Quantification Errors Research Articles

Semi-automated transcription and scoring of autobiographical memory narratives.

Autobiographical memory studies conducted with narrative methods are onerous, requiring significant resources in time and labor. We have created a semi-automated process that allows autobiographical transcribing and scoring methods to be streamlined. Our paper focuses on the Autobiographical Interview (AI; Levine, Svoboda, Hay, Winocur, & Moscovitch, Psychology and Aging, 17, 677-89, 2002), but this method can be adapted for other narrative protocols. Specifically, here we lay out a procedure that guides researchers through the four main phases of the autobiographical narrative pipeline: (1) data collection, (2) transcribing, (3) scoring, and (4) analysis. First, we provide recommendations for incorporating transcription software to augment human transcribing. We then introduce an electronic scoring procedure for tagging narratives for scoring that incorporates the traditional AI scoring method with basic keyboard shortcuts in Microsoft Word. Finally, we provide a Python script that can be used to automate counting of scored transcripts. This method accelerates the time it takes to conduct a narrative study and reduces the opportunity for error in narrative quantification. Available open access on GitHub ( https://github.com/cMadan/scoreAI ), our pipeline makes narrative methods more accessible for future research.

Fast quantitative three-dimensional ultrashort echo time (UTE) Cones magnetic resonance imaging of major tissues in the knee joint using extended sprial sampling.

The purpose of this study is to investigate the effect of extending the spiral sampling window on quantitative 3D ultrashort echo time (UTE) Cones imaging of major knee joint tissues including articular cartilage, menisci, tendons and ligaments at 3 T. Nine cadaveric human whole knee specimens were imaged on a 3 T clinical MRI scanner. A series of quantitative 3D UTE Cones imaging biomarkers including T2 *, T1 , adiabatic T1ρ , magnetization transfer ratio (MTR) and macromolecular fraction (MMF) were estimated using spiral sampling trajectories with various durations. Errors in UTE MRI biomarkers as a function of sampling time were evaluated using a nonstretched spiral trajectory as a reference standard. No significant differences were observed by increasing the spiral sampling window from 1116 to 2232 μs in the calculated T2 *, T1 , adiabatic T1ρ , MTR and MMF, as all P-values were over .05 as assessed by ANOVA with two-sided Dunnett's test. Although extending the sampling window results in signal loss for short T2 components, there was limited effect on the calculated quantitative biomarkers, with error percentages typically smaller than 5% in all the evaluated tissues. The total scan time can be reduced by up to 54% with quantification errors of less than 5% in any evaluated major tissue in the knee joint, suggesting that 3D UTE Cones MRI techniques can be greatly accelerated by using a longer spiral sampling window without causing additional quantitative bias.

Reliability of crack quantification via acousto-ultrasound active-sensing structural health monitoring using surface-mounted PZT actuators/sensors

This work presents the introduction and experimental investigation of an active-sensing acousto-ultrasound structural health monitoring approach for damage size quantification based on piezoelectric sensors/actuators mounted on multiple seemingly identical structural components. The objective of this work is to determine how reliable the damage diagnostics can be from one component to another similar (nominally identical) component using surface-mounted PZT (lead zirconate titanate) sensors/actuators, and also to evaluate how sensitive a sensor network configuration in terms of the number of sensors/actuators is with respect to its detection reliability. Extensive crack growth experiments on multiple identical coupons outfitted with the same sensor network configuration under cyclic loads were conducted to assess the damage quantification reliability from one coupon to another using the same diagnostic algorithm. The results of the study indicate that the crack size estimates obtained from the active-sensing structural health monitoring system can vary within the population of identical structural components (coupons), but the difference in quantifying damage among coupons decreases with the increase in the number of sensors and actuators used, that is, wave propagation paths. Furthermore, it is shown that the diagnostic results in terms of damage quantification converge with the increase in the number of sensors. The results of the study indicate that the diagnostic approach using a multi-path sensor network can reduce the damage quantification error from one component to another within a “hotspot” configuration (damage location is known or suspected a priori). Finally, the results of this study indicate that the more wave propagation paths used in the diagnostic active-sensing algorithm, the more reliable the damage quantification results are, provided that the same sensor network is used and installed at nominally identical locations for all coupons.

