High-resolution prestack seismic inversion using a hybrid FISTA least-squares strategy

A new inversion method to estimate high-resolution amplitude-versus-angle attributes (AVA) attributes such as intercept and gradient from prestack data is presented. The proposed technique promotes sparse-spike reflectivities that, when convolved with the source wavelet, fit the observed data. The inversion is carried out using a hybrid two-step strategy that combines fast iterative shrinkage-thresholding algorithm (FISTA) and a standard least-squares (LS) inversion. FISTA, which can be viewed as an extension of the classical gradient algorithm, provides sparse solutions by minimizing the misfit between the modeled and the observed data, and the [Formula: see text]-norm of the solution. FISTA is used to estimate the location in time of the main reflectors. Then, LS is used to retrieve the appropriate reflectivity amplitudes that honor the data. FISTA, like other iterative solvers for [Formula: see text]-norm regularization, does not require matrices in explicit form, making it easy to apply, economic in computational terms, and adequate for solving large-scale problems. As a consequence, the FISTA+LS strategy represents a simple and cost-effective new procedure to solve the AVA inversion problem. Results on synthetic and field data show that the proposed hybrid method can obtain high-resolution AVA attributes from noisy observations, making it an interesting alternative to conventional methods.

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High-Resolution Melting Is a Sensitive, Cost-Effective, Time-Saving Technique for BRAF V600E Detection in Thyroid FNAB Washing Liquid: A Prospective Cohort Study

European Thyroid Journal ◽

10.1159/000430092 ◽

2015 ◽

Vol 4 (2) ◽

pp. 73-81 ◽

Cited By ~ 6

Author(s):

Marco Marino ◽

Maria Laura Monzani ◽

Giulia Brigante ◽

Katia Cioni ◽

Bruno Madeo ◽

...

Keyword(s):

Cohort Study ◽

High Resolution ◽

Molecular Analysis ◽

Prospective Cohort Study ◽

Prospective Cohort ◽

Large Scale ◽

High Resolution Melting ◽

Cost Effective ◽

Braf V600e Mutation ◽

Braf V600e

Objective: The diagnostic accuracy of thyroid fine needle aspiration biopsy (FNAB) can be improved by the combination of cytological and molecular analysis. In this study, washing liquids of FNAB (wFNAB) were tested for the BRAF V600E mutation, using the sensitive and cost-effective technique called high-resolution melting (HRM). The aim was to demonstrate the feasibility of BRAF analysis in wFNAB and its diagnostic utility, combined with cytology. Design: Prospective cohort study. Methods: 481 patients, corresponding to 648 FNAB samples, were subjected to both cytological (on cells smeared onto a glass slide) and molecular analysis (on fluids obtained washing the FNAB needle with 1 ml of saline) of the same aspiration. BRAF V600E analysis was performed by HRM after methodological validation for application to wFNAB (technique sensitivity: 5.4%). Results: The cytological results of the FNAB were: 136 (21%) nondiagnostic (THY1); 415 (64%) benign (THY2); 80 (12.4%) indeterminate (THY3); 9 (1.4%) suspicious for malignancy (THY4); 8 (1.2%) diagnostic of malignancy (THY5). The BRAF V600E mutation was found in 5 THY2, 2 THY3, 6 THY4 and 6 THY5 samples. Papillary carcinoma diagnosis was histologically confirmed in all BRAF+ thyroidectomized patients. BRAF combined with cytology improved the diagnostic value compared to cytology alone in a subgroup of 74 operated patients. Conclusions: HRM was demonstrated to be a feasible method for BRAF analysis in wFNAB. Thanks to its sensitivity and cost-effectiveness, it might be routinely used on a large scale in clinical practice. In perspective, standby wFNAB samples could be analyzed a posteriori in case of indeterminate cytology and/or suspicious findings on ultrasound.

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Nonlinear waveform inversion of plane‐wave seismograms in stratified elastic media

Geophysics ◽

10.1190/1.1443083 ◽

1991 ◽

Vol 56 (5) ◽

pp. 664-674 ◽

Cited By ~ 56

Author(s):

F. Kormendi ◽

M. Dietrich

Keyword(s):

