Discovery of GPR183 Agonists Based on an Antagonist Scaffold

The G protein-coupled receptor GPR183/EBI2, which is activated by oxysterols, is a therapeutic target for inflammatory and metabolic diseases where both antagonists and agonists are of potential interest. Using the piperazine diamide core of the known GPR183 antagonist (E)-3-(4-bromophenyl)-1-(4-(4-methoxybenzoyl)piperazin-1-yl)prop-2-en-1-one (NIBR189) as starting point, we identified and sourced 79 structurally related compounds that were commercially available. In vitro screening of this compound collection using a Ca²⁺ mobilization assay resulted in the identification of 10 compounds with agonist properties. To enable establishment of initial structure-activity relationship trends, these were supplemented with five in-house compounds, two of which were also shown to be GPR183 agonists. Taken together, our findings suggest that the agonist activity of this compound series is dictated by the substitution pattern of one of the two distal phenyl rings, which functions as a molecular efficacy-switch.     

Biomolecular Structure and Dynamics with Neutrons: The View from Simulation

The characterization of the structure and internal dynamics of biomolecules is essential to understanding their biological function. Neutron scattering probes similar time- and length-scales to molecular dynamics simulation. Hence, simulation models of biomolecules have become invaluable in the interpretation of experimental neutron data. Here, we report on advances in the application of simulation in developing neutron scattering to investigate internal protein motions and, as an example of industrial relevance, in the derivation of physical models of use in biofuel renewable energy research.     

Time‐domain hybrid formulations for wave simulations in three‐dimensional PML‐truncated heterogeneous media

We are concerned with the numerical simulation of wave motion in arbitrarily heterogeneous, elastic, perfectly‐matched‐layer‐(PML)‐truncated media. We extend in three dimensions a recently developed two‐dimensional formulation, by treating the PML via an unsplit‐field, but mixed‐field, displacement‐stress formulation, which is then coupled to a standard displacement‐only formulation for the interior domain, thus leading to a computationally cost‐efficient hybrid scheme. The hybrid treatment leads to, at most, third‐order in time semi‐discrete forms. The formulation is flexible enough to accommodate the standard PML, as well as the multi‐axial PML. We discuss several time‐marching schemes, which can be used à la carte, depending on the application: (a) an extended Newmark scheme for third‐order in time, either unsymmetric or fully symmetric semi‐discrete forms; (b) a standard implicit Newmark for the second‐order, unsymmetric semi‐discrete forms; and (c) an explicit Runge–Kutta scheme for a first‐order in time unsymmetric system. The latter is well‐suited for large‐scale problems on parallel architectures, while the second‐order treatment is particularly attractive for ready incorporation in existing codes written originally for finite domains. We compare the schemes and report numerical results demonstrating stability and efficacy of the proposed formulations. Copyright © 2014 John Wiley & Sons, Ltd.     

Growth modulation of low density lipoprotein receptor sterol sensitivity in cultured human fibroblasts

Sterols regulate low density lipoprotein (LDL) receptor and 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase gene expressions by end product repression. Studies on cultured cells have shown that growing cells have a higher LDL uptake than quiescent cells and that incubation of cells with growth factors or mitogenic compounds leads to sterol-resistant upregulation of LDL receptor gene expression. The recent finding that elevated LDL receptor activity in acute myelogenous leukemia cells was characterized by a decreased sensitivity to downregulation by sterols raises the possibility that the mechanism behind this is related to the cellular growth rate. By using cultured human fibroblasts as a model system we therefore studied whether growth modulation of sterol sensitivity takes place in normal actively growing cells. Judging from the ability of sterols (25-hydroxycholesterol+cholesterol) to inhibit ¹²⁵I-LDL degradation, we found that the sensitivity to sterols varied markedly between cells of different densities. The lowest sensitivity to sterols and highest ¹²⁵I-LDL degradation rate were found in subconfluent cells, whereas sparse and confluent cells were the most sensitive ones. In contrast to the LDL receptor, HMG-CoA reductase sterol sensitivity did not appear to be growth regulated. We conclude that growth-dependent modulation of sterol sensitivity and LDL receptor activity takes place in normal human fibroblasts. Modulation of sterol sensitivity may be an important mechanism to ensure an adequate cholesterol supply in growing cells.     

