling are reviewed. Typical workflows and conventions encountered during processing of biophysical data from electron microscopy, tomography or small-angle X-ray scattering are described. Situs is a modular program package for the multi-scale modeling of atomic resolution structures and low-resolution biophysical data from electron microscopy, tomography or small-angle X-ray scattering. This article provides an overview of recent developments in the Situs package, with an emphasis on workflows and conventions that are important for practical applications. The modular design of the programs facilitates scripting in the bash shell that allows specific programs tomore » be combined in creative ways that go beyond the original intent of the developers. Several scripting-enabled functionalities, such as flexible transformations of data type, the use of symmetry constraints or the creation of two-dimensional projection images, are described. The processing of low-resolution biophysical maps in such workflows follows not only first principles but often relies on implicit conventions. Situs conventions related to map formats, resolution, correlation functions and feature detection are reviewed and summarized. The compatibility of the Situs workflow with CCP4 conventions and programs is discussed.« less

Novel approaches for targeting the adenosine A2A receptor.

PubMed

Yuan, Gengyang; Gedeon, Nicholas G; Jankins, Tanner C; Jones, Graham B

2015-01-01

The adenosine A2A receptor (A2AR) represents a drug target for a wide spectrum of diseases. Approaches for targeting this membrane-bound protein have been greatly advanced by new stabilization techniques. The resulting X-ray crystal structures and subsequent analyses provide deep insight to the A2AR from both static and dynamic perspectives. Application of this, along with other biophysical methods combined with fragment-based drug design (FBDD), has become a standard approach in targeting A2AR. Complementarities of in silico screening based- and biophysical screening assisted- FBDD are likely to feature in future approaches in identifying novel ligands against this key receptor. This review describes evolution of the above approaches for targeting A2AR and highlights key modulators identified. It includes a review of: adenosine receptor structures, homology modeling, X-ray structural analysis, rational drug design, biophysical methods, FBDD and in silico screening. As a drug target, the A2AR is attractive as its function plays a role in a wide spectrum of diseases including oncologic, inflammatory, Parkinson's and cardiovascular diseases. Although traditional approaches such as high-throughput screening and homology model-based virtual screening (VS) have played a role in targeting A2AR, numerous shortcomings have generally restricted their applications to specific ligand families. Using stabilization methods for crystallization, X-ray structures of A2AR have greatly accelerated drug discovery and influenced development of biophysical-in silico hybrid screening methods. Application of these new methods to other ARs and G-protein-coupled receptors is anticipated in the future.

Ontology of physics for biology: representing physical dependencies as a basis for biological processes.

PubMed

Cook, Daniel L; Neal, Maxwell L; Bookstein, Fred L; Gennari, John H

2013-12-02

In prior work, we presented the Ontology of Physics for Biology (OPB) as a computational ontology for use in the annotation and representations of biophysical knowledge encoded in repositories of physics-based biosimulation models. We introduced OPB:Physical entity and OPB:Physical property classes that extend available spatiotemporal representations of physical entities and processes to explicitly represent the thermodynamics and dynamics of physiological processes. Our utilitarian, long-term aim is to develop computational tools for creating and querying formalized physiological knowledge for use by multiscale "physiome" projects such as the EU's Virtual Physiological Human (VPH) and NIH's Virtual Physiological Rat (VPR). Here we describe the OPB:Physical dependency taxonomy of classes that represent of the laws of classical physics that are the "rules" by which physical properties of physical entities change during occurrences of physical processes. For example, the fluid analog of Ohm's law (as for electric currents) is used to describe how a blood flow rate depends on a blood pressure gradient. Hooke's law (as in elastic deformations of springs) is used to describe how an increase in vascular volume increases blood pressure. We classify such dependencies according to the flow, transformation, and storage of thermodynamic energy that occurs during processes governed by the dependencies. We have developed the OPB and annotation methods to represent the meaning-the biophysical semantics-of the mathematical statements of physiological analysis and the biophysical content of models and datasets. Here we describe and discuss our approach to an ontological representation of physical laws (as dependencies) and properties as encoded for the mathematical analysis of biophysical processes.

Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin

NASA Astrophysics Data System (ADS)

Mallick, Kaniska; Trebs, Ivonne; Boegh, Eva; Giustarini, Laura; Schlerf, Martin; Drewry, Darren T.; Hoffmann, Lucien; von Randow, Celso; Kruijt, Bart; Araùjo, Alessandro; Saleska, Scott; Ehleringer, James R.; Domingues, Tomas F.; Ometto, Jean Pierre H. B.; Nobre, Antonio D.; Leal de Moraes, Osvaldo Luiz; Hayek, Matthew; Munger, J. William; Wofsy, Steven C.

2016-10-01

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman-Monteith and Shuttleworth-Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy-atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land-surface-atmosphere exchange parameterizations across a range of spatial scales.

Effect of conductor geometry on source localization: Implications for epilepsy studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schlitt, H.; Heller, L.; Best, E.

1994-07-01

We shall discuss the effects of conductor geometry on source localization for applications in epilepsy studies. The most popular conductor model for clinical MEG studies is a homogeneous sphere. However, several studies have indicated that a sphere is a poor model for the head when the sources are deep, as is the case for epileptic foci in the mesial temporal lobe. We believe that replacing the spherical model with a more realistic one in the inverse fitting procedure will improve the accuracy of localizing epileptic sources. In order to include a realistic head model in the inverse problem, we mustmore » first solve the forward problem for the realistic conductor geometry. We create a conductor geometry model from MR images, and then solve the forward problem via a boundary integral equation for the electric potential due to a specified primary source. One the electric potential is known, the magnetic field can be calculated directly. The most time-intensive part of the problem is generating the conductor model; fortunately, this needs to be done only once for each patient. It takes little time to change the primary current and calculate a new magnetic field for use in the inverse fitting procedure. We present the results of a series of computer simulations in which we investigate the localization accuracy due to replacing the spherical model with the realistic head model in the inverse fitting procedure. The data to be fit consist of a computer generated magnetic field due to a known current dipole in a realistic head model, with added noise. We compare the localization errors when this field is fit using a spherical model to the fit using a realistic head model. Using a spherical model is comparable to what is usually done when localizing epileptic sources in humans, where the conductor model used in the inverse fitting procedure does not correspond to the actual head.« less

Two-way Coupling of a Process-Based Crop Growth Model (BioCro) and a Biogeochemistry Model (DayCent) and its Application to an Energy Crop Site in the mid-west USA

NASA Astrophysics Data System (ADS)

Jaiswal, D.; Long, S.; Parton, W. J.; Hartman, M.

2012-12-01

A coupled modeling system of crop growth model (BioCro) and biogeochemical model (DayCent) has been developed to assess the two-way interactions between plant growth and biogeochemistry. Crop growth in BioCro is simulated using a detailed mechanistic biochemical and biophysical multi-layer canopy model and partitioning of dry biomass into different plant organs according to phenological stages. Using hourly weather records, the model partitions light between dynamically changing sunlit and shaded portions of the canopy and computes carbon and water exchange with the atmosphere and through the canopy for each hour of the day, each day of the year. The model has been parameterized for the bioenergy crops sugarcane, Miscanthus and switchgrass, and validation has shown it to predict growth cycles and partitioning of biomass to a high degree of accuracy. As such it provides an ideal input for a soil biogeochemical model. DayCent is an established model for predicting long-term changes in soil C & N and soil-atmosphere exchanges of greenhouse gases. At present, DayCent uses a relatively simple productivity model. In this project BioCro has replaced this simple model to provide DayCent with a productivity and growth model equal in detail to its biogeochemistry. Dynamic coupling of these two models to produce CroCent allows for differential C: N ratios of litter fall (based on rates of senescence of different plant organs) and calibration of the model for realistic plant productivity in a mechanistic way. A process-based approach to modeling plant growth is needed for bioenergy crops because research on these crops (especially second generation feedstocks) has started only recently, and detailed agronomic information for growth, yield and management is too limited for effective empirical models. The coupled model provides means to test and improve the model against high resolution data, such as that obtained by eddy covariance and explore yield implications of different crop and soil management.

Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

PubMed Central

Molina, Iñigo; Morillo, Carmen; García-Meléndez, Eduardo; Guadalupe, Rafael; Roman, Maria Isabel

2011-01-01

One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (σ0). The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r2 = 0.88). In turn, when effective LAI (LAIeff) has been taken into account, the parameters of the scattering canopy model are better estimated (r2 = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values. PMID:22164028

The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

NASA Astrophysics Data System (ADS)

Souty, F.; Brunelle, T.; Dumas, P.; Dorin, B.; Ciais, P.; Crassous, R.; Müller, C.; Bondeau, A.

2012-10-01

Interactions between food demand, biomass energy and forest preservation are driving both food prices and land-use changes, regionally and globally. This study presents a new model called Nexus Land-Use version 1.0 which describes these interactions through a generic representation of agricultural intensification mechanisms within agricultural lands. The Nexus Land-Use model equations combine biophysics and economics into a single coherent framework to calculate crop yields, food prices, and resulting pasture and cropland areas within 12 regions inter-connected with each other by international trade. The representation of cropland and livestock production systems in each region relies on three components: (i) a biomass production function derived from the crop yield response function to inputs such as industrial fertilisers; (ii) a detailed representation of the livestock production system subdivided into an intensive and an extensive component, and (iii) a spatially explicit distribution of potential (maximal) crop yields prescribed from the Lund-Postdam-Jena global vegetation model for managed Land (LPJmL). The economic principles governing decisions about land-use and intensification are adapted from the Ricardian rent theory, assuming cost minimisation for farmers. In contrast to the other land-use models linking economy and biophysics, crops are aggregated as a representative product in calories and intensification for the representative crop is a non-linear function of chemical inputs. The model equations and parameter values are first described in details. Then, idealised scenarios exploring the impact of forest preservation policies or rising energy price on agricultural intensification are described, and their impacts on pasture and cropland areas are investigated.

Electrical Wave Propagation in a Minimally Realistic Fiber Architecture Model of the Left Ventricle

NASA Astrophysics Data System (ADS)

Song, Xianfeng; Setayeshgar, Sima

2006-03-01

Experimental results indicate a nested, layered geometry for the fiber surfaces of the left ventricle, where fiber directions are approximately aligned in each surface and gradually rotate through the thickness of the ventricle. Numerical and analytical results have highlighted the importance of this rotating anisotropy and its possible destabilizing role on the dynamics of scroll waves in excitable media with application to the heart. Based on the work of Peskin[1] and Peskin and McQueen[2], we present a minimally realistic model of the left ventricle that adequately captures the geometry and anisotropic properties of the heart as a conducting medium while being easily parallelizable, and computationally more tractable than fully realistic anatomical models. Complementary to fully realistic and anatomically-based computational approaches, studies using such a minimal model with the addition of successively realistic features, such as excitation-contraction coupling, should provide unique insight into the basic mechanisms of formation and obliteration of electrical wave instabilities. We describe our construction, implementation and validation of this model. [1] C. S. Peskin, Communications on Pure and Applied Mathematics 42, 79 (1989). [2] C. S. Peskin and D. M. McQueen, in Case Studies in Mathematical Modeling: Ecology, Physiology, and Cell Biology, 309(1996)

Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes

USDA-ARS?s Scientific Manuscript database

A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to earth system models is relative...

Biophysical modelling of phytoplankton communities from first principles using two-layered spheres: Equivalent Algal Populations (EAP) model.

PubMed

Robertson Lain, L; Bernard, S; Evers-King, H

2014-07-14

There is a pressing need for improved bio-optical models of high biomass waters as eutrophication of coastal and inland waters becomes an increasing problem. Seasonal boom conditions in the Southern Benguela and persistent harmful algal production in various inland waters in Southern Africa present valuable opportunities for the development of such modelling capabilities. The phytoplankton-dominated signal of these waters additionally addresses an increased interest in Phytoplankton Functional Type (PFT) analysis. To these ends, an initial validation of a new model of Equivalent Algal Populations (EAP) is presented here. This paper makes a first order comparison of two prominent phytoplankton Inherent Optical Property (IOP) models with the EAP model, which places emphasis on explicit bio-physical modelling of the phytoplankton population as a holistic determinant of inherent optical properties. This emphasis is shown to have an impact on the ability to retrieve the detailed phytoplankton spectral scattering information necessary for PFT applications and to successfully simulate reflectance across wide ranges of physical environments, biomass, and assemblage characteristics.

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

NASA Technical Reports Server (NTRS)

King, Michael D.; Moody, Eric G.; Schaaf, Crystal B.; Platnick, Steven

2006-01-01

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. , Over five years of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA s Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface s radiative characteristics. However, roughly 30% of the global land surface, on an annual equal-angle basis, is obscured due to persistent and transient cloud cover, while another 207% is obscured due to ephemeral and seasonal snow effects. This precludes the MOD43B3 albedo products from being directly used in some remote sensing and ground-based applications, climate models, and global change research projects. To provide researchers with the requisite spatially complete global snow-free land surface albedo dataset, an ecosystem-dependent temporal interpolation technique was developed to fill missing or lower quality data and snow covered values from the official MOD43B3 dataset with geophysically realistic values. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data.

Relevance of biophysical interactions of nanoparticles with a model membrane in predicting cellular uptake: study with TAT peptide-conjugated nanoparticles

PubMed Central

Peetla, Chiranjeevi; Rao, Kavitha S.; Labhasetwar, Vinod

2009-01-01

The aim of the study was to test the hypothesis that the biophysical interactions of the trans-activating transcriptor (TAT) peptide-conjugated nanoparticles (NPs) with a model cell membrane could predict the cellular uptake of the encapsulated therapeutic agent. To test the above hypothesis, the biophysical interactions of ritonavir-loaded poly (L-lactide) nanoparticles (RNPs), either conjugated to a TAT peptide (TAT-RNPs) or scrambled TAT peptide (sc-TAT-RNPs), were studied with an endothelial cell model membrane (EMM) using a Langmuir film balance, and the corresponding human vascular endothelial cells (HUVECs) were used to study the uptake of the encapsulated therapeutic. Biophysical interactions were determined from the changes in surface pressure (SP) of the EMM as a function of time following interaction with NPs, and the compression isotherm (π–A) of the EMM lipid mixture in the presence of NPs. In addition, the EMMs were transferred onto a silicon substrate following interactions with NPs using the Langmuir–Schaeffer (LS) technique. The transferred LS films were imaged by atomic force microscopy (AFM) to determine the changes in lipid morphology and to characterize the NP–membrane interactions. TAT-RNPs showed an increase in SP of the EMM, which was dependent upon the amount of the peptide bound to NPs and the concentration of NPs, whereas sc-TAT-RNPs and RNPs did not show any significant change in SP. The isotherm experiment showed a shift towards higher mean molecular area (mmA) in the presence of TAT-RNPs, indicating their interactions with the lipids of the EMM, whereas sc-TAT-RNPs and RNPs did not show any significant change. The AFM images showed condensation of the lipids following interaction with TAT-RNPs, indicating their penetration into the EMM, whereas RNPs did not cause any change. Surface analysis and 3-D AFM images of the EMM further confirmed penetration of TAT-RNPs into the EMM whereas RNPs were seen anchored loosely to the membrane, and were significantly less in number than TAT-RNPs. We speculate that hydrophobic tyrosine of the TAT that forms the NP–interface drives the initial interactions of TAT-RNPs with the EMM, followed by electrostatic interactions with the anionic phospholipids of the membrane. In case of sc-TAT-RNPs, hydrophilic arginine forms the NP–interface that does not interact with the EMM, despite having the similar cationic charge on these NPs as TAT-RNPs. TAT peptide alone did not show any change in SP, suggesting that the interaction occurs when the peptide is conjugated to a carrier system. HUVECs showed higher uptake of the drug with TAT-RNPs as compared to that with sc-TAT-RNPs or RNPs, suggesting that the biophysical interactions of NPs with cell membrane lipids play a role in cellular internalization of NPs. In conclusion, TAT peptide sequence and the amount of TAT conjugated to NPs significantly affect the biophysical interactions of NPs with the EMM, and these interactions correlate with the cellular delivery of the encapsulated drug. Biophysical interactions with a model membrane thus could be effectively used in developing efficient functionalized nanocarrier systems for drug delivery applications. PMID:19243206

Theory and simulations of adhesion receptor dimerization on membrane surfaces.

PubMed

Wu, Yinghao; Honig, Barry; Ben-Shaul, Avinoam

2013-03-19

The equilibrium constants of trans and cis dimerization of membrane bound (2D) and freely moving (3D) adhesion receptors are expressed and compared using elementary statistical-thermodynamics. Both processes are mediated by the binding of extracellular subdomains whose range of motion in the 2D environment is reduced upon dimerization, defining a thin reaction shell where dimer formation and dissociation take place. We show that the ratio between the 2D and 3D equilibrium constants can be expressed as a product of individual factors describing, respectively, the spatial ranges of motions of the adhesive domains, and their rotational freedom within the reaction shell. The results predicted by the theory are compared to those obtained from a novel, to our knowledge, dynamical simulations methodology, whereby pairs of receptors perform realistic translational, internal, and rotational motions in 2D and 3D. We use cadherins as our model system. The theory and simulations explain how the strength of cis and trans interactions of adhesive receptors are affected both by their presence in the constrained intermembrane space and by the 2D environment of membrane surfaces. Our work provides fundamental insights as to the mechanism of lateral clustering of adhesion receptors after cell-cell contact and, more generally, to the formation of lateral microclusters of proteins on cell surfaces. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

3-D Technology Approaches for Biological Ecologies

NASA Astrophysics Data System (ADS)

Liu, Liyu; Austin, Robert; U. S-China Physical-Oncology Sciences Alliance (PS-OA) Team

Constructing three dimensional (3-D) landscapes is an inevitable issue in deep study of biological ecologies, because in whatever scales in nature, all of the ecosystems are composed by complex 3-D environments and biological behaviors. Just imagine if a 3-D technology could help complex ecosystems be built easily and mimic in vivo microenvironment realistically with flexible environmental controls, it will be a fantastic and powerful thrust to assist researchers for explorations. For years, we have been utilizing and developing different technologies for constructing 3-D micro landscapes for biophysics studies in in vitro. Here, I will review our past efforts, including probing cancer cell invasiveness with 3-D silicon based Tepuis, constructing 3-D microenvironment for cell invasion and metastasis through polydimethylsiloxane (PDMS) soft lithography, as well as explorations of optimized stenting positions for coronary bifurcation disease with 3-D wax printing and the latest home designed 3-D bio-printer. Although 3-D technologies is currently considered not mature enough for arbitrary 3-D micro-ecological models with easy design and fabrication, I hope through my talk, the audiences will be able to sense its significance and predictable breakthroughs in the near future. This work was supported by the State Key Development Program for Basic Research of China (Grant No. 2013CB837200), the National Natural Science Foundation of China (Grant No. 11474345) and the Beijing Natural Science Foundation (Grant No. 7154221).

A Study of Child Variance, Volume 1: Conceptual Models; Conceptual Project in Emotional Disturbance.

ERIC Educational Resources Information Center

Rhodes, William C.; Tracy, Michael L.

Presented are 11 papers discussing the following six models of emotional disturbance in children: biophysical, behavioral, psychodynamic, sociological, and ecological, models, and counter theory. Emotional disturbance is defined as a distinctive human state having multiple manifestations and involving disability, deviance, and alienation. All the…

Effect of ambient light on the time needed to complete a fetal biophysical profile: A randomized controlled trial.

PubMed

Said, Heather M; Gupta, Shweta; Vricella, Laura K; Wand, Katy; Nguyen, Thinh; Gross, Gilad

2017-10-01

The objective of this study is to determine whether ambient light serves as a fetal stimulus to decrease the amount of time needed to complete a biophysical profile. This is a randomized controlled trial of singleton gestations undergoing a biophysical profile. Patients were randomized to either ambient light or a darkened room. The primary outcome was the time needed to complete the biophysical profile. Secondary outcomes included total and individual component biophysical profile scores and scores less than 8. A subgroup analysis of different maternal body mass indices was also performed. 357 biophysical profile studies were analyzed. 182 studies were performed with ambient light and 175 were performed in a darkened room. There was no difference in the median time needed to complete the biophysical profile based on exposure to ambient light (6.1min in darkened room versus 6.6min with ambient light; P=0.73). No difference was found in total or individual component biophysical profile scores. Subgroup analysis by maternal body mass index did not demonstrate shorter study times with ambient light exposure in women who were normal weight, overweight or obese. Ambient light exposure did not decrease the time needed to complete the biophysical profile. There was no evidence that ambient light altered fetal behavior observed during the biophysical profile. Copyright © 2017 Elsevier B.V. All rights reserved.

Neurophysiological bases of exponential sensory decay and top-down memory retrieval: a model.

PubMed

Zylberberg, Ariel; Dehaene, Stanislas; Mindlin, Gabriel B; Sigman, Mariano

2009-01-01

Behavioral observations suggest that multiple sensory elements can be maintained for a short time, forming a perceptual buffer which fades after a few hundred milliseconds. Only a subset of this perceptual buffer can be accessed under top-down control and broadcasted to working memory and consciousness. In turn, single-cell studies in awake-behaving monkeys have identified two distinct waves of response to a sensory stimulus: a first transient response largely determined by stimulus properties and a second wave dependent on behavioral relevance, context and learning. Here we propose a simple biophysical scheme which bridges these observations and establishes concrete predictions for neurophsyiological experiments in which the temporal interval between stimulus presentation and top-down allocation is controlled experimentally. Inspired in single-cell observations, the model involves a first transient response and a second stage of amplification and retrieval, which are implemented biophysically by distinct operational modes of the same circuit, regulated by external currents. We explicitly investigated the neuronal dynamics, the memory trace of a presented stimulus and the probability of correct retrieval, when these two stages were bracketed by a temporal gap. The model predicts correctly the dependence of performance with response times in interference experiments suggesting that sensory buffering does not require a specific dedicated mechanism and establishing a direct link between biophysical manipulations and behavioral observations leading to concrete predictions.

A fast analytical undulator model for realistic high-energy FEL simulations

NASA Astrophysics Data System (ADS)

Tatchyn, R.; Cremer, T.

1997-02-01

A number of leading FEL simulation codes used for modeling gain in the ultralong undulators required for SASE saturation in the <100 Å range employ simplified analytical models both for field and error representations. Although it is recognized that both the practical and theoretical validity of such codes could be enhanced by incorporating realistic undulator field calculations, the computational cost of doing this can be prohibitive, especially for point-to-point integration of the equations of motion through each undulator period. In this paper we describe a simple analytical model suitable for modeling realistic permanent magnet (PM), hybrid/PM, and non-PM undulator structures, and discuss selected techniques for minimizing computation time.

Realistic Gamow shell model for resonance and continuum in atomic nuclei

NASA Astrophysics Data System (ADS)

Xu, F. R.; Sun, Z. H.; Wu, Q.; Hu, B. S.; Dai, S. J.

2018-02-01

The Gamow shell model can describe resonance and continuum for atomic nuclei. The model is established in the complex-moment (complex-k) plane of the Berggren coordinates in which bound, resonant and continuum states are treated on equal footing self-consistently. In the present work, the realistic nuclear force, CD Bonn, has been used. We have developed the full \\hat{Q}-box folded-diagram method to derive the realistic effective interaction in the model space which is nondegenerate and contains resonance and continuum channels. The CD-Bonn potential is renormalized using the V low-k method. With choosing 16O as the inert core, we have applied the Gamow shell model to oxygen isotopes.

Novel high-fidelity realistic explosion damage simulation for urban environments

NASA Astrophysics Data System (ADS)

Liu, Xiaoqing; Yadegar, Jacob; Zhu, Youding; Raju, Chaitanya; Bhagavathula, Jaya

2010-04-01

Realistic building damage simulation has a significant impact in modern modeling and simulation systems especially in diverse panoply of military and civil applications where these simulation systems are widely used for personnel training, critical mission planning, disaster management, etc. Realistic building damage simulation should incorporate accurate physics-based explosion models, rubble generation, rubble flyout, and interactions between flying rubble and their surrounding entities. However, none of the existing building damage simulation systems sufficiently faithfully realize the criteria of realism required for effective military applications. In this paper, we present a novel physics-based high-fidelity and runtime efficient explosion simulation system to realistically simulate destruction to buildings. In the proposed system, a family of novel blast models is applied to accurately and realistically simulate explosions based on static and/or dynamic detonation conditions. The system also takes account of rubble pile formation and applies a generic and scalable multi-component based object representation to describe scene entities and highly scalable agent-subsumption architecture and scheduler to schedule clusters of sequential and parallel events. The proposed system utilizes a highly efficient and scalable tetrahedral decomposition approach to realistically simulate rubble formation. Experimental results demonstrate that the proposed system has the capability to realistically simulate rubble generation, rubble flyout and their primary and secondary impacts on surrounding objects including buildings, constructions, vehicles and pedestrians in clusters of sequential and parallel damage events.

Determinants of the electric field during transcranial direct current stimulation.

