Slope Of Linear Regression Research Articles

Effect of individual characteristics and aerobic training on the %HRR-%V̇O2R relationship

ABSTRACT This study aimed to assess if, during incremental exercise, considering individual characteristics can make the relationship between the percentages of heart rate (HRR) and oxygen uptake (V̇O2R) reserve either 1:1 or more accurate. Cycle ergometer data of the maximal incremental exercise tests performed by 450 healthy and sedentary participants (17-66 years) of the HERITAGE Family Study, grouped for sex, ethnicity, age, body fat, resting HR, and V̇O2max, were used to calculate the individual linear regressions between %HRR and %V̇O2R. The mean slope and intercept of the individual linear regressions of each subgroup were compared with 1 and 0 (identity line), respectively, using Hotelling tests followed by post-hoc one-sample t-tests. Two multiple linear regressions were also performed, using either the slopes or intercepts of the individual linear regressions as dependent variables and sex, age, resting HR, and V̇O2max as independent variables. The mean %HRR-%V̇O2R relationships of all subgroups differed from the identity line. Moreover, individual linear regression intercepts (8.9 ± 16.0) and slopes (0.971 ± 0.190) changed (p < 0.001) after 20 weeks of aerobic training (13.1 ± 11.1 and 0.891 ± 0.122). The multiple linear regressions could explain only 3.8% and 1.3% of the variance in the intercepts and slopes, whose variability remained high (standard error of estimate of 15.8 and 0.189). In conclusion, the %HRR-%V̇O2R relationship differs from the identity line regardless of individual characteristics and their difference increased after aerobic training. Moreover, due to the high interindividual variability, using a single equation for the whole population seems not suitable for representing the %HRR-%V̇O2R relationship of a given subject, even when several individual characteristics are considered. Highlights The association between %HRR and %V̇O2R is not 1:1 even when individuals are grouped by age, sex, ethnicity, body composition, HRrest, and V̇O2max. Using several subject characteristics to identify the individual’s %HRR-%V̇O2R relationship does not meaningfully increase its prediction accuracy or reduce the interindividual variability of %HRR-%V̇O2R relationships Using a single equation for the whole population is not suitable for representing the relationship of a given subject; hence, individual relationships should be preferred when prescribing the intensity of aerobic exercise. The individual %HRR-%V̇O2R relationship should be periodically assessed due to the potential training induced changes in the relationship.

Significance of non‐Fourier flux on a chemically reactive ternary hybrid nanofluid flow (water + Al2O3 + ZnO + Fe3O4) by a quadratically radiated tended elongating surface

AbstractThis study compares the dynamics of dissipative water–alumina–zinc oxide–iron (II, III) oxide ternary hybrid nanofluid (THNF) and water–alumina–zinc oxide binary hybrid nanofluid flows across an inclined surface when quadratic thermal radiation and chemical reaction are important. This report presents the governing equations for the transportation of both dynamics using the thermo‐physical properties of water (base fluid), alumina nanoparticles, zinc oxide nanoparticles and iron (II, III) oxide nanoparticles. To analyse heat transmission phenomena, the Cattaneo–Christov heat flux model was added into the energy equation. With proper similarity transformations, flow‐driven equations are transformed into nonlinear ordinary differential equations and then solved using the bvp4c solver. Slope of linear regression (SLR) is used to determine the rate at which physical parameters increase or decrease as a result of the influence of relevant factors. It is noticed that, when volume percent of nanoparticles (ϕ1) takes input in the range , skin friction coefficient decreases at a rate of 16.548 (in case of THNF) and 14.2018 (in case of binary hybrid nanofluid) per unit value of volume percent of nanoparticles. It is detected that when radiation parameter increases, Nusselt number decreases. The decrement rates are observed as 2.9451 (in case of THNF) and 1.15337 (in case of binary hybrid nanofluid) when radiation parameter takes input in the range . Furthermore, it is noticed that, when chemical reaction parameter increases, Sherwood number increases. The increment rates are observed as 0.1259 (in case of THNF) and 1.1263 (in case of binary hybrid nanofluid) when chemical reaction parameter takes input in the range . There was also good agreement in a validation study between the current and previous results.

