True Signal Research Articles

Mitigation of Unmodeled Error to Improve the Accuracy of Multi-GNSS PPP for Crustal Deformation Monitoring

High-rate multi-constellation global navigation satellite system (GNSS) precise point positioning (PPP) has been recognized as an efficient and reliable technique for large earthquake monitoring. However, the displacements derived from PPP are often overwhelmed by the centimeter-level noise, therefore they are usually unable to detect slight deformations which could provide new findings for geophysics. In this paper, Global Positioning System (GPS), GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS), and BeiDou navigation satellite system (BDS) data collected during the 2017 Mw 6.5 Jiuzhaigou earthquake were used to further exploit the capability of BDS-only and multi-GNSS PPP in deformation monitoring by applying sidereal filtering (SF) in the observation domain. The equation that unifies the residuals for the uncombined and undifferenced (UCUD) PPP solution on different frequencies was derived, which could greatly reduce the complexity of data processing. An unanticipated long-term periodic error term of up to ± 3 cm was found in the phase residuals associated with BDS satellites in geostationary Earth orbit (GEO), which is not due to multipath originated from the ground but is in fact satellite dependent. The period of this error is mainly longer than 2000 s and cannot be alleviated by using multi-GNSS. Compared with solutions without sidereal filtering, the application of the SF approach dramatically improves the positioning precision with respect to the weekly averaged positioning solution, by 75.2%, 42.8%, and 56.7% to 2.00, 2.23, and 5.58 cm in the case of BDS-only PPP in the east, north, and up components, respectively, and 71.2%, 27.7%, and 37.9% to 1.25, 0.81, and 3.79 cm in the case of GPS/GLONASS/BDS combined PPP, respectively. The GPS/GLONASS/BDS combined solutions augmented by the SF successfully suppress the GNSS noise, which contributes to the detection of the true seismic signal and is beneficial to the pre- and post-seismic signal analysis.

Security control for networked control systems with randomly occurring integrity check protection subject to randomly occurring zero-value attacks

This paper investigates the problem of secure control for networked control systems (NCSs) under randomly occurring zero-value attacks (ROZVAs). Specifically, ROZVAs only offset the true signal without injecting obfuscated information or noises, and possess the minimum energy of the added malicious information. To protect system stability against ROZVA, randomly occurring integrity check protection (ROICP) is introduced which prevents malicious data injection with less energy cost than persistently occurring protection. Besides the random phenomena of ROZVA and ROICP, which are characterized by two mutually independent random variables obeying the Bernoulli distribution, the randomly occurring time delays caused by ROICP are also considered in system modelling. According to the built stochastic linear system model, security analysis of the NCS with ROICP subject to ROZVA is carried out and sufficient condition for stochastic stability is derived via a linear matrix inequality (LMI) approach. Based on the proposed condition, a compensation feedback controller is designed to facilitate system stability. Finally, simulation results show the effectiveness of the proposed method.

Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration

We investigate the signal reconstruction performance of sparse linear regression in the presence of noise when piecewise continuous nonconvex penalties are used. Among such penalties, we focus on the smoothly clipped absolute deviation (SCAD) penalty. The contributions of this study are three-fold: we first present a theoretical analysis of a typical reconstruction performance, using the replica method, under the assumption that each component of the design matrix is given as an independent and identically distributed (i.i.d.) Gaussian variable. This clarifies the superiority of the SCAD estimator compared with in a wide parameter range, although the nonconvex nature of the penalty tends to lead to solution multiplicity in certain regions. This multiplicity is shown to be connected to replica symmetry breaking in the spin-glass theory, and associated phase diagrams are given. We also show that the global minimum of the mean square error between the estimator and the true signal is located in the replica symmetric phase. Second, we develop an approximate formula efficiently computing the cross-validation error without actually conducting the cross-validation, which is also applicable to the non-i.i.d. design matrices. It is shown that this formula is only applicable to the unique solution region and tends to be unstable in the multiple solution region. We implement instability detection procedures, which allows the approximate formula to stand alone and resultantly enables us to draw phase diagrams for any specific dataset. Third, we propose an annealing procedure, called nonconvexity annealing, to obtain the solution path efficiently. Numerical simulations are conducted on simulated datasets to examine these results to verify the consistency of the theoretical results and the efficiency of the approximate formula and nonconvexity annealing. The characteristic behaviour of the annealed solution in the multiple solution region is addressed. Another numerical experiment on a real-world dataset of Type Ia supernovae is conducted; its results are consistent with those of earlier studies using the formulation. A MATLAB package of numerical codes implementing the estimation of the solution path using the annealing with respect to in conjunction with the approximate CV formula and the instability detection routine is distributed in Obuchi (2019 https://github.com/T-Obuchi/SLRpackage_AcceleratedCV_matlab).