PubMed

Opitz, Alexander; Paulus, Walter; Will, Susanne; Antunes, Andre; Thielscher, Axel

2015-04-01

Transcranial direct current stimulation (tDCS) causes a complex spatial distribution of the electric current flow in the head which hampers the accurate localization of the stimulated brain areas. In this study we show how various anatomical features systematically shape the electric field distribution in the brain during tDCS. We constructed anatomically realistic finite element (FEM) models of two individual heads including conductivity anisotropy and different skull layers. We simulated a widely employed electrode montage to induce motor cortex plasticity and moved the stimulating electrode over the motor cortex in small steps to examine the resulting changes of the electric field distribution in the underlying cortex. We examined the effect of skull thickness and composition on the passing currents showing that thinner skull regions lead to higher electric field strengths. This effect is counteracted by a larger proportion of higher conducting spongy bone in thicker regions leading to a more homogenous current over the skull. Using a multiple regression model we could identify key factors that determine the field distribution to a significant extent, namely the thicknesses of the cerebrospinal fluid and the skull, the gyral depth and the distance to the anode and cathode. These factors account for up to 50% of the spatial variation of the electric field strength. Further, we demonstrate that individual anatomical factors can lead to stimulation "hotspots" which are partly resistant to electrode positioning. Our results give valuable novel insights in the biophysical foundation of tDCS and highlight the importance to account for individual anatomical factors when choosing an electrode montage. Copyright © 2015 Elsevier Inc. All rights reserved.

Urban Heat Islands in China Enhanced by Haze Pollution

NASA Astrophysics Data System (ADS)

Cao, C.; Lee, X.; Liu, S.; Oleson, K. W.; Schultz, N. M.; Xiao, W.; Zhang, M.; Zhao, L.

2015-12-01

Land conversion from natural surfaces to artificial urban structures has led to the phenomenon of urban heat island (UHI). The intensity of UHI is thought to be controlled primarily by biophysical factors such as changes in albedo, aerodynamic resistance and evapotranspiration, while influences of biogeochemical factors such as aerosol pollution have long been ignored. We hypothesize that increased downward longwave radiation associated with anthropogenic aerosols in urban air will exacerbate nighttime UHI intensity. Here we tested this hypothesis by using the MODIS satellite land surface temperature product and the Community Land Model (CLM) for 39 cities in China. Our results showed that in contrast to observations in North America and elsewhere, nighttime surface UHI of these Chinese cities (3.34 K) was greater than daytime UHI (2.06 K). Variations in the nighttime UHI among the cities were positively correlated with difference in the aerosol optical depth between urban and the adjacent rural area (confidence level p < 0.01). The CLM was able to reproduce the MODIS UHI intensity in the daytime but underestimated the observed UHI intensity at night. The model performance was improved by including an aerosol-enhanced downward longwave radiation in urban land and a more realistic anthropogenic heat flux. Our study illustrates that although climate background largely determine spatial differences in the daytime UHI, in countries like China with serious air quality problems, aerosol-induced pollution plays an important role in the night-time UHI formation. Mitigation of particulate pollution therefore has the added co-benefit by reducing UHI-related heat stress on urban residents.

A conformally flat realistic anisotropic model for a compact star

NASA Astrophysics Data System (ADS)

Ivanov, B. V.

2018-04-01

A physically realistic stellar model with a simple expression for the energy density and conformally flat interior is found. The relations between the different conditions are used without graphic proofs. It may represent a real pulsar.

Risk assessment and management of radiofrequency radiation exposure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dabala, Dana; Surducan, Emanoil; Surducan, Vasile

2013-11-13

Radiofrequency radiation (RFR) industry managers, occupational physicians, security department, and other practitioners must be advised on the basic of biophysics and the health effects of RF electromagnetic fields so as to guide the management of exposure. Information on biophysics of RFR and biological/heath effects is derived from standard texts, literature and clinical experiences. Emergency treatment and ongoing care is outlined, with clinical approach integrating the circumstances of exposure and the patient's symptoms. Experimental risk assessment model in RFR chronic exposure is proposed. Planning for assessment and monitoring exposure, ongoing care, safety measures and work protection are outlining the proper management.

Biophysical applications of neutron Compton scattering

NASA Astrophysics Data System (ADS)

Wanderlingh, U. N.; Albergamo, F.; Hayward, R. L.; Middendorf, H. D.

Neutron Compton scattering (NCS) can be applied to measuring nuclear momentum distributions and potential parameters in molecules of biophysical interest. We discuss the analysis of NCS spectra from peptide models, focusing on the characterisation of the amide proton dynamics in terms of the width of the H-bond potential well, its Laplacian, and the mean kinetic energy of the proton. The Sears expansion is used to quantify deviations from the high-Q limit (impulse approximation), and line-shape asymmetry parameters are evaluated in terms of Hermite polynomials. Results on NCS from selectively deuterated acetanilide are used to illustrate this approach.

Risk assessment and management of radiofrequency radiation exposure

NASA Astrophysics Data System (ADS)

Dabala, Dana; Surducan, Emanoil; Surducan, Vasile; Neamtu, Camelia

2013-11-01

Radiofrequency radiation (RFR) industry managers, occupational physicians, security department, and other practitioners must be advised on the basic of biophysics and the health effects of RF electromagnetic fields so as to guide the management of exposure. Information on biophysics of RFR and biological/heath effects is derived from standard texts, literature and clinical experiences. Emergency treatment and ongoing care is outlined, with clinical approach integrating the circumstances of exposure and the patient's symptoms. Experimental risk assessment model in RFR chronic exposure is proposed. Planning for assessment and monitoring exposure, ongoing care, safety measures and work protection are outlining the proper management.

Accurate, robust and reliable calculations of Poisson-Boltzmann binding energies

PubMed Central

Nguyen, Duc D.; Wang, Bao

2017-01-01

Poisson-Boltzmann (PB) model is one of the most popular implicit solvent models in biophysical modeling and computation. The ability of providing accurate and reliable PB estimation of electrostatic solvation free energy, ΔGel, and binding free energy, ΔΔGel, is important to computational biophysics and biochemistry. In this work, we investigate the grid dependence of our PB solver (MIBPB) with SESs for estimating both electrostatic solvation free energies and electrostatic binding free energies. It is found that the relative absolute error of ΔGel obtained at the grid spacing of 1.0 Å compared to ΔGel at 0.2 Å averaged over 153 molecules is less than 0.2%. Our results indicate that the use of grid spacing 0.6 Å ensures accuracy and reliability in ΔΔGel calculation. In fact, the grid spacing of 1.1 Å appears to deliver adequate accuracy for high throughput screening. PMID:28211071

In pursuit of an accurate spatial and temporal model of biomolecules at the atomistic level: a perspective on computer simulation.

PubMed

Gray, Alan; Harlen, Oliver G; Harris, Sarah A; Khalid, Syma; Leung, Yuk Ming; Lonsdale, Richard; Mulholland, Adrian J; Pearson, Arwen R; Read, Daniel J; Richardson, Robin A

2015-01-01

Despite huge advances in the computational techniques available for simulating biomolecules at the quantum-mechanical, atomistic and coarse-grained levels, there is still a widespread perception amongst the experimental community that these calculations are highly specialist and are not generally applicable by researchers outside the theoretical community. In this article, the successes and limitations of biomolecular simulation and the further developments that are likely in the near future are discussed. A brief overview is also provided of the experimental biophysical methods that are commonly used to probe biomolecular structure and dynamics, and the accuracy of the information that can be obtained from each is compared with that from modelling. It is concluded that progress towards an accurate spatial and temporal model of biomacromolecules requires a combination of all of these biophysical techniques, both experimental and computational.

Image-Based Predictive Modeling of Heart Mechanics.

PubMed

Wang, V Y; Nielsen, P M F; Nash, M P

2015-01-01

Personalized biophysical modeling of the heart is a useful approach for noninvasively analyzing and predicting in vivo cardiac mechanics. Three main developments support this style of analysis: state-of-the-art cardiac imaging technologies, modern computational infrastructure, and advanced mathematical modeling techniques. In vivo measurements of cardiac structure and function can be integrated using sophisticated computational methods to investigate mechanisms of myocardial function and dysfunction, and can aid in clinical diagnosis and developing personalized treatment. In this article, we review the state-of-the-art in cardiac imaging modalities, model-based interpretation of 3D images of cardiac structure and function, and recent advances in modeling that allow personalized predictions of heart mechanics. We discuss how using such image-based modeling frameworks can increase the understanding of the fundamental biophysics behind cardiac mechanics, and assist with diagnosis, surgical guidance, and treatment planning. Addressing the challenges in this field will require a coordinated effort from both the clinical-imaging and modeling communities. We also discuss future directions that can be taken to bridge the gap between basic science and clinical translation.

The role of bio-physical cohesive substrates on sediment winnowing and bedform development

NASA Astrophysics Data System (ADS)

Ye, Leiping; Parsons, Daniel; Manning, Andrew

2017-04-01

Existing sediment transport and bedform size predictions for natural open-channel flows in many environments are seriously impeded by a lack of process-based knowledge concerning the dynamics of complex bed sediment mixtures comprising cohesionless sand and biologically-active cohesive muds. A series of flume experiments (14 experimental runs) with different substrate mixtures of sand-clay-EPS (Extracellular Polymeric Substance) are combined with a detailed estuarine field survey (Dee estuary, NW England) to investigate the development of bedform morphologies and characteristics of suspended sediment over bio-physical cohesive substrates. The experimental results indicate that winnowing and sediment sorting can occur pervasively in bio-physical cohesive sediment - flow systems. Importantly however, the evolution of the bed and bedform dynamics, and hence turbulence production, is significantly reduced as bed substrate cohesivity increases. The estuarine subtidal zone survey also revealed that the bio-physical cohesion provided by both the clay and microorganism fractions in the bed plays a significant role in controlling the interactions between bed substrate and sediment suspension, deposition and bedform generation. The work will be presented here concludes by outlining the need to extend and revisit the effects of cohesivity in morphodynamic systems and the sets of parameters presently used in numerical modelling, particularly in the context of the impact of climate change on estuarine and coastal systems.

Radiation budget changes with dry forest clearing in temperate Argentina.

PubMed

Houspanossian, Javier; Nosetto, Marcelo; Jobbágy, Esteban G

2013-04-01

Land cover changes may affect climate and the energy balance of the Earth through their influence on the greenhouse gas composition of the atmosphere (biogeochemical effects) but also through shifts in the physical properties of the land surface (biophysical effects). We explored how the radiation budget changes following the replacement of temperate dry forests by crops in central semiarid Argentina and quantified the biophysical radiative forcing of this transformation. For this purpose, we computed the albedo and surface temperature for a 7-year period (2003-2009) from MODIS imagery at 70 paired sites occupied by native forests and crops and calculated the radiation budget at the tropopause and surface levels using a columnar radiation model parameterized with satellite data. Mean annual black-sky albedo and diurnal surface temperature were 50% and 2.5 °C higher in croplands than in dry forests. These contrasts increased the outgoing shortwave energy flux at the top of the atmosphere in croplands by a quarter (58.4 vs. 45.9 W m(-2) ) which, together with a slight increase in the outgoing longwave flux, yielded a net cooling of -14 W m(-2) . This biophysical cooling effect would be equivalent to a reduction in atmospheric CO2 of 22 Mg C ha(-1) , which involves approximately a quarter to a half of the typical carbon emissions that accompany deforestation in these ecosystems. We showed that the replacement of dry forests by crops in central Argentina has strong biophysical effects on the energy budget which could counterbalance the biogeochemical effects of deforestation. Underestimating or ignoring these biophysical consequences of land-use changes on climate will certainly curtail the effectiveness of many warming mitigation actions, particularly in semiarid regions where high radiation load and smaller active carbon pools would increase the relative importance of biophysical forcing. © 2012 Blackwell Publishing Ltd.

Increasing the Effectiveness of Vaginal Microbicides: A Biophysical Framework to Rethink Behavioral Acceptability

PubMed Central

Verguet, Stéphane; Young Holt, Bethany; Szeri, Andrew J.

2010-01-01

Background Microbicide candidates delivered via gel vehicles are intended to coat the vaginal epithelium after application. The coating process depends on intrinsic biophysical properties of the gel texture, which restricts the potential choices for an effective product: the gel first must be physically synthesizable, then acceptable to the user, and finally applied in a manner promoting timely adequate coating, so that the user adherence is optimized. We present a conceptual framework anchoring microbicide behavioral acceptability within the fulfillment of the product biophysical requirements. Methods We conducted a semi-qualitative/quantitative study targeting women aged 18–55 in Northern California to assess user preferences for microbicide gel attributes. Attributes included: (i) the wait time between application and intercourse, (ii) the gel texture and (iii) the trade-off between wait time and gel texture. Wait times were assessed using a mathematical model determining coating rates depending upon the gel's physical attributes. Results 71 women participated. Results suggest that women would independently prefer a gel spreading rapidly, in 2 to 15 minutes (P<0.0001), as well as one that is thick or slippery (P<0.02). Clearly, thick gels do not spread rapidly; hence the motivation to study the trade-off. When asked the same question ‘constrained’ by the biophysical reality, women indicated no significant preference for a particular gel thickness (and therefore waiting time) (P>0.10) for use with a steady partner, a preference for a watery gel spreading rapidly rather than one having intermediate properties for use with a casual partner (P = 0.024). Conclusions Biophysical constraints alter women's preferences regarding acceptable microbicide attributes. Product developers should offer a range of formulations in order to address all preferences. We designed a conceptual framework to rethink behavioral acceptability in terms of biophysical requirements that can help improve adherence in microbicide use ultimately enhancing microbicide effectiveness. PMID:21124931

Dealing with the Challenges of Teaching Molecular Biophysics to Biochemistry Majors through an Heuristics-Based Approach

ERIC Educational Resources Information Center

Castanho, Miguel A. R. B.

2002-01-01

The main distinction between the overlapping fields of molecular biophysics and biochemistry resides in their different approaches to the same problems. Molecular biophysics makes more use of physical techniques and focuses on quantitative data. This difference encounters two difficult pedagogical challenges when teaching molecular biophysics to…

Spline Laplacian estimate of EEG potentials over a realistic magnetic resonance-constructed scalp surface model.

PubMed

Babiloni, F; Babiloni, C; Carducci, F; Fattorini, L; Onorati, P; Urbano, A

1996-04-01

This paper presents a realistic Laplacian (RL) estimator based on a tensorial formulation of the surface Laplacian (SL) that uses the 2-D thin plate spline function to obtain a mathematical description of a realistic scalp surface. Because of this tensorial formulation, the RL does not need an orthogonal reference frame placed on the realistic scalp surface. In simulation experiments the RL was estimated with an increasing number of "electrodes" (up to 256) on a mathematical scalp model, the analytic Laplacian being used as a reference. Second and third order spherical spline Laplacian estimates were examined for comparison. Noise of increasing magnitude and spatial frequency was added to the simulated potential distributions. Movement-related potentials and somatosensory evoked potentials sampled with 128 electrodes were used to estimate the RL on a realistically shaped, MR-constructed model of the subject's scalp surface. The RL was also estimated on a mathematical spherical scalp model computed from the real scalp surface. Simulation experiments showed that the performances of the RL estimator were similar to those of the second and third order spherical spline Laplacians. Furthermore, the information content of scalp-recorded potentials was clearly better when the RL estimator computed the SL of the potential on an MR-constructed scalp surface model.

Training in Methods in Computational Neuroscience

DTIC Science & Technology

1992-08-29

in Tritonia. Roger Traub Models with realistic neurons , with an emphasis on large-scale modeling of epileptic phenomena in hippocampus. Rodolpho...Cell Model Plan: 1) Convert some of my simulations from NEURON to GENESIS (and thus learn GENESIS). 2) Develop a realistic inhibtory model . 3) Further...General Hospital, MA Course Project: Membrane Properties of a Neostriatal Neuron and Dopamine Modulation The purpose of my project was to model the

Order Matters: Sequencing Scale-Realistic versus Simplified Models to Improve Science Learning

ERIC Educational Resources Information Center

Chen, Chen; Schneps, Matthew H.; Sonnert, Gerhard

2016-01-01

Teachers choosing between different models to facilitate students' understanding of an abstract system must decide whether to adopt a model that is simplified and striking or one that is realistic and complex. Only recently have instructional technologies enabled teachers and learners to change presentations swiftly and to provide for learning…

How High Is the Tramping Track? Mathematising and Applying in a Calculus Model-Eliciting Activity

ERIC Educational Resources Information Center

Yoon, Caroline; Dreyfus, Tommy; Thomas, Michael O. J.

2010-01-01

Two complementary processes involved in mathematical modelling are mathematising a realistic situation and applying a mathematical technique to a given realistic situation. We present and analyse work from two undergraduate students and two secondary school teachers who engaged in both processes during a mathematical modelling task that required…

Talk to the Hand: U.S. Army Biophysical Testing.

PubMed

Santee, William R; Potter, Adam W; Friedl, Karl E

2017-07-01

Many people are unaware of the science underlying the biophysical properties of Soldier clothing and personal protective equipment, yet there is a well-refined biomedical methodology initiated by Army physiologists in World War II. This involves a methodical progression of systematic material testing technologies, computer modeling, and human testing that enables more efficient development and rapid evaluation of new concepts for Soldier health and performance. Sophisticated manikins that sweat and move are a central part of this testing continuum. This report briefly summarizes the evolution and use of one specialized form of the manikin technologies, the thermal hand model, and its use in research on Soldier hand-wear items that sustain dexterity and protect the hand in extreme environments. Thermal manikin testing methodologies were developed to provide an efficient and consistent analytical tool for the rapid evaluation of new clothing concepts. These methods have been upgraded since the original World War II and Korean War eras to include articulation and sweating capabilities, as characterized and illustrated in this article. The earlier "retired" versions of thermal hand models have now been transferred to the National Museum of Health and Science. The biophysical values from manikin testing are critical inputs to the U.S. Army Research Institute of Environmental Medicine mathematical models that provide predictions of soldier comfort, duration of exposure before loss of manual dexterity, and time to significant risk of freezing (skin temperature <-1°C) and nonfreezing cold injuries (skin temperature <5°C). The greater thickness of better insulated handwear reduces dexterity and also increases surface area which makes added insulation increasingly less effective in retaining heat. Measurements of both thermal resistance (insulation) and evaporative resistance (permeability) collectively characterize the biophysical properties and enable mathematical modeling of the human thermophysiological responses. This information can help guide the hand-wear development and selection process which often requires trade-offs between factors such as material, cost, and sizing. Soldier hands provide fine motor dexterity in tactical functions, ranging from pulling a trigger to pulling a parachute ripcord; thus, protecting hand function is critical to soldier readiness. Also, the importance of protection against nonbattle cold injuries was highlighted during World War II in northern Europe, in the Aleutian Islands, and later in Korea. The U.S. Army has been on the forefront of the biophysical analysis of clothing including gloves since environmental research was established at the Armored Medical Research Laboratory and Climatic Research Laboratory during World War II. U.S. Army Research Institute of Environmental Medicine does not make the equipment but works with their Natick Soldier Research, Development, and Engineering Center partners to make the equipment better. Reprint & Copyright © 2017 Association of Military Surgeons of the U.S.

A method to investigate the diffusion properties of nuclear calcium.

PubMed

Queisser, Gillian; Wittum, Gabriel

2011-10-01

Modeling biophysical processes in general requires knowledge about underlying biological parameters. The quality of simulation results is strongly influenced by the accuracy of these parameters, hence the identification of parameter values that the model includes is a major part of simulating biophysical processes. In many cases, secondary data can be gathered by experimental setups, which are exploitable by mathematical inverse modeling techniques. Here we describe a method for parameter identification of diffusion properties of calcium in the nuclei of rat hippocampal neurons. The method is based on a Gauss-Newton method for solving a least-squares minimization problem and was formulated in such a way that it is ideally implementable in the simulation platform uG. Making use of independently published space- and time-dependent calcium imaging data, generated from laser-assisted calcium uncaging experiments, here we could identify the diffusion properties of nuclear calcium and were able to validate a previously published model that describes nuclear calcium dynamics as a diffusion process.

Integrated Basin-Scale Modelling and Assessment: Lessons and Challenges in Linking Biophysical and Socioeconomic Sciences for Enhancing Sustainability Outcomes

NASA Astrophysics Data System (ADS)

Jakeman, A. J.; Croke, B. F.; Letcher, R. A.; Newham, L. T.; Norton, J. P.

2004-12-01

Integrated Assessment (IA) and Integrated Scenario Modelling (ISM) are being increasingly used to assess sustainability options and, in particular, the effects of policy changes, land use management, climate forcing and other uncontrollable drivers on a wide range of river basin outcomes. IA and ISM are processes that invoke the necessary range of biophysical and socioeconomic disciplines and embrace stakeholder involvement as an essential ingredient. The authors report on their IA studies in Australian and Asian river basins. They illustrate a range of modelling frameworks and tools that were used to perform the assessments, engage the relevant interest groups and promote systems understanding and social learning. The studies cover a range of issues and policies including poverty alleviation, industrial investments, infrastructure provision, erosion and sedimentation, water supply allocation, and ecological protection. The positive impacts of these studies are presented, as well as the lessons learnt and the challenges for modellers and disciplinary experts in advancing the reputation and performance of integrated assessment exercises.

Biophysical interactions between plant and soil: theory and practice

NASA Astrophysics Data System (ADS)

van der Ploeg, Martine

2016-04-01

Vegetation plays an essential role in the hydrological cycle, as it regulates the water flux to the atmosphere through evapotranspiration, while it is dependent on adequate water supply. Vegetation shapes the land surface by changing infiltration characteristics as a result of root growth, and controls soil moisture storage, which in turn affect runoff characteristics and groundwater recharge. Vegetation and the underlying geology are in constant interaction, wherein water plays a key role. The resilience of the coupled vegetation-soil system critically depends on its sensitivity to environmental changes. Models are a useful tool to explore interaction and feedbacks between vegetation, soil and landscape. Plants respond biochemically to their environment, while the models used for hydrology are often based on physical interactions. Gene-expression and genotype adaptation may complicate our modelling efforts in for example climate change impacts. Combination of new techniques to assess soil and plant properties facilitates assessment of biophysical interactions. This poster will review these techniques and compare the obtained insights of soil-plant relationships with the current modeling approaches.

Leatherbacks swimming in silico: modeling and verifying their momentum and heat balance using computational fluid dynamics.

PubMed

Dudley, Peter N; Bonazza, Riccardo; Jones, T Todd; Wyneken, Jeanette; Porter, Warren P

2014-01-01

As global temperatures increase throughout the coming decades, species ranges will shift. New combinations of abiotic conditions will make predicting these range shifts difficult. Biophysical mechanistic niche modeling places bounds on an animal's niche through analyzing the animal's physical interactions with the environment. Biophysical mechanistic niche modeling is flexible enough to accommodate these new combinations of abiotic conditions. However, this approach is difficult to implement for aquatic species because of complex interactions among thrust, metabolic rate and heat transfer. We use contemporary computational fluid dynamic techniques to overcome these difficulties. We model the complex 3D motion of a swimming neonate and juvenile leatherback sea turtle to find power and heat transfer rates during the stroke. We combine the results from these simulations and a numerical model to accurately predict the core temperature of a swimming leatherback. These results are the first steps in developing a highly accurate mechanistic niche model, which can assists paleontologist in understanding biogeographic shifts as well as aid contemporary species managers about potential range shifts over the coming decades.

Inhomogeneous ensembles of radical pairs in chemical compasses

PubMed Central

Procopio, Maria; Ritz, Thorsten

2016-01-01

The biophysical basis for the ability of animals to detect the geomagnetic field and to use it for finding directions remains a mystery of sensory biology. One much debated hypothesis suggests that an ensemble of specialized light-induced radical pair reactions can provide the primary signal for a magnetic compass sensor. The question arises what features of such a radical pair ensemble could be optimized by evolution so as to improve the detection of the direction of weak magnetic fields. Here, we focus on the overlooked aspect of the noise arising from inhomogeneity of copies of biomolecules in a realistic biological environment. Such inhomogeneity leads to variations of the radical pair parameters, thereby deteriorating the signal arising from an ensemble and providing a source of noise. We investigate the effect of variations in hyperfine interactions between different copies of simple radical pairs on the directional response of a compass system. We find that the choice of radical pair parameters greatly influences how strongly the directional response of an ensemble is affected by inhomogeneity. PMID:27804956

Optimal sparse approximation with integrate and fire neurons.

PubMed

Shapero, Samuel; Zhu, Mengchen; Hasler, Jennifer; Rozell, Christopher

2014-08-01

Sparse approximation is a hypothesized coding strategy where a population of sensory neurons (e.g. V1) encodes a stimulus using as few active neurons as possible. We present the Spiking LCA (locally competitive algorithm), a rate encoded Spiking Neural Network (SNN) of integrate and fire neurons that calculate sparse approximations. The Spiking LCA is designed to be equivalent to the nonspiking LCA, an analog dynamical system that converges on a ℓ(1)-norm sparse approximations exponentially. We show that the firing rate of the Spiking LCA converges on the same solution as the analog LCA, with an error inversely proportional to the sampling time. We simulate in NEURON a network of 128 neuron pairs that encode 8 × 8 pixel image patches, demonstrating that the network converges to nearly optimal encodings within 20 ms of biological time. We also show that when using more biophysically realistic parameters in the neurons, the gain function encourages additional ℓ(0)-norm sparsity in the encoding, relative both to ideal neurons and digital solvers.

Time constant determination for electrical equivalent of biological cells

NASA Astrophysics Data System (ADS)

Dubey, Ashutosh Kumar; Dutta-Gupta, Shourya; Kumar, Ravi; Tewari, Abhishek; Basu, Bikramjit

2009-04-01

The electric field interactions with biological cells are of significant interest in various biophysical and biomedical applications. In order to study such important aspect, it is necessary to evaluate the time constant in order to estimate the response time of living cells in the electric field (E-field). In the present study, the time constant is evaluated by considering the hypothesis of electrical analog of spherical shaped cells and assuming realistic values for capacitance and resistivity properties of cell/nuclear membrane, cytoplasm, and nucleus. In addition, the resistance of cytoplasm and nucleoplasm was computed based on simple geometrical considerations. Importantly, the analysis on the basis of first principles shows that the average values of time constant would be around 2-3 μs, assuming the theoretical capacitance values and the analytically computed resistance values. The implication of our analytical solution has been discussed in reference to the cellular adaptation processes such as atrophy/hypertrophy as well as the variation in electrical transport properties of cellular membrane/cytoplasm/nuclear membrane/nucleoplasm.