Partial-differential-equation-constrained amplitude-based shape detection in inverse acoustic scattering

In this article we discuss a formal framework for casting the inverse problem of detecting the location and shape of an insonified scatterer embedded within a two-dimensional homogeneous acoustic host, in terms of a partial-differential-equation-constrained optimization approach. We seek to satisfy the ensuing Karush–Kuhn–Tucker first-order optimality conditions using boundary integral equations. The treatment of evolving boundary shapes, which arise naturally during the search for the true shape, resides on the use of total derivatives, borrowing from recent work by Bonnet and Guzina [1–4] in elastodynamics. We consider incomplete information collected at stations sparsely spaced at the assumed obstacle’s backscattered region. To improve on the ability of the optimizer to arrive at the global optimum we: (a) favor an amplitude-based misfit functional; and (b) iterate over both the frequency- and wave-direction spaces through a sequence of problems. We report numerical results for sound-hard objects with shapes ranging from circles, to penny- and kite-shaped, including obstacles with arbitrarily shaped non-convex boundaries. Partial support for this work was provided by the US National Science Foundation under grant award CMS-0348484.     

On the inverse problem of soil profile reconstruction: a comparison of time-domain approaches

We discuss a one-dimensional inverse material profile reconstruction problem that arises in layered media underlain by a rigid bottom, when total wavefield surficial measurements are used to guide the reconstruction. To tackle the problem, we adopt the systematic framework of PDE-constrained optimization and construct an augmented misfit functional that is further endowed by a regularization scheme. We report on a comparison of spatial regularization schemes such as Tikhonov and total variation against a temporal scheme that treats the model parameters as time-dependent. We study numerically the effects of inexact initial estimates, data noise, and regularization parameter choices for all three schemes, and report inverted profiles for the modulus, and for simultaneous inversion of both the modulus and viscous damping. Our numerical experiments demonstrate comparable or superior performance of the time-dependent regularization over the Tikhonov and total variation schemes for both smooth and sharp target profiles, albeit at increased computational cost. Support for this work was provided by the US National Science Foundation under grant awards ATM-0325125 and CMS-0348484.     

Leukemic reticuloendotheliosis: a medical technologist's diagnosis

Leukemic reticuloendotheliosis (LRE) or "hairy cell leukemia" is a unique form of leukemia which possesses features of both a chronic and acute leukemia. Its characteristic morphology, clinical diagnosis, and treatment are discussed. A high index of suspicion should make this entity easily diagnosed by the medical technologist.     

ANASA-a stochastic reinforcement algorithm for real-valued neuralcomputation

This paper introduces ANASA (adaptive neural algorithm of stochastic activation), a new, efficient, reinforcement learning algorithm for training neural units and networks with continuous output. The proposed method employs concepts, found in self-organizing neural networks theory and in reinforcement estimator learning algorithms, to extract and exploit information relative to previous input pattern presentations. In addition, it uses an adaptive learning rate function and a self-adjusting stochastic activation to accelerate the learning process. A form of optimal performance of the ANASA algorithm is proved (under a set of assumptions) via strong convergence theorems and concepts. Experimentally, the new algorithm yields results, which are superior compared to existing associative reinforcement learning methods in terms of accuracy and convergence rates. The rapid convergence rate of ANASA is demonstrated in a simple learning task, when it is used as a single neural unit, and in mathematical function modeling problems, when it is used to train various multilayered neural networks.     

A physically based stochastic-deterministic procedure for the estimation of flood frequency

A procedure based on the method of derived distributions is proposed for the estimation of flood frequency from ungauged watersheds. The results of previous research on rainfall characteristics and watershed response are incorporated into the proposed procedure. These rainfall characteristics are storm depth, storm duration, space and time distribution. A simplified watershed model is used which has previously given good simulation of the watershed response. Some of the rainfall and watershed model parameters are stochastic in nature and are assumed to follow various probability distributions. Monte Carlo simulation is used for the generation of the various parameter values and simulation of the peak flow hydrographs. After 5000 realizations, the frequency of the hourly and daily peak flow and the flood volume is estimated. The proposed procedure is applied to eight coastal British Columbia watersheds and the results compare well with the observed data and with fitted probability distributions. The method is easy to apply, requires limited data and is shown to be reliable. Sensitivity analysis shows that the procedure is not very sensitive to uncertainty of the parameter values and is not dependent on the parameter probability distributions used.