Fast ingredient quantification in multigrain flour mixes using hyperspectral imaging

In the past decade, hyperspectral imaging has emerged and matured into one of the most powerful and rapidly growing methods for non-destructive food quality analysis and control. This paper demonstrates fast and automatic quantification of food ingredients with hyperspectral imaging in the visible and near-infrared range in combination with chemometrics and image processing techniques. The application of ingredient quantification in bread flour recipes requires high spatial resolution in addition to spectral discrimination power. Our results show that automatic and accurate quantification of all ingredients can be done, reaching pixel discrimination accuracies above 90% and ingredient quantification errors within the required 1% absolute error in weight. The classification accuracy obtained using 15 wavebands on the test images is around 15% higher than what was obtained with colour imaging.

Improved PET/CT Respiratory Motion Compensation by Incorporating Changes in Lung Density

Positron emission tomography/computed tomography (PET/CT) lung imaging is highly sensitive to motion. Although several techniques exist to diminish motion artifacts, a few accounts for both tissue displacement and changes in density due to the compression and dilation of the lungs, which cause quantification errors. This article presents an experimental framework for joint activity image reconstruction and motion estimation in PET/CT, where the PET image and the motion are directly estimated from the raw data. Direct motion estimation methods for motion-compensated PET/CT are preferable as they require a single attenuation map only and result in optimal signal-to-noise ratio (SNR). Previous implementations, however, failed to address changes in density during respiration. We propose to account for such changes using the Jacobian determinant of the deformation fields. In a feasibility study, we demonstrate on a modified extended cardiac-torso (XCAT) phantom with breathing motion—where the lung density and activity vary—that our approach achieved better quantification in the lungs than conventional PET/CT joint activity image reconstruction and motion estimation that does not account for density changes. The proposed method resulted in lower bias and variance in the activity images, reduced mean relative activity error in the lung at the reference gate (−4.84% to −3.22%) and more realistic Jacobian determinant values.

Evaluation of models to predict the influence of chemical pretreatment on the peels of Nephelium lappaceum L. based on pyrolysis kinetic parameters obtained using a combined Fraser-Suzuki function and Friedman’s isoconversional method

In the present work, two chemometric strategies were evaluated to assess the influence of the chemical pretreatment: a Kohonen self-organizing map neural network and a Support Vector Machine Learning algorithm. Kohonen ANN was able to predict the influence of chemical treatment mainly on pseudo-component lignin, since cellulose and hemicellulose have similar structures; presenting a topological error of 0.044 and a quantification error of 0.193, while the Vector Supporting Machine (SVM) algorithm was able to predict the classes with accuracy of 0.956, area under the ROC curve (AUC) of 0.992 and F1 score of 0.956, proving to be a more effective strategy in predicting the influence of the chemical treatment on the rambutan shells based on the kinetic parameters. It was also observed an increase in the activation energy for the cellulose pretreated with sodium hydroxide and with phosphoric acid, mainly due to the partial conversion of type I cellulose into type II cellulose, ranging from 152 to 155 kJ/mol versus 124 kJ/mol for the untreated sample. The thermal decomposition mechanism did not change significantly, being estimated as pseudo-zero order, using the Master-Plot method. TG-FTIR analysis reveals that the main gases emitted during pyrolysis are CO2, H2O and CO, being the carbon dioxide-related band an indicator of the influence of the chemical treatment. This analysis also reveals the release of sulfur compounds from the sample treated with sulfuric acid, indicating that the environmental point of view of this sample shows no viability as use as pyrolysis feedstock.

Multivalued Logic Assay of the Disease Marker of α-Ketoglutaric Acid by a Luminescent MOF-Based Biosensor.

