Consistency Error Research Articles

Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time-dependent confounders.

In longitudinal studies, the inverse probability of treatment weighted (IPTW) method is commonly employed to estimate the effect of time-dependent treatments on an outcome of interest. However, it has been documented that when the confounders are subject to measurement error, the naive IPTW method which simply ignores measurement error leads to biased treatment effect estimation. In the existing literature, there is a lack of measurement error correction methods that fully remove measurement error effect and produce consistent treatment effect estimation. In this article, we develop a novel consistent IPTW estimation procedure for longitudinal studies. The key step of the proposed method is to use the observed data to construct a corrected function that is unbiased of the unknown IPTW function. Simulation studies reveal that the proposed method outperforms the existing consistent and approximate measurement error correction methods for IPTW estimation of the average treatment effect. Finally, we apply the proposed method to analyze a real dataset.

Development of a multi-layer quality assurance program to evaluate the uncertainty of deformable dose accumulation in adaptive radiotherapy.

Deformable dose accumulation (DDA) has uncertainties which impede the implementation of DDA-based adaptive radiotherapy (ART) in clinic. The purpose of this study is to develop a multi-layer quality assurance (MLQA) program to evaluate uncertainties in DDA. A computer program is developed to generate a pseudo-inverse displacement vector field (DVF) for each deformable image registration (DIR) performed in Accuray's PreciseART. The pseudo-inverse DVF is first used to calculate a pseudo-inverse consistency error (PICE) and then implemented in an energy and mass congruent mapping (EMCM) method to reconstruct a deformed dose. The PICE is taken as a metric to estimate DIR uncertainties. A pseudo-inverse dose agreement rate (PIDAR) is used to evaluate the consequence of the DIR uncertainties in DDA and the principle of energy conservation is used to validate the integrity of dose mappings. The developed MLQA program was tested using the data collected from five representative cancer patients treated with tomotherapy. DIRs were performed in PreciseART to generate primary DVFs for the five patients. The fidelity index and PICE of these DVFs on average are equal to 0.028mm and 0.169mm, respectively. With the criteria of 3mm/3% and 5mm/5%, the PIDARs of the PreciseART-reconstructed doses are 73.9±4.4% and 87.2±3.3%, respectively. The PreciseART and EMCM-based dose reconstructions have their deposited energy changed by 5.6±3.9% and 2.6±1.5% in five GTVs, and by 9.2±7.8% and 4.7±3.6% in 30 OARs, respectively. A pseudo-inverse map-based EMCM program has been developed to evaluate DIR and dose mapping uncertainties. This program could also be used as a sanity check tool for DDA-based ART.

Estimation of Actual Evapotranspiration and Crop Coefficient of Transplanted Puddled Rice Using a Modified Non-Weighing Paddy Lysimeter

Lysimetric and eddy covariance techniques are commonly used to directly estimate actual crop evapotranspiration (ETa). However, these technologies are costly, laborious, and require skills which make in situ ET estimation difficult, particularly in developing countries. With this in mind, an attempt was made to determine ETa and stagewise crop coefficient (Kc) values of transplanted puddled rice using a modified non-weighing paddy lysimeter. The results were compared to indirect methods, viz., FAO Penman–Monteith and pan evaporation. Daily ETa ranged from 1.9 to 8.2 mmday−1, with a mean of 4.02 ± 1.35 mmday−1, and their comparison showed that the FAO Penman–Monteith equation performed well for the coefficient of determination (R2 of 0.63), root mean squared error (RMSE = 0.80), and mean absolute percentage error (MAPE = 13.6 %), and was highly correlated with ETa throughout the crop season. However, the pan evaporation approach was underestimated (R2 of 0.24; RMSE = 0.98; MAPE = 22.13%) due to a consistent pan coefficient value (0.71), vegetation role and measurement errors. In addition, actual Kc values were obtained as 1.13 ± 0.13, 1.27 ± 0.2, 1.23 ± 0.16, and 0.93 ± 0.18 for the initial, crop development, mid-season, and end-season stages, respectively. These estimated crop coefficient values were higher than FAO Kc values. Statistical analysis results revealed that the overall stagewise-derived average Kc values were in line with FAO values, but different from the derived pan Kc values, although found insignificant at a 5% significance level. In addition, water productivity and agro-meteorological indices were derived to evaluate the cultivar performance in this experiment. Therefore, such a methodology may be used in the absence of weighing lysimeter-derived Kc values. The derived regional Kc values can be applied to improve irrigation scheduling under similar agro-climatic conditions.

