Agilent Technologies

About: Agilent Technologies is a company organization based out in Santa Clara, California, United States. It is known for research contribution in the topics: Signal & Mass spectrometry. The organization has 7398 authors who have published 11518 publications receiving 262410 citations. The organization is also known as: Agilent Technologies, Inc..

S-nitrosation of proteins relevant to Alzheimer’s disease during early stages of neurodegeneration

Abstract: Protein S-nitrosation (SNO-protein), the nitric oxide-mediated posttranslational modification of cysteine thiols, is an important regulatory mechanism of protein function in both physiological and pathological pathways. A key first step toward elucidating the mechanism by which S-nitrosation modulates a protein’s function is identification of the targeted cysteine residues. Here, we present a strategy for the simultaneous identification of SNO-cysteine sites and their cognate proteins to profile the brain of the CK-p25–inducible mouse model of Alzheimer’s disease-like neurodegeneration. The approach—SNOTRAP (SNO trapping by triaryl phosphine)—is a direct tagging strategy that uses phosphine-based chemical probes, allowing enrichment of SNO-peptides and their identification by liquid chromatography tandem mass spectrometry. SNOTRAP identified 313 endogenous SNO-sites in 251 proteins in the mouse brain, of which 135 SNO-proteins were detected only during neurodegeneration. S-nitrosation in the brain shows regional differences and becomes elevated during early stages of neurodegeneration in the CK-p25 mouse. The SNO-proteome during early neurodegeneration identified increased S-nitrosation of proteins important for synapse function, metabolism, and Alzheimer’s disease pathology. In the latter case, proteins related to amyloid precursor protein processing and secretion are S-nitrosated, correlating with increased amyloid formation. Sequence analysis of SNO-cysteine sites identified potential linear motifs that are altered under pathological conditions. Collectively, SNOTRAP is a direct tagging tool for global elucidation of the SNO-proteome, providing functional insights of endogenous SNO proteins in the brain and its dysregulation during neurodegeneration.

Method and system for extracting data from surface array deposited features

Abstract: A method for evaluating an orientation of a molecular array having features arranged in a pattern An image of the molecular array is obtained by scanning the molecular array to determine data signals emanating from discrete positions on a surface of the molecular array An actual result of a function on pixels of the image which pixels lie in a second pattern, is calculated This actual result is compared with an expected result which would be obtained if the second pattern had a predetermined orientation on the array Array orientation can then be evaluated based on the result

Bad pixel detection and correction in an image sensing device

Abstract: A sensor includes an array of photodetectors each generating an output signal of pixel data indicative of incident light intensity. This pixel data is read out from the array one line at a time and stored in a line buffer. A bad pixel processor includes a first buffer that stores pixel data obtained from the line buffer for a certain pixel in a currently read out line and pixel signal light data for pixels adjacent to the certain pixel. An included second buffer stores features that are indicative of whether the pixels in a previously read out line were identified as bad pixels. Using the information in the first and second buffers, the processor identifies whether the certain pixel is a bad pixel. In one operation, the processor precludes any finding of the certain pixel as being a bad pixel if the features in the second buffer indicate that a pixel in the previous line that is adjacent to the certain pixel in the current line was identified as a bad pixel. In another operation, the processor applies a variable detection threshold against the certain pixel, with the value of that threshold being set higher if the features in the second buffer indicate that a pixel in the previous line that is adjacent to the certain pixel in the current line was identified as a bad pixel. When a bad pixel is detected, a correction algorithm is executed to replace the pixel data for the bad pixel with data that more accurately represents, estimates or approximates the intensity of the light that is incident on the photodetector.

Improving Peak Capacity in Fast Online Comprehensive Two-Dimensional Liquid Chromatography with Post-First-Dimension Flow Splitting

Abstract: The use of flow splitters between the two dimensions in online comprehensive two-dimensional (2D) liquid chromatography (LC × LC) has not received very much attention, in comparison with their use in 2D gas chromatography (GC × GC), where they are quite common In principle, splitting the flow after the first dimension column and performing online LC × LC on this constant fraction of the first dimension effluent should allow the two dimensions to be optimized almost independently When there is no flow splitting, any change in the first-dimension flow rate has an immediate impact on the second dimension With a flow splitter, one could, for example, double the flow rate into the first dimension column and perform a 1:1 flow split without changing the sample loop size or the sampler’s collection time Of course, the sensitivity would be diminished, but this can be partially compensated through the use of a larger injection; this will likely only amount to a small price to pay for this increased resolving p