Search

Filtration is a crucial step in the analysis of living microparticles. In particular, the selective microfiltration of phytoplankton by size and shape remains an open problem, even though these criteria are essential for their gender and/or species identification. However, microfiltration devices necessitate physical membranes which complicate their fabrication, reduce the sample flow rate and can cause unwanted particle clogging. Recent advances in microfabrication such as electrode High Precision Capillary Printing allow to rapidly build electrode patterns over wide areas. In this study, we introduce a new concept of membrane-less dielectrophoretic (DEP) microfiltration suitable for large scale microfabrication processes. The proposed design involves two pairs of interdigitated electrodes at the top and the bottom of a microfluidic channel. We use finite-element calculations to analyse how the DEP force field throughout the channel, as well as the resulting trajectories of particles depend on the geometry of the system, on the physical properties of the particles and suspending medium and on the imposed voltage and flow rates. We numerically show that in the negative DEP regime, particles are focused in the channel mid-planes and that virtual pillars array leads either to their trapping at specific stagnation points, or to their focusing along specific lines, depending on their dielectrophoretic mobility. Simulations allow to understand how particles can be captured and to quantify the particle filtration conditions by introducing a critical dielectrophoretic mobility. We further illustrate the principle of membrane-less dielectrophoretic microfiltration using the proposed setup, by considering the separation of a binary mixture of polystyrene particles with different diameters, and validate it experimentally., Comment: 24 pages, 8 figures including supplementary information excepted experimental movies

Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. They aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms -- coupled with a graphical interface -- is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to easily construct such models, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as a Supplementary Material and all models are provided at: https://physiboss.github.io/tutorial/.

Cloud detection is the first step of any complex satellite-based cloud retrieval. No instrument detects all clouds, and analyses that use a given satellite climatology can only discuss a specific subset of clouds. We attempt to clarify which subsets of clouds are detected in a robust way by passive sensors, and which require active sensors. To do so, we identify where retrievals of Cloud Amounts (CAs), based on numerous sensors and algorithms, differ the most. We investigate large uncertainties, and confront retrievals from the CALIOP lidar, which detects semitransparent clouds and directly measures their vertical distribution, whatever the surface below. We document the cloud vertical distribution, opacity and seasonal variability where CAs from passive sensors disagree most. CALIOP CAs are larger than the passive average by +0.05 (AM) and +0.07 (PM). Over land, the +0.1 average difference rises to +0.2 over the African desert, Antarctica and Greenland, where large passive disagreements are traced to unfavorable surface conditions. Over oceans, CALIOP retrievals are closer to the average of passive retrievals except over the ITCZ (+0.1). Passive CAs disagree more in tropical areas associated with large-scale subsidence, where CALIOP observes a specific multi-layer cloud population: optically thin, high-level clouds and opaque (z>7km), shallow boundary layer clouds (z<2km). We evaluate the CA and cloud vertical distribution from 8 General Circulation Models where passive retrievals disagree and CALIOP provides new information. We find that modeled clouds are not more realistic where cloud detections from passive observations have long been robust, than where active sensors provide more reliable information.

Hybrid modeling provides an effective solution to cope with multiple time scales dynamics in systems biology. Among the applications of this method, one of the most important is the cell cycle regulation. The machinery of the cell cycle, leading to cell division and proliferation, combines slow growth, spatio-temporal re-organisation of the cell, and rapid changes of regulatory proteins concentrations induced by post-translational modifications. The advancement through the cell cycle comprises a well defined sequence of stages, separated by checkpoint transitions. The combination of continuous and discrete changes justifies hybrid modelling approaches to cell cycle dynamics. We present a piecewise-smooth version of a mammalian cell cycle model, obtained by hybridization from a smooth biochemical model. The approximate hybridization scheme, leading to simplified reaction rates and binary event location functions, is based on learning from a training set of trajectories of the smooth model. We discuss several learning strategies for the parameters of the hybrid model., Comment: In Proceedings HSB 2013, arXiv:1308.5724

Systems biology uses large networks of biochemical reactions to model the functioning of biological cells from the molecular to the cellular scale. The dynamics of dissipative reaction networks with many well separated time scales can be described as a sequence of successive equilibrations of different subsets of variables of the system. Polynomial systems with separation are equilibrated when at least two monomials, of opposite signs, have the same order of magnitude and dominate the others. These equilibrations and the corresponding truncated dynamics, obtained by eliminating the dominated terms, find a natural formulation in tropical analysis and can be used for model reduction., Comment: 13 pages, 1 figure, workshop Tropical-12, Moskow, August 26-31, 2012; in press Contemporary Mathematics

Piecewise smooth hybrid systems, involving continuous and discrete variables, are suitable models for describing the multiscale regulatory machinery of the biological cells. In hybrid models, the discrete variables can switch on and off some molecular interactions, simulating cell progression through a series of functioning modes. The advancement through the cell cycle is the archetype of such an organized sequence of events. We present an approach, inspired from tropical geometry ideas, allowing to reduce, hybridize and analyse cell cycle models consisting of polynomial or rational ordinary differential equations., Comment: In Proceedings HSB 2012, arXiv:1208.3151

Biochemical networks are used in computational biology, to model the static and dynamical details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multi-scaleness is another property of these networks, that can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler networks, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state and quasi-equilibrium approximations, and provide practical recipes for model reduction of linear and nonlinear networks. We also discuss the application of model reduction to backward pruning machine learning techniques.