A new approach for numerical solution of Kuramoto-Tsuzuki equation

A fourth-order finite difference scheme for the Kuramoto-Tsuzuki equation is considered. The theoretical properties of the proposed scheme, such as the existence, uniqueness and the consistency errors are analyzed. The error estimates in a discrete maximum-norm show that the convergence rates of the difference scheme are of order O(h4+k2). Some numerical experiments are reported to confirm the advantages of the proposed difference scheme by comparing it with other existing recent numerical methods.

Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy.

To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART). To address the unique needs of ART, we designed Seq2Morph, a novel deep learning-based deformable image registration (DIR) network. Seq2Morph was built upon VoxelMorph which is a general-purpose framework for learning-based image registration. The major upgrades are (1) expansion of inputs to all weekly cone-beam computed tomography (CBCTs) acquired for monitoring treatment responses throughout a radiotherapy course, for registration to their planning CT; (2) incorporation of 3D convolutional long short-term memory between the encoder and decoder of VoxelMorph, to parse the temporal patterns of anatomical changes; and (3) addition of bidirectional pathways to calculate and minimize inverse consistency errors (ICEs). Longitudinal image sets from 50 patients, including a planning CT and 6 weekly CBCTs per patient, were utilized for network training and cross-validation. The outputs were deformation vector fields for all the registration pairs. The loss function was composed of a normalized cross-correlation for image intensity similarity, a DICE for contour similarity, an ICE, and a deformation regularization term. For performance evaluation, DICE and Hausdorff distance (HD) for the manual versus predicted contours of tumor and esophagus on weekly basis were quantified and further compared with other state-of-the-art algorithms, including conventional VoxelMorph and large deformation diffeomorphic metric mapping (LDDMM). Visualization of the hidden states of Seq2Morph revealed distinct spatiotemporal anatomy change patterns. Quantitatively, Seq2Morph performed similarly to LDDMM, but significantly outperformed VoxelMorph as measured by GTV DICE: (0.799±0.078, 0.798±0.081, and 0.773±0.078), and 50% HD (mm): (0.80±0.57, 0.88±0.66, and 0.95±0.60). The per-patient inference of Seq2Morph took 22 s, much less than LDDMM (∼30 min). Seq2Morph can provide accurate and fast DIR for longitudinal image studies by exploiting spatial-temporal patterns. It closely matches the clinical workflow and has the potential to serve both online and offline ART.

The bending of thin strips with a non-uniform transverse curvature

The non-linear flexural behaviour of a tape spring — whose cross section has a uniform curvature — is produced by the presence of an elastic instability. However, these are not the only structures that possess non-linear flexural behaviour. Panel-springs and bridged tape springs are examples of structures that can also produce this behaviour. Predicting the flexural behaviour of these structures is more challenging because their cross section is non-uniform. This work presents an analytical model that can predict the opposite-sense flexural behaviour of thin strips that have a non-uniform cross section. The novel attribute of this model is in the utilisation of a univariate polynomial series to describe the initial cross section of the strip. The model is applied to a range of bridged tape springs and panel-springs, with the results compared with solutions obtained from the Finite Element (FE) method. A consistent error of less than 5.5% is achieved between the analytical model and the FE model when the snap-through moment and propagation moment are compared. The analytical model is also compared with independent experimental data of the flexural behaviour of a traditional carpenter’s tape measure. An error of less than 1% and 7% of the snap-through moment and propagation moment, respectively, is achieved.