Comparison of \xe2 Versus a and Hit/Miss POD-Estimation Methods: A European Viewpoint

For estimating the probability of detection (POD) in non-destructive evaluation (NDE), there are two standard methods, the so-called â versus a approach and the hit/miss approach. The two approaches have different requirements for the quality and quantity of input data as well as for the underlying NDE method. There is considerable overlap between the methods, and they have different limitations, so it is of interest to study the differences arising from using each methodology. In particular, if the dataset is not ideal, the methodologies may exhibit different problems dealing with various limitations in the data. In this paper, a comparison between â versus a and hit/miss analysis was completed for two different data sets, a manual aerospace eddy-current inspection and a nuclear industry phased array ultrasonic weld inspection using a simplified online tool. It was found that the two standard methods (â vs. a and hit/miss) may give significantly different results, if the true hit/miss decision is based on inspector judgement and not automated signal threshold. The true inspector hit/miss performance shows significant variance that is not attributable to signal amplitude. Model-assisted POD was not able to model the inspector performance due to lack of representative amplitude threshold and difficulties in capturing true signal variance. The paper presents experience from practical cases and may be considered a European viewpoint.

High long-term test–retest reliability for extrastriatal 11C-raclopride binding in healthy older adults

In vivo dopamine D2-receptor availability is frequently assessed with 11C-raclopride and positron emission tomography. Due to low signal-to-noise ratios for 11C-raclopride in areas with low D2 receptor densities, the ligand has been considered unreliable for measurements outside the dopamine-dense striatum. Intriguingly, recent studies show that extrastriatal 11C-raclopride binding potential (BPND) values are (i) reliably higher than in the cerebellum (where D2-receptor levels are negligible), (ii) correlate with behavior in the expected direction, and (iii) showed good test–retest reliability in a sample of younger adults. The present work demonstrates high seven-month test–retest reliability of striatal and extrastriatal 11C-raclopride BPND values in healthy, older adults (n = 27, age: 64–78 years). Mean 11C-raclopride BPND values were stable between test sessions in subcortical nuclei, and in frontal and temporal cortices (p > 0.05). Across all structures analyzed, intraclass correlation coefficients were high (0.85–0.96), absolute variability was low (mean: 4–8%), and coefficients of variance ranged between 9 and 25%. Furthermore, regional 11C-raclopride BPND values correlated with previously determined 18F-fallypride BPND values (ρ = 0.97 and 0.92 in correlations with and without striatal values, respectively, p < 0.01) and postmortem determined D2-receptor densities (including striatum: ρ = 0.92; p < 0.001; excluding striatum: ρ = 0.75; p = 0.067). These observations suggest that extrastriatal 11C-raclopride measurements represent a true D2 signal.

Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.

Digital array radar channel error analysis

The amplitude-phase error caused by the channel inconsistency in the actual digital array radar and the mutual coupling error caused by the mutual coupling effect have a great influence on the performance of the array. This study analyses and models the error characteristics of amplitude-phase error and mutual-coupling error, respectively. Based on the narrow-band and wide-band array received signal models, the spatial signal arrival angle estimation accuracy and the beam-synthesising pattern are used as indicators to analyse the influence of errors on the array. Channel inconsistency and mutual coupling will cause the estimated signal arrival angle to deviate from the true signal direction and the spatial spectrum curve peak will become gentle. In addition, the beam pattern's sidelobe will raise, null orientation will deviate from the interference signal direction, and gain will raise.

