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Recent Submissions

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    Multicenter international assessment of a SARS-CoV-2 RT-LAMP test for point of care clinical application
    (Public Library of Science, 2022-05-11) Lu, Suying; Duplat, David; Benitez-Bolivar, Paula; Leon, Cielo; Villota, Stephany D.; Veloz-Villavicencio, Eliana; Arevalo, Valentina; Jaenes, Katariina; Guo, Yuxiu; Cicek, Seray; Robinson, Lucas; Peidis, Philippos; Pearson, Joel D.; Woodgett, Jim; Mazzulli, Tony; Ponce, Patricio; Restrepo, Silvia; Gonzalez, John M.; Bernal, Adriana; Guevara-Suarez, Marcela; Pardee, Keith; Cevallos, Varsovia E.; Gonzalez, Camila; Bremner, Rod
    Continued waves, new variants, and limited vaccine deployment mean that SARS-CoV-2 tests remain vital to constrain the coronavirus disease 2019 (COVID-19) pandemic. Affordable, point-of-care (PoC) tests allow rapid screening in non-medical settings. Reverse-transcription loop-mediated isothermal amplification (RT-LAMP) is an appealing approach. A crucial step is to optimize testing in low/medium resource settings. Here, we optimized RT-LAMP for SARS-CoV-2 and human β-actin, and tested clinical samples in multiple countries. “TTTT” linker primers did not improve performance, and while guanidine hydrochloride, betaine and/or Igepal-CA-630 enhanced detection of synthetic RNA, only the latter two improved direct assays on nasopharygeal samples. With extracted clinical RNA, a 20 min RT-LAMP assay was essentially as sensitive as RT-PCR. With raw Canadian nasopharygeal samples, sensitivity was 100% (95% CI: 67.6% - 100%) for those with RT-qPCR Ct values ≤ 25, and 80% (95% CI: 58.4% - 91.9%) for those with 25 < Ct ≤ 27.2. Highly infectious, high titer cases were also detected in Colombian and Ecuadorian labs. We further demonstrate the utility of replacing thermocyclers with a portable PoC device (FluoroPLUM). These combined PoC molecular and hardware tools may help to limit community transmission of SARS-CoV-2.
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    Getting the nod: Pediatric head motion in a transdiagnostic sample during movie- and resting-state fMRI
    (Public Library of Science, 2022-04-14) Frew, Simon; Samara, Ahmad; Shearer, Hallee; Eilbott, Jeffrey; Vanderwal, Tamara
    Head motion continues to be a major problem in fMRI research, particularly in developmental studies where an inverse relationship exists between head motion and age. Despite multifaceted and costly efforts to mitigate motion and motion-related signal artifact, few studies have characterized in-scanner head motion itself. This study leverages a large transdiagnostic public dataset (N = 1388, age 5-21y, The Healthy Brain Network Biobank) to characterize pediatric head motion in space, frequency, and time. We focus on practical aspects of head motion that could impact future study design, including comparing motion across groups (low, medium, and high movers), across conditions (movie-watching and rest), and between males and females. Analyses showed that in all conditions, high movers exhibited a different pattern of motion than low and medium movers that was dominated by x-rotation, and z- and y-translation. High motion spikes (>0.3mm) from all participants also showed this pitch-z-y pattern. Problematic head motion is thus composed of a single type of biomechanical motion, which we infer to be a nodding movement, providing a focused target for motion reduction strategies. A second type of motion was evident via spectral analysis of raw displacement data. This was observed in low and medium movers and was consistent with respiration rates. We consider this to be a baseline of motion best targeted in data preprocessing. Further, we found that males moved more than, but not differently from, females. Significant cross-condition differences in head motion were found. Movies had lower mean motion, and especially in high movers, movie-watching reduced within-run linear increases in head motion (i.e., temporal drift). Finally, we used intersubject correlations of framewise displacement (FD-ISCs) to assess for stimulus-correlated motion trends. Subject motion was more correlated in movie than rest, and 8 out of top 10 FD-ISC windows had FD below the mean. Possible reasons and future implications of these findings are discussed.
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    Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets
    (Public Library of Science, 2022-07-28) Forouzandeh, Amir; Rutar, Alex; Kalmady, Sunil V.; Greiner, Russell
    Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).
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    The impact of insurance institutional investors on corporate value from selection and creation perspective
    (Public Library of Science, 2022-07-01) Rong, Xing; Zhang, Tingting; Liu, Kai
    The majority of insurance investment funds are derived from policy liability debt funds. It differs from other institutional investors in a number of ways, including investment size, horizon, duration, risk, and so on. However, only a small portion of the extant literature focuses on in-depth and extensive analysis of Insurance Institutional Investors’ holdings (IIIs). This study analyses the impact of shareholding by insurance institutions on the value of Shanghai and Shenzhen A-share listed companies in China’s capital market. The paper offers three major contributions. First, we discovered that long-term equity-holding IIIs have both value selection and value creation functions. Second, the value creation function becomes more significant among long-term stock-holding IIIs with an increase in the period during which they retain the company’s shares; Third, fast-in and fast-out (FIFO) IIIs have a value-inhibiting effect on the held company and serve a value selection role, rather than a value creation function. This study provides more insight on the lack of academic interest in insurance institutions and serves as a foundation and reference for the design of regulatory policies for insurance institutions’ involvement in stock markets. It also gives empirical evidence for corporations to accurately analyze shareholding by insurance institutions. Furthermore, since this study concentrates on China’s capital market, it can serve as a benchmark for other nations, particularly, those designated as developing market economies.
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    Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester
    (Public Library of Science, 2022-06-22) Walker, Mark C.; Willner, Inbal; Miguel, Olivier X.; Murphy, Malia S. Q.; El-Chaar, Darine; Moretti, Felipe; Harvey, Alysha L. J. Dingwall; White, Ruth Rennicks; Muldoon, Katherine A.; Carrington, Andre M.; Hawken, Steven; Aviv, Richard I.
    Objective To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. Methods All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability. Results The dataset included 289 sagittal fetal ultrasound images; 129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% Cl: 88-98%), sensitivity 92% (95% Cl: 79-100%), specificity 94% (95% Cl: 91-96%), and the area under the ROC curve 0.94 (95% Cl: 0.89-1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area. Conclusions Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.