Inhomogeneous ensembles of radical pairs in chemical compasses

NASA Astrophysics Data System (ADS)

Procopio, Maria; Ritz, Thorsten

2016-11-01

The biophysical basis for the ability of animals to detect the geomagnetic field and to use it for finding directions remains a mystery of sensory biology. One much debated hypothesis suggests that an ensemble of specialized light-induced radical pair reactions can provide the primary signal for a magnetic compass sensor. The question arises what features of such a radical pair ensemble could be optimized by evolution so as to improve the detection of the direction of weak magnetic fields. Here, we focus on the overlooked aspect of the noise arising from inhomogeneity of copies of biomolecules in a realistic biological environment. Such inhomogeneity leads to variations of the radical pair parameters, thereby deteriorating the signal arising from an ensemble and providing a source of noise. We investigate the effect of variations in hyperfine interactions between different copies of simple radical pairs on the directional response of a compass system. We find that the choice of radical pair parameters greatly influences how strongly the directional response of an ensemble is affected by inhomogeneity.

An isoline separating relatively warm from relatively cool wintertime forest surface temperatures for the southeastern United States

NASA Astrophysics Data System (ADS)

Wickham, J.; Wade, T. G.; Riitters, K. H.

2014-09-01

Forest-oriented climate mitigation policies promote forestation as a means to increase uptake of atmospheric carbon to counteract global warming. Some have pointed out that a carbon-centric forest policy may be overstated because it discounts biophysical aspects of the influence of forests on climate. In extra-tropical regions, many climate models have shown that forests tend to be warmer than grasslands and croplands because forest albedos tend to be lower than non-forest albedos. A lower forest albedo results in higher absorption of solar radiation and increased sensible warming that is not offset by the cooling effects of carbon uptake in extra-tropical regions. However, comparison of forest warming potential in the context of climate models is based on a coarse classification system of tropical, temperate, and boreal. There is considerable variation in climate within the broad latitudinal zonation of tropical, temperate, and boreal, and the relationship between biophysical (albedo) and biogeochemical (carbon uptake) mechanisms may not be constant within these broad zones. We compared wintertime forest and non-forest surface temperatures for the southeastern United States and found that forest surface temperatures shifted from being warmer than non-forest surface temperatures north of approximately 36°N to cooler south of 36°N. Our results suggest that the biophysical aspects of forests' influence on climate reinforce the biogeochemical aspects of forests' influence on climate south of 36°N. South of 36°N, both biophysical and biogeochemical properties of forests appear to support forestation as a climate mitigation policy. We also provide some quantitative evidence that evergreen forests tend to have cooler wintertime surface temperatures than deciduous forests that may be attributable to greater evapotranspiration rates.

Bio-physical characterisation of polynyas as a key foraging habitat for juvenile male southern elephant seals (Mirounga leonina) in Prydz Bay, East Antarctica

PubMed Central

Bestley, Sophie; Corney, Stuart; Welsford, Dirk; Labrousse, Sara; Sumner, Michael; Hindell, Mark

2017-01-01

Antarctic coastal polynyas are persistent open water areas in the sea ice zone, and regions of high biological productivity thought to be important foraging habitat for marine predators. This study quantified southern elephant seal (Mirounga leonina) habitat use within and around the polynyas of the Prydz Bay region (63°E– 88°E) in East Antarctica, and examined the bio-physical characteristics structuring polynyas as foraging habitat. Output from a climatological regional ocean model was used to provide context for in situ temperature-salinity vertical profiles collected by tagged elephant seals and to characterise the physical properties structuring polynyas. Biological properties were explored using remotely-sensed surface chlorophyll (Chl-a) and, qualitatively, historical fish assemblage data. Spatially gridded residence time of seals was examined in relation to habitat characteristics using generalized additive mixed models. The results showed clear polynya usage during early autumn and increasingly concentrated usage during early winter. Bathymetry, Chl-a, surface net heat flux (representing polynya location), and bottom temperature were identified as significant bio-physical predictors of the spatio-temporal habitat usage. The findings from this study confirm that the most important marine habitats for juvenile male southern elephant seals within Prydz Bay region are polynyas. A hypothesis exists regarding the seasonal evolution of primary productivity, coupling from surface to subsurface productivity and supporting elevated rates of secondary production in the upper water column during summer-autumn. An advancement to this hypothesis is proposed here, whereby this bio-physical coupling is likely to extend throughout the water column as it becomes fully convected during autumn-winter, to also promote pelagic-benthic linkages important for benthic foraging within polynyas. PMID:28902905

The bio-physics of condensation of divalent cations into the bacterial wall has implications for growth of Gram-positive bacteria.

PubMed

Rauch, Cyril; Cherkaoui, Mohammed; Egan, Sharon; Leigh, James

2017-02-01

The anionic-polyelectrolyte nature of the wall of Gram-positive bacteria has long been suspected to be involved in homeostasis of essential cations and bacterial growth. A better understanding of the coupling between the biophysics and the biology of the wall is essential to understand some key features at play in ion-homeostasis in this living system. We consider the wall as a polyelectrolyte gel and balance the long-range electrostatic repulsion within this structure against the penalty entropy required to condense cations around wall polyelectrolytes. The resulting equations define how cations interact physically with the wall and the characteristic time required for a cation to leave the wall and enter into the bacterium to enable its usage for bacterial metabolism and growth. The model was challenged against experimental data regarding growth of Gram-positive bacteria in the presence of varying concentration of divalent ions. The model explains qualitatively and quantitatively how divalent cations interact with the wall as well as how the biophysical properties of the wall impact on bacterial growth (in particular the initiation of bacterial growth). The interplay between polymer biophysics and the biology of Gram positive bacteria is defined for the first time as a new set of variables that contribute to the kinetics of bacterial growth. Providing an understanding of how bacteria capture essential metal cations in way that does not follow usual binding laws has implications when considering the control of such organisms and their ability to survive and grow in extreme environments. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Ontology of physics for biology: representing physical dependencies as a basis for biological processes

PubMed Central

2013-01-01

Background In prior work, we presented the Ontology of Physics for Biology (OPB) as a computational ontology for use in the annotation and representations of biophysical knowledge encoded in repositories of physics-based biosimulation models. We introduced OPB:Physical entity and OPB:Physical property classes that extend available spatiotemporal representations of physical entities and processes to explicitly represent the thermodynamics and dynamics of physiological processes. Our utilitarian, long-term aim is to develop computational tools for creating and querying formalized physiological knowledge for use by multiscale “physiome” projects such as the EU’s Virtual Physiological Human (VPH) and NIH’s Virtual Physiological Rat (VPR). Results Here we describe the OPB:Physical dependency taxonomy of classes that represent of the laws of classical physics that are the “rules” by which physical properties of physical entities change during occurrences of physical processes. For example, the fluid analog of Ohm’s law (as for electric currents) is used to describe how a blood flow rate depends on a blood pressure gradient. Hooke’s law (as in elastic deformations of springs) is used to describe how an increase in vascular volume increases blood pressure. We classify such dependencies according to the flow, transformation, and storage of thermodynamic energy that occurs during processes governed by the dependencies. Conclusions We have developed the OPB and annotation methods to represent the meaning—the biophysical semantics—of the mathematical statements of physiological analysis and the biophysical content of models and datasets. Here we describe and discuss our approach to an ontological representation of physical laws (as dependencies) and properties as encoded for the mathematical analysis of biophysical processes. PMID:24295137

Biophysical Properties of 9 KCNQ1 Mutations Associated with Long QT Syndrome (LQTS)

PubMed Central

Yang, Tao; Chung, Seo-Kyung; Zhang, Wei; Mullins, Jonathan G.L.; McCulley, Caroline H.; Crawford, Jackie; MacCormick, Judith; Eddy, Carey-Anne; Shelling, Andrew N.; French, John K.; Yang, Ping; Skinner, Jonathan R.; Roden, Dan M.; Rees, Mark I.

2009-01-01

Background Inherited long QT syndrome (LQTS) is characterized by prolonged QT interval on the EKG, syncope and sudden death due to ventricular arrhythmia. Causative mutations occur mostly in cardiac potassium and sodium channel subunit genes. Confidence in mutation pathogenicity is usually reached through family genotype-phenotype tracking, control population studies, molecular modelling and phylogenetic alignments, however, biophysical testing offers a higher degree of validating evidence. Methods and Results By using in-vitro electrophysiological testing of transfected mutant and wild-type LQTS constructs into Chinese Hamster Ovary cells, we investigated the biophysical properties of 9 KCNQ1 missense mutations (A46T, T265I, F269S, A302V, G316E, F339S, R360G, H455Y, and S546L) identified in a New Zealand based LQTS screening programme. We demonstrate through electrophysiology and molecular modeling that seven of the missense mutations have profound pathological dominant negative loss-of-function properties confirming their likely disease-causing nature. This supports the use of these mutations in diagnostic family screening. Two mutations (A46T, T265I) show suggestive evidence of pathogenicity within the experimental limits of biophysical testing, indicating that these variants are disease-causing via delayed or fast activation kinetics. Further investigation of the A46T family has revealed an inconsistent co-segregation of the variant with the clinical phenotype. Conclusions Electrophysiological characterisation should be used to validate LQTS pathogenicity of novel missense channelopathies. When such results are inconclusive, great care should be taken with genetic counselling and screening of such families, and alternative disease causing mechanisms should be considered. PMID:19808498

Trends in Biophysical Research and Their Implications for Medical Libraries

PubMed Central

Chen, Ching-chih

1973-01-01

This is a statistical survey of the trends in biophysical research as reflected by papers presented at four Biophysical Society (BPS) annual meetings between 1958 and 1972 and by the funding sources of the reported projects. The study reveals that biophysical research has grown quite substantially, particularly since 1968. Although biophysics is truly interdisciplinary, since 1968 there has been more pronounced emphasis on biomedically oriented problems and a tendency toward more specific and more highly specialized problems. Between 1958 and 1972, most biophysicists were academic researchers, 50% of whom were biomedical scientists. Over three quarters of the ongoing biophysical research projects during this period were supported by governmental agencies, and among them, the National Institutes of Health was the largest single funding source. PMID:4573970

Evaluation of the biophysical limitations on photosynthesis of four varietals of Brassica rapa

NASA Astrophysics Data System (ADS)

Pleban, J. R.; Mackay, D. S.; Aston, T.; Ewers, B.; Weinig, C.

2014-12-01

Evaluating performance of agricultural varietals can support the identification of genotypes that will increase yield and can inform management practices. The biophysical limitations of photosynthesis are amongst the key factors that necessitate evaluation. This study evaluated how four biophysical limitations on photosynthesis, stomatal response to vapor pressure deficit, maximum carboxylation rate by Rubisco (Ac), rate of photosynthetic electron transport (Aj) and triose phosphate use (At) vary between four Brassica rapa genotypes. Leaf gas exchange data was used in an ecophysiological process model to conduct this evaluation. The Terrestrial Regional Ecosystem Exchange Simulator (TREES) integrates the carbon uptake and utilization rate limiting factors for plant growth. A Bayesian framework integrated in TREES here used net A as the target to estimate the four limiting factors for each genotype. As a first step the Bayesian framework was used for outlier detection, with data points outside the 95% confidence interval of model estimation eliminated. Next parameter estimation facilitated the evaluation of how the limiting factors on A different between genotypes. Parameters evaluated included maximum carboxylation rate (Vcmax), quantum yield (ϕJ), the ratio between Vc-max and electron transport rate (J), and trios phosphate utilization (TPU). Finally, as trios phosphate utilization has been shown to not play major role in the limiting A in many plants, the inclusion of At in models was evaluated using deviance information criteria (DIC). The outlier detection resulted in a narrowing in the estimated parameter distributions allowing for greater differentiation of genotypes. Results show genotypes vary in the how limitations shape assimilation. The range in Vc-max , a key parameter in Ac, was 203.2 - 223.9 umol m-2 s-1 while the range in ϕJ, a key parameter in AJ, was 0.463 - 0.497 umol m-2 s-1. The added complexity of the TPU limitation did not improve model performance in the genotypes assessed based on DIC. By identifying how varietals differ in their biophysical limitations on photosynthesis genotype selection can be informed for agricultural goals. Further work aims at applying this approach to a fifth limiting factor on photosynthesis, mesophyll conductance.

Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data

USGS Publications Warehouse

Thenkabail, P.S.; Mariotto, I.; Gumma, M.K.; Middleton, E.M.; Landis, D.R.; Huemmrich, K.F.

2013-01-01

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy ~70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using ~20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was ~ 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysi- al characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI.

Exploring the biophysical properties of phytosterols in the plasma membrane for novel cancer prevention strategies.

PubMed

Fakih, Omar; Sanver, Didem; Kane, David; Thorne, James L

2018-05-03

Cancer is a global problem with no sign that incidences are reducing. The great costs associated with curing cancer, through developing novel treatments and applying patented therapies, is an increasing burden to developed and developing nations alike. These financial and societal problems will be alleviated by research efforts into prevention, or treatments that utilise off-patent or repurposed agents. Phytosterols are natural components of the diet found in an array of seeds, nuts and vegetables and have been added to several consumer food products for the management of cardio-vascular disease through their ability to lower LDL-cholesterol levels. In this review, we provide a connected view between the fields of structural biophysics and cellular and molecular biology to evaluate the growing evidence that phytosterols impair oncogenic pathways in a range of cancer types. The current state of understanding of how phytosterols alter the biophysical properties of plasma membrane is described, and the potential for phytosterols to be repurposed from cardio-vascular to oncology therapeutics. Through an overview of the types of biophysical and molecular biology experiments that have been performed to date, this review informs the reader of the molecular and biophysical mechanisms through which phytosterols could have anti-cancer properties via their interactions with the plasma cell membrane. We also outline emerging and under-explored areas such as computational modelling, improved biomimetic membranes and ex vivo tissue evaluation. Focus of future research in these areas should improve understanding, not just of phytosterols in cancer cell biology but also to give insights into the interaction between the plasma membrane and the genome. These fields are increasingly providing meaningful biological and clinical data but iterative experiments between molecular biology assays, biosynthetic membrane studies and computational membrane modelling improve and refine our understanding of the role of different sterol components of the plasma membrane. Copyright © 2018 Elsevier B.V. and Société Française de Biochimie et Biologie Moléculaire (SFBBM). All rights reserved.

Structural Refinement of Proteins by Restrained Molecular Dynamics Simulations with Non-interacting Molecular Fragments.

PubMed

Shen, Rong; Han, Wei; Fiorin, Giacomo; Islam, Shahidul M; Schulten, Klaus; Roux, Benoît

2015-10-01

The knowledge of multiple conformational states is a prerequisite to understand the function of membrane transport proteins. Unfortunately, the determination of detailed atomic structures for all these functionally important conformational states with conventional high-resolution approaches is often difficult and unsuccessful. In some cases, biophysical and biochemical approaches can provide important complementary structural information that can be exploited with the help of advanced computational methods to derive structural models of specific conformational states. In particular, functional and spectroscopic measurements in combination with site-directed mutations constitute one important source of information to obtain these mixed-resolution structural models. A very common problem with this strategy, however, is the difficulty to simultaneously integrate all the information from multiple independent experiments involving different mutations or chemical labels to derive a unique structural model consistent with the data. To resolve this issue, a novel restrained molecular dynamics structural refinement method is developed to simultaneously incorporate multiple experimentally determined constraints (e.g., engineered metal bridges or spin-labels), each treated as an individual molecular fragment with all atomic details. The internal structure of each of the molecular fragments is treated realistically, while there is no interaction between different molecular fragments to avoid unphysical steric clashes. The information from all the molecular fragments is exploited simultaneously to constrain the backbone to refine a three-dimensional model of the conformational state of the protein. The method is illustrated by refining the structure of the voltage-sensing domain (VSD) of the Kv1.2 potassium channel in the resting state and by exploring the distance histograms between spin-labels attached to T4 lysozyme. The resulting VSD structures are in good agreement with the consensus model of the resting state VSD and the spin-spin distance histograms from ESR/DEER experiments on T4 lysozyme are accurately reproduced.

Gulf of Maine Harmful Algal Bloom in summer 2005 – Part 2: Coupled Bio-physical Numerical Modeling

PubMed Central

He, Ruoying; McGillicuddy, Dennis J.; Keafer, Bruce A.; Anderson, Donald M.

2017-01-01

A coupled physical/biological modeling system was used to hindcast the 2005 Alexandrium fundyense bloom in the Gulf of Maine and investigate the relative importance of factors governing the bloom’s initiation and development. The coupled system consists of a state-of-the-art, free-surface primitive equation Regional Ocean Modeling System (ROMS) tailored for the Gulf of Maine (GOM) using a multi-nested configuration, and a population dynamics model for A. fundyense. The system was forced by realistic momentum and buoyancy fluxes, tides, river runoff, observed A. fundyense benthic cyst abundance, and climatological nutrient fields. Extensive comparisons were made between simulated (both physical and biological) fields and in-situ observations, revealing that the hindcast model is capable of reproducing the temporal evolution and spatial distribution of the 2005 bloom. Sensitivity experiments were then performed to distinguish the roles of three major factors hypothesized to contribute to the bloom: 1) the high abundance of cysts in western GOM sediments; 2) strong northeaster storms with prevailing downwelling-favorable winds; and 3) a large amount of fresh water input due to abundant rainfall and heavy snowmelt. Results suggested that the high abundance of cysts in western GOM was the primary factor of the 2005 bloom. Wind forcing was an important regulator, as episodic bursts of northeast winds caused onshore advection of offshore populations. These downwelling favorable winds accelerated the alongshore flow, resulting in transport of high cell concentrations into Massachusetts Bay. A large regional bloom would still have happened, however, even with normal or typical winds for that period. Anomalously high river runoff in 2005 resulted in stronger buoyant plumes/currents, which facilitated the transport of cell population to the western GOM. While affecting nearshore cell abundance in Massachusetts Bay, the buoyant plumes were confined near to the coast, and had limited impact on the gulf-wide bloom distribution. PMID:28366964

Bundling of elastic filaments induced by hydrodynamic interactions

NASA Astrophysics Data System (ADS)

Man, Yi; Page, William; Poole, Robert J.; Lauga, Eric

2017-12-01

Peritrichous bacteria swim in viscous fluids by rotating multiple helical flagellar filaments. As the bacterium swims forward, all its flagella rotate in synchrony behind the cell in a tight helical bundle. When the bacterium changes its direction, the flagellar filaments unbundle and randomly reorient the cell for a short period of time before returning to their bundled state and resuming swimming. This rapid bundling and unbundling is, at its heart, a mechanical process whereby hydrodynamic interactions balance with elasticity to determine the time-varying deformation of the filaments. Inspired by this biophysical problem, we present in this paper what is perhaps the simplest model of bundling whereby two or more straight elastic filaments immersed in a viscous fluid rotate about their centerline, inducing rotational flows which tend to bend the filaments around each other. We derive an integrodifferential equation governing the shape of the filaments resulting from mechanical balance in a viscous fluid at low Reynolds number. We show that such equation may be evaluated asymptotically analytically in the long-wavelength limit, leading to a local partial differential equation governed by a single dimensionless bundling number. A numerical study of the dynamics predicted by the model reveals the presence of two configuration instabilities with increasing bundling numbers: first to a crossing state where filaments touch at one point and then to a bundled state where filaments wrap along each other in a helical fashion. We also consider the case of multiple filaments and the unbundling dynamics. We next provide an intuitive physical model for the crossing instability and show that it may be used to predict analytically its threshold and adapted to address the transition to a bundling state. We then use a macroscale experimental implementation of the two-filament configuration in order to validate our theoretical predictions and obtain excellent agreement. This long-wavelength model of bundling will be applicable to other problems in biological physics and provides the groundwork for further, more realistic, models of flagellar bundling.

Functional identification of spike-processing neural circuits.

PubMed

Lazar, Aurel A; Slutskiy, Yevgeniy B

2014-02-01

We introduce a novel approach for a complete functional identification of biophysical spike-processing neural circuits. The circuits considered accept multidimensional spike trains as their input and comprise a multitude of temporal receptive fields and conductance-based models of action potential generation. Each temporal receptive field describes the spatiotemporal contribution of all synapses between any two neurons and incorporates the (passive) processing carried out by the dendritic tree. The aggregate dendritic current produced by a multitude of temporal receptive fields is encoded into a sequence of action potentials by a spike generator modeled as a nonlinear dynamical system. Our approach builds on the observation that during any experiment, an entire neural circuit, including its receptive fields and biophysical spike generators, is projected onto the space of stimuli used to identify the circuit. Employing the reproducing kernel Hilbert space (RKHS) of trigonometric polynomials to describe input stimuli, we quantitatively describe the relationship between underlying circuit parameters and their projections. We also derive experimental conditions under which these projections converge to the true parameters. In doing so, we achieve the mathematical tractability needed to characterize the biophysical spike generator and identify the multitude of receptive fields. The algorithms obviate the need to repeat experiments in order to compute the neurons' rate of response, rendering our methodology of interest to both experimental and theoretical neuroscientists.

Biophysical Characterization of Supported Lipid Bilayers Using Parallel Dual-Wavelength Surface Plasmon Resonance and Quartz Crystal Microbalance Measurements.

PubMed

Parkkila, Petteri; Elderdfi, Mohamed; Bunker, Alex; Viitala, Tapani

2018-06-25

Supported lipid bilayers (SLBs) have been used extensively as an effective model of biological membranes, in the context of in vitro biophysics research, and the membranes of liposomes, in the context of the development of nanoscale drug delivery devices. Despite numerous surface-sensitive techniques having been applied to their study, the comprehensive optical characterization of SLBs using surface plasmon resonance (SPR) has not been conducted. In this study, Fresnel multilayer analysis is utilized to effectively calculate layer parameters (thickness and refractive indices) with the aid of dual-wavelength and dispersion coefficient analysis, in which the linear change in the refractive index as a function of wavelength is assumed. Using complementary information from impedance-based quartz crystal microbalance experiments, biophysical properties, for example, area-per-lipid-molecule and the quantity of lipid-associated water molecules, are calculated for different lipid types and mixtures, one of which is representative of a raft-forming lipid mixture. It is proposed that the hydration layer beneath the bilayer is, in fact, an integral part of the measured optical signal. Also, the traditional Jung model analysis and the ratio of SPR responses are investigated in terms of assessing the structure of the lipid layer that is formed.

Biophysics: for HTS hit validation, chemical lead optimization, and beyond.

PubMed

Genick, Christine C; Wright, S Kirk

2017-09-01

There are many challenges to the drug discovery process, including the complexity of the target, its interactions, and how these factors play a role in causing the disease. Traditionally, biophysics has been used for hit validation and chemical lead optimization. With its increased throughput and sensitivity, biophysics is now being applied earlier in this process to empower target characterization and hit finding. Areas covered: In this article, the authors provide an overview of how biophysics can be utilized to assess the quality of the reagents used in screening assays, to validate potential tool compounds, to test the integrity of screening assays, and to create follow-up strategies for compound characterization. They also briefly discuss the utilization of different biophysical methods in hit validation to help avoid the resource consuming pitfalls caused by the lack of hit overlap between biophysical methods. Expert opinion: The use of biophysics early on in the drug discovery process has proven crucial to identifying and characterizing targets of complex nature. It also has enabled the identification and classification of small molecules which interact in an allosteric or covalent manner with the target. By applying biophysics in this manner and at the early stages of this process, the chances of finding chemical leads with novel mechanisms of action are increased. In the future, focused screens with biophysics as a primary readout will become increasingly common.

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

USGS Publications Warehouse

Mariotto, Isabella; Thenkabail, Prasad S.; Huete, Alfredo; Slonecker, E. Terrence; Platonov, Alexander

2013-01-01

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ1 (400–2500 nm) versus λ2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm).Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (> 90%) over MBBs data (varied between 45 and 84%).Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).

New horizons in Biophysics

PubMed Central

2011-01-01

This editorial celebrates the re-launch of PMC Biophysics previously published by PhysMath Central, in its new format as BMC Biophysics published by BioMed Central with an expanded scope and Editorial Board. BMC Biophysics will fill its own niche in the BMC series alongside complementary companion journals including BMC Bioinformatics, BMC Medical Physics, BMC Structural Biology and BMC Systems Biology. PMID:21595996

LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons.

PubMed

Lindén, Henrik; Hagen, Espen; Lęski, Szymon; Norheim, Eivind S; Pettersen, Klas H; Einevoll, Gaute T

2013-01-01

Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (≳500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.

Space Based Ornithology: On the Wings of Migration and Biophysics

NASA Technical Reports Server (NTRS)

Smith, James A.

2005-01-01

The study of bird migration on a global scale is one of the compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Space based technology offers new opportunities to shed understanding on the distribution and migration of organisms on the planet and their sensitivity to human disturbances and environmental changes. Migration is an incredibly diverse and complex behavior. A broad outline of space based research must address three fundamental questions: (1) where could birds be, i.e. what is their fundamental niche constrained by their biophysical limits? (2) where do we actually find birds, i.e. what is their realizable niche as modified by local or regional abiotic and biotic factors, and (3) how do they get there (and how do we know?), that is what are their migration patterns and associated mechanisms? Our working hypothesis is that individual organism biophysical models of energy and water balance, driven by satellite measurements of spatio-temporal gradients in climate and habitat, will help us to explain the variability in avian species richness and distribution. Dynamic state variable modeling provides one tool for studying bird migration across multiple scales and can be linked to mechanistic models describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. Further these models provide an ecological forecasting tool for science and application users to address what are the possible consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration.

