Nodal distance distributions in cluster flight spacecraft network

2020 ◽  
Vol 43 (17) ◽  
pp. 9968-9982
Author(s):  
Jinrong Mo ◽  
Shengbo Hu ◽  
Yanfeng Shi ◽  
Xiaowei Song ◽  
Tingting Yan
Keyword(s):  
Nodal Distance ◽  
Distance Distributions

scholarly journals Studies of transmembrane peptides by pulse dipolar spectroscopy with semi-rigid TOPP spin labels

2021 ◽  
Author(s):  
Igor Tkach ◽  
Ulf Diederichsen ◽  
Marina Bennati
Keyword(s):  
Molecular Level ◽  
Dipolar Interaction ◽  
Spin Labels ◽  
Paramagnetic Centers ◽  
Structural Adaptation ◽  
Paramagnetic Resonance ◽  
Transmembrane Peptides ◽  
Distance Distributions ◽  
Lipid Environment ◽  
Electron Paramagnetic

AbstractElectron paramagnetic resonance (EPR)-based pulsed dipolar spectroscopy measures the dipolar interaction between paramagnetic centers that are separated by distances in the range of about 1.5–10 nm. Its application to transmembrane (TM) peptides in combination with modern spin labelling techniques provides a valuable tool to study peptide-to-lipid interactions at a molecular level, which permits access to key parameters characterizing the structural adaptation of model peptides incorporated in natural membranes. In this mini-review, we summarize our approach for distance and orientation measurements in lipid environment using novel semi-rigid TOPP [4-(3,3,5,5-tetramethyl-2,6-dioxo-4-oxylpiperazin-1-yl)-L-phenylglycine] labels specifically designed for incorporation in TM peptides. TOPP labels can report single peak distance distributions with sub-angstrom resolution, thus offering new capabilities for a variety of TM peptide investigations, such as monitoring of various helix conformations or measuring of tilt angles in membranes. Graphical Abstract

scholarly journals Influence of Prior Distributions and Fragility assessment methods in the estimation of the Magnitude of a Historical Seismic Event

2018 ◽  
Vol 149 ◽  
pp. 02038 ◽  
Author(s):  
Eduardo Charters Morais ◽  
László Gergely Vigh ◽  
János KrÄhling
Keyword(s):  
Stone Masonry ◽  
Fragility Functions ◽  
Backbone Curve ◽  
Prior Distributions ◽  
Magnitude Distribution ◽  
Structural Capacity ◽  
Capacity Spectrum ◽  
Distance Distributions ◽  
Dynamic Methods ◽  
Modelling Strategies

The production of fragility functions describing the probable behaviour and damage on historical buildings is a key step in a method for the estimation of the magnitude of historical seismic events that uses a Bayes'. The fragilities are estimated by integrating the structural capacity with the seismic demand using either static methods, as the Capacity Spectrum Method (CSM), or dynamic methods, as Incremental Dynamic (IDA) and Multiple Stripes Analysis (MSA). Uncertainties in both resistance, demand, and distance and magnitude models propagate to the posterior magnitude distribution. The present paper studies the effect of uncertainties related both to the production of fragility functions and prior distributions, in the estimation of the magnitude of the 1763 Komárom earthquake (in historical Hungary). In the XVIII century most of the structures in the region were built of earth, adobe, clay or stone masonry, which is complex to model. While micro or detailed macro-modelling strategies are computationally costly, simplified macro-approaches are often more efficient, but require a pre-identification of the failure mode(s) and the determination of the backbone curve. For this study, a simplified macro-model of a Hungarian peasant house archetype is calibrated for CSM and IDA. The physical and geometrical uncertainties are incorporated in the fragilities using Monte-Carlo simulation. Prior magnitude and distance distributions are studied. The final magnitude estimates are presented and discussed.

scholarly journals Extraction of accurate cytoskeletal actin velocity distributions from noisy measurements