Analysis of fully discrete finite element methods for 2D Navier–Stokes equations with critical initial data

First-order convergence in time and space is proved for a fully discrete semi-implicit finite element method for the two-dimensional Navier–Stokes equations with L2 initial data in convex polygonal domains, without extra regularity assumptions or grid-ratio conditions. The proof utilises the smoothing properties of the Navier–Stokes equations in the analysis of the consistency errors, an appropriate duality argument, and the smallness of the numerical solution in the discrete L2(0, tm; H1) norm when tm is smaller than some constant. Numerical examples are provided to support the theoretical analysis.

Photometric redshift uncertainties in weak gravitational lensing shear analysis: models and marginalization

ABSTRACT Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the uncertainties on the sample redshift distribution n(z). Due to the challenge of running Markov chain Monte Carlo (MCMC) in the high-dimensional parameter spaces in which the n(z) uncertainties may be parametrized, it is common practice to simplify the n(z) parametrization or combine MCMC chains that each have a fixed n(z) resampled from the n(z) uncertainties. In this work, we propose a statistically principled Bayesian resampling approach for marginalizing over the n(z) uncertainty using multiple MCMC chains. We self-consistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC three-year shape catalogue, and find that these methods recover statistically consistent error bars for the cosmological parameter constraints for predicted HSC three-year analysis, implying that using the most computationally efficient of the approaches is appropriate. However, we find that for data sets with the constraining power of the full HSC survey data set (and, by implication, those upcoming surveys with even tighter constraints), the choice of method for marginalizing over n(z) uncertainty among the several methods from the literature may modify the 1σ uncertainties on Ωm–S8 constraints by ∼4 per cent, and a careful model selection is needed to ensure credible parameter intervals.

Estimation of Cumulative Organ Maximum Dose and Confidence Intervals for MRI-Guided Adaptive Radiotherapy of Abdominal Cancer

<h3>Purpose/Objective(s)</h3> The maximum dose (Dmax), e.g., D0.03cc (dose to 0.03cc volume), of adjacent organs at risk (OARs) is a primary dose-limiting constraint in MRI-guided adaptive radiotherapy (MRgART) of abdominal cancer. We utilized inverse consistency error (ICE) maps to estimate voxel-wise uncertainties in deformable image registration (DIR) based dose accumulation (DDA) and confidence intervals in cumulative OARs Dmax during MRgART for pancreatic cancer. <h3>Materials/Methods</h3> Motion-average reference (first fraction) and subsequent daily MRIs, collected in MRgART for five pancreatic cancer patients treated with 33 Gy in 5 fractions on a 1.5T MR-Linac, were registered using a free-form DIR algorithm based on feature similarity scoring metrics and were used to accumulate daily doses. Forward and reverse DIRs were performed, and ICE maps were calculated in the reference image. The correlation between ICE and actual registration error (RE) was assessed by identifying anatomical landmarks (63) in a reference-daily image pair and measuring the landmark REs (after forward-DIR) and ICEs (after forward and reverse DIRs). To account for the deformation vector field spatial uncertainty affecting the DDA, for each voxel in the reference image we associated a cloud of dose grid points centered at the voxel landing coordinates in the daily image with a radius proportional to the ICE length. We considered that the cloud would encompass the "true" registration point with a high likelihood. The uncertainty in the dose mapped to each voxel from each fraction was determined as the standard deviation of the grid point doses within the cloud. The maxima and minima of the dose distributions were computed from the cumulative uncertainties, and confidence intervals for Dmax were determined. <h3>Results</h3> The measured REs and ICEs for landmarks indicated a positive correlation between these errors (Pearson's correlation coefficient r=0.795, p-value=7.04 × 10⁻¹⁵), RE being about twice ICE. The ICEs averaged 1.5 mm and 2.5 mm in the duodenum and stomach. For the duodenum, cumulative D0.03cc and D1cc at the end of treatment ranged from 85.7% to 100.4% of the plan constraint value (confidence intervals included). The uncertainty intervals were between 1.1% and 2.2% in four cases and approximately 7% for the fifth case. Cumulative D0.03cc in the stomach varied from 89.4% to 102.8%, with uncertainty intervals that extended from 2.5% to 6.4%. All Dmax lower limits were below 100%. <h3>Conclusion</h3> Voxel-wise uncertainties in DIR-based dose accumulation during MRgART of abdominal cancer can be estimated using an ICE-based technique. The derived confidence intervals for cumulative Dmax (D1cc or D0.03cc) of OARs (the duodenum and stomach) can be utilized to guide daily dose adaptation throughout MRgART.