Docosahexaenoic acid modifies the clustering and size of lipid rafts and the lateral organization and surface expression of MHC class I of EL4 cells.

PubMed

Shaikh, Saame Raza; Rockett, Benjamin Drew; Salameh, Muhammad; Carraway, Kristen

2009-09-01

An emerging molecular mechanism by which docosahexaenoic acid (DHA) exerts its effects is modification of lipid raft organization. The biophysical model, based on studies with liposomes, shows that DHA avoids lipid rafts because of steric incompatibility between DHA and cholesterol. The model predicts that DHA does not directly modify rafts; rather, it incorporates into nonrafts to modify the lateral organization and/or conformation of membrane proteins, such as the major histocompatibility complex (MHC) class I. Here, we tested predictions of the model at a cellular level by incorporating oleic acid, eicosapentaenoic acid (EPA), and DHA, compared with a bovine serum albumin (BSA) control, into the membranes of EL4 cells. Quantitative microscopy showed that DHA, but not EPA, treatment, relative to the BSA control diminished lipid raft clustering and increased their size. Approximately 30% of DHA was incorporated directly into rafts without changing the distribution of cholesterol between rafts and nonrafts. Quantification of fluorescence colocalization images showed that DHA selectively altered MHC class I lateral organization by increasing the fraction of the nonraft protein into rafts compared with BSA. Both DHA and EPA treatments increased antibody binding to MHC class I compared with BSA. Antibody titration showed that DHA and EPA did not change MHC I conformation but increased total surface levels relative to BSA. Taken together, our findings are not in agreement with the biophysical model. Therefore, we propose a model that reconciles contradictory viewpoints from biophysical and cellular studies to explain how DHA modifies lipid rafts on several length scales. Our study supports the notion that rafts are an important target of DHA's mode of action.

Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080

PubMed Central

Fischer, Günther; Shah, Mahendra; N. Tubiello, Francesco; van Velhuizen, Harrij

2005-01-01

A comprehensive assessment of the impacts of climate change on agro-ecosystems over this century is developed, up to 2080 and at a global level, albeit with significant regional detail. To this end an integrated ecological–economic modelling framework is employed, encompassing climate scenarios, agro-ecological zoning information, socio-economic drivers, as well as world food trade dynamics. Specifically, global simulations are performed using the FAO/IIASA agro-ecological zone model, in conjunction with IIASAs global food system model, using climate variables from five different general circulation models, under four different socio-economic scenarios from the intergovernmental panel on climate change. First, impacts of different scenarios of climate change on bio-physical soil and crop growth determinants of yield are evaluated on a 5′×5′ latitude/longitude global grid; second, the extent of potential agricultural land and related potential crop production is computed. The detailed bio-physical results are then fed into an economic analysis, to assess how climate impacts may interact with alternative development pathways, and key trends expected over this century for food demand and production, and trade, as well as key composite indices such as risk of hunger and malnutrition, are computed. This modelling approach connects the relevant bio-physical and socio-economic variables within a unified and coherent framework to produce a global assessment of food production and security under climate change. The results from the study suggest that critical impact asymmetries due to both climate and socio-economic structures may deepen current production and consumption gaps between developed and developing world; it is suggested that adaptation of agricultural techniques will be central to limit potential damages under climate change. PMID:16433094

Effects of damping on mode shapes, volume 1

NASA Technical Reports Server (NTRS)

Gates, R. M.

1977-01-01

Displacement, velocity, and acceleration admittances were calculated for a realistic NASTRAN structural model of space shuttle for three conditions: liftoff, maximum dynamic pressure and end of solid rocket booster burn. The realistic model of the orbiter, external tank, and solid rocket motors included the representation of structural joint transmissibilities by finite stiffness and damping elements. Methods developed to incorporate structural joints and their damping characteristics into a finite element model of the space shuttle, to determine the point damping parameters required to produce realistic damping in the primary modes, and to calculate the effect of distributed damping on structural resonances through the calculation of admittances.

Modeling of ultrasonic wave propagation in composite laminates with realistic discontinuity representation.

PubMed

Zelenyak, Andreea-Manuela; Schorer, Nora; Sause, Markus G R

2018-02-01

This paper presents a method for embedding realistic defect geometries of a fiber reinforced material in a finite element modeling environment in order to simulate active ultrasonic inspection. When ultrasonic inspection is used experimentally to investigate the presence of defects in composite materials, the microscopic defect geometry may cause signal characteristics that are difficult to interpret. Hence, modeling of this interaction is key to improve our understanding and way of interpreting the acquired ultrasonic signals. To model the true interaction of the ultrasonic wave field with such defect structures as pores, cracks or delamination, a realistic three dimensional geometry reconstruction is required. We present a 3D-image based reconstruction process which converts computed tomography data in adequate surface representations ready to be embedded for processing with finite element methods. Subsequent modeling using these geometries uses a multi-scale and multi-physics simulation approach which results in quantitative A-Scan ultrasonic signals which can be directly compared with experimental signals. Therefore, besides the properties of the composite material, a full transducer implementation, piezoelectric conversion and simultaneous modeling of the attached circuit is applied. Comparison between simulated and experimental signals provides very good agreement in electrical voltage amplitude and the signal arrival time and thus validates the proposed modeling approach. Simulating ultrasound wave propagation in a medium with a realistic shape of the geometry clearly shows a difference in how the disturbance of the waves takes place and finally allows more realistic modeling of A-scans. Copyright © 2017 Elsevier B.V. All rights reserved.

Realistic micromechanical modeling and simulation of two-phase heterogeneous materials

NASA Astrophysics Data System (ADS)

Sreeranganathan, Arun

This dissertation research focuses on micromechanical modeling and simulations of two-phase heterogeneous materials exhibiting anisotropic and non-uniform microstructures with long-range spatial correlations. Completed work involves development of methodologies for realistic micromechanical analyses of materials using a combination of stereological techniques, two- and three-dimensional digital image processing, and finite element based modeling tools. The methodologies are developed via its applications to two technologically important material systems, namely, discontinuously reinforced aluminum composites containing silicon carbide particles as reinforcement, and boron modified titanium alloys containing in situ formed titanium boride whiskers. Microstructural attributes such as the shape, size, volume fraction, and spatial distribution of the reinforcement phase in these materials were incorporated in the models without any simplifying assumptions. Instrumented indentation was used to determine the constitutive properties of individual microstructural phases. Micromechanical analyses were performed using realistic 2D and 3D models and the results were compared with experimental data. Results indicated that 2D models fail to capture the deformation behavior of these materials and 3D analyses are required for realistic simulations. The effect of clustering of silicon carbide particles and associated porosity on the mechanical response of discontinuously reinforced aluminum composites was investigated using 3D models. Parametric studies were carried out using computer simulated microstructures incorporating realistic microstructural attributes. The intrinsic merit of this research is the development and integration of the required enabling techniques and methodologies for representation, modeling, and simulations of complex geometry of microstructures in two- and three-dimensional space facilitating better understanding of the effects of microstructural geometry on the mechanical behavior of materials.

Chained Bell Inequality Experiment with High-Efficiency Measurements

NASA Astrophysics Data System (ADS)

Tan, T. R.; Wan, Y.; Erickson, S.; Bierhorst, P.; Kienzler, D.; Glancy, S.; Knill, E.; Leibfried, D.; Wineland, D. J.

2017-03-01

We report correlation measurements on two 9Be+ ions that violate a chained Bell inequality obeyed by any local-realistic theory. The correlations can be modeled as derived from a mixture of a local-realistic probabilistic distribution and a distribution that violates the inequality. A statistical framework is formulated to quantify the local-realistic fraction allowable in the observed distribution without the fair-sampling or independent-and-identical-distributions assumptions. We exclude models of our experiment whose local-realistic fraction is above 0.327 at the 95% confidence level. This bound is significantly lower than 0.586, the minimum fraction derived from a perfect Clauser-Horne-Shimony-Holt inequality experiment. Furthermore, our data provide a device-independent certification of the deterministically created Bell states.

Regression approach to non-invasive determination of bilirubin in neonatal blood

NASA Astrophysics Data System (ADS)

Lysenko, S. A.; Kugeiko, M. M.

2012-07-01

A statistical ensemble of structural and biophysical parameters of neonatal skin was modeled based on experimental data. Diffuse scattering coefficients of the skin in the visible and infrared regions were calculated by applying a Monte-Carlo method to each realization of the ensemble. The potential accuracy of recovering the bilirubin concentration in dermis (which correlates closely with that in blood) was estimated from spatially resolved spectrometric measurements of diffuse scattering. The possibility to determine noninvasively the bilirubin concentration was shown by measurements of diffuse scattering at λ = 460, 500, and 660 nm at three source-detector separations under conditions of total variability of the skin biophysical parameters.

Radoslav K. Andjus (1926-2003): a brief summary of his life and work.

PubMed

Stojilkovic, Stanko S; Zivadinović, Dragoslava; Hegedis, Aleksandar; Marjanović, Marina

2005-06-01

Radoslav K. Andjus was a professor of physiology and biophysics at the University of Belgrade, Serbia, from 1953 to 1992. He was an elected member of the Serbian Academy of Sciences and Arts, the International Academy of Astronautics, and the Montenegrin Academy of Sciences and Arts. He published over 190 papers in domestic and international journals and three textbooks. The main field of his research was thermophysiology. He studied hypothermia, suspended animation and resuscitation, hibernation and biological rhythms, temperature adaptation and acclimation, and cryoprotection. Professor Andjus also contributed significantly to the fields of brain metabolism, endocrinology, electroretinography, as well as biophysical modeling and theoretical biology.

Biophysical and economic limits to negative CO2 emissions

NASA Astrophysics Data System (ADS)

Smith, Pete; Davis, Steven J.; Creutzig, Felix; Fuss, Sabine; Minx, Jan; Gabrielle, Benoit; Kato, Etsushi; Jackson, Robert B.; Cowie, Annette; Kriegler, Elmar; van Vuuren, Detlef P.; Rogelj, Joeri; Ciais, Philippe; Milne, Jennifer; Canadell, Josep G.; McCollum, David; Peters, Glen; Andrew, Robbie; Krey, Volker; Shrestha, Gyami; Friedlingstein, Pierre; Gasser, Thomas; Grübler, Arnulf; Heidug, Wolfgang K.; Jonas, Matthias; Jones, Chris D.; Kraxner, Florian; Littleton, Emma; Lowe, Jason; Moreira, José Roberto; Nakicenovic, Nebojsa; Obersteiner, Michael; Patwardhan, Anand; Rogner, Mathis; Rubin, Ed; Sharifi, Ayyoob; Torvanger, Asbjørn; Yamagata, Yoshiki; Edmonds, Jae; Yongsung, Cho

2016-01-01

To have a >50% chance of limiting warming below 2 °C, most recent scenarios from integrated assessment models (IAMs) require large-scale deployment of negative emissions technologies (NETs). These are technologies that result in the net removal of greenhouse gases from the atmosphere. We quantify potential global impacts of the different NETs on various factors (such as land, greenhouse gas emissions, water, albedo, nutrients and energy) to determine the biophysical limits to, and economic costs of, their widespread application. Resource implications vary between technologies and need to be satisfactorily addressed if NETs are to have a significant role in achieving climate goals.

Measurement of surface physical properties and radiation balance for KUREX-91 study

NASA Technical Reports Server (NTRS)

Walter-Shea, Elizabeth A.; Blad, Blaine L.; Mesarch, Mark A.; Hays, Cynthia J.

1992-01-01

Biophysical properties and radiation balance components were measured at the Streletskaya Steppe Reserve of the Russian Republic in July 1991. Steppe vegetation parameters characterized include leaf area index (LAI), leaf angle distribution, mean tilt angle, canopy height, leaf spectral properties, leaf water potential, fraction of absorbed photosynthetically active radiation (APAR), and incoming and outgoing shortwave and longwave radiation. Research results, biophysical parameters, radiation balance estimates, and sun-view geometry effects on estimating APAR are discussed. Incoming and outgoing radiation streams are estimated using bidirectional spectral reflectances and bidirectional thermal emittances. Good agreement between measured and modeled estimates of the radiation balance were obtained.

Mechanisms controlling primary and new production in a global ecosystem model - Part I: Validation of the biological simulation

NASA Astrophysics Data System (ADS)

Popova, E. E.; Coward, A. C.; Nurser, G. A.; de Cuevas, B.; Fasham, M. J. R.; Anderson, T. R.

2006-12-01

A global general circulation model coupled to a simple six-compartment ecosystem model is used to study the extent to which global variability in primary and export production can be realistically predicted on the basis of advanced parameterizations of upper mixed layer physics, without recourse to introducing extra complexity in model biology. The "K profile parameterization" (KPP) scheme employed, combined with 6-hourly external forcing, is able to capture short-term periodic and episodic events such as diurnal cycling and storm-induced deepening. The model realistically reproduces various features of global ecosystem dynamics that have been problematic in previous global modelling studies, using a single generic parameter set. The realistic simulation of deep convection in the North Atlantic, and lack of it in the North Pacific and Southern Oceans, leads to good predictions of chlorophyll and primary production in these contrasting areas. Realistic levels of primary production are predicted in the oligotrophic gyres due to high frequency external forcing of the upper mixed layer (accompanying paper Popova et al., 2006) and novel parameterizations of zooplankton excretion. Good agreement is shown between model and observations at various JGOFS time series sites: BATS, KERFIX, Papa and HOT. One exception is the northern North Atlantic where lower grazing rates are needed, perhaps related to the dominance of mesozooplankton there. The model is therefore not globally robust in the sense that additional parameterizations are needed to realistically simulate ecosystem dynamics in the North Atlantic. Nevertheless, the work emphasises the need to pay particular attention to the parameterization of mixed layer physics in global ocean ecosystem modelling as a prerequisite to increasing the complexity of ecosystem models.

Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference

PubMed Central

Hines, Keegan E.; Bankston, John R.; Aldrich, Richard W.

2015-01-01

The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. PMID:25650922

Assessment of economic and water quality impacts of land use change using a simple bioeconomic model.

PubMed

Bhattarai, Gandhi; Srivastava, Puneet; Marzen, Luke; Hite, Diane; Hatch, Upton

2008-07-01

The objective of this study is to assess the economic and water quality impact of land use change in a small watershed in the Wiregrass region of Alabama. The study compares changes in water quality and revenue from agricultural and timber production due to changes in land use between years 1992 and 2001. The study was completed in two stages. In the first stage, a biophysical model was used to estimate the effect of land use change on nitrogen and phosphorus runoff and sediment deposition in the main channel; in the second stage, farm enterprise budgeting tools were used to estimate the economic returns for the changes in land use condition. Both biophysical and economic results are discussed, and a case for complex optimization to develop a decision support system is presented.

Interactions and diffusion in fine-stranded β-lactoglobulin gels determined via FRAP and binding.

PubMed

Schuster, Erich; Hermansson, Anne-Marie; Ohgren, Camilla; Rudemo, Mats; Lorén, Niklas

2014-01-07

The effects of electrostatic interactions and obstruction by the microstructure on probe diffusion were determined in positively charged hydrogels. Probe diffusion in fine-stranded gels and solutions of β-lactoglobulin at pH 3.5 was determined using fluorescence recovery after photobleaching (FRAP) and binding, which is widely used in biophysics. The microstructures of the β-lactoglobulin gels were characterized using transmission electron microscopy. The effects of probe size and charge (negatively charged Na2-fluorescein (376Da) and weakly anionic 70kDa FITC-dextran), probe concentration (50 to 200 ppm), and β-lactoglobulin concentration (9% to 12% w/w) on the diffusion properties and the electrostatic interaction between the negatively charged probes and the positively charged gels or solutions were evaluated. The results show that the diffusion of negatively charged Na2-fluorescein is strongly influenced by electrostatic interactions in the positively charged β-lactoglobulin systems. A linear relationship between the pseudo-on binding rate constant and the β-lactoglobulin concentration for three different probe concentrations was found. This validates an important assumption of existing biophysical FRAP and binding models, namely that the pseudo-on binding rate constant equals the product of the molecular binding rate constant and the concentration of the free binding sites. Indicators were established to clarify whether FRAP data should be analyzed using a binding-diffusion model or an obstruction-diffusion model. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Social and Biophysical Predictors of Public Perceptions of Extreme Fires

NASA Astrophysics Data System (ADS)

Hall, T. E.; Kooistra, C. M.; Paveglio, T.; Gress, S.; Smith, A. M.

2013-12-01

To date, what constitutes an 'extreme' fire has been approached separately by biophysical and social scientists. Research on the biophysical characteristics of fires has identified potential dimensions of extremity, including fire size and vegetation mortality. On the social side, factors such as the degree of immediate impact to one's life and property or the extent of social disruption in the community contribute to a perception of extremity. However, some biophysical characteristics may also contribute to perceptions of extremity, including number of simultaneous ignitions, rapidity of fire spread, atypical fire behavior, and intensity of smoke. Perceptions of these impacts can vary within and across communities, but no studies to date have investigated such perceptions in a comprehensive way. In this study, we address the question, to what extent is the magnitude of impact of fires on WUI residents' well-being explained by measurable biophysical characteristics of the fire and subjective evaluations of the personal and community-level impacts of the fire? We bring together diverse strands of psychological theory, including landscape perception, mental models, risk perception, and community studies. The majority of social science research on fires has been in the form of qualitative case studies, and our study is methodologically unique by using a nested design (hierarchical modeling) to enable generalizable conclusions across a wide range of fires and human communities. We identified fires that burned in 2011 or 2012 in the northern Rocky Mountain region that were at least 1,000 acres and that intersected (within 15 km) urban clusters or identified Census places. For fires where an adequately large number of households was located in proximity to the fire, we drew random samples of approximately 150 individuals for each fire. We used a hybrid internet (Qualtrics) and mail survey, following the Dillman method, to measure individual perceptions. We developed two composite dependent variables: (1) subjective perceptions of the atypicality of the fire; and (2) perceptions of the fire's impact to individual and community well-being. The impact measures were adapted from the hazards and disasters literature and used a multi-item measure of emotional response during and immediately after the fire. Independent variables included both biophysical characteristics of each fire (such as size, duration, and burn severity), obtained from remotely sensed imagery, and perceptual variables measured in the survey. All measures were pilot tested for adequate psychometric properties using a sample of 150 individuals from an on-line panel who had been affected by a wildfire within the past two years. Factor analysis techniques will be used to reduce the data to latent constructs for use in regression modeling. Hierarchical linear modeling will be used to identify factors predicting the impact of fires on individuals (level 1) and whether those factors differ by fire (level 2). Our study provides a unique interdisciplinary perspective on extreme disturbance events, and findings will help land managers and community leaders anticipate how individuals may respond to future fires, as well as how to ameliorate the negative impacts of those fires.

New parsimonious simulation methods and tools to assess future food and environmental security of farm populations

PubMed Central

Antle, John M.; Stoorvogel, Jetse J.; Valdivia, Roberto O.

2014-01-01

This article presents conceptual and empirical foundations for new parsimonious simulation models that are being used to assess future food and environmental security of farm populations. The conceptual framework integrates key features of the biophysical and economic processes on which the farming systems are based. The approach represents a methodological advance by coupling important behavioural processes, for example, self-selection in adaptive responses to technological and environmental change, with aggregate processes, such as changes in market supply and demand conditions or environmental conditions as climate. Suitable biophysical and economic data are a critical limiting factor in modelling these complex systems, particularly for the characterization of out-of-sample counterfactuals in ex ante analyses. Parsimonious, population-based simulation methods are described that exploit available observational, experimental, modelled and expert data. The analysis makes use of a new scenario design concept called representative agricultural pathways. A case study illustrates how these methods can be used to assess food and environmental security. The concluding section addresses generalizations of parametric forms and linkages of regional models to global models. PMID:24535388

Interactions between marine biota and ENSO: a conceptual model analysis

NASA Astrophysics Data System (ADS)

Heinemann, M.; Timmermann, A.; Feudel, U.

2011-01-01

We develop a conceptual coupled atmosphere-ocean-ecosystem model for the tropical Pacific to investigate the interaction between marine biota and the El Niño-Southern Oscillation (ENSO). Ocean and atmosphere are represented by a two-box model for the equatorial Pacific cold tongue and the warm pool, including a simplified mixed layer scheme. Marine biota are represented by a three-component (nutrient, phytoplankton, and zooplankton) ecosystem model. The atmosphere-ocean model exhibits an oscillatory state which qualitatively captures the main physics of ENSO. During an ENSO cycle, the variation of nutrient upwelling, and, to a small extent, the variation of photosynthetically available radiation force an ecosystem oscillation. The simplified ecosystem in turn, due to the effect of phytoplankton on the absorption of shortwave radiation in the water column, leads to (1) a warming of the tropical Pacific, (2) a reduction of the ENSO amplitude, and (3) a prolongation of the ENSO period. We qualitatively investigate these bio-physical coupling mechanisms using continuation methods. It is demonstrated that bio-physical coupling may play a considerable role in modulating ENSO variability.

New parsimonious simulation methods and tools to assess future food and environmental security of farm populations.

PubMed

Antle, John M; Stoorvogel, Jetse J; Valdivia, Roberto O

2014-04-05

This article presents conceptual and empirical foundations for new parsimonious simulation models that are being used to assess future food and environmental security of farm populations. The conceptual framework integrates key features of the biophysical and economic processes on which the farming systems are based. The approach represents a methodological advance by coupling important behavioural processes, for example, self-selection in adaptive responses to technological and environmental change, with aggregate processes, such as changes in market supply and demand conditions or environmental conditions as climate. Suitable biophysical and economic data are a critical limiting factor in modelling these complex systems, particularly for the characterization of out-of-sample counterfactuals in ex ante analyses. Parsimonious, population-based simulation methods are described that exploit available observational, experimental, modelled and expert data. The analysis makes use of a new scenario design concept called representative agricultural pathways. A case study illustrates how these methods can be used to assess food and environmental security. The concluding section addresses generalizations of parametric forms and linkages of regional models to global models.

Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI

PubMed Central

Farooq, Hamza; Xu, Junqian; Nam, Jung Who; Keefe, Daniel F.; Yacoub, Essa; Georgiou, Tryphon; Lenglet, Christophe

2016-01-01

Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data. PMID:27982056

Computational models of O-LM cells are recruited by low or high theta frequency inputs depending on h-channel distributions

PubMed Central

Sekulić, Vladislav; Skinner, Frances K

2017-01-01

Although biophysical details of inhibitory neurons are becoming known, it is challenging to map these details onto function. Oriens-lacunosum/moleculare (O-LM) cells are inhibitory cells in the hippocampus that gate information flow, firing while phase-locked to theta rhythms. We build on our existing computational model database of O-LM cells to link model with function. We place our models in high-conductance states and modulate inhibitory inputs at a wide range of frequencies. We find preferred spiking recruitment of models at high (4–9 Hz) or low (2–5 Hz) theta depending on, respectively, the presence or absence of h-channels on their dendrites. This also depends on slow delayed-rectifier potassium channels, and preferred theta ranges shift when h-channels are potentiated by cyclic AMP. Our results suggest that O-LM cells can be differentially recruited by frequency-modulated inputs depending on specific channel types and distributions. This work exposes a strategy for understanding how biophysical characteristics contribute to function. DOI: http://dx.doi.org/10.7554/eLife.22962.001 PMID:28318488

Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions

PubMed Central

Vazquez-Anderson, Jorge; Mihailovic, Mia K.; Baldridge, Kevin C.; Reyes, Kristofer G.; Haning, Katie; Cho, Seung Hee; Amador, Paul; Powell, Warren B.

2017-01-01

Abstract Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA–RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA–RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA–mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs. PMID:28334800

Mathematical modeling in realistic mathematics education

NASA Astrophysics Data System (ADS)

Riyanto, B.; Zulkardi; Putri, R. I. I.; Darmawijoyo

2017-12-01

The purpose of this paper is to produce Mathematical modelling in Realistics Mathematics Education of Junior High School. This study used development research consisting of 3 stages, namely analysis, design and evaluation. The success criteria of this study were obtained in the form of local instruction theory for school mathematical modelling learning which was valid and practical for students. The data were analyzed using descriptive analysis method as follows: (1) walk through, analysis based on the expert comments in the expert review to get Hypothetical Learning Trajectory for valid mathematical modelling learning; (2) analyzing the results of the review in one to one and small group to gain practicality. Based on the expert validation and students’ opinion and answers, the obtained mathematical modeling problem in Realistics Mathematics Education was valid and practical.

A way forward for fire-caused tree mortality prediction: Modeling a physiological consequence of fire

Treesearch

Kathleen L. Kavanaugh; Matthew B. Dickinson; Anthony S. Bova

2010-01-01

Current operational methods for predicting tree mortality from fire injury are regression-based models that only indirectly consider underlying causes and, thus, have limited generality. A better understanding of the physiological consequences of tree heating and injury are needed to develop biophysical process models that can make predictions under changing or novel...

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

Modeling coupled interactions of carbon, water, and ozone exchange between terrestrial ecosystems and the atmosphere

Treesearch

Ned Nikolova; Karl F. Zeller

2003-01-01

A new biophysical model (FORFLUX) is presented to study the simultaneous exchange of ozone, carbon dioxide, and water vapor between terrestrial ecosystems and the atmosphere. The model mechanistically couples all major processes controlling ecosystem flows trace gases and water implementing recent concepts in plant eco-physiology, micrometeorology, and soil hydrology....