2020 ◽  
Author(s):  
Cayla M. Miller ◽  
Elgin Korkmazhan ◽  
Alexander R. Dunn
Keyword(s):  
Single Molecule ◽  
Actin Filaments ◽  
Dynamical Behavior ◽  
Jump Process ◽  
Pairwise Distance ◽  
Actin Dynamics ◽  
Cell Cortex ◽  
Superresolution Microscopy ◽  
Distance Distributions ◽  
Primary Fibroblasts

Dynamic remodeling of the actin cytoskeleton allows cells to migrate, change shape, and exert mechanical forces on their surroundings. How the complex dynamical behavior of the cytoskeleton arises from the interactions of its molecular components remains incompletely understood. Tracking the movement of individual actin filaments in living cells can in principle provide a powerful means of addressing this question. However, single-molecule fluorescence imaging measurements that could provide this information are limited by low signal-to-noise ratios, with the result that the localization errors for individual fluorophore fiducials attached to filamentous (F)-actin are comparable to the distances traveled by actin filaments between measurements. In this study we tracked the movement F-actin labeled with single-molecule densities of the fluorogenic label SiR-actin in primary fibroblasts and endothelial cells. We then used a Bayesian statistical approach to estimate true, underlying actin filament velocity distributions from the tracks of individual actin-associated fluorophores along with quantified localization uncertainties. This analysis approach is broadly applicable to inferring statistical pairwise distance distributions arising from noisy point localization measurements such as occur in superresolution microscopy. We found that F-actin velocity distributions were better described by a statistical jump process, in which filaments exist in mechanical equilibria punctuated by abrupt, jump-like movements, than by models incorporating combinations of diffusive motion and drift. A model with exponentially distributed time- and length-scales for filament jumps recapitulated F-actin velocity distributions measured for the cell cortex, integrin-based adhesions, and actin stress fibers, indicating that a common physical model can potentially describe F-actin dynamics in a variety of cellular contexts.

scholarly journals Probability of successful larval dispersal declines fivefold over 1 km in a coral reef fish

2011 ◽  
Vol 279 (1735) ◽  
pp. 1883-1888 ◽  
Author(s):  
Peter M. Buston ◽  
Geoffrey P. Jones ◽  
Serge Planes ◽  
Simon R. Thorrold
Keyword(s):  
Larval Dispersal ◽  
Isolation By Distance ◽  
Dispersal Distance ◽  
Population Connectivity ◽  
Marine Ecology ◽  
Sea Anemones ◽  
Marine Population ◽  
Amphiprion Percula ◽  
Distance Distributions ◽  
Dispersal Distances

A central question of marine ecology is, how far do larvae disperse? Coupled biophysical models predict that the probability of successful dispersal declines as a function of distance between populations. Estimates of genetic isolation-by-distance and self-recruitment provide indirect support for this prediction. Here, we conduct the first direct test of this prediction, using data from the well-studied system of clown anemonefish ( Amphiprion percula ) at Kimbe Island, in Papua New Guinea. Amphiprion percula live in small breeding groups that inhabit sea anemones. These groups can be thought of as populations within a metapopulation. We use the x- and y -coordinates of each anemone to determine the expected distribution of dispersal distances (the distribution of distances between each and every population in the metapopulation). We use parentage analyses to trace recruits back to parents and determine the observed distribution of dispersal distances. Then, we employ a logistic model to (i) compare the observed and expected dispersal distance distributions and (ii) determine the relationship between the probability of successful dispersal and the distance between populations. The observed and expected dispersal distance distributions are significantly different ( p < 0.0001). Remarkably, the probability of successful dispersal between populations decreases fivefold over 1 km. This study provides a framework for quantitative investigations of larval dispersal that can be applied to other species. Further, the approach facilitates testing biological and physical hypotheses for the factors influencing larval dispersal in unison, which will advance our understanding of marine population connectivity.

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