A new method for abnormal underground rocks identification using ground penetrating radar

Surrounding rock quality of underground caverns, which determines the safety and stability, is of great importance in geological engineering. Ground penetrating radar (GPR) has potential to accurately image the subterranean profile. Against two major issues that affect GPR detection, a new method to process GPR data for abnormal underground rocks identification was investigated. An improved amplitude gain function and an elimination method were proposed, which can amplify weak signals and separate true signals from superimposed waveforms, and it was further developed into real-time algorithm. In field measurements, after processing the GPR data of two pre-buried pipelines, the imaging profile can accurately present their locations. Surrounding rock quality of a subterranean cavern was detected by GPR, and the elimination results can clearly show the multi-joint areas. The real-time algorithm has capacity to discover abnormal places during the survey instead of afterwards, which can improve the engineer efficiency.

On the resolutions of ocean altimetry maps

Abstract. The Data Unification and Altimeter Combination System (DUACS) produces sea level global and regional maps that serve oceanographic applications, climate forecasting centers, and geophysics and biology communities. These maps are generated using an optimal interpolation method applied to altimeter observations. They are provided on a global 1∕4∘ × 1∕4∘ (longitude × latitude) and daily grid resolution framework (1∕8∘ × 1∕8∘ longitude × latitude grid for the regional products) through the Copernicus Marine Environment Monitoring Service (CMEMS). Yet, the dynamical content of these maps does not have full 1∕4∘ spatial and 1 d temporal resolutions due to the filtering properties of the optimal interpolation. In the present study, we estimate the effective spatial and temporal resolutions of the newly reprocessed delayed-time DUACS maps (a.k.a. DUACS-DT2018). Our approach is based on the ratio between the spectral content of the mapping error and the spectral content of independent true signals (along-track and tide gauge observations), also known as the noise-to-signal ratio. We found that the spatial resolution of the DUACS-DT2018 global maps based on sampling by three altimeters simultaneously ranges from ∼100 km wavelength at high latitude to ∼800 km wavelength in the equatorial band and the mean temporal resolution is ∼34 d. The mean effective spatial resolution at midlatitude is estimated to be ∼200 km. The mean effective spatial resolution is ∼130 km for the regional Mediterranean Sea and for the regional Black Sea products. An intercomparison with previous DUACS reprocessing systems (a.k.a., DUACS-DT2010 and DUACS-DT2014) highlights the progress of the system over the past 8 years, in particular a gain of resolution in highly turbulent regions. The same diagnostic applied to maps constructed with two altimeters and maps with three altimeters confirms a modest increase in resolving capabilities and accuracies in the DUACS maps with the number of missions.

Lost data recovery for structural health monitoring based on convolutional neural networks

Signal transmission loss of using wireless sensors for structural health monitoring is a usual case, which undermines the reliability of the sensors for monitoring the structural conditions. The measured vibration data with a high data loss ratio can hardly be used for the analysis, that is, modal identification, as it will lead to significant errors in the results. This paper proposes a novel approach based on convolutional neural networks for recovering the lost vibration data for structural health monitoring. The used network is a fully feed-forward convolutional neural network with bottleneck architecture and skip connection, which constructs the nonlinear relationships between the incomplete signal with data loss measured from the sensors with the transmission loss and the complete true signal. The trained network extracts the robust higher representation features of the measured incomplete signals using the compression layers and expands those features gradually throughout the reconstruction layers to recover and obtain the complete true signals. The long-term vibration data from Dowling Hall Footbridge are employed to validate the effectiveness and robustness of the proposed approach for the lost data recovery. Two case studies are conducted to validate the recovery accuracy for single-channel and multiple-channel cases, respectively. The effect of sampling rate on the recovery accuracy is also investigated. The proposed approach exhibits the outstanding capability of lost data recovery, even when the signals have severe data loss ratios up to 90%. To further demonstrate the reliability of the recovered signals for data analysis, modal identification results by using the recovered signals with different data loss ratios show a very good agreement with those obtained from the complete true data.