Plane Wave ◽

Least Squares ◽

Wave Velocity ◽

Generalized Least Squares ◽

P Wave ◽

Gradient Algorithm ◽

Stratified Medium ◽

Inversion Method ◽

Inversion Algorithm ◽

Frequency Wave

We present a method for determining the elastic parameters of a horizontally stratified medium from its plane‐wave reflectivity. The nonlinear inverse problem is iteratively solved by using a generalized least‐squares formalism. The proposed method uses the (relatively) fast convergence properties of the conjugate gradient algorithm and achieves computational efficiency through analytical solutions for calculating the reference and perturbational wavefields. The solution method is implemented in the frequency‐wave slowness domain and can be readily adapted to various source‐receiver configurations. The behavior of the algorithm conforms to the predictions of generalized least‐squares inverse theory: the inversion scheme yields satisfactory results as long as the correct velocity trends are introduced in the starting model. In practice, the inversion algorithm should be applied first in the precritical region because of the strong nonlinear behavior of postcritical data with respect to velocity perturbations. The suggested inversion strategy consists of first inverting for the density and P‐wave velocity (or P‐wave impedance) by considering plane waves in the low slowness region (near‐normal angles of incidence), then in optimizing for the S‐wave velocity by progressively including contributions from the high slowness region (steep angles of incidence). Numerical experiments performed with noise‐free synthetic data prove that the proposed inversion method satisfactorialy reconstructs the elastic properties of a stratified medium from a limited set of plane‐wave components, at a reasonable computing cost.

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MXtrodes: MXene-infused bioelectronic interfaces for multiscale electrophysiology and stimulation

10.1101/2021.03.01.433237 ◽

2021 ◽

Author(s):

Nicolette Driscoll ◽

Brian Erickson ◽

Brendan B. Murphy ◽

Andrew G. Richardson ◽

Gregory Robbins ◽

...

Keyword(s):

High Resolution ◽

Animal Models ◽

Electrochemical Properties ◽

Large Scale ◽

Treatment Strategies ◽

Small Animal ◽

Cost Effective ◽

Solution Processing ◽

Small Animal Models ◽

Paradigm Shifting

Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a novel class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti3C2 MXene and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials, and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in a number of applications ranging from mapping large scale neuromuscular networks in humans to delivering cortical microstimulation in small animal models. Finally, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.

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Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species

Remote Sensing ◽

10.3390/rs13173503 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3503

Author(s):

Roberto Rodriguez ◽

Ryan L. Perroy ◽

James Leary ◽

Daniel Jenkins ◽

Max Panoff ◽

...

Keyword(s):

Image Analysis ◽

High Resolution ◽

Large Scale ◽

Invasive Plant ◽

Image Interpretation ◽

Cost Effective ◽

Human Detection ◽

Aerial Imagery ◽

Miconia Calvescens ◽

Significant Factors

Timely, accurate maps of invasive plant species are critical for making appropriate management decisions to eliminate emerging target populations or contain infestations. High-resolution aerial imagery is routinely used to map, monitor, and detect invasive plant populations. While conventional image interpretation involving human analysts is straightforward, it can require high demands for time and resources to produce useful intelligence. We compared the performance of human analysts with a custom Retinanet-based deep convolutional neural network (DNN) for detecting individual miconia (Miconia calvescens DC) plants, using high-resolution unmanned aerial system (UAS) imagery collected over lowland tropical forests in Hawai’i. Human analysts (n = 38) examined imagery at three linear scrolling speeds (100, 200 and 300 px/s), achieving miconia detection recalls of 74 ± 3%, 60 ± 3%, and 50 ± 3%, respectively. The DNN achieved 83 ± 3% recall and completed the image analysis in 1% of the time of the fastest scrolling speed tested. Human analysts could discriminate large miconia leaf clusters better than isolated individual leaves, while the DNN detection efficacy was independent of leaf cluster size. Optically, the contrast in the red and green color channels and all three (i.e., red, green, and blue) signal to clutter ratios (SCR) were significant factors for human detection, while only the red channel contrast, and the red and green SCRs were significant factors for the DNN. A linear cost analysis estimated the operational use of a DNN to be more cost effective than human photo interpretation when the cumulative search area exceeds a minimum area. For invasive species like miconia, which can stochastically spread propagules across thousands of ha, the DNN provides a more efficient option for detecting incipient, immature miconia across large expanses of forested canopy. Increasing operational capacity for large-scale surveillance with a DNN-based image analysis workflow can provide more rapid comprehension of invasive plant abundance and distribution in forested watersheds and may become strategically vital to containing these invasions.

