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1. An Iterative Algorithm for Accurate Estimation of Power System Forced Oscillation Parameters

   https://doi.org/10.1109/PESGM52003.2023.10253231  

   Dosiek, Luke; Hosur, Sanjay (July 2023, 2023 IEEE Power & Energy Society General Meeting (PESGM))

   This paper proposes an iterative method of estimating power system forced oscillation (FO) amplitude, frequency, phase, and start/stop times from measured data. It combines three algorithms with favorable asymptotic statistical properties: a periodogram-based iterative frequency estimator, a Discrete-Time Fourier Transform (DTFT)-based method of estimating amplitude and phase, and a changepoint detection (CPD) method for estimating the FO start and stop samples. Each of these have been shown in the literature to be approximate maximum likelihood estimators (MLE), meaning that for large enough sample size or signal-to-noise ratio (SNR), they can be unbiased and reach the Cramer-Rao Lower Bound in variance. The proposed method is shown through Monte Carlo simulations of a low-order model of the Western Electricity Coordinating Council (WECC) power system to achieve statistical efficiency for low SNR values. The proposed method is validated with data measured from the January 11, 2019 US Eastern Interconnection (EI) FO event. It is shown to accurately extract the FO parameters and remove electromechanical mode meter bias, even with a time-varying FO amplitude. 

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2. An unbiased, efficient sleep–wake detection algorithm for a population with sleep disorders: change point decoder

   https://doi.org/10.1093/sleep/zsaa011  

   Cakmak, Ayse S; Da Poian, Giulia; Willats, Adam; Haffar, Ammer; Abdulbaki, Rami; Ko, Yi-An; Shah, Amit J; Vaccarino, Viola; Bliwise, Donald L; Rozell, Christopher; et al (February 2020, Sleep)

   null (Ed.)

   Abstract Study Objectives The usage of wrist-worn wearables to detect sleep–wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep–wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep. Methods Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD’s performance was compared with the performance of the OA in relation to PSG. Results On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of −22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep–wake transitions with an error of 64.4. The OA method’s performance was 28.6 min, −0.03%, and −17.2, respectively. Conclusions The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep–wake transitions. The CPD could be used as an alternate framework to investigate sleep–wake dynamics within the conventional time frame of 30-s epochs. 

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3. Detecting recurrent passenger mutations in melanoma by targeted UV damage sequencing

   https://doi.org/10.1038/s41467-023-38265-3  

   Selvam, Kathiresan; Sivapragasam, Smitha; Poon, Gregory M.; Wyrick, John J. (December 2023, Nature Communications)

   Abstract Sequencing of melanomas has identified hundreds of recurrent mutations in both coding and non-coding DNA. These include a number of well-characterized oncogenic driver mutations, such as coding mutations in the BRAF and NRAS oncogenes, and non-coding mutations in the promoter of telomerase reverse transcriptase ( TERT ). However, the molecular etiology and significance of most of these mutations is unknown. Here, we use a new method known as CPD-capture-seq to map UV-induced cyclobutane pyrimidine dimers (CPDs) with high sequencing depth and single nucleotide resolution at sites of recurrent mutations in melanoma. Our data reveal that many previously identified drivers and other recurrent mutations in melanoma occur at CPD hotspots in UV-irradiated melanocytes, often associated with an overlapping binding site of an E26 transformation-specific (ETS) transcription factor. In contrast, recurrent mutations in the promoters of a number of known or suspected cancer genes are not associated with elevated CPD levels. Our data indicate that a subset of recurrent protein-coding mutations are also likely caused by ETS-induced CPD hotspots. This analysis indicates that ETS proteins profoundly shape the mutation landscape of melanoma and reveals a method for distinguishing potential driver mutations from passenger mutations whose recurrence is due to elevated UV damage. 

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4. Online Change Point Detection for Random Dot Product Graphs

   https://doi.org/10.1109/IEEECONF53345.2021.9723374  

   Marenco, Bernardo; Bermolen, Paola; Fiori, Marcelo; Larroca, Federico; Mateos, Gonzalo (October 2021, 2021 55th Asilomar Conference on Signals, Systems, and Computers)

   Given a sequence of random graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. To this end, we adopt the Random Dot Product Graph (RDPG) model which postulates each node has an associated latent vector, and inner products between these vectors dictate the edge formation probabilities. Existing approaches for graph change-point detection (CPD) rely either on extensive computation, or they store and process the entire observed time series. In this paper we consider the cumulative sum of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal model. This reference distribution is inferred via spectral embeddings of the first few graphs in the sequence, and the monitoring function can be updated in an efficient, online fashion. We characterize the distribution of this running statistic, allowing us to select appropriate thresholding parameters that guarantee error-rate control. The end result is a lightweight online CPD algorithm, with a proven capability to flag distribution shifts in the arriving graphs. The novel method is tested on both synthetic and real network data, corroborating its effectiveness in quickly detecting changes in the input graph sequence. 

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5. Label-free detection of DNA hybridization with a compact LSPR-based fiber-optic sensor

   https://doi.org/10.1039/C7AN00249A  

   Kaye, Savannah; Zeng, Zheng; Sanders, Mollye; Chittur, Krishnan; Koelle, Paula M.; Lindquist, Robert; Manne, Upender; Lin, Yongbin; Wei, Jianjun (January 2017, The Analyst)

   A miniaturized, robust, localized surface plasmon resonance (LSPR)-coupled fiber-optic (FO) nanoprobe providing an integrated and portable solution for detection of DNA hybridization and measurement of DNA concentrations has been demonstrated. The FO nanoprobe was created by constructing arrays of metallic nanostructures on the end facets of optical fibers utilizing nanofabrication technologies, including electron beam lithography and lift-off processes. The LSPR-FO nanoprobe device offers real-time, label-free, and low-sample-volume quantification of single-strand DNA in water with high sensitivity and selectivity, achieving a limit of detection around 10 fM. These results demonstrate the feasibility of the LSPR-FO nanoprobe device as a compact and low-cost biosensor for detection of short-strand DNA. 

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