An Estimate of Uncertainty in Deformable Image Registration-Based Accumulated Biological Equivalent Dose for Re-Irradiation

<h3>Purpose/Objective(s)</h3> We describe a dose volume histogram (DVH) overlay technique to estimate uncertainty in deformable image registration (DIR) based accumulated biologically equivalent dose (BED) for re-irradiation planning. We compare applicability of two DIR methods, contour-based (C-DIR) and image intensity and contour hybrid (H-DIR) DIR for BED accumulation. <h3>Materials/Methods</h3> Patient CT-based re-irradiation plans to the same anatomic regions as in their initial irradiations over a four-year period were analyzed. Among the organs-at-risk (OARs) for which BED was accumulated were stomach, heart, and esophagus. Physical dose distributions were converted to BED using OAR-specific parameters based on a linear-quadratic-linear model. For C-DIR, the registration was based only upon the contours drawn on both images. H-DIR is an image intensity-based DIR technique which includes contour-based registration to augment the DIR results. All prior BEDs were warped to the most recent CT image for accumulation. Data input to the DVH overlay workflow were the two CT images to be registered, the deformation vector field, the BED on the earlier CT, and the contours on the later CT. The later-CT contours were warped to the earlier CT, and the earlier-CT BED was warped to the later CT; BED DVHs from both images were then calculated and overlaid. For a fully invertible DIR, the two DVHs for the OAR should be identical; differences between the DVHs were interpreted as BED uncertainties due to inverse consistency error. For a given DVH point, the uncertainty was obtained from the differences between the overlaid DVHs. <h3>Results</h3> For an abdomen case, the stomach accumulated BED_max is 3% higher using C-DIR versus H-DIR; ΔBED_max is approximately 2% for both DIR methods. The higher isodoses were distal to the stomach for all plans; H-DIR, which accounts for regions distal to the OAR, is appropriate for re-irradiation BED calculations. For a thoracic case, both DIR methods yield similar results for the heart. The difference between H-DIR and C-DIR for accumulated heart BED_max is 2.4%; ΔBED_max is 0.9% and 0.6% for H-DIR and C-DIR, respectively. For the esophagus, accumulated BED_max is 54% higher using H-DIR instead of C-DIR, and ΔBED_max is much lower for C-DIR (0.2%) than for H-DIR (1.7%). Visual inspection showed that the BED distribution on the esophagus using H-DIR is unreasonable, attributed to H-DIR relying too heavily upon image intensity features. For accurate BED accumulation in the esophagus, C-DIR is essential during the re-irradiation planning for this case. <h3>Conclusion</h3> The DVH overlay technique can estimate the uncertainty in accumulated BED for re-irradiation scenarios. Caution should be exercised in using various DIR methods before applying DIR dose accumulation for re-irradiation planning. Contour-based DIR may be the preferred registration method for re-irradiation dose accumulation.