We use the Litvinov-Maslov correspondence principle to reduce and hybridize networks of biochemical reactions. We apply this method to a cell cycle oscillator model. The reduced and hybridized model can be used as a hybrid model for the cell cycle. We also propose a practical recipe for detecting quasi-equilibrium QE reactions and quasi-steady state QSS species in biochemical models with rational rate functions and use this recipe for model reduction. Interestingly, the QE/QSS invariant manifold of the smooth model and the reduced dynamics along this manifold can be put into correspondence to the tropical variety of the hybridization and to sliding modes along this variety, respectively, Comment: conference SASB 2011, to be published in Electronic Notes in Theoretical Computer Science

In their comment, Poole et al. (2009) aim to show it is highly improbable that the observations described in Chepfer and Noel (2009), and described as "NAT-like" therein, are produced by Nitric Acid Trihydrate (NAT) particles. In this reply, we attempt to show why there is, in our opinion, too little evidence to reject this interpretation right away.

The optical properties of cold tropical tropopause clouds are examined on a global scale, using two years of space-borne lidar observations from CALIPSO (June 2006 ? May 2008). The linear depolarization ratio, color ratio and backscatter signal are analyzed in tropical clouds colder than 200 K in a way similar to recent studies of Polar Stratospheric Clouds (PSCs). It is found that the three categories of particles encountered in PSC (Ice, Sulfate Ternary Solutions or STS, and Nitric Acid Trihydrate or NAT) do also occur in tropical cold cloud layers. Particles with optical properties similar to NAT are few, but they cover the tropical belt and represent about 20% of cold cloud tropical particles. The optical behavior of these particles requires them to be small, non-spherical, optically thin, and persistent in the TTL at temperatures colder than 200 K; NAT particles and very small ice crystals meet these criteria.

Ground-based and satellite observations have hinted at the existence of polar stratospheric clouds (PSCs) with relatively high optical depths, even if optical depth values are hard to come by. This study documents a type II PSC observed from spaceborne lidar, with visible optical depths up to 0.8. Comparisons with multiple temperature fields, including reanalyses and results from mesoscale simulations, suggest that intense small-scale temperature fluctuations due to gravity waves play an important role in its formation, while nearby observations show the presence of a potentially related type Ia PSC farther downstream inside the polar vortex. Following this first case, the geographic distribution and microphysical properties of PSCs with optical depths above 0.3 are explored over Antarctica during the 2006 and 2007 austral winters. These clouds are rare (less than 1% of profiles) and concentrated over areas where strong winds hit steep ground slopes in the Western Hemisphere, especially over the peninsula. Such PSCs are colder than the general PSC population, and their detection is correlated with daily temperature minima across Antarctica. Lidar and depolarization ratios within these clouds suggest they are most likely ice-based (type II). Similarities between the case study and other PSCs suggest they might share the same formation mechanisms.

This study present a comparison of lidar observations of midlatitude cirrus clouds over the SIRTA observatory between 2002 and 2006 with multiple tropopauses (MT) retrieved from radiosounding temperature profiles. The temporal variability of MT properties (frequency, thickness) are discussed. Results show a marked annual cycle, with MT frequency reaching its lowest point in May (~18% occurrence of MT) and slowly rising to more than 40% in DJF. The average thickness of the MT also follows an annual cycle, going from less than 1 km in spring to 1.5 km in late autumn. Comparison with lidar observations show that cirrus clouds show a preference for being located close below the 1st tropopause. When the cloud top is above the 1st tropopause (7% of observations), in 20% of cases the cloud base is above it as well, resulting in a cirrus cloud "sandwiched" between the two tropopauses. Compared to the general distribution of cirrus, cross-tropopause cirrus show a higher frequency of large optical depths, while inter-tropopause cirrus show almost exclusively low optical depths (Tau < 0.03 in 90% of cases) typical of subvisible clouds. Results suggest the occurrence of inter-tropopause cirrus clouds is correlated with the frequency of multiple tropopauses.

We document, for the first time, how detailed vertical profiles of cloud fraction (CF) change diurnally between 51 deg S and 51 deg N, by taking advantage of 15 months of measurements from the Cloud-Aerosol Transport System (CATS) lidar on the non-sun-synchronous International Space Station (ISS). Over the tropical ocean in summer, we find few high clouds during daytime. At night they become frequent over a large altitude range (11-16 km between 22:00 and 04:00 LT). Over the summer tropical continents, but not over ocean, CATS observations reveal mid-level clouds (4-8 km above sea level or a.s.l.) persisting all day long, with a weak diurnal cycle (minimum at noon). Over the Southern Ocean, diurnal cycles appear for the omnipresent low-level clouds (minimum between noon and 15:00) and high-altitude clouds (minimum between 08:00 and 14:00). Both cycles are time shifted, with high-altitude clouds following the changes in low-altitude clouds by several hours. Over all continents at all latitudes during summer, the low-level clouds develop upwards and reach a maximum occurrence at about 2.5 km a.s.l. in the early afternoon (around 14:00). Our work also shows that (1) the diurnal cycles of vertical profiles derived from CATS are consistent with those from ground-based active sensors on a local scale, (2) the cloud profiles derived from CATS measurements at local times of 01:30 and 13:30 are consistent with those observed from CALIPSO at similar times, and (3) the diurnal cycles of low and high cloud amounts (CAs) derived from CATS are in general in phase with those derived from geostationary imagery but less pronounced. Finally, the diurnal variability of cloud profiles revealed by CATS strongly suggests that CALIPSO measurements at 01:30 and 13:30 document the daily extremes of the cloud fraction profiles over ocean and are more representative of daily averages over land, except at altitudes above 10 km where they capture part of the diurnal variability. These findings are applicable to other instruments with local overpass times similar to CALIPSO's, such as all the other A-Train instruments and the future Earth-CARE mission.