Improving the spatial representation of soil properties and hydrology using topographically derived initialization processes in the SWAT model

USDA-ARS?s Scientific Manuscript database

Topography exerts critical controls on many hydrologic, geomorphologic, and environmental biophysical processes. Unfortunately many watershed modeling systems use topography only to define basin boundaries and stream channels and do not explicitly account for the topographic controls on processes su...

A remote-sensing driven tool for estimating crop stress and yields

USDA-ARS?s Scientific Manuscript database

Biophysical crop simulation models are normally forced with precipitation data recorded with either gages or ground-based radar. However, ground based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would...

Integrating social science into empirical models of coupled human and natural systems

Treesearch

Jeffrey D. Kline; Eric M. White; A Paige Fischer; Michelle M. Steen-Adams; Susan Charnley; Christine S. Olsen; Thomas A. Spies; John D. Bailey

2017-01-01

Coupled human and natural systems (CHANS) research highlights reciprocal interactions (or feedbacks) between biophysical and socioeconomic variables to explain system dynamics and resilience. Empirical models often are used to test hypotheses and apply theory that represent human behavior. Parameterizing reciprocal interactions presents two challenges for social...

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

NASA Astrophysics Data System (ADS)

Verrelst, Jochem; Malenovský, Zbyněk; Van der Tol, Christiaan; Camps-Valls, Gustau; Gastellu-Etchegorry, Jean-Philippe; Lewis, Philip; North, Peter; Moreno, Jose

2018-06-01

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

Conjugate problems of transport phenomena under quasi-steady microaccelerations in realistic spaceflight.

PubMed

Polezhaev, V I; Nikitin, S A

2009-04-01

A new model for spatial convective transport processes conjugated with the measured or calculated realistic quasi-steady microaccelerations is presented. Rotation around the mass center, including accelerated rotation, gravity gradient, and aerodynamical drag are taken into account. New results of the effect on mixing and concentration inhomogeneities of the elementary convective processes are presented. The mixing problem in spacecraft enclosures, concentration inhomogeneities due to convection induced by body forces in realistic spaceflight, and the coupling of this kind of convection with thermocapillary convection on the basis of this model are discussed.

Investigation of Periodic-Disturbance Identification and Rejection in Spacecraft

DTIC Science & Technology

2006-08-01

linear theory. Therefore, it is of interest to examine its efficacy on the current nonlinear spacecraft model. In addition, the robustness of the...School, Monterey, California 93943 Spacecraft periodic-disturbance rejection using a realistic spacecraft hardware simulator and its associated models...is investigated. The effectiveness of the dipole-type disturbance rejection filter on the current realistic nonlinear rigid-body spacecraft model is

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

PubMed Central

Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

2013-01-01

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

A method of emotion contagion for crowd evacuation

NASA Astrophysics Data System (ADS)

Cao, Mengxiao; Zhang, Guijuan; Wang, Mengsi; Lu, Dianjie; Liu, Hong

2017-10-01

The current evacuation model does not consider the impact of emotion and personality on crowd evacuation. Thus, there is large difference between evacuation results and the real-life behavior of the crowd. In order to generate more realistic crowd evacuation results, we present a method of emotion contagion for crowd evacuation. First, we combine OCEAN (Openness, Extroversion, Agreeableness, Neuroticism, Conscientiousness) model and SIS (Susceptible Infected Susceptible) model to construct the P-SIS (Personalized SIS) emotional contagion model. The P-SIS model shows the diversity of individuals in crowd effectively. Second, we couple the P-SIS model with the social force model to simulate emotional contagion on crowd evacuation. Finally, the photo-realistic rendering method is employed to obtain the animation of crowd evacuation. Experimental results show that our method can simulate crowd evacuation realistically and has guiding significance for crowd evacuation in the emergency circumstances.

Specifications of insilicoML 1.0: a multilevel biophysical model description language.

PubMed

Asai, Yoshiyuki; Suzuki, Yasuyuki; Kido, Yoshiyuki; Oka, Hideki; Heien, Eric; Nakanishi, Masao; Urai, Takahito; Hagihara, Kenichi; Kurachi, Yoshihisa; Nomura, Taishin

2008-12-01

An extensible markup language format, insilicoML (ISML), version 0.1, describing multi-level biophysical models has been developed and available in the public domain. ISML is fully compatible with CellML 1.0, a model description standard developed by the IUPS Physiome Project, for enhancing knowledge integration and model sharing. This article illustrates the new specifications of ISML 1.0 that largely extend the capability of ISML 0.1. ISML 1.0 can describe various types of mathematical models, including ordinary/partial differential/difference equations representing the dynamics of physiological functions and the geometry of living organisms underlying the functions. ISML 1.0 describes a model using a set of functional elements (modules) each of which can specify mathematical expressions of the functions. Structural and logical relationships between any two modules are specified by edges, which allow modular, hierarchical, and/or network representations of the model. The role of edge-relationships is enriched by key words in order for use in constructing a physiological ontology. The ontology is further improved by the traceability of history of the model's development and by linking between different ISML models stored in the model's database using meta-information. ISML 1.0 is designed to operate with a model database and integrated environments for model development and simulations for knowledge integration and discovery.

Challenges for modeling global gene regulatory networks during development: insights from Drosophila.

PubMed

Wilczynski, Bartek; Furlong, Eileen E M

2010-04-15

Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative "coarse-grain" models operating at the gene level to very "fine-grain" quantitative models operating at the biophysical "transcription factor-DNA level". Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review, we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well-studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Building biophysics in mid-century China: the University of Science and Technology of China.

PubMed

Luk, Yi Lai Christine

2015-01-01

Biophysics has been either an independent discipline or an element of another discipline in the United States, but it has always been recognized as a stand-alone discipline in the People's Republic of China (PRC) since 1949. To inquire into this apparent divergence, this paper investigates the formational history of biophysics in China by examining the early institutional history of one of the best-known and prestigious science and technology universities in the PRC, the University of Science and Technology of China (USTC). By showing how the university and its biophysics program co-evolved with national priorities from the school's founding in 1958 to the eve of the Cultural Revolution in 1966, the purpose of this paper is to assess the development of a scientific discipline in the context of national demands and institutional politics. Specific materials for analysis include the school's admission policies, curricula, students' dissertations, and research program. To further contextualize the institutional setting of Chinese biophysics, this paper begins with a general history of proto-biophysical institutions in China during the Nationalist-Communist transitional years. This paper could be of interest to historians wanting to know more about the origin of the biophysics profession in China, and in particular how research areas that constitute biophysics changed in tandem with socio-political contingencies.

Historical and Critical Review on Biophysical Economics

NASA Astrophysics Data System (ADS)

Adigüzel, Yekbun

2016-07-01

Biophysical economics is initiated with the long history of the relation of economics with ecological basis and biophysical perspectives of the physiocrats. It inherently has social, economic, biological, environmental, natural, physical, and scientific grounds. Biological entities in economy like the resources, consumers, populations, and parts of production systems, etc. could all be dealt by biophysical economics. Considering this wide scope, current work is a “biophysical economics at a glance” rather than a comprehensive review of the full range of topics that may just be adequately covered in a book-length work. However, the sense of its wide range of applications is aimed to be provided to the reader in this work. Here, modern approaches and biophysical growth theory are presented after the long history and an overview of the concepts in biophysical economics. Examples of the recent studies are provided at the end with discussions. This review is also related to the work by Cleveland, “Biophysical Economics: From Physiocracy to Ecological Economics and Industrial Ecology” [C. J. Cleveland, in Advances in Bioeconomics and Sustainability: Essay in Honor of Nicholas Gerogescu-Roegen, eds. J. Gowdy and K. Mayumi (Edward Elgar Publishing, Cheltenham, England, 1999), pp. 125-154.]. Relevant parts include critics and comments on the presented concepts in a parallelized fashion with the Cleveland’s work.

A Linear Stochastic Dynamical Model of ENSO. Part II: Analysis.

NASA Astrophysics Data System (ADS)

Thompson, C. J.; Battisti, D. S.

2001-02-01

In this study the behavior of a linear, intermediate model of ENSO is examined under stochastic forcing. The model was developed in a companion paper (Part I) and is derived from the Zebiak-Cane ENSO model. Four variants of the model are used whose stabilities range from slightly damped to moderately damped. Each model is run as a simulation while being perturbed by noise that is uncorrelated (white) in space and time. The statistics of the model output show the moderately damped models to be more realistic than the slightly damped models. The moderately damped models have power spectra that are quantitatively quite similar to observations, and a seasonal pattern of variance that is qualitatively similar to observations. All models produce ENSOs that are phase locked to the annual cycle, and all display the `spring barrier' characteristic in their autocorrelation patterns, though in the models this `barrier' occurs during the summer and is less intense than in the observations (inclusion of nonlinear effects is shown to partially remedy this deficiency). The more realistic models also show a decadal variability in the lagged autocorrelation pattern that is qualitatively similar to observations.Analysis of the models shows that the greatest part of the variability comes from perturbations that project onto the first singular vector, which then grow rapidly into the ENSO mode. Essentially, the model output represents many instances of the ENSO mode, with random phase and amplitude, stimulated by the noise through the optimal transient growth of the singular vectors.The limit of predictability for each model is calculated and it is shown that the more realistic (moderately damped) models have worse potential predictability (9-15 months) than the deterministic chaotic models that have been studied widely in the literature. The predictability limits are strongly correlated with the stability of the models' ENSO mode-the more highly damped models having much shorter limits of predictability. A comparison of the two most realistic models shows that even though these models have similar statistics, they have very different predictability limits. The models have a strong seasonal dependence to their predictability limits.The results of this study (with the companion paper) suggest that the linear, stable dynamical model of ENSO is indeed a plausible hypothesis for the observed ENSO. With very reasonable levels of stochastic forcing, the model produces realistic levels of variance, has a realistic spectrum, and qualitatively reproduces the observed seasonal pattern of variance, the autocorrelation pattern, and the ENSO-like decadal variability.

Location, location, location

NASA Astrophysics Data System (ADS)

van der Hilst, Floor

2018-03-01

The sustainability of biomass production for energy depends on site-specific biophysical and socio-economic conditions. New research using high-resolution ecosystem process modelling shows the trade-offs between economic and environmental performance of biomass production for an ethanol biorefinery.

Molecular and Cellular Biophysics

NASA Astrophysics Data System (ADS)

Jackson, Meyer B.

2006-01-01

Molecular and Cellular Biophysics provides advanced undergraduate and graduate students with a foundation in the basic concepts of biophysics. Students who have taken physical chemistry and calculus courses will find this book an accessible and valuable aid in learning how these concepts can be used in biological research. The text provides a rigorous treatment of the fundamental theories in biophysics and illustrates their application with examples. Conformational transitions of proteins are studied first using thermodynamics, and subsequently with kinetics. Allosteric theory is developed as the synthesis of conformational transitions and association reactions. Basic ideas of thermodynamics and kinetics are applied to topics such as protein folding, enzyme catalysis and ion channel permeation. These concepts are then used as the building blocks in a treatment of membrane excitability. Through these examples, students will gain an understanding of the general importance and broad applicability of biophysical principles to biological problems. Offers a unique synthesis of concepts across a wide range of biophysical topics Provides a rigorous theoretical treatment, alongside applications in biological systems Author has been teaching biophysics for nearly 25 years

A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging

NASA Astrophysics Data System (ADS)

Solomon, Justin; Samei, Ehsan

2014-11-01

Realistic three-dimensional (3D) mathematical models of subtle lesions are essential for many computed tomography (CT) studies focused on performance evaluation and optimization. In this paper, we develop a generic mathematical framework that describes the 3D size, shape, contrast, and contrast-profile characteristics of a lesion, as well as a method to create lesion models based on CT data of real lesions. Further, we implemented a technique to insert the lesion models into CT images in order to create hybrid CT datasets. This framework was used to create a library of realistic lesion models and corresponding hybrid CT images. The goodness of fit of the models was assessed using the coefficient of determination (R2) and the visual appearance of the hybrid images was assessed with an observer study using images of both real and simulated lesions and receiver operator characteristic (ROC) analysis. The average R2 of the lesion models was 0.80, implying that the models provide a good fit to real lesion data. The area under the ROC curve was 0.55, implying that the observers could not readily distinguish between real and simulated lesions. Therefore, we conclude that the lesion-modeling framework presented in this paper can be used to create realistic lesion models and hybrid CT images. These models could be instrumental in performance evaluation and optimization of novel CT systems.

Spatialized Application of Remotely Sensed Data Assimilation Methods for Farmland Drought Monitoring Using Two Different Crop Models

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Casa, Raffaele; Pignatti, Stefano; Castaldi, Fabio; Yang, Hao; Guijun, Yang

2016-08-01

The aim of this work was to develop a tool to evaluate the effect of water stress on yield losses at the farmland and regional scale, by assimilating remotely sensed biophysical variables into crop growth models. Biophysical variables were retrieved from HJ1A, HJ1B and Landsat 8 images, using an algorithm based on the training of artificial neural networks on PROSAIL.For the assimilation, two crop models of differing degree of complexity were used: Aquacrop and SAFY. For Aquacrop, an optimization procedure to reduce the difference between the remotely sensed and simulated CC was developed. For the modified version of SAFY, the assimilation procedure was based on the Ensemble Kalman Filter.These procedures were tested in a spatialized application, by using data collected in the rural area of Yangling (Shaanxi Province) between 2013 and 2015Results were validated by utilizing yield data both from ground measurements and statistical survey.

Integrating environmental and socio-economic indicators of a linked catchment-coastal system using variable environmental intensity.

PubMed

Dymond, John R; Davie, Tim J A; Fenemor, Andrew D; Ekanayake, Jagath C; Knight, Ben R; Cole, Anthony O; de Oca Munguia, Oscar Montes; Allen, Will J; Young, Roger G; Basher, Les R; Dresser, Marc; Batstone, Chris J

2010-09-01

Can we develop land use policy that balances the conflicting views of stakeholders in a catchment while moving toward long term sustainability? Adaptive management provides a strategy for this whereby measures of catchment performance are compared against performance goals in order to progressively improve policy. However, the feedback loop of adaptive management is often slow and irreversible impacts may result before policy has been adapted. In contrast, integrated modelling of future land use policy provides rapid feedback and potentially improves the chance of avoiding unwanted collapse events. Replacing measures of catchment performance with modelled catchment performance has usually required the dynamic linking of many models, both biophysical and socio-economic-and this requires much effort in software development. As an alternative, we propose the use of variable environmental intensity (defined as the ratio of environmental impact over economic output) in a loose coupling of models to provide a sufficient level of integration while avoiding significant effort required for software development. This model construct was applied to the Motueka Catchment of New Zealand where several biophysical (riverine water quantity, sediment, E. coli faecal bacteria, trout numbers, nitrogen transport, marine productivity) models, a socio-economic (gross output, gross margin, job numbers) model, and an agent-based model were linked. An extreme set of land use scenarios (historic, present, and intensive) were applied to this modelling framework. Results suggest that the catchment is presently in a near optimal land use configuration that is unlikely to benefit from further intensification. This would quickly put stress on water quantity (at low flow) and water quality (E. coli). To date, this model evaluation is based on a theoretical test that explores the logical implications of intensification at an unlikely extreme in order to assess the implications of likely growth trajectories from present use. While this has largely been a desktop exercise, it would also be possible to use this framework to model and explore the biophysical and economic impacts of individual or collective catchment visions. We are currently investigating the use of the model in this type of application.

Integrating Environmental and Socio-Economic Indicators of a Linked Catchment-Coastal System Using Variable Environmental Intensity

NASA Astrophysics Data System (ADS)

Dymond, John R.; Davie, Tim J. A.; Fenemor, Andrew D.; Ekanayake, Jagath C.; Knight, Ben R.; Cole, Anthony O.; de Oca Munguia, Oscar Montes; Allen, Will J.; Young, Roger G.; Basher, Les R.; Dresser, Marc; Batstone, Chris J.

2010-09-01

Can we develop land use policy that balances the conflicting views of stakeholders in a catchment while moving toward long term sustainability? Adaptive management provides a strategy for this whereby measures of catchment performance are compared against performance goals in order to progressively improve policy. However, the feedback loop of adaptive management is often slow and irreversible impacts may result before policy has been adapted. In contrast, integrated modelling of future land use policy provides rapid feedback and potentially improves the chance of avoiding unwanted collapse events. Replacing measures of catchment performance with modelled catchment performance has usually required the dynamic linking of many models, both biophysical and socio-economic—and this requires much effort in software development. As an alternative, we propose the use of variable environmental intensity (defined as the ratio of environmental impact over economic output) in a loose coupling of models to provide a sufficient level of integration while avoiding significant effort required for software development. This model construct was applied to the Motueka Catchment of New Zealand where several biophysical (riverine water quantity, sediment, E. coli faecal bacteria, trout numbers, nitrogen transport, marine productivity) models, a socio-economic (gross output, gross margin, job numbers) model, and an agent-based model were linked. An extreme set of land use scenarios (historic, present, and intensive) were applied to this modelling framework. Results suggest that the catchment is presently in a near optimal land use configuration that is unlikely to benefit from further intensification. This would quickly put stress on water quantity (at low flow) and water quality ( E. coli). To date, this model evaluation is based on a theoretical test that explores the logical implications of intensification at an unlikely extreme in order to assess the implications of likely growth trajectories from present use. While this has largely been a desktop exercise, it would also be possible to use this framework to model and explore the biophysical and economic impacts of individual or collective catchment visions. We are currently investigating the use of the model in this type of application.

Dynamics of entanglement and uncertainty relation in coupled harmonic oscillator system: exact results

NASA Astrophysics Data System (ADS)

Park, DaeKil

2018-06-01

The dynamics of entanglement and uncertainty relation is explored by solving the time-dependent Schrödinger equation for coupled harmonic oscillator system analytically when the angular frequencies and coupling constant are arbitrarily time dependent. We derive the spectral and Schmidt decompositions for vacuum solution. Using the decompositions, we derive the analytical expressions for von Neumann and Rényi entropies. Making use of Wigner distribution function defined in phase space, we derive the time dependence of position-momentum uncertainty relations. To show the dynamics of entanglement and uncertainty relation graphically, we introduce two toy models and one realistic quenched model. While the dynamics can be conjectured by simple consideration in the toy models, the dynamics in the realistic quenched model is somewhat different from that in the toy models. In particular, the dynamics of entanglement exhibits similar pattern to dynamics of uncertainty parameter in the realistic quenched model.

Georgia Tech Vertical Lift Research Center of Excellence

DTIC Science & Technology

2017-12-14

Technical Project Summaries Task 1.1 (GT-1): Next Generation VABS for More Realistic Modeling of Composite Blades ...Methodology for the Prediction of Rotor Blade Ice Formation and Shedding ..................................................................... 20...software disclosures and technology transfer efforts. Task 1.1 (GT-1): Next Generation VABS for More Realistic Modeling of Composite Blades PIs

Realistic simplified gaugino-higgsino models in the MSSM

NASA Astrophysics Data System (ADS)

Fuks, Benjamin; Klasen, Michael; Schmiemann, Saskia; Sunder, Marthijn

2018-03-01

We present simplified MSSM models for light neutralinos and charginos with realistic mass spectra and realistic gaugino-higgsino mixing, that can be used in experimental searches at the LHC. The formerly used naive approach of defining mass spectra and mixing matrix elements manually and independently of each other does not yield genuine MSSM benchmarks. We suggest the use of less simplified, but realistic MSSM models, whose mass spectra and mixing matrix elements are the result of a proper matrix diagonalisation. We propose a novel strategy targeting the design of such benchmark scenarios, accounting for user-defined constraints in terms of masses and particle mixing. We apply it to the higgsino case and implement a scan in the four relevant underlying parameters {μ , tan β , M1, M2} for a given set of light neutralino and chargino masses. We define a measure for the quality of the obtained benchmarks, that also includes criteria to assess the higgsino content of the resulting charginos and neutralinos. We finally discuss the distribution of the resulting models in the MSSM parameter space as well as their implications for supersymmetric dark matter phenomenology.

Analysis of the Impact of Realistic Wind Size Parameter on the Delft3D Model

NASA Astrophysics Data System (ADS)

Washington, M. H.; Kumar, S.

2017-12-01

The wind size parameter, which is the distance from the center of the storm to the location of the maximum winds, is currently a constant in the Delft3D model. As a result, the Delft3D model's output prediction of the water levels during a storm surge are inaccurate compared to the observed data. To address these issues, an algorithm to calculate a realistic wind size parameter for a given hurricane was designed and implemented using the observed water-level data for Hurricane Matthew. A performance evaluation experiment was conducted to demonstrate the accuracy of the model's prediction of water levels using the realistic wind size input parameter compared to the default constant wind size parameter for Hurricane Matthew, with the water level data observed from October 4th, 2016 to October 9th, 2016 from National Oceanic and Atmospheric Administration (NOAA) as a baseline. The experimental results demonstrate that the Delft3D water level output for the realistic wind size parameter, compared to the default constant size parameter, matches more accurately with the NOAA reference water level data.

SPH simulations of WBC adhesion to the endothelium: the role of haemodynamics and endothelial binding kinetics.

PubMed

Gholami, Babak; Comerford, Andrew; Ellero, Marco

2015-11-01

A multiscale Lagrangian particle solver introduced in our previous work is extended to model physiologically realistic near-wall cell dynamics. Three-dimensional simulation of particle trajectories is combined with realistic receptor-ligand adhesion behaviour to cover full cell interactions in the vicinity of the endothelium. The selected stochastic adhesion model, which is based on a Monte Carlo acceptance-rejection method, fits in our Lagrangian framework and does not compromise performance. Additionally, appropriate inflow/outflow boundary conditions are implemented for our SPH solver to enable realistic pulsatile flow simulation. The model is tested against in-vitro data from a 3D geometry with a stenosis and sudden expansion. In both steady and pulsatile flow conditions, results show close agreement with the experimental ones. Furthermore we demonstrate, in agreement with experimental observations, that haemodynamics alone does not account for adhesion of white blood cells, in this case U937 monocytic human cells. Our findings suggest that the current framework is fully capable of modelling cell dynamics in large arteries in a realistic and efficient manner.

Biophysical modelling of intra-ring variations in tracheid features and wood density of Pinus pinaster trees exposed to seasonal droughts

Treesearch

Sarah Wilkinson; Jerome Ogee; Jean-Christophe Domec; Mark Rayment; Lisa Wingate

2015-01-01

Process-based models that link seasonally varying environmental signals to morphological features within tree rings are essential tools to predict tree growth response and commercially important wood quality traits under future climate scenarios. This study evaluated model portrayal of radial growth and wood anatomy observations within a mature maritime pine (Pinus...

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

USGS Publications Warehouse

Tadesse, Tsegaye; Brown, Jesslyn F.; Hayes, M.J.

2005-01-01

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

Estimating the biophysical properties of neurons with intracellular calcium dynamics.

PubMed

Ye, Jingxin; Rozdeba, Paul J; Morone, Uriel I; Daou, Arij; Abarbanel, Henry D I

2014-06-01

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

Estimating the biophysical properties of neurons with intracellular calcium dynamics

NASA Astrophysics Data System (ADS)

Ye, Jingxin; Rozdeba, Paul J.; Morone, Uriel I.; Daou, Arij; Abarbanel, Henry D. I.

2014-06-01

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V (t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

Colloquium: Biophysical principles of undulatory self-propulsion in granular media

NASA Astrophysics Data System (ADS)

Goldman, Daniel I.

2014-07-01

Biological locomotion, movement within environments through self-deformation, encompasses a range of time and length scales in an organism. These include the electrophysiology of the nervous system, the dynamics of muscle activation, the mechanics of the skeletal system, and the interaction mechanics of such structures within natural environments like water, air, sand, and mud. Unlike the many studies of cellular and molecular scale biophysical processes, movement of entire organisms (like flies, lizards, and snakes) is less explored. Further, while movement in fluids like air and water is also well studied, little is known in detail of the mechanics that organisms use to move on and within flowable terrestrial materials such as granular media, ensembles of small particles that collectively display solid, fluid, and gaslike behaviors. This Colloquium reviews recent progress to understand principles of biomechanics and granular physics responsible for locomotion of the sandfish, a small desert-dwelling lizard that "swims" within sand using undulation of its body. Kinematic and muscle activity measurements of sand swimming using high speed x-ray imaging and electromyography are discussed. This locomotion problem poses an interesting challenge: namely, that equations that govern the interaction of the lizard with its environment do not yet exist. Therefore, complementary modeling approaches are also described: resistive force theory for granular media, multiparticle simulation modeling, and robotic physical modeling. The models reproduce biomechanical and neuromechanical aspects of sand swimming and give insight into how effective locomotion arises from the coupling of the body movement and flow of the granular medium. The argument is given that biophysical study of movement provides exciting opportunities to investigate emergent aspects of living systems that might not depend sensitively on biological details.

Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance.

