Diphenyl-Methane Based Thyromimetic Inhibitors for Transthyretin Amyloidosis

Thyromimetics, whose physicochemical characteristics are analog to thyroid hormones (THs) and their derivatives, are promising candidates as novel therapeutics for neurodegenerative and metabolic pathologies. In particular, sobetirome (GC-1), one of the initial halogen-free thyromimetics, and newly synthesized IS25 and TG68, with optimized ADME-Tox profile, have recently attracted attention owing to their superior therapeutic benefits, selectivity, and enhanced permeability. Here, we further explored the functional capabilities of these thyromimetics to inhibit transthyretin (TTR) amyloidosis. TTR is a homotetrameric transporter protein for THs, yet it is also responsible for severe amyloid fibril formation, which is facilitated by tetramer dissociation into non-native monomers. By combining nuclear magnetic resonance (NMR) spectroscopy, computational simulation, and biochemical assays, we found that GC-1 and newly designed diphenyl-methane-based thyromimetics, namely IS25 and TG68, are TTR stabilizers and efficient suppressors of TTR aggregation. Based on these observations, we propose the novel potential of thyromimetics as a multi-functional therapeutic molecule for TTR-related pathologies, including neurodegenerative diseases.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Semi‐Markovian modelling and performance analysis of variable rate traffic in ATM networks

Semi‐Markovian processes (SMP) serve as a versatile means to model various traffic types generated by sources or superposed from links in ATM networks. An efficient discrete time analysis method is proposed for SMP/G/1 queueing systems to evaluate ATM switches with non‐renewal input. Some basic results are derived for the autocorrelation function of semi‐Markovian processes and for the effect of state space reduction on autocorrelation, which show how to represent correlated traffic with given characteristic by an adequate SMP model of limited size. This revised version was published online in June 2006 with corrections to the Cover Date.     

Simulation of the submerged energy nozzle-mold water model system using laser-optical and computational fluid dynamics methods

The present work describes quantitative digital particle image velocimetry measurements of a full-scale water model of a thin slab mold. Different casting speeds and two submerged entry nozzles with one and two outlet ports have been investigated. The flow pattern of the single-port nozzle shows a counterclockwise-rotating double vortex that is nearly steady-state but leads to high stationary surface waves. The flow jets out of the two-port nozzle oscillate and produce a transient flow pattern with low wave amplitudes. The amplitudes for the one-port nozzle show a linear variation with the volumetric flow rate. The experimental results lead to a good interpretation of the flow phenomena and are used to validate steady-state numerical simulations with the commercial program, CFX, on the basis of the Reynolds equations. To describe anisotropic turbulence effects, the Reynolds stress model (RSM) is used for the flat single-port nozzle and the standard k-ɛ model for the mold flow. The calculated mean velocities and wave amplitudes, predicted from pressure distribution at the water surface, are generally in the consensus of the experimental data. An erratum to this article is available at .