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Expansion of a triplet repeat tract in exon 1 of the HTT gene causes Huntington’s disease (HD). The mutant HTT protein (mHTT) has numerous aberrant interactions with diverse, pleiomorphic effects. Lowering mHTT is a promising approach to treat HD, but it is unclear when lowering should be initiated, how much is necessary, and what duration should occur to achieve benefits. Furthermore, the effects of mHTT lowering on brain lipids have not been assessed. Using a mHtt-inducible mouse model, we analyzed mHtt lowering initiated at different ages and sustained for different time-periods. mHTT protein in cytoplasmic and synaptic compartments of the striatum was reduced 38-52%; however, there was minimal lowering of mHTT in nuclear and perinuclear regions where aggregates formed at 12 months of age. Total striatal lipids were reduced in 9-month-old LacQ140 mice and preserved by mHtt lowering. Subclasses important for white matter structure and function including ceramide (Cer), sphingomyelin (SM), and monogalactosyldiacylglycerol (MGDG), contributed to the reduction in total lipids. Phosphatidylinositol (PI), phosphatidylserine (PS), and bismethyl phosphatidic acid (BisMePA) were also changed in LacQ140 mice. Levels of all subclasses except ceramide were preserved by mHtt lowering. mRNA expression profiling indicated that a transcriptional mechanism contributes to changes in myelin lipids, and some but not all changes can be prevented by mHtt lowering. Our findings suggest that early and sustained reduction in mHtt can prevent changes in levels of select striatal proteins and most lipids, but a misfolded, degradation-resistant form of mHTT hampers some benefits in the long term.

Chemically modified siRNAs distinguish between mutant and normal huntingtin based on a single nucleotide difference and lower mutant huntingtin specifically in patient derived cells and in a mouse model of Huntington’s disease.

In steel manufacturing process, flat products are greatly concerned with the surface quality and the possibilities of its on-line inspection. The visual control is obviously unable to continuously check the surface of the moving product, and the control at the ending stage remains not suitable although, it may provide information about process trends and parameters history. So, strip surface defects that are not detected yield to product downgrading or to costly rework operations for producer and/or end users. With such needed quality level, steel surface inspection systems are more and more implemented for detecting defects and allowing correction at appropriate time. Based on Computer vision, these applications make a use of detection and classification algorithms to identify these arising defects. The present work is related to a Project of a scientific and economic impact: The Development of an on-line inspection system for strip surface defects identification during the thermo-mechanical treatment in hot rolling process. We asses, in this work, some approaches in labeling each of the defects belonging to a database created for this aim. This Dataset is compound of five, among the most frequent, surface defect types and with 108 variants of each one. Obtained results shown the importance of the choice of a relevant image features extractor.