α-Ketoglutaric acid (α-KA) is an important endogenous metabolite in the Krebs cycle and considered a critical marker for various diseases. Several approaches for identifying α-KA have been reported. However, most of them are limited to single-signal transduction modes in one assay and working in the suspension state, which easily cause quantification errors and lead to false-positive detection results. Herein, a multivalue-responsive and semi-solid EuMOF-gel (EuMOG) logic biosensor is first designed and fabricated, which can realize the specific, rapid, and easy-to-differentiate naked-eye detection of the disease-associated marker of α-KA in serum through implementing one-to-two logic operation with three multicolor gates. The implementation of one-to-two logic operation can be rationally achieved by assembling 2,6-naphthalenedicarboxylic acid as both the energy donor and blue-colored output 1, photoactive Eu3+ ions as the red-emissive output 2, and α-KA stimuli as an input and energy blocker between the dual spectrum-resolved outputs into one unit. With the specific input of α-KA among various analytes, the binary code of the logic decoder switches from (0,1) to (1,0) with an obvious color change from red to cyan. Moreover, by integrating the output chromaticity into the logical operations, the concentration levels of α-KA could be easily evaluated with the intelligent EuMOG detector, which has three multicolor gates in parallel encoding low (0,1), normal (1,1), and danger (1,0) statuses. Such multivalued logic biosensor provides a facile strategy to improve the analysis reliability of disease-associated biomarkers.

Dual-Energy CT-Based Iodine Quantification in Liver Tumors – Impact of Scan-, Patient-, and Position-Related Factors

To quantify the contribution of lesion location and patient positioning, dual-energy approach, patient size, and radiation dose to the error of dual-energy CT-based iodine quantification (DECT-IQ) in liver tumors. A phantom with four liver lesions (diameter 15 mm; iodine concentration 0-5 mgI/mL) and two sizes was used. One lesion emulated a subdiaphragmatic lesion. Both sizes were imaged in dual-energy mode on (1) a dual-source DECT (DS-DE) at 100/Sn150 kV and (2) a single-source split-filter DECT (SF-DE) at AuSn120 kV at two radiation doses (8 and 12 mGy). Scans were performed at seven different vertical table positions (from -6 to + 6 cm from the gantry isocenter). Iodine concentration was repeatedly measured and absolute errors (errorabs) were calculated. Errors were compared using robust repeated-measures ANOVAs with post-hoc comparisons. A linear mixed effect model was used to determine the factors influencing the error of DECT-IQ. The linear mixed effect models showed that errors were significantly influenced by DECT approach, phantom size, and lesion location (all p < 0.001). The impact of lesion location on the error was stronger in SF-DE compared to DS-DE. Radiation dose did not significantly influence error (p = 0.22). When averaged across all setups, errorabs was significantly higher for SF-DE (2.08 ± 1.92 mgI/mL) compared to DS-DE (0.37 ± 0.29 mgI/mL) (all p < 0.001). Artefacts were found in the subdiaphragmatic lesion for SF-DE with significantly increased errorabs compared to DS-DE (p < 0.001). Errorabs was significantly higher in the large compared to the medium phantom for DS-DE (0.30 ± 0.23 mgI/mL vs. 0.43 ± 0.33 mgI/mL) and SF-DE (1.68 ± 1.99 vs. 2.36 ± 1.81 mgI/mL) (p < 0.001). The dual-energy approach, patient size, and lesion location modified by patient position significantly impacted DECT-IQ in simulated liver tumors.

Effect of attenuation mismatches in time of flight PET reconstruction

While the pursuit of better time resolution in positron emission tomography (PET) is rapidly evolving, little work has been performed on time of flight (TOF) image quality at high time resolution in the presence of modelling inconsistencies. This works focuses on the effect of using the wrong attenuation map in the system model, causing perturbations in the reconstructed radioactivity image. Previous work has usually considered the effects to be local to the area where there is attenuation mismatch, and has shown that the quantification errors in this area tend to reduce with improved time resolution. This publication shows however that errors in the PET image at a distance from the mismatch increase with time resolution. The errors depend on the reconstruction algorithm used. We quantify the errors in the hypothetical case of perfect time resolution for maximum likelihood reconstructions. In addition, we perform reconstructions on simulated and patient data. In particular, for respiratory-gated reconstructions from a wrong attenuation map, increased errors are observed with improved time resolutions in areas close to the lungs (e.g. from 13.3% in non-TOF to up to 20.9% at 200 ps in the left ventricle).

