[doi:10.106311.3437639]“
“The effects of annealing on the digestibility, morphology, and physicochemical

characteristics of four types of granular sweet potato starches [Yulmi (YM), Yeonwhangmi (YHM), sweet potato starch from Samyang Genex (SSPS), and commercial sweet potato starch (CSPS)] were investigated. Annealing was performed at 55A degrees C and 90% moisture content for 72 h. Morphology, Selleck C188-9 the branched chain distribution of amylopectin, and the X-ray diffraction pattern remained unchanged during the annealing process. The slowly digestible starch content in annealed YM, YHM, and SSPS starches increased, but did not change in annealed CSPS. The gelatinization temperatures increased, but the gelatinization temperature range decreased with annealing. The swelling factor and amylose leaching decreased, while the close packing concentration increased. Rapid Visco Analyser analysis revealed that annealed starches possessed thermal stability and higher pasting temperatures.

It is suggested that the enhanced packing arrangement formed during annealing impacts the digestibility and physicochemical properties of sweet potato starches.”
“In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “”just-in-time”" assembly of select cellular structures and molecular machines. However, present in all time-series SRT1720 ic50 gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene Pictilisib in vitro or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “”single-cell”"-like information from population-level time-series

expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.