PubMed

Messina, Carlos D; Podlich, Dean; Dong, Zhanshan; Samples, Mitch; Cooper, Mark

2011-01-01

The effectiveness of breeding strategies to increase drought resistance in crops could be increased further if some of the complexities in gene-to-phenotype (G → P) relations associated with epistasis, pleiotropy, and genotype-by-environment interactions could be captured in realistic G → P models, and represented in a quantitative manner useful for selection. This paper outlines a promising methodology. First, the concept of landscapes was extended from the study of fitness landscapes used in evolutionary genetics to the characterization of yield-trait-performance landscapes for agricultural environments and applications in plant breeding. Second, the E(NK) model of trait genetic architecture was extended to incorporate biophysical, physiological, and statistical components. Third, a graphical representation is proposed to visualize the yield-trait performance landscape concept for use in selection decisions. The methodology was demonstrated at a particular stage of a maize breeding programme with the objective of improving the drought tolerance of maize hybrids for the US Western Corn-Belt. The application of the framework to the genetic improvement of drought tolerance in maize supported selection of Doubled Haploid (DH) lines with improved levels of drought tolerance based on physiological genetic knowledge, prediction of test-cross yield within the target population of environments, and their predicted potential to sustain further genetic progress with additional cycles of selection. The existence of rugged yield-performance landscapes with multiple peaks and intervening valleys of lower performance, as shown in this study, supports the proposition that phenotyping strategies, and the directions emphasized in genomic selection can be improved by creating knowledge of the topology of yield-trait performance landscapes.

Integrating a Detailed Agricultural Model in a Global Economic Framework: New methods for assessment of climate mitigation and adaptation opportunities

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Calvin, K.; Zhang, X.; Wise, M.; West, T. O.

2010-12-01

Climate change and food security are global issues increasingly linked through human decision making that takes place across all scales from on-farm management actions to international climate negotiations. Understanding how agricultural systems can respond to climate change, through mitigation or adaptation, while still supplying sufficient food to feed a growing global population, thus requires a multi-sector tool in a global economic framework. Integrated assessment models are one such tool, however they are typically driven by historical aggregate statistics of production in combination with exogenous assumptions of future trends in agricultural productivity; they are not yet capable of exploring agricultural management practices as climate adaptation or mitigation strategies. Yet there are agricultural models capable of detailed biophysical modeling of farm management and climate impacts on crop yield, soil erosion and C and greenhouse gas emissions, although these are typically applied at point scales that are incompatible with coarse resolution integrated assessment modeling. To combine the relative strengths of these modeling systems, we are using the agricultural model EPIC (Environmental Policy Integrated Climate), applied in a geographic data framework for regional analyses, to provide input to the global economic model GCAM (Global Change Assessment Model). The initial phase of our approach focuses on a pilot region of the Midwest United States, a highly productive agricultural area. We apply EPIC, a point based biophysical process model, at 60 m spatial resolution within this domain and aggregate the results to GCAM agriculture and land use subregions for the United States. GCAM is then initialized with multiple management options for key food and bioenergy crops. Using EPIC to distinguish these management options based on grain yield, residue yield, soil C change and cost differences, GCAM then simulates the optimum distribution of the available management options to meet demands for food and energy over the next century. The coupled models provide a new platform for evaluating future changes in agricultural management based on food demand, bioenergy demand, and changes in crop yield and soil C under a changing climate. This framework can be applied to evaluate the economically and biophysically optimal distribution of management under future climates.

A taxonomy-based approach to shed light on the babel of mathematical analogies for rice simulation

USDA-ARS?s Scientific Manuscript database

For most biophysical domains, different models are available and the extent to which their structures differ with respect to differences in outputs was never quantified. We use a taxonomy-based approach to address the question with thirteen rice models. Classification keys and binary attributes for ...

Landscape-level terrestrial methane flux observed from a very tall tower

Treesearch

Ankur R. Desai; Ke Xu; Hanqin Tian; Peter Weishampel; Jonathan Thom; Dan Baumann; Arlyn E. Andrews; Druce D. Cook; Jennifer Y. King; Randall Kolka

2015-01-01

Simulating the magnitude and variability of terrestrial methane sources and sinks poses a challenge to ecosystem models because the biophysical and biogeochemical processes that lead to methane emissions from terrestrial and freshwater ecosystems are, by their nature, episodic and spatially disjunct. As a consequence, model predictions of regional methane emissions...

The Assessment of Hyperactivity in Preschool Populations: A Multidisciplinary Perspective.

ERIC Educational Resources Information Center

Rosenberg, Michael S.; And Others

1989-01-01

The variety of methods available for the assessment of hyperactivity in preschool populations is reviewed. Specific procedures for assessment are presented from a multidisciplinary perspective, integrating biophysical, behavioral, cognitive, and ecological models. (Author/JDD)

An integrated multidisciplinary model describing initiation of cancer and the Warburg hypothesis

PubMed Central

2013-01-01

Background In this paper we propose a chemical physics mechanism for the initiation of the glycolytic switch commonly known as the Warburg hypothesis, whereby glycolytic activity terminating in lactate continues even in well-oxygenated cells. We show that this may result in cancer via mitotic failure, recasting the current conception of the Warburg effect as a metabolic dysregulation consequent to cancer, to a biophysical defect that may contribute to cancer initiation. Model Our model is based on analogs of thermodynamic concepts that tie non-equilibrium fluid dynamics ultimately to metabolic imbalance, disrupted microtubule dynamics, and finally, genomic instability, from which cancers can arise. Specifically, we discuss how an analog of non-equilibrium Rayleigh-Benard convection can result in glycolytic oscillations and cause a cell to become locked into a higher-entropy state characteristic of cancer. Conclusions A quantitative model is presented that attributes the well-known Warburg effect to a biophysical mechanism driven by a convective disturbance in the cell. Contrary to current understanding, this effect may precipitate cancer development, rather than follow from it, providing new insights into carcinogenesis, cancer treatment, and prevention. PMID:23758735

An information-based approach to change-point analysis with applications to biophysics and cell biology.

PubMed

Wiggins, Paul A

2015-07-21

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Climate-driven range shifts of the king penguin in a fragmented ecosystem

NASA Astrophysics Data System (ADS)

Cristofari, Robin; Liu, Xiaoming; Bonadonna, Francesco; Cherel, Yves; Pistorius, Pierre; Le Maho, Yvon; Raybaud, Virginie; Stenseth, Nils Christian; Le Bohec, Céline; Trucchi, Emiliano

2018-03-01

Range shift is the primary short-term species response to rapid climate change, but it is often hampered by natural or anthropogenic habitat fragmentation. Different critical areas of a species' niche may be exposed to heterogeneous environmental changes and modelling species response under such complex spatial and ecological scenarios presents well-known challenges. Here, we use a biophysical ecological niche model validated through population genomics and palaeodemography to reconstruct past range shifts and identify future vulnerable areas and potential refugia of the king penguin in the Southern Ocean. Integrating genomic and demographic data at the whole-species level with specific biophysical constraints, we present a refined framework for predicting the effect of climate change on species relying on spatially and ecologically distinct areas to complete their life cycle (for example, migratory animals, marine pelagic organisms and central-place foragers) and, in general, on species living in fragmented ecosystems.

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Retrieval of biophysical parameters with AVIRIS and ISM: The Landes Forest, south west France

NASA Technical Reports Server (NTRS)

Zagolski, F.; Gastellu-Etchegorry, J. P.; Mougin, E.; Giordano, G.; Marty, G.; Letoan, T.; Beaudoin, A.

1992-01-01

The first steps of an experiment for investigating the capability of airborne spectrometer data for retrieval of biophysical parameters of vegetation, especially water conditions are presented. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and ISM data were acquired in the frame of the 1991 NASA/JPL and CNES campaigns on the Landes, South west France, a large and flat forest area with mainly maritime pines. In-situ measurements were completed at that time; i.e. reflectance spectra, atmospheric profiles, sampling for further laboratory analyses of elements concentrations (lignin, water, cellulose, nitrogen,...). All information was integrated in an already existing data base (age, LAI, DBH, understory cover,...). A methodology was designed for (1) obtaining geometrically and atmospherically corrected reflectance data, (2) registering all available information, and (3) analyzing these multi-source informations. Our objective is to conduct comparative studies with simulation reflectance models, and to improve these models, especially in the MIR.

Thermal characterization and syngas production from the pyrolysis of biophysical dried and traditional thermal dried sewage sludge.

PubMed

Han, Rong; Zhao, Chenxi; Liu, Jinwen; Chen, Aixia; Wang, Hongtao

2015-12-01

A novel method for energy recycling from sewage sludge was developed through biophysical drying coupled with fast pyrolysis. Thermal decomposition properties of biophysical-dried sludge (BDS) and thermal-dried sludge (TDS) were characterized through thermogravimetric (TG) coupled with mass spectrometry (MS) analysis. BDS exhibited typical peaks in each differential thermogravimetric (DTG) region and presented slower mass loss rates in H, C, and L regions (180-550°C) but remarkable weight loss in region I (>550°C) compared with TDS. The charring process centered at region I, was responsible for the prominent H2 emission from BDS. The pseudo multicomponent model showed that the Em values of BDS and TDS were 48.84 and 37.75 kJ/mol, respectively. Furthermore, fast pyrolysis of BDS was proven to facilitate syngas and char formation more than TDS. For the yielded syngas, the thermal conversion of BDS was characterized by high H2 and CH4 content beyond 700°C. Copyright © 2015. Published by Elsevier Ltd.

Benefits of object-oriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology.

PubMed

Gesenhues, Jonas; Hein, Marc; Ketelhut, Maike; Habigt, Moriz; Rüschen, Daniel; Mechelinck, Mare; Albin, Thivaharan; Leonhardt, Steffen; Schmitz-Rode, Thomas; Rossaint, Rolf; Autschbach, Rüdiger; Abel, Dirk

2017-04-01

Computational models of biophysical systems generally constitute an essential component in the realization of smart biomedical technological applications. Typically, the development process of such models is characterized by a great extent of collaboration between different interdisciplinary parties. Furthermore, due to the fact that many underlying mechanisms and the necessary degree of abstraction of biophysical system models are unknown beforehand, the steps of the development process of the application are iteratively repeated when the model is refined. This paper presents some methods and tools to facilitate the development process. First, the principle of object-oriented (OO) modeling is presented and the advantages over classical signal-oriented modeling are emphasized. Second, our self-developed simulation tool ModeliChart is presented. ModeliChart was designed specifically for clinical users and allows independently performing in silico studies in real time including intuitive interaction with the model. Furthermore, ModeliChart is capable of interacting with hardware such as sensors and actuators. Finally, it is presented how optimal control methods in combination with OO models can be used to realize clinically motivated control applications. All methods presented are illustrated on an exemplary clinically oriented use case of the artificial perfusion of the systemic circulation.

Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI.

PubMed

Wang, Jun; Breen, Daniel; Akinin, Abraham; Broccard, Frederic; Abarbanel, Henry D I; Cauwenberghs, Gert

2017-12-01

Representing the biophysics of neuronal dynamics and behavior offers a principled analysis-by-synthesis approach toward understanding mechanisms of nervous system functions. We report on a set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit. The analog VLSI chip, NeuroDyn, features 384 digitally programmable parameters specifying for 4 generalized Hodgkin-Huxley neurons coupled through 12 conductance-based chemical synapses. The parameters also describe reversal potentials, maximal conductances, and spline regressed kinetic functions for ion channel gating variables. In one set of experiments, we assimilated membrane potential recorded from one of the neurons on the chip to the model structure upon which NeuroDyn was designed using the known current input sequence. We arrived at the programmed parameters except for model errors due to analog imperfections in the chip fabrication. In a related set of experiments, we replicated songbird individual neuron dynamics on NeuroDyn by estimating and configuring parameters extracted using data assimilation from intracellular neural recordings. Faithful emulation of detailed biophysical neural dynamics will enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits. Neuroscience applications include studying the relationship between molecular properties of neurons and the emergence of different spike patterns or different brain behaviors. Clinical applications include studying and predicting effects of neuromodulators or neurodegenerative diseases on ion channel kinetics.

Microfluidics-Based in Vivo Mimetic Systems for the Study of Cellular Biology

PubMed Central

2015-01-01

Conspectus The human body is a complex network of molecules, organelles, cells, tissues, and organs: an uncountable number of interactions and transformations interconnect all the system’s components. In addition to these biochemical components, biophysical components, such as pressure, flow, and morphology, and the location of all of these interactions play an important role in the human body. Technical difficulties have frequently limited researchers from observing cellular biology as it occurs within the human body, but some state-of-the-art analytical techniques have revealed distinct cellular behaviors that occur only in the context of the interactions. These types of findings have inspired bioanalytical chemists to provide new tools to better understand these cellular behaviors and interactions. What blocks us from understanding critical biological interactions in the human body? Conventional approaches are often too naïve to provide realistic data and in vivo whole animal studies give complex results that may or may not be relevant for humans. Microfluidics offers an opportunity to bridge these two extremes: while these studies will not model the complexity of the in vivo human system, they can control the complexity so researchers can examine critical factors of interest carefully and quantitatively. In addition, the use of human cells, such as cells isolated from donated blood, captures human-relevant data and limits the use of animals in research. In addition, researchers can adapt these systems easily and cost-effectively to a variety of high-end signal transduction mechanisms, facilitating high-throughput studies that are also spatially, temporally, or chemically resolved. These strengths should allow microfluidic platforms to reveal critical parameters in the human body and provide insights that will help with the translation of pharmacological advances to clinical trials. In this Account, we describe selected microfluidic innovations within the last 5 years that focus on modeling both biophysical and biochemical interactions in cellular communication, such as flow and cell–cell networks. We also describe more advanced systems that mimic higher level biological networks, such as organ on-a-chip and animal on-a-chip models. Since the first papers in the early 1990s, interest in the bioanalytical use of microfluidics has grown significantly. Advances in micro-/nanofabrication technology have allowed researchers to produce miniaturized, biocompatible assay platforms suitable for microfluidic studies in biochemistry and chemical biology. Well-designed microfluidic platforms can achieve quick, in vitro analyses on pico- and femtoliter volume samples that are temporally, spatially, and chemically resolved. In addition, controlled cell culture techniques using a microfluidic platform have produced biomimetic systems that allow researchers to replicate and monitor physiological interactions. Pioneering work has successfully created cell–fluid, cell–cell, cell–tissue, tissue–tissue, even organ-like level interfaces. Researchers have monitored cellular behaviors in these biomimetic microfluidic environments, producing validated model systems to understand human pathophysiology and to support the development of new therapeutics. PMID:24555566

Microfluidics-based in vivo mimetic systems for the study of cellular biology.

PubMed

Kim, Donghyuk; Wu, Xiaojie; Young, Ashlyn T; Haynes, Christy L

2014-04-15

The human body is a complex network of molecules, organelles, cells, tissues, and organs: an uncountable number of interactions and transformations interconnect all the system's components. In addition to these biochemical components, biophysical components, such as pressure, flow, and morphology, and the location of all of these interactions play an important role in the human body. Technical difficulties have frequently limited researchers from observing cellular biology as it occurs within the human body, but some state-of-the-art analytical techniques have revealed distinct cellular behaviors that occur only in the context of the interactions. These types of findings have inspired bioanalytical chemists to provide new tools to better understand these cellular behaviors and interactions. What blocks us from understanding critical biological interactions in the human body? Conventional approaches are often too naïve to provide realistic data and in vivo whole animal studies give complex results that may or may not be relevant for humans. Microfluidics offers an opportunity to bridge these two extremes: while these studies will not model the complexity of the in vivo human system, they can control the complexity so researchers can examine critical factors of interest carefully and quantitatively. In addition, the use of human cells, such as cells isolated from donated blood, captures human-relevant data and limits the use of animals in research. In addition, researchers can adapt these systems easily and cost-effectively to a variety of high-end signal transduction mechanisms, facilitating high-throughput studies that are also spatially, temporally, or chemically resolved. These strengths should allow microfluidic platforms to reveal critical parameters in the human body and provide insights that will help with the translation of pharmacological advances to clinical trials. In this Account, we describe selected microfluidic innovations within the last 5 years that focus on modeling both biophysical and biochemical interactions in cellular communication, such as flow and cell-cell networks. We also describe more advanced systems that mimic higher level biological networks, such as organ on-a-chip and animal on-a-chip models. Since the first papers in the early 1990s, interest in the bioanalytical use of microfluidics has grown significantly. Advances in micro-/nanofabrication technology have allowed researchers to produce miniaturized, biocompatible assay platforms suitable for microfluidic studies in biochemistry and chemical biology. Well-designed microfluidic platforms can achieve quick, in vitro analyses on pico- and femtoliter volume samples that are temporally, spatially, and chemically resolved. In addition, controlled cell culture techniques using a microfluidic platform have produced biomimetic systems that allow researchers to replicate and monitor physiological interactions. Pioneering work has successfully created cell-fluid, cell-cell, cell-tissue, tissue-tissue, even organ-like level interfaces. Researchers have monitored cellular behaviors in these biomimetic microfluidic environments, producing validated model systems to understand human pathophysiology and to support the development of new therapeutics.

Comparison of biophysical factors influencing on emphysema quantification with low-dose CT

NASA Astrophysics Data System (ADS)

Heo, Chang Yong; Kim, Jong Hyo

2014-03-01

Emphysema Index(EI) measurements in MDCT is known to be influenced by various biophysical factors such as total lung volume, and body size. We investigated the association of the four biophysical factors with emphysema index in low-dose MDCT. In particular, we attempted to identify a potentially stronger biophysical factor than total lung volume. A total of 400 low-dose MDCT volumes taken at 120kVp, 40mAs, 1mm thickness, and B30f reconstruction kernel were used. The lungs, airways, and pulmonary vessels were automatically segmented, and two Emphysema Indices, relative area below -950HU(RA950) and 15th percentile(Perc15), were extracted from the segmented lungs. The biophysical factors such as total lung volume(TLV), mode of lung attenuation(ModLA), effective body diameter(EBD), and the water equivalent body diameter(WBD) were estimated from the segmented lung and body area. The association of biophysical factors with emphysema indices were evaluated by correlation coefficients. The mean emphysema indices were 8.3±5.5(%) in RA950, and -930±18(HU) in Perc15. The estimates of biophysical factors were 4.7±1.0(L) in TLV, -901±21(HU) in ModLA, 26.9±2.2(cm) in EBD, and 25.9±2.6(cm) in WBD. The correlation coefficients of biophysical factors with RA950 were 0.73 in TLV, 0.94 in ModLA, 0.31 in EBD, and 0.18 WBD, the ones with Perc15 were 0.74 in TLV, 0.98 in ModLA, 0.29 in EBD, and 0.15 WBD. Study results revealed that two biophysical factors, TLV and ModLA, mostly affects the emphysema indices. In particular, the ModLA exhibited strongest correlation of 0.98 with Perc15, which indicating the ModLA is the most significant confounding biophysical factor in emphysema indices measurement.

GERMcode: A Stochastic Model for Space Radiation Risk Assessment

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee Y.; Ponomarev, Artem L.; Cucinotta, Francis A.

2012-01-01

A new computer model, the GCR Event-based Risk Model code (GERMcode), was developed to describe biophysical events from high-energy protons and high charge and energy (HZE) particles that have been studied at the NASA Space Radiation Laboratory (NSRL) for the purpose of simulating space radiation biological effects. In the GERMcode, the biophysical description of the passage of HZE particles in tissue and shielding materials is made with a stochastic approach that includes both particle track structure and nuclear interactions. The GERMcode accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections. For NSRL applications, the GERMcode evaluates a set of biophysical properties, such as the Poisson distribution of particles or delta-ray hits for a given cellular area and particle dose, the radial dose on tissue, and the frequency distribution of energy deposition in a DNA volume. By utilizing the ProE/Fishbowl ray-tracing analysis, the GERMcode will be used as a bi-directional radiation transport model for future spacecraft shielding analysis in support of Mars mission risk assessments. Recent radiobiological experiments suggest the need for new approaches to risk assessment that include time-dependent biological events due to the signaling times for activation and relaxation of biological processes in cells and tissue. Thus, the tracking of the temporal and spatial distribution of events in tissue is a major goal of the GERMcode in support of the simulation of biological processes important in GCR risk assessments. In order to validate our approach, basic radiobiological responses such as cell survival curves, mutation, chromosomal aberrations, and representative mouse tumor induction curves are implemented into the GERMcode. Extension of these descriptions to other endpoints related to non-targeted effects and biochemical pathway responses will be discussed.

Reservoir studies with geostatistics to forecast performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, R.W.; Behrens, R.A.; Emanuel, A.S.

1991-05-01

In this paper example geostatistics and streamtube applications are presented for waterflood and CO{sub 2} flood in two low-permeability sandstone reservoirs. Thy hybrid approach of combining fine vertical resolution in cross-sectional models with streamtubes resulted in models that showed water channeling and provided realistic performance estimates. Results indicate that the combination of detailed geostatistical cross sections and fine-grid streamtube models offers a systematic approach for realistic performance forecasts.

Personalized Instruction with Bootstrap Tutors in an Introductory Biophysics Course

ERIC Educational Resources Information Center

Roper, L. David

1974-01-01

Discusses the conduct of an introductory biophysics course with a personalized instruction by using tutors selected from the students themselves. Included are three tables of text contents, a sample of a terminal questionnaire, and a list of biophysics references. (CC)

Foreword for Special Issue on Environmental Biophysics

USDA-ARS?s Scientific Manuscript database

This special issue on Environmental Biophysics is presented in honor of Dr. John Norman. Over the past four decades, Dr. Norman has dedicated himself to building bridges between disparate scientific disciplines for a better understanding and prediction of biophysical interactions. The consummate i...

Biophysics of the Senses

NASA Astrophysics Data System (ADS)

Presley, Tennille D.

2016-12-01

Biophysics of the Senses connects fundamental properties of physics to biological systems, relating them directly to the human body. It includes discussions of the role of charges and free radicals in disease and homeostasis, how aspects of mechanics impact normal body functions, human bioelectricity and circuitry, forces within the body, and biophysical sensory mechanisms. This is an exciting view of how sensory aspects of biophysics are utilized in everyday life for students who are curious but struggle with the connection between biology and physics.

Hemodynamic Changes Caused by Flow Diverters in Rabbit Aneurysm Models: Comparison of Virtual and Realistic FD Deployments Based on Micro-CT Reconstruction

PubMed Central

Fang, Yibin; Yu, Ying; Cheng, Jiyong; Wang, Shengzhang; Wang, Kuizhong; Liu, Jian-Min; Huang, Qinghai

2013-01-01

Adjusting hemodynamics via flow diverter (FD) implantation is emerging as a novel method of treating cerebral aneurysms. However, most previous FD-related hemodynamic studies were based on virtual FD deployment, which may produce different hemodynamic outcomes than realistic (in vivo) FD deployment. We compared hemodynamics between virtual FD and realistic FD deployments in rabbit aneurysm models using computational fluid dynamics (CFD) simulations. FDs were implanted for aneurysms in 14 rabbits. Vascular models based on rabbit-specific angiograms were reconstructed for CFD studies. Real FD configurations were reconstructed based on micro-CT scans after sacrifice, while virtual FD configurations were constructed with SolidWorks software. Hemodynamic parameters before and after FD deployment were analyzed. According to the metal coverage (MC) of implanted FDs calculated based on micro-CT reconstruction, 14 rabbits were divided into two groups (A, MC >35%; B, MC <35%). Normalized mean wall shear stress (WSS), relative residence time (RRT), inflow velocity, and inflow volume in Group A were significantly different (P<0.05) from virtual FD deployment, but pressure was not (P>0.05). The normalized mean WSS in Group A after realistic FD implantation was significantly lower than that of Group B. All parameters in Group B exhibited no significant difference between realistic and virtual FDs. This study confirmed MC-correlated differences in hemodynamic parameters between realistic and virtual FD deployment. PMID:23823503

Engineered biomaterial and biophysical stimulation as combinatorial strategies to address prosthetic infection by pathogenic bacteria.

PubMed

Boda, Sunil Kumar; Basu, Bikramjit

2017-10-01

A plethora of antimicrobial strategies are being developed to address prosthetic infection. The currently available methods for implant infection treatment include the use of antibiotics and revision surgery. Among the bacterial strains, Staphylococcus species pose significant challenges particularly, with regard to hospital acquired infections. In order to combat such life threatening infectious diseases, researchers have developed implantable biomaterials incorporating nanoparticles, antimicrobial reinforcements, surface coatings, slippery/non-adhesive and contact killing surfaces. This review discusses a few of the biomaterial and biophysical antimicrobial strategies, which are in the developmental stage and actively being pursued by several research groups. The clinical efficacy of biophysical stimulation methods such as ultrasound, electric and magnetic field treatments against prosthetic infection depends critically on the stimulation protocol and parameters of the treatment modality. A common thread among the three biophysical stimulation methods is the mechanism of bactericidal action, which is centered on biophysical rupture of bacterial membranes, the generation of reactive oxygen species (ROS) and bacterial membrane depolarization evoked by the interference of essential ion-transport. Although the extent of antimicrobial effect, normally achieved through biophysical stimulation protocol is insufficient to warrant therapeutic application, a combination of antibiotic/ROS inducing agents and biophysical stimulation methods can elicit a clinically relevant reduction in viable bacterial numbers. In this review, we present a detailed account of both the biomaterial and biophysical approaches for achieving maximum bacterial inactivation. Summarizing, the biophysical stimulation methods in a combinatorial manner with material based strategies can be a more potent solution to control bacterial infections. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 2174-2190, 2017. © 2016 Wiley Periodicals, Inc.

Resolving Cognitive Conflict in a Realistic Situation with Modeling Characteristics: Coping with a Changing Reference in Fractions

ERIC Educational Resources Information Center

Shahbari, Juhaina Awawdeh; Peled, Irit

2015-01-01

This study investigates the effect of using a realistic situation with modeling characteristics in creating and resolving a cognitive conflict to promote understanding of a changing reference in fraction calculations. The study was conducted among 96 seventh graders divided into 2 experimental groups and 1 control group. The experimental groups…

Improving the forecast for biodiversity under climate change.