Sequential phase feeding algorithm with minimal beam pointing error for centrosymmetric planar array antenna

Phased array antennas using digital phase shifters for beam steering are characterized by beam pointing error. This study proposes a sequential phase feeding algorithm (SPFA) with minimal beam pointing error for a centrosymmetric planar array antenna. The theoretical expression of a beam pointing error caused by phase quantification error is derived. On the basis of the derivation results, the phase shift of each element is sequentially quantified through truncating or carrying to achieve a smaller beam pointing error. The proposed algorithm improves the beam pointing accuracy of the centrosymmetric planar array antenna, and the computational load is acceptable. Simulation results verify the effectiveness of SPFA.

Quantification for non-targeted LC/MS screening without standard substances

Non-targeted and suspect analyses with liquid chromatography/electrospray/high-resolution mass spectrometry (LC/ESI/HRMS) are gaining importance as they enable identification of hundreds or even thousands of compounds in a single sample. Here, we present an approach to address the challenge to quantify compounds identified from LC/HRMS data without authentic standards. The approach uses random forest regression to predict the response of the compounds in ESI/HRMS with a mean error of 2.2 and 2.0 times for ESI positive and negative mode, respectively. We observe that the predicted responses can be transferred between different instruments via a regression approach. Furthermore, we applied the predicted responses to estimate the concentration of the compounds without the standard substances. The approach was validated by quantifying pesticides and mycotoxins in six different cereal samples. For applicability, the accuracy of the concentration prediction needs to be compatible with the effect (e.g. toxicology) predictions. We achieved the average quantification error of 5.4 times, which is well compatible with the accuracy of the toxicology predictions.

Research on Consistency Enhancement of Wireless Channel Key Against RF Fingerprint

The development of the physical layer key extraction technology provides a new type of encryption mechanism that uses the reciprocity and time varying of wireless channels to generate keys. This encryption mechanism can achieve informational security. However, the wireless channels in the real environment are not completely reciprocal, which results in low consistency in generating the keys. In this paper, we study the influence of RF fingerprint on channel reciprocity, and propose a calibration matrix algorithm resist the RF fingerprint effect. Based on the RF fingerprinting model established previously, we propose two calibration methods from the perspective of RF gain and I/Q gain. The superiority of the two calibration methods is proved by the average system capacity and the linear quantification error rate. Next, we extend the calibration matrix algorithm of narrowband systems to wideband OFDM systems. The applicability of the calibration matrix algorithm in OFDM systems is proved by the linear quantification error rate of the uplink and downlink channels. In order to prove the reliability of the algorithm in the real environment, we use a USRP to collect multiple sets of measured data, and analyse the data through the calibration algorithm. The final result shows that this algorithm makes the uplink and downlink channel reciprocity compensated.

Obstacles to detecting isoforms using full-length scRNA-seq data

BackgroundEarly single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses.ResultsIn this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results.ConclusionsTo accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.

2D electrophoresis image brightness correction based on gradient interval histogram

BackgroundTwo-dimensional electrophoresis (2DE) is one of the most widely applied techniques in comparative proteomics. The basic task of 2DE is to identify differential protein expression by quantitative analysis of 2DE images. To reduce the errors of spot quantification in 2DE images, a novel brightness correction method based on gradient interval histogram (GIH) is proposed in this paper.ResultsFirst, GIH equalization is proposed to enhance the protein spot edges, especially the weak protein spots in the 2DE image. Second, to eliminate the overall brightness shift, GIH matching is applied to the 2DE images that need to be compared. Finally, the proposed method is verified by subjective quality evaluation and quantitative analysis of protein spots in real 2DE images.ConclusionsThe experimental results show that the average error of the quantification of corresponding protein spots in the resulting image pairs is less than 3%, which is significantly superior to that of the existing methods.

