Edwards, Nathan J. | Search NASA

DOE OSTI2021

Hardware intrusion detection system

An apparatus for intrusion detection includes processing circuitry, a switch, signal detection circuitry, and an analog-to-digital converter (“ADC”). The processing circuitry is coupled to send a challenge signal to a device when the device is coupled to the processing circuitry. The switch is coupled to be enabled and disabled by the processing circuitry. The switch is for coupling to the device to receive a response signal in response to the challenge signal sent by the processing circuitry. The signal detection circuitry is coupled to receive the response signal in via the switch, when the processing circuitry enables the switch. The ADC is coupled to take measurements of the signal detection circuitry at a first output. The processing circuitry is coupled to the ADC and configured to analyze whether an intruder is present in the device based on the measurements of the signal detection circuitry.

Edwards, Nathan J.↗

DOE OSTI2021

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

59 BASIC BIOLOGICAL SCIENCES↗