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At least 685 records · Page 38

Delivery-Risk-Aware Flexibility Scheduling and Dispatch for Aggregated Flexible Loads

Flexible loads like smart thermostats and water heaters can shift energy consumption and provide flexibility to the grid. However, this flexibility is dependent on occupant behavior and can lead to delivery risk, which causes utilities and grid operations to consider them as unreliable for purposes of grid operation. To date, they have not been well integrated into wholesale electricity markets or ancillary service offerings. With proper consideration of uncertainty and risk, these resources can be one of the most cost-effective sources of flexibility. This work uses stochastic optimization to quantify and bid flexibility from a fleet of flexible resources while considering their delivery risk.

DER↗

Modeling the Growth Rates of Tetragonal Lysozyme Crystal Faces

The measured macroscopic growth rates of the (110) and (101) faces of tetragonal lysozyme show an unexpectedly complex dependence on the supersaturation. The growth rates decay asymptotically to zero when the supersaturation is lowered to zero and increase rapidly when the supersaturation is increased. When supersaturations are increased still further the growth rates attain a maximum before starting to decrease. However, growth of these crystals is known to proceed by the classical dislocation and 2D nucleation growth mechanisms. This anomaly can be explained if growth is assumed to occur not by monomer units but by lysozyme aggregates. Analysis of the molecular packing of these crystals revealed that they were constructed of strongly bonded 4(sub 3) helices, while weaker bonds were responsible for binding the helices to each other. It follows that during crystal growth the stronger bonds are formed before the weaker ones. Thus, the growth of these crystals could be viewed as a two step process: aggregate growth units corresponding to the 4(sub 3) helix are first formed in the bulk solution by stronger intermolecular bonds and then attached to the crystal face by weaker bonds on dislocation hillocks or 2D islands. This will lead to a distribution of aggregates in the solution with monomers and lower order aggregates being predominant at low supersaturations and higher order aggregates being predominant at high supersaturations. If the crystal grows mostly by higher order aggregates, such as tetramers and octamers, it would explain the anomalous dependence of the growth rates on the supersaturation. Besides the analysis of molecular packing, a comprehensive analysis of the measured (110) and (101) growth rates was also undertaken in this study. The distribution of aggregates in lysozyme nutrient solutions at various solution conditions were determined from reversible aggregation reactions at equilibrium. The supersaturation was defined for each aggregate species with respect to its concentration at saturation in order to apply growth rate models to this process. The measured growth rates were then compared with the predicted ones from several dislocation and 2D nucleation growth models, employing tetramer and octamer growth units in polydisperse solutions and monomer units in monodisperse solutions. For the (110) face, the calculations consistently showed that the measured growth rates followed the expected model relations with octamer growth units. For the (101) face, it is not possible to obtain a clear agreement between the predicted and measured growth rates for a single growth unit as done for the (110) face. However, the calculations do indicate that the average size of the growth unit is between a tetramer and an octamer. This suggests that tetramers, octamers and other intermediate size growth units all participate in the growth process for this face. These calculations show that it is possible to model the macroscopic protein crystal growth rates if the molecular level processes can be account for, particularly protein aggregation processes in the bulk solution. Our recent investigations of tetragonal lysozyme crystals employing high resolution atomic force microscopy scans have further confirmed the growth of these crystals by aggregate growth units corresponding to 4(sub 3) helices.

Li, Meirong↗

Silica Nanodepletors: Targeting and Clearing Alzheimer's beta-Amyloid Plaques

Abnormal accumulation of beta-amyloid (A beta) peptide aggregates in the brain is a major hallmark of Alzheimer's disease (AD). A beta aggregates interfere with neuronal communications, ultimately causing neuronal damage and brain atrophy. Much effort has been made to develop AD treatments that suppress A beta aggregate formation, thereby attenuating A beta-induced neurotoxicity. Here, the design of A beta nanodepletors consisting of ultralarge mesoporous silica nanostructures and anti-A beta single-chain variable fragments, with the goal of targeting and eliminating aggregative A beta monomers, is reported. The A beta nanodepletors impart a notable decline in A beta aggregate formation, resulting in significant mitigation of A beta-induced neurotoxicity in vitro. Furthermore, stereotaxic injections of A beta nanodepletors into the brain of an AD mouse model system successfully suppress A beta plaque formation in vivo up to approximate to 30%, suggesting that A beta nanodepletors can serve as a promising antiamylodoisis material.

Jung, Huijin↗

Identifying amyloid-related diseases by mapping mutations in low-complexity protein domains to pathologies

Proteins including FUS, hnRNPA2, and TDP-43 reversibly aggregate into amyloid-like fibrils through interactions of their low-complexity domains (LCDs). Mutations in LCDs can promote irreversible amyloid aggregation and disease. We introduce a computational approach to identify mutations in LCDs of disease-associated proteins predicted to increase propensity for amyloid aggregation. We identify several disease-related mutations in the intermediate filament protein keratin-8 (KRT8). Atomic structures of wild-type and mutant KRT8 segments confirm the transition to a pleated strand capable of amyloid formation. Biochemical analysis reveals KRT8 forms amyloid aggregates, and the identified mutations promote aggregation. Aggregated KRT8 is found in Mallory–Denk bodies, observed in hepatocytes of livers with alcoholic steatohepatitis (ASH). We demonstrate that ethanol promotes KRT8 aggregation, and KRT8 amyloids co-crystallize with alcohol. Lastly, KRT8 aggregation can be seeded by liver extract from people with ASH, consistent with the amyloid nature of KRT8 aggregates and the classification of ASH as an amyloid-related condition.

59 BASIC BIOLOGICAL SCIENCES↗