Population-specific long-range linkage disequilibrium in the human genome and its influence on identifying common disease variants

Despite the availability of large-scale sequencing data, long-range linkage disequilibrium (LRLD) has not been extensively studied. The theoretical aspects of LRLD estimates were studied to determine the best estimation method for the sequencing data of three different populations of African (AFR), European (EUR), and East-Asian (EAS) descent from the 1000 Genomes Project. Genome-wide LRLDs excluding centromeric regions revealed clear population specificity, presenting substantially more population-specific LRLDs than coincident LRLDs. Clear relationships between the functionalities of the regions in LRLDs denoted long-range interactions in the genome. The proportions of gene regions were increased in LRLD variants, and the coding sequence (CDS)-CDS LRLDs showed obvious functional similarities between genes in LRLDs. Application to theoretical case-control associations confirmed that the LRLDs in genome-wide association studies (GWASs) could contribute to false signals, although the impacts might not be severe in most cases. LRLDs with variants with functional similarity exist in the human genome indicating possible gene-gene interactions, and they differ depending on populations. Based on the current study, LRLDs should be examined in GWASs to identify true signals. More importantly, population specificity in LRLDs should be examined in relevant studies.

Residual Congestion on Discharge for Heart Failure Assessed by Impedance Technique Predicts 30-Day and One-Year Heart Failure Death. Results of IMPEDANCE-HF Extended Trial

Introduction The aim of secondary analyses of the IMPEDANCE-HF extended trial was to find out if residual congestion on discharge for HF could predict 30-day and one-year Heart Failure death. Methods The IMPEDANCE-HF extended trial was a randomized controlled single-blinded trial of HF with LVEF ≤ 45% and NYHA class II-IV patients (ClinicalTrials.gov NCT01315223). Half of the patients (N=145) were assigned to the active Lung Impedance (LI)-guided treatment arm where clinicians were based therapy on LI level. The other half was assigned to the control arm where LI values were recorded but not conveyed to the clinical treatment team. A non-invasive impedance device (“CardioSet”, Tel Aviv) was used to assess the lung fluid content. Unlike the existing impedance devices, the present device can differentiate a true signal from the lungs from the noise signal of surrounding chest wall and enables to measure a small change in lung fluid content. A method to determine individual “dry” baseline LI (BLI) for each HF patient has been reported. BLI for each patient was used to calculate a new parameter, the ∆LIR= [(current LI/BLI)-1] × 100%, which determinate the degree of pulmonary congestion in time of measurement. Method. Degree of pulmonary congestion at discharge for HF hospitalization was divided into 5 categories. (1) ∆LIR= 0 to - 18% - minimal interstitial edema (IE), (2) ∆LIR= - 18.1 to -28% - mild IE, (3) ∆LIR= -28.1 to - 38 - moderate IE, (4) ∆LIR= -38.1 to -48% - severe IE to mild alveolar edema (AE) and (5) ∆LIR Results LI-guided patients were followed for 61.9.4±39.6 months and control patients for 46.7±33.3 months (p Conclusions The degree of pre-discharge pulmonary congestion as assessed by LI is a very robust and reliable predictor for 30-day and one-year HF death. Discharge of patients with pulmonary congestion corresponding to categories 4 and 5 is unacceptable and associated with 30-day and one-year mortality more than 80%.

Evaluation of methods for adjusting population stratification in genome-wide association studies: Standard versus categorical principal component analysis.

Unaccounted population stratification can lead to false-positive findings and can mask the true association signals in identification of disease-related genetic variants. The computational simplicity of principal component analysis (PCA) makes it a widely used method for population stratification adjustment. However, given that genotype data are generally represented by numerical values 0, 1, and 2, corresponding to the number of minor alleles, it is more reasonable to consider genotype data as categorical data. Because PCA is inherently only suitable for continuous variables, it is not appropriate to directly apply PCA on genotype data. Second, although common variants have been extensively studied, little is known about the stratification of rare variants and its impact on association tests. Over the last decade, there has been a shift in the genome-wide association studies toward studying low-frequency (minor allele frequency [MAF] between 0.01 and 0.05) and rare (MAF less than 0.01) variants, which are now widely reputed as complex trait determinants. The fact that rare variants are not stratified in the same way as common variants necessitates the development of statistical methods that can capture stratification patterns for low-frequency and rare variants. To address these limitations, we investigate performances of generalized PCA and similarity-matrix-based PCA methods to detect underlying structures for rare and common variants. We demonstrate, through simulated and real datasets, that a special case of generalized PCA (i.e., logistic PCA) is able to adjust for population stratification in rare variants much more effectively than standard PCA while their performances are comparable for common variants.