Reliability of Gridded Precipitation Products for Water Management Studies: The Case of the Ankavia River Basin in Madagascar

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, gridded precipitation products (GPPs) from merged satellite-/gauge-/model-based rainfall products constitute an attractive alternative. The quality of which must, nevertheless, be verified. The objective of this study was to evaluate, at different time scales, the reliability of 6 GPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The GPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), the European Centre Medium-Range Weather Forecasts ECMWF Reanalysis on global land surface (ERA5-Land), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 Final (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), and the African Rainfall Estimation (RFEv2) products. The results suggest that IMERG (R2 = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other GPPs at the daily scale, followed by RFEv2 (R2 = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R2 = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All GPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0–0.2 mm/day). ARC2, IMERG, PERSIANN, and RFEv2 all underestimate rainfall occurrence in the 0.2–150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (&gt;150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range of 2–100 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R2 = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at an hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R2 = 0.20). Overall, the IMERG product can be regarded as the most reliable gridded precipitation source at monthly, daily, and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall &gt;100 mm/day may, nevertheless, restrict its use.

Family functioning, contributions to college expenses, access to mentors, and college student’s health and flourishing: Examining moderation by family structure

Objective: To evaluate how family functioning, family contributions to college expenses, and access to mentors are associated with college student’s self-reported health and flourishing, and to test for moderation by family structure. Participants: Undergraduate college students (N = 238) recruited through an email list-serve at a large midwestern state university. Methods: Participants completed an online survey (distributed through Qualtrics) in February 2020. Data were analyzed using linear regression (in SPSS 28) and simple slope analyses. Results: College students’ access to mentors is associated with their self-reported health, and family structure moderates the association between family strengths and self-reported health. Family functioning and access to mentors are both associated with college students’ flourishing. Conclusions: College students’ health and flourishing may benefit from access to mentors and functional family dynamics. Though students from post-divorce families see fewer benefits from family strengths compared to peers in nuclear biological families.

Fine‐tuning the performance of abundance estimation based on environmental DNA (eDNA) focusing on eDNA particle size and marker length

AbstractReliable abundance estimation is a primary challenge in environmental DNA (eDNA) analysis, which has been addressed by considering the effects of eDNA transport and degradation. However, these eDNA spatial dynamics depend on the cellular and molecular structure of eDNA, with its persistence state (particle size and DNA fragment length) being essential for improved abundance estimation. To address the issue, we used datasets obtained from two types of aquarium experiments (targeting zebrafish [Danio rerio] and Japanese jack mackerel [Trachurus japonicus]) and compared the relationships between eDNA concentration and species abundance among different eDNA size fractions and target marker lengths. We reared the fish in experimental tanks with different individual numbers or biomass densities, filtered rearing water using different pore size filters, and quantified eDNA concentrations targeting different fragment lengths or genetic regions. Consequently, both experiments showed that the accuracy and sensitivity in abundance estimation were improved (i.e., R2 values and slopes of linear regressions increased) when targeting eDNA at the 3‐ to 10 μm size fraction. On the contrary, targeting eDNA at the &gt;10 μm size fraction yielded a lower R2 value. This result indicates that the relationship between eDNA concentration and species abundance can be worsened when extremely larger size fractions are targeted. Conversely, the target marker length negatively affected the R2 value. This study proposes that the relationship between eDNA concentration and species abundance relies on the complex interactions between the particle size, persistence, and spatial heterogeneity of eDNA in water.

Consistency Evaluation of Two Loading Devices in Measuring the Perception of Dyspnea.