PubMed

Urban, M C; Bocedi, G; Hendry, A P; Mihoub, J-B; Pe'er, G; Singer, A; Bridle, J R; Crozier, L G; De Meester, L; Godsoe, W; Gonzalez, A; Hellmann, J J; Holt, R D; Huth, A; Johst, K; Krug, C B; Leadley, P W; Palmer, S C F; Pantel, J H; Schmitz, A; Zollner, P A; Travis, J M J

2016-09-09

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity. Copyright © 2016, American Association for the Advancement of Science.

Reconstructed Historical Land Cover and Biophysical Parameters for Studies of Land-Atmosphere Interactions within the Eastern United States

NASA Technical Reports Server (NTRS)

Steyaert, Louis T.; Knox, Robert G.

2007-01-01

The local environment where we live within the Earth's biosphere is often taken for granted. This environment can vary depending on whether the land cover is a forest, grassland, wetland, water body, bare soil, pastureland, agricultural field, village, residential suburb, or an urban complex with concrete, asphalt, and large buildings. In general, the type and characteristics of land cover influence surface temperatures, sunlight exposure and duration, relative humidity, wind speed and direction, soil moisture amount, plant life, birds, and other wildlife in our backyards. The physical and biological properties (biophysical characteristics) of land cover help to determine our surface environment because they directly affect surface radiation, heat, and soil moisture processes, and also feedback to regional weather and climate. Depending on the spatial scale and land use intensity, land cover changes can have profound impacts on our local and regional environment. Over the past 350 years, the eastern half of the United States, an area extending from the grassland prairies of the Great Plains to the Gulf and Atlantic coasts, has experienced extensive land cover and land use changes that began with land clearing in the 1600s, led to extensive deforestation and intensive land use practices by 1920, and then evolved to the present-day landscape. Determining the consequences of such land cover changes on regional and global climate is a major research issue. Such research requires detailed historical land cover data and modeling experiments simulating historical climates. Given the need to understand the effects of historical land cover changes in the eastern United States, some questions include: - What were the most important land cover transformations and how did they alter biophysical characteristics of the land cover at key points in time since the mid-1600s? - How have land cover and land use changes over the past 350 years affected the land surface environment including surface weather, hydrologic, and climatic variability? - How do the potential effects of regional human-induced land cover change on the environment compare to similar changes that are caused by the natural variations of the Earth's climate system? To help answer these questions, we reconstructed a fractional land cover and biophysical parameter dataset for the eastern United States at 1650, 1850, 1920, and 1992 time-slices. Each land cover fraction is associated with a biophysical parameter class, a suite of parameters defining the biophysical characteristics of that kind of land cover. This new dataset is designed for use in computer models of land-atmosphere interactions, to understand and quantify the effects of historical land cover changes on the water, energy, and carbon cycles

Large scale in vivo recordings to study neuronal biophysics.

PubMed

Giocomo, Lisa M

2015-06-01

Over the last several years, technological advances have enabled researchers to more readily observe single-cell membrane biophysics in awake, behaving animals. Studies utilizing these technologies have provided important insights into the mechanisms generating functional neural codes in both sensory and non-sensory cortical circuits. Crucial for a deeper understanding of how membrane biophysics control circuit dynamics however, is a continued effort to move toward large scale studies of membrane biophysics, in terms of the numbers of neurons and ion channels examined. Future work faces a number of theoretical and technical challenges on this front but recent technological developments hold great promise for a larger scale understanding of how membrane biophysics contribute to circuit coding and computation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Cell biophysics: How to eat on the go

NASA Astrophysics Data System (ADS)

Levine, Herbert

2016-12-01

Dendritic cells use components of their cytoskeleton to both move and ingest pieces of infected cells. This competition for protein resources can give rise to a complex set of states that may be understood with an advection-diffusion model.

Biophysics and Thermodynamics: The Scientific Building Blocks of Bio-inspired Drug Delivery Nano Systems.

PubMed

Demetzos, Costas

2015-06-01

Biophysics and thermodynamics are considered as the scientific milestones for investigating the properties of materials. The relationship between the changes of temperature with the biophysical variables of biomaterials is important in the process of the development of drug delivery systems. Biophysics is a challenge sector of physics and should be used complementary with the biochemistry in order to discover new and promising technological platforms (i.e., drug delivery systems) and to disclose the 'silence functionality' of bio-inspired biological and artificial membranes. Thermal analysis and biophysical approaches in pharmaceuticals present reliable and versatile tools for their characterization and for the successful development of pharmaceutical products. The metastable phases of self-assembled nanostructures such as liposomes should be taken into consideration because they represent the thermal events can affect the functionality of advanced drug delivery nano systems. In conclusion, biophysics and thermodynamics are characterized as the building blocks for design and development of bio-inspired drug delivery systems.

Performance evaluation of spectral vegetation indices using a statistical sensitivity function

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2007-01-01

A great number of spectral vegetation indices (VIs) have been developed to estimate biophysical parameters of vegetation. Traditional techniques for evaluating the performance of VIs are regression-based statistics, such as the coefficient of determination and root mean square error. These statistics, however, are not capable of quantifying the detailed relationship between VIs and biophysical parameters because the sensitivity of a VI is usually a function of the biophysical parameter instead of a constant. To better quantify this relationship, we developed a “sensitivity function” for measuring the sensitivity of a VI to biophysical parameters. The sensitivity function is defined as the first derivative of the regression function, divided by the standard error of the dependent variable prediction. The function elucidates the change in sensitivity over the range of the biophysical parameter. The Student's t- or z-statistic can be used to test the significance of VI sensitivity. Additionally, we developed a “relative sensitivity function” that compares the sensitivities of two VIs when the biophysical parameters are unavailable.

Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions.

PubMed

Vazquez-Anderson, Jorge; Mihailovic, Mia K; Baldridge, Kevin C; Reyes, Kristofer G; Haning, Katie; Cho, Seung Hee; Amador, Paul; Powell, Warren B; Contreras, Lydia M

2017-05-19

Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA-RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA-RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA-mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Realist complex intervention science: Applying realist principles across all phases of the Medical Research Council framework for developing and evaluating complex interventions

PubMed Central

Fletcher, Adam; Jamal, Farah; Moore, Graham; Evans, Rhiannon E.; Murphy, Simon; Bonell, Chris

2016-01-01

The integration of realist evaluation principles within randomised controlled trials (‘realist RCTs’) enables evaluations of complex interventions to answer questions about what works, for whom and under what circumstances. This allows evaluators to better develop and refine mid-level programme theories. However, this is only one phase in the process of developing and evaluating complex interventions. We describe and exemplify how social scientists can integrate realist principles across all phases of the Medical Research Council framework. Intervention development, modelling, and feasibility and pilot studies need to theorise the contextual conditions necessary for intervention mechanisms to be activated. Where interventions are scaled up and translated into routine practice, realist principles also have much to offer in facilitating knowledge about longer-term sustainability, benefits and harms. Integrating a realist approach across all phases of complex intervention science is vital for considering the feasibility and likely effects of interventions for different localities and population subgroups. PMID:27478401

Computational Difficulties in the Identification and Optimization of Control Systems.

DTIC Science & Technology

1980-01-01

necessary and Identify by block number) - -. 3. iABSTRACT (Continue on revers, side It necessary and Identify by block number) As more realistic models ...Island 02912 ABSTRACT As more realistic models for resource management are developed, the need for efficient computational techniques for parameter...optimization (optimal control) in "state" models which This research was supported in part by ttfe National Science Foundation under grant NSF-MCS 79-05774

Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change

Treesearch

Wen J. Wang; Hong S. He; Frank R. Thompson; Martin A. Spetich; Jacob S. Fraser

2018-01-01

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are notwell represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts.We investigate how species biological...

Phase precession through acceleration of local theta rhythm: a biophysical model for the interaction between place cells and local inhibitory neurons.

PubMed

Castro, Luísa; Aguiar, Paulo

2012-08-01

Phase precession is one of the most well known examples within the temporal coding hypothesis. Here we present a biophysical spiking model for phase precession in hippocampal CA1 which focuses on the interaction between place cells and local inhibitory interneurons. The model's functional block is composed of a place cell (PC) connected with a local inhibitory cell (IC) which is modulated by the population theta rhythm. Both cells receive excitatory inputs from the entorhinal cortex (EC). These inputs are both theta modulated and space modulated. The dynamics of the two neuron types are described by integrate-and-fire models with conductance synapses, and the EC inputs are described using non-homogeneous Poisson processes. Phase precession in our model is caused by increased drive to specific PC/IC pairs when the animal is in their place field. The excitation increases the IC's firing rate, and this modulates the PC's firing rate such that both cells precess relative to theta. Our model implies that phase coding in place cells may not be independent from rate coding. The absence of restrictive connectivity constraints in this model predicts the generation of phase precession in any network with similar architecture and subject to a clocking rhythm, independently of the involvement in spatial tasks.

Biophysically based mathematical modeling of interstitial cells of Cajal slow wave activity generated from a discrete unitary potential basis.

PubMed

Faville, R A; Pullan, A J; Sanders, K M; Koh, S D; Lloyd, C M; Smith, N P

2009-06-17

Spontaneously rhythmic pacemaker activity produced by interstitial cells of Cajal (ICC) is the result of the entrainment of unitary potential depolarizations generated at intracellular sites termed pacemaker units. In this study, we present a mathematical modeling framework that quantitatively represents the transmembrane ion flows and intracellular Ca2+ dynamics from a single ICC operating over the physiological membrane potential range. The mathematical model presented here extends our recently developed biophysically based pacemaker unit modeling framework by including mechanisms necessary for coordinating unitary potential events, such as a T-Type Ca2+ current, Vm-dependent K+ currents, and global Ca2+ diffusion. Model simulations produce spontaneously rhythmic slow wave depolarizations with an amplitude of 65 mV at a frequency of 17.4 cpm. Our model predicts that activity at the spatial scale of the pacemaker unit is fundamental for ICC slow wave generation, and Ca2+ influx from activation of the T-Type Ca2+ current is required for unitary potential entrainment. These results suggest that intracellular Ca2+ levels, particularly in the region local to the mitochondria and endoplasmic reticulum, significantly influence pacing frequency and synchronization of pacemaker unit discharge. Moreover, numerical investigations show that our ICC model is capable of qualitatively replicating a wide range of experimental observations.

The Influence of Life History Variability on Population Connectivity: Development and Application of a Trait-Based Biophysical Model of Individuals

NASA Astrophysics Data System (ADS)

Wong-Ala, J.; Neuheimer, A. B.; Hixon, M.; Powell, B.

2016-02-01

Connectivity estimates, which measure the exchange of individuals among populations, are necessary to create effective reserves for marine life. Connectivity can be influenced by a combination of biology (e.g. spawning time) and physics (e.g. currents). In the past a dispersal model was created in an effort to explain connectivity for the highly sought after reef fish Lau`ipala (Yellow Tang, Zebrasoma flavescens) around Hawai`i Island using physics alone, but this was shown to be insufficient. Here we created an individual based model (IBM) to describe Lau`ipala life history and behavior forced with ocean currents and temperature (via coupling to a physical model) to examine biophysical interactions. The IBM allows for tracking of individual fish from spawning to settlement, and individual variability in modeled processes. We first examined the influence of different reproductive (e.g. batch vs. constant spawners), developmental (e.g. pelagic larval duration), and behavioral (e.g. active vs. passive buoyancy control) traits on modeled connectivity estimates for larval reef fish around Hawai`i Island and compared results to genetic observations of parent-offspring pair distribution. Our model is trait-based which allows individuals to vary in life history strategies enabling mechanistic links between predictions and underlying traits and straightforward applications to other species and sites.

Modeling the Normal and Neoplastic Cell Cycle with 'Realistic Boolean Genetic Networks': Their Application for Understanding Carcinogenesis and Assessing Therapeutic Strategies

NASA Technical Reports Server (NTRS)

Szallasi, Zoltan; Liang, Shoudan

2000-01-01

In this paper we show how Boolean genetic networks could be used to address complex problems in cancer biology. First, we describe a general strategy to generate Boolean genetic networks that incorporate all relevant biochemical and physiological parameters and cover all of their regulatory interactions in a deterministic manner. Second, we introduce 'realistic Boolean genetic networks' that produce time series measurements very similar to those detected in actual biological systems. Third, we outline a series of essential questions related to cancer biology and cancer therapy that could be addressed by the use of 'realistic Boolean genetic network' modeling.

Statistical and Biophysical Models for Predicting Total and Outdoor Water Use in Los Angeles

NASA Astrophysics Data System (ADS)

Mini, C.; Hogue, T. S.; Pincetl, S.

2012-04-01

Modeling water demand is a complex exercise in the choice of the functional form, techniques and variables to integrate in the model. The goal of the current research is to identify the determinants that control total and outdoor residential water use in semi-arid cities and to utilize that information in the development of statistical and biophysical models that can forecast spatial and temporal urban water use. The City of Los Angeles is unique in its highly diverse socio-demographic, economic and cultural characteristics across neighborhoods, which introduces significant challenges in modeling water use. Increasing climate variability also contributes to uncertainties in water use predictions in urban areas. Monthly individual water use records were acquired from the Los Angeles Department of Water and Power (LADWP) for the 2000 to 2010 period. Study predictors of residential water use include socio-demographic, economic, climate and landscaping variables at the zip code level collected from US Census database. Climate variables are estimated from ground-based observations and calculated at the centroid of each zip code by inverse-distance weighting method. Remotely-sensed products of vegetation biomass and landscape land cover are also utilized. Two linear regression models were developed based on the panel data and variables described: a pooled-OLS regression model and a linear mixed effects model. Both models show income per capita and the percentage of landscape areas in each zip code as being statistically significant predictors. The pooled-OLS model tends to over-estimate higher water use zip codes and both models provide similar RMSE values.Outdoor water use was estimated at the census tract level as the residual between total water use and indoor use. This residual is being compared with the output from a biophysical model including tree and grass cover areas, climate variables and estimates of evapotranspiration at very high spatial resolution. A genetic algorithm based model (Shuffled Complex Evolution-UA; SCE-UA) is also being developed to provide estimates of the predictions and parameters uncertainties and to compare against the linear regression models. Ultimately, models will be selected to undertake predictions for a range of climate change and landscape scenarios. Finally, project results will contribute to a better understanding of water demand to help predict future water use and implement targeted landscaping conservation programs to maintain sustainable water needs for a growing population under uncertain climate variability.

The Future Role of Information Technology in Erosion Modelling

USDA-ARS?s Scientific Manuscript database

Natural resources management and decision-making is a complex process requiring cooperation and communication among federal, state, and local stakeholders balancing biophysical and socio-economic concerns. Predicting soil erosion is common practice in natural resource management for assessing the e...

Chapter 5 - Development of biophysical gradient layers for the LANDFIRE Prototype Project

Treesearch

Lisa Holsinger; Robert E. Keane; Russell Parsons; Eva Karau

2006-01-01

Distributions of plant species are generally continuous, gradually changing across landscapes and blending into each other due to the influence of, and interactions between, a complex array of biophysical gradients (Whittaker 1967; 1975). Key biophysical gradients for understanding vegetation distributions include moisture, temperature, evaporative demand, nutrient...

Single Particle Orientation and Rotational Tracking (SPORT) in biophysical studies

NASA Astrophysics Data System (ADS)

Gu, Yan; Ha, Ji Won; Augspurger, Ashley E.; Chen, Kuangcai; Zhu, Shaobin; Fang, Ning

2013-10-01

The single particle orientation and rotational tracking (SPORT) techniques have seen rapid development in the past 5 years. Recent technical advances have greatly expanded the applicability of SPORT in biophysical studies. In this feature article, we survey the current development of SPORT and discuss its potential applications in biophysics, including cellular membrane processes and intracellular transport.The single particle orientation and rotational tracking (SPORT) techniques have seen rapid development in the past 5 years. Recent technical advances have greatly expanded the applicability of SPORT in biophysical studies. In this feature article, we survey the current development of SPORT and discuss its potential applications in biophysics, including cellular membrane processes and intracellular transport. Electronic supplementary information (ESI) available: Three supplementary movies and an experimental section. See DOI: 10.1039/c3nr02254d

Watershed-Scale Heterogeneity of the Biophysical Controls on Soil Respiration

NASA Astrophysics Data System (ADS)

Riveros, D. A.; Pacific, V. J.; McGlynn, B. L.; Welsch, D. L.; Epstein, H. E.; Muth, D. J.; Marshall, L.; Wraith, J.

2006-12-01

Large gaps exist in our understanding of the variability of soil respiration response to changing hydrologic conditions across spatial and temporal scales. Determining the linkages between the hydrologic cycle and the biophysical controls of soil respiration from the local point, to the plot, to the watershed scale is critical to understanding the dynamics of net ecosystem CO2 exchange (NEE). To study the biophysical controls of soil respiration, we measured soil CO2 concentration, soil CO2 flux, dissolved CO2 in stream water, soil moisture, soil temperature, groundwater dynamics, and precipitation at 20-minute intervals throughout the growing season at 4 sites and at weekly intervals at 62 sites covering the range of topographic position, slope, aspect, land cover, and upslope accumulated area conditions in a 555-ha subalpine watershed in central Montana. Our goal was to quantify watershed-scale heterogeneity in soil CO2 concentrations and surface efflux and gain understanding of the biophysical controls on soil respiration. We seek to improve our ability to evaluate and predict soil respiration responses to a dynamic hydrologic cycle across multiple temporal and spatial scales. We found that time lags between biophysical controls and soil respiration can occur from hourly to daily scales. The sensitivity of soil respiration to changes in environmental conditions is controlled by the antecedent soil moisture and by topographic position. At the watershed scale, significant differences in soil respiration exist between upland (dry) and lowland (wet) sites. However, differences in the magnitude and timing of soil respiration also exist within upland settings due to heterogeneity in soil temperature, soil moisture, and soil organic matter. Finally, we used a process-based model to simulate respiration at different times of the year across spatial locations. Our simulations highlight the importance of autotrophic and heterotrophic respiration (production) over diffusivity and soil physical properties (transport). Our work begins to address the disconnect between point, footprint, watershed scale estimates of ecosystem respiration and the role of a dynamic hydrologic cycle.

Dependence of soil respiration on soil temperature and soil moisture in successional forests in Southern China

USGS Publications Warehouse

Tang, X.-L.; Zhou, G.-Y.; Liu, S.-G.; Zhang, D.-Q.; Liu, S.-Z.; Li, Ji; Zhou, C.-Y.

2006-01-01

The spatial and temporal variations in soil respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, soil respiration rates, soil temperature, and soil moisture were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of soil respiration and its biophysical dependence in these forests. The relationships between biophysical factors and soil respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of soil respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). Soil respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (±SD) soil respiration rate in the DNR forests was (9.0 ± 4.6) Mg CO2-C/hm2per year, ranging from (6.1 ± 3.2) Mg CO2-C/hm2per year in early successional forests to (10.7 ± 4.9) Mg CO2-C/hm2 per year in advanced successional forests. Soil respiration was correlated with both soil temperature and moisture. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of soil respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, moisture increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more soil moisture is needed to maintain their activities.

High accuracy mantle convection simulation through modern numerical methods - II: realistic models and problems

NASA Astrophysics Data System (ADS)

Heister, Timo; Dannberg, Juliane; Gassmöller, Rene; Bangerth, Wolfgang

2017-08-01

Computations have helped elucidate the dynamics of Earth's mantle for several decades already. The numerical methods that underlie these simulations have greatly evolved within this time span, and today include dynamically changing and adaptively refined meshes, sophisticated and efficient solvers, and parallelization to large clusters of computers. At the same time, many of the methods - discussed in detail in a previous paper in this series - were developed and tested primarily using model problems that lack many of the complexities that are common to the realistic models our community wants to solve today. With several years of experience solving complex and realistic models, we here revisit some of the algorithm designs of the earlier paper and discuss the incorporation of more complex physics. In particular, we re-consider time stepping and mesh refinement algorithms, evaluate approaches to incorporate compressibility, and discuss dealing with strongly varying material coefficients, latent heat, and how to track chemical compositions and heterogeneities. Taken together and implemented in a high-performance, massively parallel code, the techniques discussed in this paper then allow for high resolution, 3-D, compressible, global mantle convection simulations with phase transitions, strongly temperature dependent viscosity and realistic material properties based on mineral physics data.

The NASA Space Radiobiology Risk Assessment Project

NASA Astrophysics Data System (ADS)

Cucinotta, Francis A.; Huff, Janice; Ponomarev, Artem; Patel, Zarana; Kim, Myung-Hee

The current first phase (2006-2011) has the three major goals of: 1) optimizing the conventional cancer risk models currently used based on the double-detriment life-table and radiation quality functions; 2) the integration of biophysical models of acute radiation syndromes; and 3) the development of new systems radiation biology models of cancer processes. The first-phase also includes continued uncertainty assessment of space radiation environmental models and transport codes, and relative biological effectiveness factors (RBE) based on flight data and NSRL results, respectively. The second phase of the (2012-2016) will: 1) develop biophysical models of central nervous system risks (CNS); 2) achieve comphrensive systems biology models of cancer processes using data from proton and heavy ion studies performed at NSRL; and 3) begin to identify computational models of biological countermeasures. Goals for the third phase (2017-2021) include: 1) the development of a systems biology model of cancer risks for operational use at NASA; 2) development of models of degenerative risks, 2) quantitative models of counter-measure impacts on cancer risks; and 3) indiviudal based risk assessments. Finally, we will support a decision point to continue NSRL research in support of NASA's exploration goals beyond 2021, and create an archival of NSRL research results for continued analysis. Details on near term goals, plans for a WEB based data resource of NSRL results, and a space radiation Wikepedia are described.

A biophysical approach to daunorubicin interaction with model membranes: relevance for the drug's biological activity.

PubMed

Alves, Ana Catarina; Ribeiro, Daniela; Horta, Miguel; Lima, José L F C; Nunes, Cláudia; Reis, Salette

2017-08-01

Daunorubicin is extensively used in chemotherapy for diverse types of cancer. Over the years, evidence has suggested that the mechanisms by which daunorubicin causes cytotoxic effects are also associated with interactions at the membrane level. The aim of the present work was to study the interplay between daunorubicin and mimetic membrane models composed of different ratios of 1,2-dimyristoyl- sn -glycero- 3 -phosphocholine (DMPC), sphingomyelin (SM) and cholesterol (Chol). Several biophysical parameters were assessed using liposomes as mimetic model membranes. Thereby, the ability of daunorubicin to partition into lipid bilayers, its apparent location within the membrane and its effect on membrane fluidity were investigated. The results showed that daunorubicin has higher affinity for lipid bilayers composed of DMPC, followed by DMPC : SM, DMPC : Chol and lastly by DMPC : SM : Chol. The addition of SM or Chol into DMPC membranes not only increases the complexity of the model membrane but also decreases its fluidity, which, in turn, reduces the amount of anticancer drug that can partition into these mimetic models. Fluorescence quenching studies suggest a broad distribution of the drug across the bilayer thickness, with a preferential location in the phospholipid tails. The gathered data support that daunorubicin permeates all types of membranes to different degrees, interacts with phospholipids through electrostatic and hydrophobic bonds and causes alterations in the biophysical properties of the bilayers, namely in membrane fluidity. In fact, a decrease in membrane fluidity can be observed in the acyl region of the phospholipids. Ultimately, such outcomes can be correlated with daunorubicin's biological action, where membrane structure and lipid composition have an important role. In fact, the results indicate that the intercalation of daunorubicin between the phospholipids can also take place in rigid domains, such as rafts that are known to be involved in different receptor processes, which are important for cellular function. © 2017 The Author(s).

Valuing investments in sustainable land management using an integrated modelling framework to support a watershed conservation scheme in the Upper Tana River, Kenya

NASA Astrophysics Data System (ADS)

Hunink, Johannes E.; Bryant, Benjamin P.; Vogl, Adrian; Droogers, Peter

2015-04-01

We analyse the multiple impacts of investments in sustainable land use practices on ecosystem services in the Upper Tana basin (Kenya) to support a watershed conservation scheme (a "water fund"). We apply an integrated modelling framework, building on previous field-based and modelling studies in the basin, and link biophysical outputs to economic benefits for the main actors in the basin. The first step in the modelling workflow is the use of a high-resolution spatial prioritization tool (Resource Investment Optimization System -- RIOS) to allocate the type and location of conservation investments in the different subbasins, subject to budget constraints and stakeholder concerns. We then run the Soil and Water Assessment Tool (SWAT) using the RIOS-identified investment scenarios to produce spatially explicit scenarios that simulate changes in water yield and suspended sediment. Finally, in close collaboration with downstream water users (urban water supply and hydropower) we link those biophysical outputs to monetary metrics, including: reduced water treatment costs, increased hydropower production, and crop yield benefits for upstream farmers in the conservation area. We explore how different budgets and different spatial targeting scenarios influence the return of the investments and the effectiveness of the water fund scheme. This study is novel in that it presents an integrated analysis targeting interventions in a decision context that takes into account local environmental and socio-economic conditions, and then relies on detailed, process-based, biophysical models to demonstrate the economic return on those investments. We conclude that the approach allows for an analysis on different spatial and temporal scales, providing conclusive evidence to stakeholders and decision makers on the contribution and benefits of the land-based investments in this basin. This is serving as foundational work to support the implementation of the Upper Tana-Nairobi Water Fund, a public-private partnership to safeguard ecosystem service provision and food security.