Political uncertainty and financial market reactions: A new test

Recent literature highlights the crucial role of understanding the mechanism between political uncertainty and financial market reactions. Along the lines of this topic, our study stresses a clear causal framework. Exploiting one unique natural experiment of the Taiwan Strait Crisis (1995-96), we provide a simple testing strategy which could precisely quantify the effects of political shocks on stock markets. This approach combines the features of one innovative panel estimator and new statistical learning methods for causal inference. Our results indicate, separating true signal from noise via the optimal benchmark, the political crisis had a substantial and significant negative impact on Taiwan's stock prices. This finding is consistent with the empirical evidence of risk premium in recent studies. Moreover, the optimal counterfactual could be an alternative option for the ceteris paribus assumption in non-lab controlled settings. Finally, this study shows predictor selection is needed for a convincing causal estimate in counterfactual studies.

Sensitivity Analysis of Leakage Correction of GRACE Data in Southwest China Using A-Priori Model Simulations: Inter-Comparison of Spherical Harmonics, Mass Concentration and In Situ Observations.

The Gravity Recovery and Climate Experiment (GRACE) level-2 spherical harmonic (SH) solutions are noisy and thus require filtering. Filtering reduces noise but affects signal quality via signal leakage. Generally, a leakage correction is required for GRACE applications to remove leakage signal and recover the true signal. Forward modelling based on some a priori information is a widely used approach for leakage correction of GRACE data. The a priori information generally relies on global hydrological model simulations. There are many global hydrological models and therefore it is of interest to explore how different global hydrology model simulations influence leakage correction results. This study investigated the sensitivity of three leakage correction methods (additive method, scaling factor method and multiplicative method) to five global hydrology model simulations (four models from the Global Land Data Assimilation System (GLDAS) and the WaterGAP Global Hydrology Model (WGHM)). The sensitivity analysis was performed with observational data in Southwest China and one sub-region, Guangxi. Results show that although large differences were identified among the five global model simulations, the additive and scaling factor methods are less affected by the choice of a priori model in comparison to the multiplicative approach. For the additive and scaling factor methods, WGHM outperforms the other four GLDAS models in leakage correction of GRACE data. GRACE data corrected with the multiplicative method shows the highest amount of error, indicating this method is not applicable for leakage correction in the study area. This study also assessed the level-3 mascon (mass concentration) solutions of GRACE data. The mascon-based results are nearly as good as the leakage corrected results based on SH solutions.

A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis.

BackgroundData errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases.ResultsApplication to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis-associations was small and/or the number of samples was large. Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in multiple cancer datasets. Our procedure was not only able to identify sample-labeling errors but also to unambiguously identify the source of the errors. Our results demonstrate that these errors should be identified and corrected before integrative analysis.ConclusionsOur results indicate that sample-labeling errors were common in large multi-omics datasets. These errors should be corrected before integrative analysis.

Abstract 4176: Familial lung cancer exhibits multiple novel linked haplotypes within pedigrees