PurposeThis study aimed to assess the consistency of hand-held electronic incremental threshold loading device (I-TLD) and traditional constant threshold loading device (C-TLD) in measuring the perception of dyspnea (POD) in humans.Patients and methodsThirty-eight patients with stable chronic obstructive pulmonary disease (COPD) and 41 non-COPD subjects were recruited for the study, all of whom were subjected to an external loading breathing test by gradually increasing the inspiratory load starting from 0 to 5, 10, 20, and 30 cmH2O oral pressure using I-TLD and C-TLD. The Borg score measurement was performed immediately after the loading breath of each level. The linear regression slope a of Borg scores vs percentage of oral pressure from the patients’ maximum represented patients’ POD. The consistency of POD measured by the two devices was analyzed by two Related Samples Wilcoxon test, Spearman correlation analysis, and Bland-Altman analysis.ResultsThere was no significant difference in slope a measured by the two devices in all subjects. The Spearman correlation analysis revealed that the slope a measured by the two devices in the inspiratory loading breath test had a significant correlation: in COPD patients, r = 0.678, (p < 0.001) and in non-COPD subjects, r = 0.603, (p < 0.001). For the results of the Bland-Altman analysis of the whole subjects, 3.8% (3/79) points were outside of the 95% LoA confidence interval (CI) (−10.380, 9.457), and the LoA CI was acceptable, which depicted that the two devices were consistent in their estimation.ConclusionI-TLD was consistent with C-TLD in measuring POD in COPD patients and non-COPD subjects. I-TLD may be used as an alternative method to replace C-TLD to measure POD in COPD patients and non-COPD subjects.

Correlations of 18F-FDG PET/CT Metabolic Parameters and Metabolic Heterogeneity with Human Epidermal Growth Factor Receptor 2 Expression in Patients with Gastric Cancer

Objective To investigate the value of 18F-FDG PET/CT metabolic parameters and metabolic heterogeneity for predicting the expression of human epidermal growth factor receptor 2 (HER2) in patients with gastric cancer. Methods A total of 45 patients with gastric cancer confirmed by surgical pathology between September 2016 and May 2021 were enrolled in this study.All the patients underwent 18F-FDG PET/CT examination before surgery.The maximum standardized uptake value (SUVmax),metabolic tumor volume (MTV),and total lesion glycolysis (TLG) of primary gastric cancer were measured,and the linear regression slope of MTV corresponding to different SUVmax thresholds (40% SUVmax and 80% SUVmax) was calculated.The absolute value of the slope was deemed to represent the metabolic heterogeneity of primary gastric cancer,termed the heterogeneity index (HI).Univariate and multivariate Logistic regression analyses were conducted to evaluate the correlations of 18F-FDG PET/CT metabolic parameters and HI with HER2 expression. Results The 45 patients included 10 with positive HER2 expression and 35 with negative result.The MTV (P=0.043) and HI (P=0.048) were lower in the patients with positive HER2 expression than in the patients with negative HER2 expression.The MTV and HI had the optimal thresholds of 12.10 cm3 and 3.71,respectively,which respectively showed the accuracy of 62.2% and 57.8% for predicting HER2 expression.The univariate Logistic regression showed that the tumor differentiation degree,MTV,and HI were correlated with HER2 expression,while the multivariate Logistic regression showed that only the tumor differentiation degree (OR=20.130,95%CI=1.843-219.860,P=0.014) was an independent predictor for HER2 expression.A further stratified analysis of the tumor differentiation degree showed that HER2 expression only varied among different MTV threshold groups in patients with moderately/well differentiated gastric cancer (P=0.031). Conclusions MTV and HI were associated with HER2 expression in gastric cancer,whereas neither played an independent predictive role.Therefore,these factors should be combined with clinicopathological characteristics of patients to jointly guide treatment decisions.

Toward Atmospheric Correction Algorithms for Sentinel-3/OLCI Images of Productive Waters