The association between beverage-specific alcohol consumption and mortality among road users in Russia, 1965–2019

A vast body of literature suggests a relationship between alcohol consumption and road traffic fatalities. Despite an impressive downward trend in road traffic fatalities in Russia, the death rate is still unacceptably high. Far fewer studies have differentiated the association by road users and types of alcoholic beverages. This population-based study aims to estimate the associations of total and beverage-specific alcohol per capita (15+) consumption (APC) based on official alcohol sales statistics and road traffic mortality using police data on the number of deaths by road users. The study covers the period 1965–2019. We employed a first-order difference linear regression model with robust and autocorrelation consistent standard errors, controlling for a level of motorisation. To examine the possible evolution of the phenomenon, we repeated models separately for three consecutive periods (1965–1984, 1985–2002, 2003–2019). The findings suggest that an annual 1-litre increase in APC (in litres of pure alcohol) associated with a corresponding increase in the death rates (per 100,000 inhabitants) of both unprotected road users and motor vehicle occupants by about 0.3 (p < 0.01) and 0.4 (p < 0.05), respectively. A beverage-specific analysis for 1965–2019 revealed a positive and significant association between mortality of pedestrians and cyclists and the consumption of strong alcoholic beverages (p < 0.05) as well as mortality of drivers and passengers and the consumption of weaker alcoholic beverages, primarily beer (p < 0.01). Various road safety strategies should be applied to prevent road traffic fatalities of road users.

Convergence Analysis under Consistent Error Bounds

We introduce the notion of consistent error bound functions which provides a unifying framework for error bounds for multiple convex sets. This framework goes beyond the classical Lipschitzian and Hölderian error bounds and includes logarithmic and entropic error bounds found in the exponential cone. It also includes the error bounds obtainable under the theory of amenable cones. Our main result is that the convergence rate of several projection algorithms for feasibility problems can be expressed explicitly in terms of the underlying consistent error bound function. Another feature is the usage of Karamata theory and functions of regular variations which allows us to reason about convergence rates while bypassing certain complicated expressions. Finally, applications to conic feasibility problems are given and we show that a number of algorithms have convergence rates depending explicitly on the singularity degree of the problem.

Chemical Probe-Based Nanopore Sequencing to Selectively Assess the RNA Modifications.

Nanopore direct RNA sequencing (dRNA-Seq) reads reveal RNA modifications through consistent error profiles specific to a modified nucleobase. However, a null data set is required to identify actual RNA modification-associated errors for distinguishing it from confounding highly intrinsic sequencing errors. Here, we reveal that inosine creates a signature mismatch error in dRNA-Seq reads and obviates the need for a null data set by harnessing the selective reactivity of acrylonitrile for validating the presence of actual inosine modifications. Selective reactivity of acrylonitrile toward inosine altered multiple dRNA-Seq parameters like signal intensity and trace value. We also deduced the stoichiometry of inosine modification through deviation in signal intensity and trace value using this chemical biology approach. Furthermore, we devised Nano ICE-Seq, a protocol to overcome the low coverage issue associated with direct RNA sequencing. Taken together, our chemical probe-based approach may facilitate the knockout-free detection of disease-associated RNA modifications in clinical scenarios.

Segmentation of Intima – Media Thickness in Intravascular Ultrasound Images for Detection of Atherosclerosis

Cardiovascular Diseases (CVDs) are the leading cause of deaths, and adverse CVDs are related to Coronary Artery Disease (CAD). CAD is caused due to the accumulation of fatty lesions called plaques on the vessels that nourish the heart with blood. The Intravascular Ultrasound (IVUS) imaging modality has captured considerable attention in the diagnosis of CVDs in recent years. Generally coronary artery consists of three distinct regions: Media, Intima and Luminal region. Intima-Media Thickness (IMT) is perceived as a significant indicator in the risk evaluation process, tracking the amount of atherosclerosis development. In this paper, IVUS image is preprocessed using Total Variance Regularization for speckle noise removal and the contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Region of Interest (RoI) is extracted using segmentation techniques such as Multi-Level Set Based, Otsu’s segmentation, Active Contour and Watershed segmentation and their performances are compared. The performance metrics used are Jaccard Index (JAC), Dice Coefficient (DC), Cohen Kappa Coefficient (KAP), Variation of Information (VOI), Global Consistency Error (GCE), and Rand Index (RI). From the analysis, it is observed that the Multi-Level Set based technique has a high JAC, DICE, KAP and RI. These values indicate the similarity between the segmented and ground truth image. Also the value of GI, indicates the less error measurement between segmented image and ground truth image. The significance of using Multi- level set based technique is that it uses the B-spline function-based curvature updation. This function is less dependent on the degree, smoothness and domain partition of the image, resulting in increased segmenting accuracy.

