Simulating Brownian suspensions with fluctuating hydrodynamics

Fluctuating hydrodynamics has been successfully combined with several computational methods to rapidly compute the correlated random velocities of Brownian particles. In the overdamped limit where both particle and fluid inertia are ignored, one must also account for a Brownian drift term in order to successfully update the particle positions. In this paper, we present an efficient computational method for the dynamic simulation of Brownian suspensions with fluctuating hydrodynamics that handles both computations and provides a similar approximation as Stokesian Dynamics for dilute and semidilute suspensions. This advancement relies on combining the fluctuating force-coupling method (FCM) with a new midpoint time-integration scheme we refer to as the drifter-corrector (DC). The DC resolves the drift term for fluctuating hydrodynamics-based methods at a minimal computational cost when constraints are imposed on the fluid flow to obtain the stresslet corrections to the particle hydrodynamic interactions. With the DC, this constraint need only be imposed once per time step, reducing the simulation cost to nearly that of a completely deterministic simulation. By performing a series of simulations, we show that the DC with fluctuating FCM is an effective and versatile approach as it reproduces both the equilibrium distribution and the evolution of particulate suspensions in periodic as well as bounded domains. In addition, we demonstrate that fluctuating FCM coupled with the DC provides an efficient and accurate method for large-scale dynamic simulation of colloidal dispersions and the study of processes such as colloidal gelation

Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction

Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding.Comment: 13 pages, 7 Postscript figure

Tempo and mode of early gene loss in endosymbiotic bacteria from insects

BACKGROUND: Understanding evolutionary processes that drive genome reduction requires determining the tempo (rate) and the mode (size and types of deletions) of gene losses. In this study, we analysed five endosymbiotic genome sequences of the gamma-proteobacteria (three different Buchnera aphidicola strains, Wigglesworthia glossinidia, Blochmannia floridanus) to test if gene loss could be driven by the selective importance of genes. We used a parsimony method to reconstruct a minimal ancestral genome of insect endosymbionts and quantified gene loss along the branches of the phylogenetic tree. To evaluate the selective or functional importance of genes, we used a parameter that measures the level of adaptive codon bias in E. coli (i.e. codon adaptive index, or CAI), and also estimates of evolutionary rates (Ka) between pairs of orthologs either in free-living bacteria or in pairs of symbionts. RESULTS: Our results demonstrate that genes lost in the early stages of symbiosis were on average less selectively constrained than genes conserved in any of the extant symbiotic strains studied. These results also extend to more recent events of gene losses (i.e. among Buchnera strains) that still tend to concentrate on genes with low adaptive bias in E. coli and high evolutionary rates both in free-living and in symbiotic lineages. In addition, we analyzed the physical organization of gene losses for early steps of symbiosis acquisition under the hypothesis of a common origin of different symbioses. In contrast with previous findings we show that gene losses mostly occurred through loss of rather small blocks and mostly in syntenic regions between at least one of the symbionts and present-day E. coli. CONCLUSION: At both ancient and recent stages of symbiosis evolution, gene loss was at least partially influenced by selection, highly conserved genes being retained more readily than lowly conserved genes: although losses might result from drift due to the bottlenecking of endosymbiontic populations, we demonstrated that purifying selection also acted by retaining genes of greater selective importance

Modeling effects of patchiness and biological variability on transport rates within bioturbated sediments

Bioturbation models are typically one-dimensional, with the underlying assumption that tracer gradients are predominantly vertical, and that sediment reworking is laterally homogeneous. These models implicitly assume that bioturbation activity does not vary with horizontal location on the sediment surface. Benthic organisms, however, are often patchily distributed. Moreover, due to natural variability, bioturbation activity varies among individuals within a population, and hence, among bioturbated patches. Here we analyze a 1D model formulation that explicitly includes patchiness, exemplified by conveyor-belt transport. The patchiness is represented with one coefficient αb, as the fraction of bioturbated areas of the total area. First, all the mixed patches are considered to feature the same bioturbation rates. Then variability of these rates among patches is introduced in the model. The model is analyzed through different scenarios to assess the influence of patchiness and biological variability on the resulting tracer profiles (luminophores, 234Th and 210Pb). With patchiness, the principal feature of the resulting profiles is exponential decrease of tracer concentrations near the SWI, due to the accumulation of particles in the nonbioturbated patches, and the presence of subsurface peaks or anomalous concentrations at depth, as the result of particle transport in the bioturbated patches. This pattern is unusual compared to published patterns for conveyor-belt transport. Adding intra-population variability in bioturbation rates induces biodiffusive-like transport, especially with luminophores. This theoretical work provides new insights about the influence of patch structure on particle dispersion within sediments and proposes a new applicable approach to model various bioturbation processes (type and rates of transport) that can be horizontally distributed in sediments