Atmospheric correction of remote sensing imagery over optically complex waters is still a challenging task. Even algorithms showing a good accuracy for moderate and extremely turbid waters need to be tested when being used for eutrophic inland basins. Such a test was carried out in this study on the example of a Sentinel-3/OLCI image of the productive waters of the Gorky Reservoir during the period of intense blue-green algal bloom using data on the concentration of chlorophyll a and remote sensing reflectance measured from the motorboat at many points of the reservoir. The accuracy of four common atmospheric correction (AC) algorithms was examined. All of them showed unsatisfactory accuracy due to incorrect determination of atmospheric aerosol parameters and aerosol radiance. The calculated aerosol optical depth (AOD) spectra varied widely (AOD(865) = 0.005 − 0.692) even over a small area (up to 10 × 10 km) and correlated with the measured chlorophyll a. As a result, a part of the high water-leaving signal caused by phytoplankton bloom was taken as an atmosphere signal. A significant overestimation of atmospheric aerosol parameters, as a consequence, led to a strong underestimation of the remote sensing reflectance and low accuracy of the considered AC algorithms. To solve this problem, an algorithm with a fixed AOD was proposed. The fixed AOD spectrum was determined in the area with relatively “clean” water as 5 percentiles of AOD in all water pixels. The proposed algorithm made it possible to obtain the remote sensing reflectance with high accuracy. The slopes of linear regression are close to 1 and the intercepts tend to zero in almost all spectral bands. The determination coefficients are more than 0.9; the bias, mean absolute percentage error, and root-mean-square error are notably lower than for other AC algorithms.

A Spatial Long-Term Trend Analysis of Estimated Chlorophyll-a Concentrations in Utah Lake Using Earth Observation Data

We analyzed chlorophyll-a (chl-a) concentrations in shallow, turbid Utah Lake using Landsat data from 1984 to 2021. Utah Lake is ~40 km by 21 km, has a surface area of ~390 km2, an average depth of ~3 m, and loses ~50% of inflow to evaporation. This limits spatial mixing, allowing us to evaluate impacts on smaller lake regions. We evaluated long-term trends at the pixel level and for areas related to boundary conditions. We created 17 study areas based on differences in shoreline development and nutrient inflows. We expected impacted areas to exhibit increasing chl-a trends, as population growth and development in the Utah Lake watershed have been significant. We used the non-parametric Mann–Kendall test to evaluate trends. The majority of the lake exhibited decreasing trends, with a few pixels in Provo and Goshen Bays exhibiting slight increasing or no trends. We estimated trend magnitudes using Sen’s slope and fitted linear regression models. Trend magnitudes in all pixels (and regions), both decreasing and increasing, were small; with the largest decreasing and increasing trends being about −0.05 and −0.005 µg/L/year, and about 0.1 and 0.005 µg/L/year for the Sen’s slope and linear regression slope, respectively. Over the ~40 year-period, this would result in average decreases of 2 to 0.2 µg/L or increases of 4 and 0.2 µg/L. All the areas exhibited decreasing trends, but the monthly trends in some areas exhibited no trends rather than decreasing trends. Monthly trends for some areas showed some indications that algal blooms are occurring earlier, though evidence is inconclusive. We found essentially no change in algal concentrations in Utah Lake at either the pixel level or for the analysis regions since the 1980′s; despite significant population expansion; increased nutrient inflows; and land-use changes. This result matches prior research and supports the hypothesis that algal growth in Utah Lake is not limited by direct nutrient inflows but limited by other factors.

Comparison of DeepStrain and Feature Tracking for Cardiac MRI Strain Analysis.

Myocardial feature tracking (FT) provides a comprehensive analysis of myocardial deformation from cine balanced steady-state free-precession images (bSSFP). However, FT remains time-consuming, precluding its clinical adoption. To compare left-ventricular global radial strain (GRS) and global circumferential strain (GCS) values measured using automated DeepStrain analysis of short-axis cine images to those calculated using manual commercially available FT analysis. Retrospective, single-center. A total of 30 healthy subjects and 120 patients with cardiac disease for DeepStrain development. For evaluation, 47 healthy subjects (36 male, 53 ± 5 years) and 533 patients who had undergone a clinical cardiac MRI (373 male, 59 ± 14 years). FIELD STRENGTH/SEQUENCE: bSSFP sequence at 1.5T (Phillips) and 3T (Siemens). Automated DeepStrain measurements of GRS and GCS were compared to commercially available FT (Circle, cvi42) measures obtained by readers with 1 year and 3 years of experience. Comparisons were performed overall and stratified by scanner manufacturer. Paired t-test, linear regression slope, Pearson correlation coefficient (r). Overall, FT and DeepStrain measurements of GCS were not significantly different (P=0.207), but measures of GRS were significantly different. Measurements of GRS from Philips (slope=1.06 [1.03 1.08], r=0.85) and Siemens (slope=1.04 [0.99 1.09], r= 0.83) data showed a very strong correlation and agreement between techniques. Measurements of GCS from Philips (slope=0.98 [0.98 1.01], r=0.91) and Siemens (slope=1.0 [0.96 1.03], r=0.88) data similarly showed a very strong correlation. The average analysis time per subject was 4.1 ± 1.2minutes for FT and 34.7 ± 3.3 seconds for DeepStrain, representing a 7-fold reduction in analysis time. This study demonstrated high correlation of myocardial GCS and GRS measurements between freely available fully automated DeepStrain and commercially available manual FT software, with substantial time-saving in the analysis. 3 TECHNICAL EFFICACY: Stage 3.

