4,021 research outputs found
A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data
Background: A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity
information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.
Results: In this work, a Bayesian neural network approach is employed to analyze ion intensity information present in 13878 different MS/MS spectra. The influence of a library of 35 features on peptide fragmentation is examined under different proton mobility conditions. Useful rules
involved in peptide fragmentation are found and subsets of features which have significant influence on fragmentation pathway of peptides are characterised. An intensity model is built based on the selected features and the model can make an accurate prediction of the intensity patterns for given MS/MS spectra. The predictions include not only the mean values of spectra intensity but also the
variances that can be used to tolerate noises and system biases within experimental MS/MS spectra.
Conclusion: The intensity patterns of fragmentation spectra are informative and can be used to analyze the influence of various characteristics of fragmented peptides on their fragmentation pathway. The features with significant influence can be used in turn to predict spectra intensities. Such information can help develop more reliable algorithms for peptide and protein identification
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A physiological marker of recognition memory in adults with autism spectrum disorder? The Pupil Old/New Effect
This study investigated the pupil Old/New effect in individuals with Autism Spectrum Disorder (ASD) and typical development (TD). Participants studied verbal and visual meaningful and meaningless materials in black and white on a computer screen. Pupil sizes were measured while participants performed a Remember (episodic memory with context) /Know (semantic memory, no context) recognition memory test. ASD compared to TD individuals showed significantly reduced recognition rates for all materials. Both groups showed better memory for visual compared to verbal (picture superiority effect) and meaningful compared to meaningless materials. A pupil size ratio (pupil size for test item divided by baseline) for old (studied) and new (unstudied) materials indicated larger pupils for old compared to new materials only for the TD but not the ASD group. Pupil size in response to old versus new items was positively related to recognition accuracy, confirming that the pupil Old/New effect reflects a memory phenomenon in the ASD group. In addition, this study suggests an involvement of the noradrenergic neurotransmitter system in the abnormal hippocampal functioning in ASD. Implications of these findings as well as their underlying neurophysiology will be discussed in relation to current theories of memory in ASD
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