Phase Contrast CT Enabled Three-Material Decomposition in Spectral CT Imaging.

Three-material decomposition is crucial for material quantification when more than two elemental materials, including a K-edge material, are presented in an image object. In principle, three-material decomposition requires a triple energy scan which cannot be directly accomplished using a conventional dual energy CT system. In this work, a new scheme to enable three-material decomposition by employing phase contrast CT was presented. When a grating interferometer is added, a conventional absorption dual energy CT system can be upgraded to a phase contrast dual energy CT system which provides an additional phase signal related to the real part of the refractive index of an image object, along with the absorption signal under two different x-ray spectra. In this work, a three-material decomposition method was proposed for the aforementioned dual energy phase contrast CT system. Physical experimental studies were performed on a benchtop x-ray Talbot-Lau interferometer system to validate the proposed method. A physical phantom, containing calcium and iodine inserts of known concentrations, was used as the image object. A rotation-rotation dual energy phase contrast CT scan was performed under 40 and 80 kVp tube potentials. For each view angle, a phase stepping procedure with five phase steps was performed. After the phase retrieval procedure and image reconstruction using the standard filtered-back projection, the solutions were decomposed into the calcium, iodine and water bases based on the proposed decomposition method. For all the solutions, the relative quantification errors of the concentrations were within 10%.

Three-Dimensional GRE T1ρ mapping of the brain using tailored variable flip-angle scheduling.

To introduce a new approach called tailored variable flip-angle (VFA) scheduling for SNR-efficient 3D T1ρ mapping of the brain using a magnetization-prepared gradient-echo sequence. Simulations were used to assess the relative SNR efficiency, quantitative accuracy, and spatial blurring of tailored VFA scheduling for T1ρ mapping of brain tissue compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS), a state-of-the-art technique for accurate 3D gradient-echo T1ρ mapping. Simulations were also used to calculate optimal imaging parameters for tailored VFA scheduling versus MAPSS, without and with nulling of CSF. Four participants were imaged at 3T MRI to demonstrate the feasibility of tailored VFA scheduling for T1ρ mapping of the brain. Using MAPSS as a reference standard, in vivo data were used to validate the relative SNR efficiency and quantitative accuracy of the new approach. Tailored VFA scheduling can provide a 2-fold to 4-fold gain in the SNR of the resulting T1ρ map as compared with MAPSS when using identical sequence parameters while limiting T1ρ quantification errors to 2% or less. In vivo whole-brain 3D T1ρ maps acquired with tailored VFA scheduling had superior SNR efficiency than is achievable with MAPSS, and the SNR efficiency improved with a greater number of views per segment. Tailored VFA scheduling is an SNR-efficient GRE technique for 3D T1ρ mapping of the brain that provides increased flexibility in choice of imaging parameters compared with MAPSS, which may benefit a variety of applications.

Blood flow quantification in dialysis access using digital subtraction angiography: A retrospective study

Background and objectiveVascular access is the “lifeline” of end-stage renal disease patients, which is surgically constructed to remove blood-waste and return artificially filtered blood into circulation. The arteriovenous shunting causes an abrupt change in blood flow and results in increased fluidic stress, which predisposes to access stenosis and thrombosis. While access flow is crucial to evaluate interventional endpoint, application to measure flow using digital angiogram is not yet available. The goal of this study was to determine the feasibility of flow quantification in dialysis access using a software tool and to guide the design of an imaging protocol. Methods173 digital subtraction angiographic (DSA) images were retrospectively analyzed to evaluate access flow in a custom-programming environment. Four bolus transit time algorithms and a distance calculation method were assessed for flow computation. Gamma variate function was applied to remove secondary flow and intensity outliers in the bolus time-intensity curves and evaluated for enhancement in computational accuracy. The percent deviations of flow rates computed from dilution of iodinated radio-contrast material were compared with in situ catheter-based flow measurement. ResultsAmong the implemented bolus transit time algorithms, quantification error (mean ± standard error) of cross-correlation algorithm without and with gamma variate curve fitting was 35 ± 1% and 22 ± 1%, respectively. All other algorithms had quantification error >27%. The bias and limits of agreement of the cross-correlation algorithm with gamma variate curve fit was −94 ml/min and [−353, 165] mL/min, respectively. ConclusionsThe cross-correlation algorithm with gamma variate curve fit had the best accuracy and reproducibility for image-based blood flow computation. To further enhance accuracy, images may need to be acquired with a dedicated injection protocol with predetermined parameters such as the duration, rate and mode of bolus injection, and the acquisition frame rate.