An artificial neural network‐based model to predict chronic kidney disease in aged cats

Background Chronic kidney disease (CKD) frequently causes death in older cats; its early detection is challenging. Objectives To build a sensitive and specific model for early prediction of CKD in cats using artificial neural network (ANN) techniques applied to routine health screening data. Animals Data from 218 healthy cats ≥7 years of age screened at the Royal Veterinary College (RVC) were used for model building. Performance was tested using data from 3546 cats in the Banfield Pet Hospital records and an additional 60 RCV cats—all initially without a CKD diagnosis. Methods Artificial neural network (ANN) modeling used a multilayer feed‐forward neural network incorporating a back‐propagation algorithm. Clinical variables from single cat visits were selected using factorial discriminant analysis. Independent submodels were built for different prediction time frames. Two decision threshold strategies were investigated. Results Input variables retained were plasma creatinine and blood urea concentrations, and urine specific gravity. For prediction of CKD within 12 months, the model had accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 88%, 87%, 70%, 53%, and 92%, respectively. An alternative decision threshold increased specificity and PPV to 98% and 87%, but decreased sensitivity and NPV to 42% and 79%, respectively. Conclusions and Clinical Importance A model was generated that identified cats in the general population ≥7 years of age that are at risk of developing CKD within 12 months. These individuals can be recommended for further investigation and monitoring more frequently than annually. Predictions were based on single visits using common clinical variables

Mutations in Hydin impair ciliary motility in mice

Chlamydomonas reinhardtii hydin is a central pair protein required for flagellar motility, and mice with Hydin defects develop lethal hydrocephalus. To determine if defects in Hydin cause hydrocephalus through a mechanism involving cilia, we compared the morphology, ultrastructure, and activity of cilia in wild-type and hydin mutant mice strains. The length and density of cilia in the brains of mutant animals is normal. The ciliary axoneme is normal with respect to the 9 + 2 microtubules, dynein arms, and radial spokes but one of the two central microtubules lacks a specific projection. The hydin mutant cilia are unable to bend normally, ciliary beat frequency is reduced, and the cilia tend to stall. As a result, these cilia are incapable of generating fluid flow. Similar defects are observed for cilia in trachea. We conclude that hydrocephalus in hydin mutants is caused by a central pair defect impairing ciliary motility and fluid transport in the brain

Smoothened receptor signaling regulates the developmental shift of GABA polarity in rat somatosensory cortex.

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordSonic Hedgehog (Shh) and its patched-smoothened receptor complex control a variety of functions in the developing central nervous system such as neural cell proliferation and differentiation. Recently, Shh signaling components have been found to be expressed at the synaptic level in the postnatal brain, suggesting a potential role in the regulation of synaptic transmission. Using in utero electroporation of constitutively active and negative-phenotype forms of the Shh signal transducer smoothened (Smo), we studied the role of Smo signaling in the development and maturation of GABAergic transmission in the somatosensory cortex. Our results show that enhancing Smo activity during development accelerates the shift from depolarizing to hyperpolarizing GABA in dependence on functional expression of potassium-chloride cotransporter type 2 (KCC2). On the other hand, blocking Smo activity maintains GABA response in a depolarizing state in mature cortical neurons resulting in altered chloride homeostasis and increased seizure susceptibility. This study reveals an unexpected function of Smo signaling on the regulation of chloride homeostasis through the control of KCC2 cell surface stability and on the timing of the GABA inhibitory/excitatory shift in brain maturation

Bidecadal North Atlantic ocean circulation variability controlled by timing of volcanic eruptions

International audienceWhile bidecadal climate variability has been evidenced in several North Atlantic paleoclimaterecords, its drivers remain poorly understood. Here we show that the subset of CMIP5historical climate simulations that produce such bidecadal variability exhibits a robustsynchronization, with a maximum in Atlantic Meridional Overturning Circulation (AMOC) 15years after the 1963 Agung eruption. The mechanisms at play involve salinity advection fromthe Arctic and explain the timing of Great Salinity Anomalies observed in the 1970s and the1990s. Simulations, as well as Greenland and Iceland paleoclimate records, indicate thatcoherent bidecadal cycles were excited following five Agung-like volcanic eruptions of the lastmillennium. Climate simulations and a conceptual model reveal that destructive interferencecaused by the Pinatubo 1991 eruption may have damped the observed decreasing trend of theAMOC in the 2000s. Our results imply a long-lasting climatic impact and predictabilityfollowing the next Agung-like eruption