Assessing the performance of historical skins and bones for museomics using wolf specimens as a case study

Advances in the field of museomics have promoted a high sampling demand for natural history collections (NHCs), eventually resulting in damage to invaluable resources to understand historical biodiversity. It is thus essential to achieve a consensus about which historical tissues present the best sources of DNA. In this study, we evaluated the performance of different historical tissues from Iberian wolf NHCs in genome-wide assessments. We targeted three tissues—bone (jaw and femur), maxilloturbinal bone, and skin—that have been favored by traditional taxidermy practices for mammalian carnivores. Specifically, we performed shotgun sequencing and target capture enrichment for 100,000 single nucleotide polymorphisms (SNPs) selected from the commercial Canine HD BeadChip across 103 specimens from 1912 to 2005. The performance of the different tissues was assessed using metrics based on endogenous DNA content, uniquely high-quality mapped reads after capture, and enrichment proportions. All samples succeeded as DNA sources, regardless of their collection year or sample type. Skin samples yielded significantly higher amounts of endogenous DNA compared to both bone types, which yielded equivalent amounts. There was no evidence for a direct effect of tissue type on capture efficiency; however, the number of genotyped SNPs was strictly associated with the starting amount of endogenous DNA. Evaluation of genotyping accuracy for distinct minimum read depths across tissue types showed a consistent overall low genotyping error rate (&amp;lt;7%), even at low (3x) coverage. We recommend the use of skins as reliable and minimally destructive sources of endogenous DNA for whole-genome and target enrichment approaches in mammalian carnivores. In addition, we provide a new 100,000 SNP capture array validated for historical DNA (hDNA) compatible to the Canine HD BeadChip for high-quality DNA. The increasing demand for NHCs as DNA sources should encourage the generation of genomic datasets comparable among studies.

Validity Of Linear Position Transducer To Measure Velocity During Barbell Back Squats

PURPOSE:Examine the validity of a linear position transducer (LPT) measuring velocity and displacement (D) relative to a validated criterion (CTRN) during the barbell back squat (BS). METHODS:Eight men and women (25 ± 5 years, 171 ± 5 cm, 79.6 ± 11.6 kg) with six months of prior resistance training experience completed two trials at least 48 hours apart. During visit one participants completed an informed consent and one-repetition maximum (1RM) for the BS. During visit two participants performed two sets of three BS repetitions at 30, 50, 60, and 70% 1RM. LPT and CTRN were attached to the inside sleeves of the barbell with the retractable belts perpendicular to the floor. Barbell position collected by LPT was sampled at a rate of 100 Hz and filtered on a rolling 0.10s average with a custom spreadsheet calculating peak velocity (PV), mean velocity (MV), and D. Repetition onset was defined as a filtered velocity of 0.15 m•s-1. CTRN data employed a 30 cm movement threshold and assessed for PV, MV and D. Intraclass Correlation Coefficients (ICC) and paired samples t-tests were used to assess validity and differences between both unfiltered and filtered data for LPT relative to CTRN. RESULTS:Data is displayed in Table 1. Strong ICCs were observed for PV and MV between LPT and CTRN, with filtered data reflecting higher ICCs at all intensities. Differences were noted between the LPT and CTRN for both PV and MCV whether LPT data were filtered or unfiltered at all intensities, except for PV at 30% using unfiltered data (p > 0.05). Moderate to strong ICCs were observed for D between devices, though when measured with CTRN, D was about 6 cm greater than observed with LPT (p < 0.05). CONCLUSION:Data indicate LPT provides valid measures of PV, MCV, and D, though there is likely consistent error between the LPT and CTRN. Filtered data typically display higher ICC compared to unfiltered data. Differences in D are likely due to the onset velocity for LPT, which may have caused differences between devices for PV and MV.

Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants

We derive consistency, asymptotic normality, and standard error estimation for the tail conditional allocation, also known as the marginal expected shortfall, under minimal conditions and thus geared toward widest applicability. These advances have become possible due to a newly developed technique that hinges on compound sums of concomitants. An insurance inspired numerical study has been designed to illustrate the performance of the obtained results.

QT Ratio: A simple solution to individual QT correction

Preclinical risk assessment of drug-induced arrhythmias is critical for drug development and relies on heart rate corrected QT interval (QT) prolongation as a biomarker for arrhythmia risk. However, the methods used to correct QT vary in complexity and don't account for all changes in the QT-rate relationship. Thus, we developed the novel Ratio QT correction method which characterizes that relationship at each timepoint using the ratio between QT, adjusted for a species-specific constant, and rate (RR interval). This ratio represents the slope between the intercept and the datapoint being corrected, which is then used in a linear equation like individual methods. A unique correction coefficient for each datapoint avoids assuming static relationships. We hypothesize that the simple and dynamic nature of the Ratio method will provide more consistent rate correction and error reduction compared to Bazett's and individual regression methods. Comparisons were made using ECG data from non-human primates (NHPs) treated with dofetilide or moxifloxacin, separated into small groups (n = 4). The methods were compared based on corrected QT vs RR slopes, standard error, and minimal detectable difference (MDD) for each method. The Ratio method resulted in smaller corrected QT-rate relationship slopes than Bazett's, more closely matching those of individual methods. It produced similar or lower MDDs compared to individual and Bazett's correction, respectively, with more consistent reduction in standard error. This simple and effective method has the potential for easy translatability across species.

Differential Operator Approximation Based Tightly Coupled Multiphysics Solver Using Cascaded Fourier Network

AbstractCalculating the unknown coupled multiphysics fields from the known boundary condition is of great significance in computational physics. Existing classical algorithms are usually time consuming and resource demanding. Explosive growth in deep learning (DL) has provided a substitutive way to accelerate the calculation process by fully making use of the parallel computing capability of the graphics processing unit. In this work, a cascaded DL framework is presented to compute the coupled multiphysics fields related to electrical potential, temperature, velocity, etc. The whole framework is comprised of an input module and several Fourier modules, which achieves to acquire the coupled fields from the explicit boundary conditions. Compared with conventional networks, the Fourier network emerges consistent average error among different resolutions which saves training costs. As a result, a fully trained network can obtain the expected physics fields in real time, offering rosy prospects for practical scenarios.

Determining the performance of a temperature sensor embedded into a mouthguard

ObjectiveThis study aimed to determine the steady-state errors of oral-based temperature sensors, that are embedded in mouthguards, using a robust assessment process.Materials and methodsFour electronic boards with temperature sensors were encapsulated in mouthguards made from ethylene-vinyl acetate (EVA). The error and time to reach steady-state temperature were determined using a thermostatic water bath during three different conditions (34, 38.5 and 43 °C). Subsequently, a case study of one volunteer wearing the instrumented mouthguard is presented.ResultsThe water bath tests showed that a mean absolute error of 0.2 °C was reached after a maximum of 690 s across all test conditions. The case study yielded an absolute error was 0.2 °C after 1110 s.ConclusionThese results show that an instrumented mouthguard with temperature sensing capabilities can yield a consistent steady-state error that is close to the clinical requirements across a range of temperatures. However, the time it takes to reach steady-state temperature needs to be considered for these systems to correctly interpret the outcomes.