Performance of Deep Learning Models in Automatic Measurement of Ellipsoid Zone Area on Baseline Optical Coherence Tomography (OCT) Images From the Rate of Progression of USH2A-Related Retinal Degeneration (RUSH2A) Study

PurposePreviously, we have shown the capability of a hybrid deep learning (DL) model that combines a U-Net and a sliding-window (SW) convolutional neural network (CNN) for automatic segmentation of retinal layers from OCT scan images in retinitis pigmentosa (RP). We found that one of the shortcomings of the hybrid model is that it tends to underestimate ellipsoid zone (EZ) width or area, especially when EZ extends toward or beyond the edge of the macula. In this study, we trained the model with additional data which included more OCT scans having extended EZ. We evaluated its performance in automatic measurement of EZ area on SD-OCT volume scans obtained from the participants of the RUSH2A natural history study by comparing the model’s performance to the reading center’s manual grading.Materials and MethodsDe-identified Spectralis high-resolution 9-mm 121-line macular volume scans as well as their EZ area measurements by a reading center were transferred from the management center of the RUSH2A study under the data transfer and processing agreement. A total of 86 baseline volume scans from 86 participants of the RUSH2A study were included to evaluate two hybrid models: the original RP240 model trained on 480 mid-line B-scans from 220 patients with retinitis pigmentosa (RP) and 20 participants with normal vision from a single site, and the new RP340 model trained on a revised RP340 dataset which included RP240 dataset plus an additional 200 mid-line B-scans from another 100 patients with RP. There was no overlap of patients between training and evaluation datasets. EZ and apical RPE in each B-scan image were automatically segmented by the hybrid model. EZ areas were determined by interpolating the discrete 2-dimensional B-scan EZ-RPE layer over the scan area. Dice similarity, correlation, linear regression, and Bland-Altman analyses were conducted to assess the agreement between the EZ areas measured by the hybrid model and by the reading center.ResultsFor EZ area > 1 mm2, average dice coefficients ± SD between the EZ band segmentations determined by the DL model and the manual grading were 0.835 ± 0.132 and 0.867 ± 0.105 for RP240 and RP340 hybrid models, respectively (p < 0.0005; n = 51). When compared to the manual grading, correlation coefficients (95% CI) were 0.991 (0.987–0.994) and 0.994 (0.991–0.996) for RP240 and RP340 hybrid models, respectively. Linear regression slopes (95% CI) were 0.918 (0.896–0.940) and 0.995 (0.975–1.014), respectively. Bland-Altman analysis revealed a mean difference ± SD of -0.137 ± 1.131 mm2 and 0.082 ± 0.825 mm2, respectively.ConclusionAdditional training data improved the hybrid model’s performance, especially reducing the bias and narrowing the range of the 95% limit of agreement when compared to manual grading. The close agreement of DL models to manual grading suggests that DL may provide effective tools to significantly reduce the burden of reading centers to analyze OCT scan images. In addition to EZ area, our DL models can also provide the measurements of photoreceptor outer segment volume and thickness to further help assess disease progression and to facilitate the study of structure and function relationship in RP.

