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

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

Improved Procedures for Purification of the Bandeiraea simplicifolia I Isolectins and Bandeiraea simplicifolia II Lectin by Affinity Chromatography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66350/1/j.1432-1033.1980.tb07197.x.pd

Cr/Sc multilayer radiator for parametric EUV radiation in "water-window" spectral range

The results of experimental investigation of parametric radiation generated by 5.7 MeV electrons in a multilayer structure consisting of 100 Cr/Sc bi-layers deposited on a Si[3]N[4] membrane are presented. The multilayer structure was specially created for generation of parametric radiation with photon energy in "water-window" spectral range. First test measurements of angular distributions of radiation have been done and discussed

Summer Temperature Trend Over the Past Two Millennia Using Air Content in Himalayan Ice

Two Himalayan ice cores display a factor-two decreasing trend of air content over the past two millennia, in contrast to the relatively stable values in Greenland and Antarctica ice cores over the same period. Because the air content can be related with the relative frequency and intensity of melt phenomena, its variations along the Himalayan ice cores provide an indication of summer temperature trend. Our reconstruction point toward an unprecedented warming trend in the 20th century but does not depict the usual trends associated with Medieval Warm Period (MWP), or Little Ice Age (LIA)

1-D-ice flow modelling at EPICA Dome C and Dome Fuji, East Antarctica

One-dimensional (1-D) ice flow models are used to construct the age scales at the Dome C and Dome Fuji drilling sites (East Antarctica). The poorly constrained glaciological parameters at each site are recovered by fitting independent age markers identified within each core. We reconstruct past accumulation rates, that are larger than those modelled using the classical vapour saturation pressure relationship during glacial periods by up to a factor 1.5. During the Early Holocene, changes in reconstructed accumulation are not linearly related to changes in ice isotopic composition. A simple model of past elevation changes is developed and shows an amplitude variation of 110–120 m at both sites. We suggest that there is basal melting at Dome C (0.56±0.19 mm/yr). The reconstructed velocity profile is highly non-linear at both sites, which suggests complex ice flow effects. This induces a non-linear thinning function in both drilling sites, which is also characterized by bumps corresponding to variations in ice thickness with time