Abstract Lung cancer (LC) kills more people in the United States each year than any other cancer. While it is well known that a variety of environmental factors (particularly tobacco smoke) strongly increase the risk of LC, there are multiple associated genetic variants with small contributions to risk. High aggregation of LC within rare individual families suggest that there are high-risk genetic variants as well. However, these genetic risk factors for LC are under studied due to the rapid fatality of LC. We studied 28 highly aggregated extended high-risk familial lung cancer (HRFLC) families collected from eight different sites across the US. Whole exome sequencing was performed on 290 individuals from these families to identify potential risk variants for HRFLC using genetic linkage analysis. Quality control was performed on the sequence data, filtering on parameters such as read depth, genotype quality, missingness, and Mendelian inconsistencies. Identity-by-descent (IBD) values were also calculated to verify correct familial relationships. Quality control procedures left approximately 400,000 SNVs and indels for analysis.Parametric two-point linkage analysis was performed assuming an autosomal dominant mode of inheritance. Disease allele frequency was set to 1% with a penetrance of 80% for carriers and 1% phenocopy rate. While we did not identify any genome-wide significant variants across the 28 families, multiple suggestive variants were identified. The largest cluster of suggestive variants was located at 14q32 in the CATSPERB gene. Given the likely locus heterogeneity in LC (combined with the lack of power in some families), it is not surprising that none of the variants were significant across the families; looking at the individual family results proved more informative. Long haplotypes linked to LC risk were identified in multiple families. These long runs of positive linkages, which have little to no negative linkage evidence across them, are characteristic of true linkage signals in these types of analysis. Two of the most interesting linked regions were at 7p36.1 and 4q21.23-28.23. The 7p signal (observed in a single family) was genome-wide suggestive and located within the SSPO gene. SSPO has been implicated in breast and skin cancer (melanoma); it is a novel lung cancer signal. The 4q linkage (again observed in a single family) covers a large chunk of 4q and contains multiple potential candidate genes, however, the best candidate gene is PTPN13, a gene implicated in lung cancer but never in familial lung cancer. We are currently evaluating the individual family results of several other pedigrees and plan to perform additional analyses to confirm these linkages. Citation Format: Anthony Musolf, Haiming Sun, Bilal A. Moiz, Claudio W. Pikielny, Mariza de Andrade, Diptasri Mandal, Colette Gaba, Ping Yang, Yafang Li, Ming You, Richard K. Wilson, Elena Y. Kupert, Marshall W. Anderson, Ann G. Schwartz, Susan M. Pinney, Christopher I. Amos, Ramaswamy Govindan, Joan E. Bailey-Wilson. Familial lung cancer exhibits multiple novel linked haplotypes within pedigrees [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4176.

Abstract 1675: Analytical validation of MSI High detection with GuardantOMNI

Abstract Background: Late stage therapy selection is advancing beyond the detection of classical driver alterations (SNV, Indel, CNV, Fusion) to the use of aggregate biomarkers comprising combinations of SNVs, Indels, and fusions, that reflect the underlying mutational process, indicate the cellular dysfunction, and suggest the neoantigen signature. GuardantOMNI (OMNI), is a highly sensitive 500-gene cfDNA sequencing test designed to detect primary somatic mutations as well as aggregate biomarkers including TMB and DDR pathway defects. The newest version of OMNI introduces MSI biomarker detection, which is an indication for the first FDA-approved pan-cancer therapy. Here we present the first analytical validation study of OMNI MSI-H detection. Methods: Probabilistic models for MSI detection were designed to interrogate instability across &amp;gt;1300 loci, differentiating PCR and sequencing induced artifacts from true biological signals using molecular barcoding as implemented in the Guardant digital sequencing platform. Cut-offs were selected based on the analysis of the training healthy donor cohort and locked prior to validation. Analytical performance was assessed using pre-characterized cell lines, cancer-free donor-samples, and clinical patient samples. Qualitative and quantitative orthogonal confirmation was provided by next-generation sequencing (NGS) and published information. Results: 350 samples were processed for this study, using both 5ng and 30ng cfDNA input levels, and all samples passed sequencing QC metrics established prior to testing. Cell line-based dilution studies performed in triplicate spanning 5ng and 30ng input range as well as in silico simulation demonstrated 95% limit of detection (LoD) at 0.1% Analysis of 121 healthy donor and 16 MSS advanced cancer patient samples identified no false positives (FP). In 240 patient samples with orthogonal confirmation of MSI status from tissue or panel NGS, GuardantOMNI demonstrated 100% analytic sensitivity (3/3) and 100% analytic specificity (237/237) with a PPV of 1 and and NPV 1. Retrospective analysis of 2000+ clinical samples detected MSI high in 16% of colorectal cancers, 4% of prostate cancers, and 0.6% of non-small cell lung cancers. Conclusions: Here we have demonstrated the performance of MSI-H detection with GuardantOMNI (OMNI), which has the potential to accelerate clinical trial enrollment, research and discovery with a single, non-invasive blood sample. Citation Format: Jing Zhao, Alexander Artyomenko, Carlo Artieri, John Latham, Stephen R. Fairclough, Catalin Barbacioru, Elena Helman, John Strickler, Darya Chudova, Richard Lanman, AmirAli Talasaz. Analytical validation of MSI High detection with GuardantOMNI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1675.