Soft known-value constraints for improved quantitation in multivariate curve resolution.

Multivariate curve resolution (MCR) is a powerful tool in chemometrics that has been involved in the solution of many analytical problems. The introduction of partial or incomplete knowledge of reference values as known-value constraints in an MCR model can considerably reduce the extent of rotational ambiguity for all components. Known-value constraints can provide enough information for MCR methods to perform both the identification and quantitative analysis of first-order data sets. In practice, in addition to noise and non-ideal behavior, limitations in the reference methods or procedures cause deviation in measured known values. It is shown that deviation in the measured known values, when used as known-value constraints, may result in considerable quantification errors in MCR results and can challenge identification analysis. This contribution investigates the importance and effect of soft known-value constraints on the accuracy of MCR solutions. The influence of noise levels, the amount of deviation of known values from true values, and the interaction of these two factors were evaluated with simulated data. An illustration using soft known-value constraints is given for a batch reaction experiment.

Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context

Battery management systems may rely on mathematical models to provide higher performance than standard charging protocols. Electrochemical models allow us to capture the phenomena occurring inside a lithium-ion cell and therefore, could be the best model choice. However, to be of practical value, they require reliable model parameters. Uncertainty quantification and optimal experimental design concepts are essential tools for identifying systems and estimating parameters precisely. Approximation errors in uncertainty quantification result in sub-optimal experimental designs and consequently, less-informative data, and higher parameter unreliability. In this work, we propose a highly efficient design of experiment method based on global parameter sensitivities. This novel concept is applied to the single-particle model with electrolyte and thermal dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The proposed method avoids the simplifying assumption of output-parameter linearization (i.e., local parameter sensitivities) used in conventional Fisher information matrix-based experimental design strategies. Thus, the optimized current input profile results in experimental data of higher information content and in turn, in more precise parameter estimates.

Influence of contrast agent dispersion on bolus-based MRI myocardial perfusion measurements: A computational fluid dynamics study.

Bolus-based dynamic contrast agent (CA) perfusion measurements of the heart are subject to systematic errors due to CA bolus dispersion in the coronary arteries. To better understand these effects on quantification of myocardial blood flow and myocardial perfusion reserve (MPR), an in-silico model of the coronary arteries down to the pre-arteriolar vessels has been developed. In this work, a computational fluid dynamics analysis is performed to investigate these errors on the basis of realistic 3D models of the left and right porcine coronary artery trees, including vessels at the pre-arteriolar level. Using advanced boundary conditions, simulations of blood flow and CA transport are conducted at rest and under stress. These are evaluated with regard to dispersion (assessed by the width of CA concentration time curves and associated vascular transport functions) and errors of myocardial blood flow and myocardial perfusion reserve quantification. Contrast agent dispersion increases with traveled distance as well as vessel diameter, and decreases with higher flow velocities. Overall, the average myocardial blood flow errors are -28% ± 16% and -8.5% ± 3.3% at rest and stress, respectively, and the average myocardial perfusion reserve error is 26% ± 22%. The calculated values are different in the left and right coronary tree. Contrast agent dispersion is dependent on a complex interplay of several different factors characterizing the cardiovascular bed, including vessel size and integrated vascular length. Quantification errors evoked by the observed CA dispersion show nonnegligible distortion in dynamic CA bolus-based perfusion measurements. We expect future improvements of quantitative perfusion measurements to make the systematic errors described here more apparent.