A dome subjected to compression forces: A comparison study between the mathematical model, the catenary rotation surface and the paraboloid

Singular minimal surfaces are models in architecture of domes where compression forces are the only ones acting on the dome. This paper investigates if catenary rotation surfaces and paraboloids are good candidates to substitute the singular minimal surfaces as designs for the construction of domes. Assuming that these surfaces have the same surface area and the same boundary curve, a numerical comparison of the heights of their centers of gravity and the curvatures is presented. This method is performed with Mathematica together with an analysis based on the slope linear regression line. The numerical results demonstrate that the centers of gravity of the two candidates are considerably close to that of the mathematical model. It is also proved that the paraboloids adjust better than catenary rotation surfaces.

Geophysical and geochemical characterization reveals topography controls on critical zone structure in a low hilly region

AbstractCharacterizing the structure of the critical zone (CZ), which is often tens of metres deep, is challenging and crucial to understand earth surface processes. Existing findings on the effect of topography on CZ thickness focused exclusively on mountainous regions with steep slopes, not considering low hilly regions with gentle slopes. Seismic refraction and borehole surveys were conducted to characterize the effects of topography on a red soil CZ structure in the low hilly region of Jiangxi Province, subtropical China. This CZ is derived from Quaternary red clay (QRC) overlying Cretaceous sandstone bedrock. Regolith with a thickness of 8.2–9.5 m consisted of an upper layer of QRC (4.8–6.2 m thick) and a lower layer of weathered sandstone (2.6–4.0 m thick). The boundary between weathered and unweathered sandstone was identified by a Ca depletion profile and calcite weathering front. The boundary between the QRC and weathered sandstone was identified by morphological and textural features and a feldspar weathering front. The QRC had seismic refraction velocities &lt;1.3 km s−1, the weathered sandstone had velocities of 1.3–1.7 km s−1, while the sandstone bedrock had velocities &gt;1.7 km s−1. Following the profile sequence downslope, regolith thickness decreased linearly with decreasing ground elevation. The linear fitted regression slope was &lt;1. This was predominantly determined by QRC thickness, which in turn was mainly controlled by the difference between slopes of ground and bedrock surface and by surface erosion. Following the profile sequence across the slope, regolith thickness decreased linearly with decreasing ground elevation, with a slope of ~1. This was controlled by QRC thickness, which in turn was mainly controlled by surface erosion only, since the bedrock surface was nearly level. These results suggest that topography can control CZ structure in hilly regions with gentle slopes.

Abstract 4138: Prediction of progression-free survival in patients with primary glioblastoma: MRI T2 relaxivity and deep learning

Abstract Introduction: We aimed to evaluate T2 relaxation times in the follow-up of patients with glioblastoma (GBM), the most common and aggressive primary brain tumor with nearly universal recurrence following standard therapy. Methods: In this partially retrospective study, 53 patients (mean age 45.05 years ± 12.6) with non-recurrent GBM followed at our department roughly every 2 months after surgery and combined chemo-radiotherapy were included (&amp;gt;180 days progression-free survival [PFS] as determined by RANO,1 ≥3 MRI examinations in the PFS interval [same scanner and protocol]). Thirty-six patients additionally received Tumor-Treating Fields (TTFields) therapy. All data used were prior to progression. T2 relaxation rates (1/T2) were calculated voxel-wise assuming mono-exponential decay. Whole-brain (WB) histogram values were extracted from 1/T2 maps including skewness, kurtosis, mean, median, 15 and 85 percentile values and variance. We additionally segmented 1/T2 maps using a 5 compartment Gaussian mixture model (Python v3.8.2), producing mean, variance and voxel percentage for each component. To evaluate predictive potential with respect to PFS, we used a deep recurrent neural network (long short-term memory [LSTM] model) in Tensorflow v2.3.1 (4 timesteps); the features used included the above metrics as well as TTFields treatment status and scanner. Twenty subjects were included in the predictive model, as inclusion criteria were stricter (progression, ≤120 days between all MRIs [mean 43.6 days], ≥4 PFS MRIs). Two models were tested: a regression model (days to progression) and a classification model (±18 months PFS; models differed in output layer). Both models were run 10 times; mean results are presented. Results: We found WB median 1/T2 correlated with PFS, as values decreased prior to progression. WB median 1/T2 linear regression slopes also differed in progression versus pseudo-progression, as values were relatively stable in pseudo-progression. The deep LSTM regression model achieved an R2 of 97%. The deep LSTM classification model achieved a mean macro precision of 86%, recall 85% and F1 accuracy of 85% in predicting progression within 18 months. Conclusions: We found very intriguing results with WB median 1/T2 measurements in distinguishing progression from pseudo-progression, and in suggesting progression despite otherwise unremarkable imaging. A more complex predictive model trained on pre-progression data, using a fully-automated segmentation method followed by deep learning, showed very promising results. The present results may find utility in the monitoring of GBM patients and may be advantageous to include in multimodal predictive models. Reference: 1. Wen PY et al. J Clin Oncol 2010;28:1963-1972. Citation Format: Aaron M. Rulseh, Josef Vymazal. Prediction of progression-free survival in patients with primary glioblastoma: MRI T2 relaxivity and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4138.

