84 research outputs found
Theoretical Framework for Microscopic Osmotic Phenomena
The basic ingredients of osmotic pressure are a solvent fluid with a soluble
molecular species which is restricted to a chamber by a boundary which is
permeable to the solvent fluid but impermeable to the solute molecules. For
macroscopic systems at equilibrium, the osmotic pressure is given by the
classical van't Hoff Law, which states that the pressure is proportional to the
product of the temperature and the difference of the solute concentrations
inside and outside the chamber. For microscopic systems the diameter of the
chamber may be comparable to the length-scale associated with the solute-wall
interactions or solute molecular interactions. In each of these cases, the
assumptions underlying the classical van't Hoff Law may no longer hold. In this
paper we develop a general theoretical framework which captures corrections to
the classical theory for the osmotic pressure under more general relationships
between the size of the chamber and the interaction length scales. We also show
that notions of osmotic pressure based on the hydrostatic pressure of the fluid
and the mechanical pressure on the bounding walls of the chamber must be
distinguished for microscopic systems. To demonstrate how the theoretical
framework can be applied, numerical results are presented for the osmotic
pressure associated with a polymer of N monomers confined in a spherical
chamber as the bond strength is varied
A Stochastic Immersed Boundary Method for Fluid-Structure Dynamics at Microscopic Length Scales
In this work it is shown how the immersed boundary method of (Peskin2002) for
modeling flexible structures immersed in a fluid can be extended to include
thermal fluctuations. A stochastic numerical method is proposed which deals
with stiffness in the system of equations by handling systematically the
statistical contributions of the fastest dynamics of the fluid and immersed
structures over long time steps. An important feature of the numerical method
is that time steps can be taken in which the degrees of freedom of the fluid
are completely underresolved, partially resolved, or fully resolved while
retaining a good level of accuracy. Error estimates in each of these regimes
are given for the method. A number of theoretical and numerical checks are
furthermore performed to assess its physical fidelity. For a conservative
force, the method is found to simulate particles with the correct Boltzmann
equilibrium statistics. It is shown in three dimensions that the diffusion of
immersed particles simulated with the method has the correct scaling in the
physical parameters. The method is also shown to reproduce a well-known
hydrodynamic effect of a Brownian particle in which the velocity
autocorrelation function exhibits an algebraic tau^(-3/2) decay for long times.
A few preliminary results are presented for more complex systems which
demonstrate some potential application areas of the method.Comment: 52 pages, 11 figures, published in journal of computational physic
Stochastic Eulerian-Lagrangian Methods for Fluid-Structure Interactions with Thermal Fluctuations and Shear Boundary Conditions
A computational approach is introduced for the study of the rheological
properties of complex fluids and soft materials. The approach allows for a
consistent treatment of microstructure elastic mechanics, hydrodynamic
coupling, thermal fluctuations, and externally driven shear flows. A mixed
description in terms of Eulerian and Lagrangian reference frames is used for
the physical system. Microstructure configurations are represented in a
Lagrangian reference frame. Conserved quantities, such as momentum of the fluid
and microstructures, are represented in an Eulerian reference frame. The
mathematical formalism couples these different descriptions using general
operators subject to consistency conditions. Thermal fluctuations are taken
into account in the formalism by stochastic driving fields introduced in
accordance with the principles of statistical mechanics. To study the
rheological responses of materials subject to shear, generalized periodic
boundary conditions are developed where periodic images are shifted relative to
the unit cell to induce shear. Stochastic numerical methods are developed for
the formalism. As a demonstration of the methods, results are presented for the
shear responses of a polymeric fluid, lipid vesicle fluid, and a gel-like
material.Comment: 36 pages, 10 figures, stochastic computational methods for soft
materials and complex fluids at microstructure leve
A Numerical Model for Brownian Particles Fluctuating in Incompressible Fluids
We present a numerical method that consistently implements thermal
fluctuations and hydrodynamic interactions to the motion of Brownian particles
dispersed in incompressible host fluids. In this method, the thermal
fluctuations are introduced as random forces acting on the Brownian particles.
The hydrodynamic interactions are introduced by directly resolving the fluid
motions with the particle motion as a boundary condition to be satisfied. The
validity of the method has been examined carefully by comparing the present
numerical results with the fluctuation-dissipation theorem whose analytical
form is known for dispersions of a single spherical particle. Simulations are
then performed for more complicated systems, such as a dispersion composed of
many spherical particles and a single polymeric chain in a solvent.Comment: 6 pages, 8 figure
Assessment of suitable observation conditions for a monthly operational remote sensing based crop monitoring system.
Abstract: Cloud cover is the main issue to consider when remote sensing images are used to identify, map and monitor croplands, especially over the summer season (October to March in Brazi). This paper aims at evaluating clear sky conditions over four Brazilian states (Sa?o Paulo, Parana?, Santa Catarina, and Rio Grande do Sul) to assess suitable observation conditions for a monthly basis operational crop monitoring system. Cloudiness was analyzed using MODIS Cloud Mask product (MOD35), which presents four labels for cloud cover status: cloudy, uncertainty, probably clear and confident clear. R software was used to compute average values of clear sky with a confidence interval of 95% for each month between July 1st, 2000 and June 30th, 2013. Results showed significant differences within and between the four tested states. Moreover, the period from November to March presented 50% less clear sky areas when compared to April to October
Cloud cover assessment for operational crop monitoring systems in tropical areas.
Abstract: The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no signi?cant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles(UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information
Myc depletion induces a pluripotent dormant state mimicking diapause
Mouse embryonic stem cells (ESCs) are maintained in a naive ground state of pluripotency in the presence of MEK and GSK3 inhibitors. Here, we show that ground-state ESCs express low Myc levels. Deletion of both c-myc and N-myc (dKO) or pharmacological inhibition of Myc activity strongly decreases transcription, splicing, and protein synthesis, leading to proliferation arrest. This process is reversible and occurs without affecting pluripotency, suggesting that Myc-depleted stem cells enter a state of dormancy similar to embryonic diapause. Indeed, c-Myc is depleted in diapaused blastocysts, and the differential expression signatures of dKO ESCs and diapaused epiblasts are remarkably similar. Following Myc inhibition, pre-implantation blastocysts enter biosynthetic dormancy but can progress through their normal developmental program after transfer into pseudo-pregnant recipients. Our study shows that Myc controls the biosynthetic machinery of stem cells without affecting their potency, thus regulating their entry and exit from the dormant state.This work was supported by the FOR2033 and SFB873 funded by the Deutsche Forschungsgemeinschaft (DFG), the Dietmar Hopp Foundation (all to A.T.), and the Wellcome Trust (to A.S.)
Reviews and syntheses: remotely sensed optical time series for monitoring vegetation productivity
Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
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
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