Narrow-band search for gravitational waves from known pulsars using the second LIGO observing run

Isolated spinning neutron stars, asymmetric with respect to their rotation axis, are expected to be sources of continuous gravitational waves. The most sensitive searches for these sources are based on accurate matched filtering techniques, that assume the continuous wave to be phase-locked with the pulsar beamed emission. While matched filtering maximizes the search sensitivity, a significant signal-to-noise ratio loss will happen in case of a mismatch between the assumed and the true signal phase evolution. Narrow-band algorithms allow for a small mismatch in the frequency and spin-down values of the pulsar while integrating coherently the entire data set. In this paper we describe a narrow-band search using LIGO O2 data for the continuous wave emission of 33 pulsars. No evidence for a continuous wave signal has been found and upper-limits on the gravitational wave amplitude, over the analyzed frequency and spin-down volume, have been computed for each of the targets. In this search we have surpassed the spin-down limit for some of the pulsars already present in the O1 LIGO narrow-band search, such as J1400\textminus6325 J1813\textminus1246, J1833\textminus1034, J1952+3252, and for new targets such as J0940\textminus5428 and J1747\textminus2809. For J1400\textminus6325, J1833\textminus1034 and J1747\textminus2809 this is the first time the spin-down limit is surpassed.

Siberian Trap volcanism, global warming and the Permian-Triassic mass extinction: New insights from Armenian Permian-Triassic sections

AbstractPermian-Triassic boundary sections from Armenia were studied for carbon isotopes of carbonates as well as oxygen isotopes of conodont apatite in order to constrain the global significance of earlier reported variations in the isotope proxies and elaborate the temporal relationship between carbon cycle changes, global warming and Siberian Trap volcanism. Carbon isotope records of the Chanakhchi and Vedi II sections show a 3–5‰ negative excursion that start in the Clarkina nodosa (C. yini) conodont Zone (latest Permian) with minimum values recorded in Hindeodus parvus to Isarcicella isarcica conodont zones (earliest Triassic). Sea surface temperatures (SST) reconstructed from oxygen isotopes of conodont apatite increase by 8–10 °C over an extrapolated time interval of ∼39 ka with the onset of global warming occurring in the C. iranica (C. meishanensis) Zone of the latest Permian. Climate warming documented in the Armenian sections is comparable to published time-equivalent shifts in SST in Iran and South China suggesting that this temperature change represents a true global signature. By correlating the Armenian and Iranian section with the radiometrically well-dated Meishan GSSP (Global Stratotype Section and Point) section (South China), the negative shift in δ13C is estimated to have occurred 12–128 ka prior to the onset of global warming. This temporal offset is unexpected given the synchrony in changes in atmospheric CO2 and global temperature as seen in Pleistocene ice core records. The negative δ13C excursion is explained by the addition of emission of isotopically light CO2 and CH4 from thermogenic heating of organic carbon-rich sediments by Siberian Trap sill intrusions. However, the observed time lag in the δ13C and δ18O shifts questions the generally assumed cause-effect relationship between emission of thermogenically produced greenhouse gases and global warming. The onset of temperature rise coincides with a significant enrichment in Hg/TOC (total organic carbon) ratios arguing for a major volcanic event at the base of the extinction interval. Whether global warming was a major factor for the Late Permian mass extinction depends on the duration of the extinction interval. Warming only starts at the base of the extinction interval, but with the extinction encompassing a time interval of 60 ± 48 ka, global climate warming in conjunction with temperature-related stressors as hypoxia and reduced nutrient availability may have been one of the major triggers of the most devastating biotic crisis in Earth history.