Regional comparison of tundra carbon budget response over the Alaska North Slope to varying environmental conditions as informed by in situ and flux tower measurements, remote sensing and biophysical modeling

NASA Astrophysics Data System (ADS)

Shirley, S.; Watts, J. D.; Kimball, J. S.; Zhang, Z.; Poulter, B.; Klene, A. E.; Jones, L. A.; Kim, Y.; Oechel, W. C.; Zona, D.; Euskirchen, E. S.

2017-12-01

A warming Arctic climate is contributing to shifts in landscape moisture and temperature regimes, a shortening of the non-frozen season, and increases in the depth of annual active layer. The changing environmental conditions make it difficult to determine whether tundra ecosystems are a carbon sink or source. At present, eddy covariance flux towers and biophysical measurements within the tower footprint provide the most direct assessment of change to the tundra carbon balance. However, these measurements have a limited spatial footprint and exist over relatively short timescales. Thus, terrestrial ecosystem models are needed to provide an improved understanding of how changes in landscape environmental conditions impact regional carbon fluxes. This study examines the primary drivers thought to affect the magnitude and variability of tundra-atmosphere CO2 and CH4 fluxes over the Alaska North Slope. Also investigated is the ability of biophysical models to capture seasonal flux characteristics over the 9 tundra tower sites examined. First, we apply a regression tree approach to ascertain which remotely sensed environmental variables best explain observed variability in the tower fluxes. Next, we compare flux estimates obtained from multiple process models including Terrestrial Carbon Flux (TCF) and the Lund-Potsdam-Jena Wald Schnee und Landschaft (LPJ-wsl), and Soil Moisture Active Passive Level 4 Carbon (SMAP L4_C) products. Our results indicate that out of 7 variables examined vegetation greenness, temperature, and moisture are more significant predictors of carbon flux magnitude over the tundra tower sites. This study found that satellite data-driven models, due to the ability of remote sensing instruments to capture the physical principles and processes driving tundra carbon flux, are more effective at estimating the magnitude and spatiotemporal variability of CO2 and CH4 fluxes in northern high latitude ecosystems.

Simulation of radiofrequency ablation in real human anatomy.

PubMed

Zorbas, George; Samaras, Theodoros

2014-12-01

The objective of the current work was to simulate radiofrequency ablation treatment in computational models with realistic human anatomy, in order to investigate the effect of realistic geometry in the treatment outcome. The body sites considered in the study were liver, lung and kidney. One numerical model for each body site was obtained from Duke, member of the IT'IS Virtual Family. A spherical tumour was embedded in each model and a single electrode was inserted into the tumour. The same excitation voltage was used in all cases to underline the differences in the resulting temperature rise, due to different anatomy at each body site investigated. The same numerical calculations were performed for a two-compartment model of the tissue geometry, as well as with the use of an analytical approximation for a single tissue compartment. Radiofrequency ablation (RFA) therapy appears efficient for tumours in liver and lung, but less efficient in kidney. Moreover, the time evolution of temperature for a realistic geometry differs from that for a two-compartment model, but even more for an infinite homogenous tissue model. However, it appears that the most critical parameters of computational models for RFA treatment planning are tissue properties rather than tissue geometry. Computational simulations of realistic anatomy models show that the conventional technique of a single electrode inside the tumour volume requires a careful choice of both the excitation voltage and treatment time in order to achieve effective treatment, since the ablation zone differs considerably for various body sites.

Identifying potential recommendation domains for conservation agriculture in Ethiopia, Kenya, and Malawi.

PubMed

Tesfaye, Kindie; Jaleta, Moti; Jena, Pradyot; Mutenje, Munyaradzi

2015-02-01

Conservation agriculture (CA) is being promoted as an option for reducing soil degradation, conserving water, enhancing crop productivity, and maintaining yield stability. However, CA is a knowledge- and technology-intensive practice, and may not be feasible or may not perform better than conventional agriculture under all conditions and farming systems. Using high resolution (≈1 km(2)) biophysical and socioeconomic geospatial data, this study identified potential recommendation domains (RDs) for CA in Ethiopia, Kenya, and Malawi. The biophysical variables used were soil texture, surface slope, and rainfall while the socioeconomic variables were market access and human and livestock population densities. Based on feasibility and comparative performance of CA over conventional agriculture, the biophysical and socioeconomic factors were first used to classify cultivated areas into three biophysical and three socioeconomic potential domains, respectively. Combinations of biophysical and socioeconomic domains were then used to develop potential RDs for CA based on adoption potential within the cultivated areas. About 39, 12, and 5% of the cultivated areas showed high biophysical and socioeconomic potential while 50, 39, and 21% of the cultivated areas showed high biophysical and medium socioeconomic potential for CA in Malawi, Kenya, and Ethiopia, respectively. The results indicate considerable acreages of land with high CA adoption potential in the mixed crop-livestock systems of the studied countries. However, there are large differences among countries depending on biophysical and socio-economic conditions. The information generated in this study could be used for targeting CA and prioritizing CA-related agricultural research and investment priorities in the three countries.

Identifying Potential Recommendation Domains for Conservation Agriculture in Ethiopia, Kenya, and Malawi

NASA Astrophysics Data System (ADS)

Tesfaye, Kindie; Jaleta, Moti; Jena, Pradyot; Mutenje, Munyaradzi

2015-02-01

Conservation agriculture (CA) is being promoted as an option for reducing soil degradation, conserving water, enhancing crop productivity, and maintaining yield stability. However, CA is a knowledge- and technology-intensive practice, and may not be feasible or may not perform better than conventional agriculture under all conditions and farming systems. Using high resolution (≈1 km2) biophysical and socioeconomic geospatial data, this study identified potential recommendation domains (RDs) for CA in Ethiopia, Kenya, and Malawi. The biophysical variables used were soil texture, surface slope, and rainfall while the socioeconomic variables were market access and human and livestock population densities. Based on feasibility and comparative performance of CA over conventional agriculture, the biophysical and socioeconomic factors were first used to classify cultivated areas into three biophysical and three socioeconomic potential domains, respectively. Combinations of biophysical and socioeconomic domains were then used to develop potential RDs for CA based on adoption potential within the cultivated areas. About 39, 12, and 5 % of the cultivated areas showed high biophysical and socioeconomic potential while 50, 39, and 21 % of the cultivated areas showed high biophysical and medium socioeconomic potential for CA in Malawi, Kenya, and Ethiopia, respectively. The results indicate considerable acreages of land with high CA adoption potential in the mixed crop-livestock systems of the studied countries. However, there are large differences among countries depending on biophysical and socio-economic conditions. The information generated in this study could be used for targeting CA and prioritizing CA-related agricultural research and investment priorities in the three countries.

Realistic molecular model of kerogen's nanostructure

NASA Astrophysics Data System (ADS)

Bousige, Colin; Ghimbeu, Camélia Matei; Vix-Guterl, Cathie; Pomerantz, Andrew E.; Suleimenova, Assiya; Vaughan, Gavin; Garbarino, Gaston; Feygenson, Mikhail; Wildgruber, Christoph; Ulm, Franz-Josef; Pellenq, Roland J.-M.; Coasne, Benoit

2016-05-01

Despite kerogen's importance as the organic backbone for hydrocarbon production from source rocks such as gas shale, the interplay between kerogen's chemistry, morphology and mechanics remains unexplored. As the environmental impact of shale gas rises, identifying functional relations between its geochemical, transport, elastic and fracture properties from realistic molecular models of kerogens becomes all the more important. Here, by using a hybrid experimental-simulation method, we propose a panel of realistic molecular models of mature and immature kerogens that provide a detailed picture of kerogen's nanostructure without considering the presence of clays and other minerals in shales. We probe the models' strengths and limitations, and show that they predict essential features amenable to experimental validation, including pore distribution, vibrational density of states and stiffness. We also show that kerogen's maturation, which manifests itself as an increase in the sp2/sp3 hybridization ratio, entails a crossover from plastic-to-brittle rupture mechanisms.

Realistic molecular model of kerogen's nanostructure.

PubMed

Bousige, Colin; Ghimbeu, Camélia Matei; Vix-Guterl, Cathie; Pomerantz, Andrew E; Suleimenova, Assiya; Vaughan, Gavin; Garbarino, Gaston; Feygenson, Mikhail; Wildgruber, Christoph; Ulm, Franz-Josef; Pellenq, Roland J-M; Coasne, Benoit

2016-05-01

Despite kerogen's importance as the organic backbone for hydrocarbon production from source rocks such as gas shale, the interplay between kerogen's chemistry, morphology and mechanics remains unexplored. As the environmental impact of shale gas rises, identifying functional relations between its geochemical, transport, elastic and fracture properties from realistic molecular models of kerogens becomes all the more important. Here, by using a hybrid experimental-simulation method, we propose a panel of realistic molecular models of mature and immature kerogens that provide a detailed picture of kerogen's nanostructure without considering the presence of clays and other minerals in shales. We probe the models' strengths and limitations, and show that they predict essential features amenable to experimental validation, including pore distribution, vibrational density of states and stiffness. We also show that kerogen's maturation, which manifests itself as an increase in the sp(2)/sp(3) hybridization ratio, entails a crossover from plastic-to-brittle rupture mechanisms.

BIOPHYSICAL EVALUATION OF INDIVIDUAL COMPONENT LEVELS AND SELECTED CONFIGURATIONS OF THE UNITED STATES MARINE CORPS COLD-WEATHER CLOTHING ENSEMBLE

DTIC Science & Technology

2018-01-02

TECHNICAL REPORT NO. T18-01 DATE January 2018 BIOPHYSICAL EVALUATION OF INDIVIDUAL COMPONENT...USARIEM TECHNICAL REPORT T18-01 BIOPHYSICAL EVALUATION OF INDIVIDUAL COMPONENT LEVELS AND...OF TABLES Table Page Table 1. Clothing and individual equipment descriptions ................................................. 3 Table 2. Surface

Biophysical Regulation of Cell Behavior—Cross Talk between Substrate Stiffness and Nanotopography

PubMed Central

Yang, Yong; Wang, Kai; Gu, Xiaosong; Leong, Kam W.

2017-01-01

The stiffness and nanotopographical characteristics of the extracellular matrix (ECM) influence numerous developmental, physiological, and pathological processes in vivo. These biophysical cues have therefore been applied to modulate almost all aspects of cell behavior, from cell adhesion and spreading to proliferation and differentiation. Delineation of the biophysical modulation of cell behavior is critical to the rational design of new biomaterials, implants, and medical devices. The effects of stiffness and topographical cues on cell behavior have previously been reviewed, respectively; however, the interwoven effects of stiffness and nanotopographical cues on cell behavior have not been well described, despite similarities in phenotypic manifestations. Herein, we first review the effects of substrate stiffness and nanotopography on cell behavior, and then focus on intracellular transmission of the biophysical signals from integrins to nucleus. Attempts are made to connect extracellular regulation of cell behavior with the biophysical cues. We then discuss the challenges in dissecting the biophysical regulation of cell behavior and in translating the mechanistic understanding of these cues to tissue engineering and regenerative medicine. PMID:29071164

Ecosystem services sustainability in the Mediterranean Sea: assessment of status and trends using multiple modelling approaches

NASA Astrophysics Data System (ADS)

Liquete, Camino; Piroddi, Chiara; Macías, Diego; Druon, Jean-Noël; Zulian, Grazia

2016-09-01

Mediterranean ecosystems support important processes and functions that bring direct benefits to human society. Yet, marine ecosystem services are usually overlooked due to the challenges in identifying and quantifying them. This paper proposes the application of several biophysical and ecosystem modelling approaches to assess spatially and temporally the sustainable use and supply of selected marine ecosystem services. Such services include food provision, water purification, coastal protection, lifecycle maintenance and recreation, focusing on the Mediterranean region. Overall, our study found a higher number of decreasing than increasing trends in the natural capacity of the ecosystems to provide marine and coastal services, while in contrast the opposite was observed to be true for the realised flow of services to humans. Such a study paves the way towards an effective support for Blue Growth and the European maritime policies, although little attention is paid to the quantification of marine ecosystem services in this context. We identify a key challenge of integrating biophysical and socio-economic models as a necessary step to further this research.

A Biophysical Neural Model To Describe Spatial Visual Attention

NASA Astrophysics Data System (ADS)

Hugues, Etienne; José, Jorge V.

2008-02-01

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.

An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

NASA Astrophysics Data System (ADS)

Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

2017-12-01

Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

Ecosystem services sustainability in the Mediterranean Sea: assessment of status and trends using multiple modelling approaches

PubMed Central

Liquete, Camino; Piroddi, Chiara; Macías, Diego; Druon, Jean-Noël; Zulian, Grazia

2016-01-01

Mediterranean ecosystems support important processes and functions that bring direct benefits to human society. Yet, marine ecosystem services are usually overlooked due to the challenges in identifying and quantifying them. This paper proposes the application of several biophysical and ecosystem modelling approaches to assess spatially and temporally the sustainable use and supply of selected marine ecosystem services. Such services include food provision, water purification, coastal protection, lifecycle maintenance and recreation, focusing on the Mediterranean region. Overall, our study found a higher number of decreasing than increasing trends in the natural capacity of the ecosystems to provide marine and coastal services, while in contrast the opposite was observed to be true for the realised flow of services to humans. Such a study paves the way towards an effective support for Blue Growth and the European maritime policies, although little attention is paid to the quantification of marine ecosystem services in this context. We identify a key challenge of integrating biophysical and socio-economic models as a necessary step to further this research. PMID:27686533

A Biophysical Neural Model To Describe Spatial Visual Attention

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hugues, Etienne; Jose, Jorge V.

2008-02-14

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We firstmore » constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.« less

Hydrodynamic entrainment in micro-confined suspensions and its implications for two-point microrheology

NASA Astrophysics Data System (ADS)

Aponte-Rivera, Christian; Zia, Roseanna N.

2017-11-01

We study hydrodynamic entrainment in spherically confined colloidal suspensions of hydrodynamically interacting particles as a model system for intracellular and other micro-confined biophysical transport. Modeling of transport and rheology in such materials requires an accurate description of the microscopic forces driving particle motion and of particle interactions with nearby boundaries. We carry out dynamic simulations of concentrated, spherically confined colloids as a model system to study the effect of 3D confinement on entrainment and rheology. We show that entrainment between two tracer particles exhibits qualitatively different functional dependence on inter-particle separation as compared to an unbound suspension, and develop a scaling theory that collapses the concentrated mobility of spherically confined suspensions for all volume fractions and particle to cavity size ratios onto a master curve. For widely separated particles, the master curve can be predicted via a Green's function, which suggests a framework with which to conduct two-point microrheology measurements near confining boundaries. The implications of these results for experiments in micro-confined biophysical systems, such as the interior of eukaryotic cells, are discussed.

ADVANCED COMPUTATIONAL METHODS IN DOSE MODELING: APPLICATION OF COMPUTATIONAL BIOPHYSICAL TRANSPORT, COMPUTATIONAL CHEMISTRY, AND COMPUTATIONAL BIOLOGY

EPA Science Inventory

Computational toxicology (CompTox) leverages the significant gains in computing power and computational techniques (e.g., numerical approaches, structure-activity relationships, bioinformatics) realized over the last few years, thereby reducing costs and increasing efficiency i...

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies.

PubMed

DeRolph, Christopher R; Schramm, Michael P; Bevelhimer, Mark S

2016-10-01

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.

Stability of discrete memory states to stochastic fluctuations in neuronal systems

PubMed Central

Miller, Paul; Wang, Xiao-Jing

2014-01-01

Noise can degrade memories by causing transitions from one memory state to another. For any biological memory system to be useful, the time scale of such noise-induced transitions must be much longer than the required duration for memory retention. Using biophysically-realistic modeling, we consider two types of memory in the brain: short-term memories maintained by reverberating neuronal activity for a few seconds, and long-term memories maintained by a molecular switch for years. Both systems require persistence of (neuronal or molecular) activity self-sustained by an autocatalytic process and, we argue, that both have limited memory lifetimes because of significant fluctuations. We will first discuss a strongly recurrent cortical network model endowed with feedback loops, for short-term memory. Fluctuations are due to highly irregular spike firing, a salient characteristic of cortical neurons. Then, we will analyze a model for long-term memory, based on an autophosphorylation mechanism of calcium/calmodulin-dependent protein kinase II (CaMKII) molecules. There, fluctuations arise from the fact that there are only a small number of CaMKII molecules at each postsynaptic density (putative synaptic memory unit). Our results are twofold. First, we demonstrate analytically and computationally the exponential dependence of stability on the number of neurons in a self-excitatory network, and on the number of CaMKII proteins in a molecular switch. Second, for each of the two systems, we implement graded memory consisting of a group of bistable switches. For the neuronal network we report interesting ramping temporal dynamics as a result of sequentially switching an increasing number of discrete, bistable, units. The general observation of an exponential increase in memory stability with the system size leads to a trade-off between the robustness of memories (which increases with the size of each bistable unit) and the total amount of information storage (which decreases with increasing unit size), which may be optimized in the brain through biological evolution. PMID:16822041

Passive dendrites enable single neurons to compute linearly non-separable functions.

PubMed

Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

2013-01-01

Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions.

The future of crystallography in drug discovery

PubMed Central

Zheng, Heping; Hou, Jing; Zimmerman, Matthew D; Wlodawer, Alexander; Minor, Wladek

2014-01-01

Introduction X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule–ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery. Areas covered This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of ‘Big Data’ analysis, biochemical experimental design and structure-based drug discovery. Expert opinion Although X-ray crystallography is one of the most detailed ‘microscopes’ available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its future application in drug discovery by the development of specific tools that would allow realistic interpretation of the outcome coordinates and/or support testing of these hypotheses. PMID:24372145

Input-dependent frequency modulation of cortical gamma oscillations shapes spatial synchronization and enables phase coding.

PubMed

Lowet, Eric; Roberts, Mark; Hadjipapas, Avgis; Peter, Alina; van der Eerden, Jan; De Weerd, Peter

2015-02-01

Fine-scale temporal organization of cortical activity in the gamma range (∼25-80Hz) may play a significant role in information processing, for example by neural grouping ('binding') and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.

Passive Dendrites Enable Single Neurons to Compute Linearly Non-separable Functions

PubMed Central

Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

2013-01-01

Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions. PMID:23468600

Input-Dependent Frequency Modulation of Cortical Gamma Oscillations Shapes Spatial Synchronization and Enables Phase Coding

PubMed Central

Lowet, Eric; Roberts, Mark; Hadjipapas, Avgis; Peter, Alina; van der Eerden, Jan; De Weerd, Peter

2015-01-01

Fine-scale temporal organization of cortical activity in the gamma range (∼25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity. PMID:25679780

Urban thermal environment and its biophysical parameters derived from satellite remote sensing imagery

NASA Astrophysics Data System (ADS)

Zoran, Maria A.; Savastru, Roxana S.; Savastru, Dan M.; Tautan, Marina N.; Baschir, Laurentiu V.

2013-10-01

In frame of global warming, the field of urbanization and urban thermal environment are important issues among scientists all over the world. This paper investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Bucharest metropolitan area of Romania based on satellite remote sensing imagery Landsat TM/ETM+, time series MODIS Terra/Aqua data and IKONOS acquired during 1990 - 2012 period. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also retrieved from thermal infrared band of Landsat TM/ETM+, from MODIS Terra/Aqua datasets. Based on these parameters, the urban growth, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results indicated that the metropolitan area ratio of impervious surface in Bucharest increased significantly during two decades investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Catalyzing action towards the sustainability of deltas: deltas as integrated socio-ecological systems and sentinels of regional and global change

NASA Astrophysics Data System (ADS)

Foufoula-Georgiou, E.; Tessler, Z. D.; Brondizio, E.; Overeem, I.; Renaud, F.; Sebesvari, Z.; Nicholls, R. J.; Anthony, E.

2016-12-01

Deltas are highly dynamic and productive environments: they are food baskets of the world, home to biodiverse and rich ecosystems, and they play a central role in food and water security. However, they are becoming increasingly vulnerable to risks arising from human activities, land subsidence, regional water management, global sea-level rise, and climate extremes. Our Belmont Forum DELTAS project (BF-DELTAS: Catalyzing actions towards delta sustainability) encompasses an international network of interdisciplinary research collaborators with focal areas in the Mekong, Ganges Brahmaputra, and the Amazon deltas. The project is organized around five main modules: (1) developing an analytical framework for assessing delta vulnerability and scenarios of change (Delta-SRES), (2) developing an open-acess, science-based integrative modeling framework for risk assessment and decision support (Delta-RADS), (3) developing tools to support quantitative mapping of the bio-physical and socio-economic environments of deltas and consolidate bio-physical and social data within shared data repositories (Delta-DAT), (4) developing Global Delta Vulnerability Indices (Delta-GDVI) that capture current and projected scenarios for major deltas around the world , and (5) collaborating with regional stakeholders to put the science, modeling, and data into action (Delta-ACT). In this talk, a research summary will be presented on three research domains around which significant collaborative work was developed: advancing biophysical classification of deltas, understanding deltas as coupled socio-ecological systems, and analyzing and informing social and environmental vulnerabilities in delta regions.

Towards an integrated economic assessment of climate change impacts on agriculture

NASA Astrophysics Data System (ADS)

Lotze-Campen, H.; Piontek, F.; Stevanovic, M.; Popp, A.; Bauer, N.; Dietrich, J.; Mueller, C.; Schmitz, C.

2012-12-01

For a detailed understanding of the effects of climate change on global agricultural production systems, it is essential to consider the variability of climate change patterns as projected by General Circulation Models (GCMs), their bio-physical impact on crops and the response in land-use patterns and markets. So far, approaches that account for the interaction of bio-physical and economic impacts are largely lacking. We present an integrative analysis by using a soft-coupled system of a biophysical impact model (LPJmL, Bondeau et al. 2007), an economically driven land use model (MAgPIE, Lotze-Campen et al. 2008) and an integrated assessment model (ReMIND-R, Leimbach et al. 2010) to study climate change impacts and economic damages in the agricultural sector. First, the dynamic global vegetation and hydrology model LPJmL is used to derive climate change impacts on crop yields for wheat, maize, soy, rice and other major crops. A range of different climate projections is used, taken from the dataset provided by the Intersectoral Impact Model Intercomparison Project (ISI-MIP, www.isi-mip.org), which bias-corrected the latest CMIP5 climate data (Taylor et al. 2011). Crop yield impacts cover scenarios with and without CO2 fertilization as well as different Representative Concentration Pathways (RCPs) and different GCMs. With increasing temperature towards the end of the century yields generally decrease in tropical and subtropical regions, while they tend to benefit in higher latitudes. LPJmL results have been compared to other global crop models in the Agricultural Model Intercomparison and Improvement Project (AgMIP, www.agmip.org). Second, changes in crop yields are analysed with the spatially explicit agro-economic model MAgPIE, which covers their interaction with economic development and changes in food demand. Changes in prices as well as welfare changes of producer and consumer surplus are taken as economic indicators. Due to climate-change related reductions in crop productivity, producers in some regions face adaptation costs through either intensification or spatial expansion of agricultural production. Impacts are relatively small in the first half of the century, but intensify later. Additional adaptation options are investigated through the use of different levels of trade liberalization in the model (Schmitz et al. 2012). MAgPIE results also have been compared to other global agro-economic models in AgMIP. Third, climate-induced changes are aggregated for major world regions as the sum of producer and consumer surplus across spatial units. Different equity weighting schemes are investigated based on Frankhauser et al. (1997), in order to take spatial differences in population density and economic wealth into account. Finally, agricultural damages are implemented into the macro-economic framework of ReMIND-R. This approach of a detailed study of climate change impacts along the effect chain from bio-physical impacts to economic assessment is an important next step in the development of damage assessments with regard to long-term climate change. It will be extended in the future to other impact areas. The separate models involved have benefitted from checks for robustness in the course of AgMIP and other model intercomparison exercises.

Mesoscale Simulations of a Florida Sea Breeze Using the PLACE Land Surface Model Coupled to a 1.5-Order Turbulence Parameterization

NASA Technical Reports Server (NTRS)

Lynn, Barry H.; Stauffer, David R.; Wetzel, Peter J.; Tao, Wei-Kuo; Perlin, Natal; Baker, R. David; Munoz, Ricardo; Boone, Aaron; Jia, Yiqin

1999-01-01

A sophisticated land-surface model, PLACE, the Parameterization for Land Atmospheric Convective Exchange, has been coupled to a 1.5-order turbulent kinetic energy (TKE) turbulence sub-model. Both have been incorporated into the Penn State/National Center for Atmospheric Research (PSU/NCAR) mesoscale model MM5. Such model improvements should have their greatest effect in conditions where surface contrasts dominate over dynamic processes, such as the simulation of warm-season, convective events. A validation study used the newly coupled model, MM5 TKE-PLACE, to simulate the evolution of Florida sea-breeze moist convection during the Convection and Precipitation Electrification Experiment (CaPE). Overall, eight simulations tested the sensitivity of the MM5 model to combinations of the new and default model physics, and initialization of soil moisture and temperature. The TKE-PLACE model produced more realistic surface sensible heat flux, lower biases for surface variables, more realistic rainfall, and cloud cover than the default model. Of the 8 simulations with different factors (i.e., model physics or initialization), TKE-PLACE compared very well when each simulation was ranked in terms of biases of the surface variables and rainfall, and percent and root mean square of cloud cover. A factor separation analysis showed that a successful simulation required the inclusion of a multi-layered, land surface soil vegetation model, realistic initial soil moisture, and higher order closure of the planetary boundary layer (PBL). These were needed to realistically model the effect of individual, joint, and synergistic contributions from the land surface and PBL on the CAPE sea-breeze, Lake Okeechobee lake breeze, and moist convection.