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Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data

Natural Hazards and Earth System Science ◽

10.5194/nhess-9-303-2009 ◽

2009 ◽

Vol 9 (2) ◽

pp. 303-314 ◽

Cited By ~ 212

Author(s):

S. Martinis ◽

A. Twele ◽

S. Voigt

Keyword(s):

High Resolution ◽

Real Time ◽

Large Scale ◽

Ground Truth ◽

Training Data ◽

Experimental Investigations ◽

Inundation Area ◽

Global Threshold ◽

Flood Detection ◽

Thresholding Algorithm

Abstract. In this paper, an automatic near-real time (NRT) flood detection approach is presented, which combines histogram thresholding and segmentation based classification, specifically oriented to the analysis of single-polarized very high resolution Synthetic Aperture Radar (SAR) satellite data. The challenge of SAR-based flood detection is addressed in a completely unsupervised way, which assumes no training data and therefore no prior information about the class statistics to be available concerning the area of investigation. This is usually the case in NRT-disaster management, where the collection of ground truth information is not feasible due to time-constraints. A simple thresholding algorithm can be used in the most of the cases to distinguish between "flood" and "non-flood" pixels in a high resolution SAR image to detect the largest part of an inundation area. Due to the fact that local gray-level changes may not be distinguished by global thresholding techniques in large satellite scenes the thresholding algorithm is integrated into a split-based approach for the derivation of a global threshold by the analysis and combination of the split inherent information. The derived global threshold is then integrated into a multi-scale segmentation step combining the advantages of small-, medium- and large-scale per parcel segmentation. Experimental investigations performed on a TerraSAR-X Stripmap scene from southwest England during large scale flooding in the summer 2007 show high classification accuracies of the proposed split-based approach in combination with image segmentation and optional integration of digital elevation models.

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A modified protocol of Capture-C allows affordable and flexible high-resolution promoter interactome analysis

Scientific Reports ◽

10.1038/s41598-020-72496-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Arkadiy K. Golov ◽

Dmitrii A. Abashkin ◽

Nikolay V. Kondratyev ◽

Sergey V. Razin ◽

Alexey A. Gavrilov ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Cost Effective ◽

Probe Design ◽

Functional Link ◽

Chromosome Conformation ◽

Transcription Start Sites ◽

Relative Closeness ◽

Cost Effective Alternative ◽

Regulatory Sites

Abstract Large-scale epigenomic projects have mapped hundreds of thousands of potential regulatory sites in the human genome, but only a small proportion of these elements are proximal to transcription start sites. It is believed that the majority of these sequences are remote promoter-activating genomic sites scattered within several hundreds of kilobases from their cognate promoters and referred to as enhancers. It is still unclear what principles, aside from relative closeness in the linear genome, determine which promoter(s) is controlled by a given enhancer; however, this understanding is of great fundamental and clinical relevance. In recent years, C-methods (chromosome conformation capture-based methods) have become a powerful tool for the identification of enhancer–promoter spatial contacts that, in most cases, reflect their functional link. Here, we describe a new hybridisation-based promoter Capture-C protocol that makes use of biotinylated dsDNA probes generated by PCR from a custom pool of long oligonucleotides. The described protocol allows high-resolution promoter interactome description, providing a flexible and cost-effective alternative to the existing promoter Capture-C modifications. Based on the obtained data, we propose several tips on probe design that could potentially improve the results of future experiments.

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Bayesian seismic inversion for stratigraphic horizon, lithology, and fluid prediction

Geophysics ◽

10.1190/geo2019-0170.1 ◽

2020 ◽

Vol 85 (3) ◽

pp. R207-R221

Author(s):

Odd Kolbjørnsen ◽

Arild Buland ◽

Ragnar Hauge ◽

Per Røe ◽

Abel Onana Ndingwan ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Computational Cost ◽

Seismic Inversion ◽

Sample Point ◽

Coupled Models ◽

Prior Model ◽

Full Field ◽

One Step ◽

Bayesian Seismic Inversion

We have developed an efficient methodology for Bayesian prediction of lithology and pore fluid, and layer-bounding horizons, in which we include and use spatial geologic prior knowledge such as vertical ordering of stratigraphic layers, possible lithologies and fluids within each stratigraphic layer, and layer thicknesses. The solution includes probabilities for lithologies and fluids and horizons and their associated uncertainties. The computational cost related to the inversion of large-scale, spatially coupled models is a severe challenge. Our approach is to evaluate all possible lithology and fluid configurations within a local neighborhood around each sample point and combine these into a consistent result for the complete trace. We use a one-step nonstationary Markov prior model for lithology and fluid probabilities. This enables prediction of horizon times, which we couple laterally to decrease the uncertainty. We have tested the algorithm on a synthetic case, in which we compare the inverted lithology and fluid probabilities to results from other algorithms. We have also run the algorithm on a real case, in which we find that we can make high-resolution predictions of horizons, even for horizons within tuning distance from each other. The methodology gives accurate predictions and has a performance making it suitable for full-field inversions.

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