Spectral Simulation on the Flow Patterns and Thermal Control of Radiative Nanofluid Spraying on an Inclined Revolving Disk Considering the Effect of Nanoparticle Diameter and Solid–Liquid Interfacial Layer

Abstract The current work enlightens the flow pattern and thermal scenario of a nanofluid spraying on an inclined permeable rotating disk. The whirling disk is assumed to revolve with the angular speed Ω. The water-based alumina (Al2O3) nanofluid is considered as a functioning liquid. The nanofluid spraying is treated to be magnetically influenced and thermally radiative. The perception of the nanoparticles' diameter and solid–liquid interfacial layer is incorporated precisely at the nanolevel to observe the thermal variations of the nanofluidic motion. How the magnetic effect, permeability, and nanolayer affect nanofluidic transportation is revealed in detail. The leading flow equations are altered nondimensional using apposite similarity translation, and the spectral quasi-linearization method (SQLM) is instigated to tackle those multi-ordered nonlinear equations. Various three-dimensional figures, graphs, and tables are described to detect and analyze the hydrothermal variations. The linear regression slope technique is addressed to extract the reduction or enhancement rate of heat transference. Also, the probable error is estimated statistically to assure that hydrothermal characteristic is correlated with physical parameters. The consequences indicate enhanced heat transport for nanolayers, but reduced heat transmission for nanoparticles' diameter. Thermal profile intensifies for thickness parameter and impermeable surface, whereas decreases for nanoparticles' diameter.

Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns

This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradient descent and least-squares techniques. The performance of the model is examined by several quality metrics such as coefficient of determination (R2), Root-Mean-Squared-Error (RMSE), Mean-Absolute-Error (MAE), Index of Agreement (IA) and Slope of linear regression. Monte Carlo random sampling technique is employed to propagate input variations to the output response. Moreover, the performance of ANFIS is also compared with other machine learning models including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Ensemble. Results show that the ANFIS model yields higher prediction performance than other machine learning models, for both training and testing data points and regarding all quality metrics. For instance, using training data points, the ANFIS model exhibits a RMSE of 0.0283 compared to 0.0342, 0.0588, 0.0291, and 0.0464 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding gain values are+17.3%,+51.9%,+2.8%, and+39.0%, respectively). On the other hand, using testing data points, the ANFIS model exhibits a RMSE of 0.0276 compared to 0.0393, 0.0987, 0.0403, and 0.0460 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding values of gain are+29.8%,+72.1%,+31.5%, and+40.0%, respectively). The same observation can be made for other quality metrics. It can be concluded that the ANFIS model outperforms other models for both training and testing datasets. The ANFIS model is also compared with existing works in the past, showing its improvement in prediction results. Finally, sensitivity analysis is performed to determine the degree of effect of the input parameters on the ultimate load.

Effects of viscous variation, thermal radiation, and Arrhenius reaction: The case of MHD nanofluid flow containing gyrotactic microorganisms over a convectively heated surface

The numerical exploration of viscous variation, thermal radiation, and Arrhenius reaction modifications on an electrically conducting nanofluid flow through a convectively heated surface is scrutinized in this study. By means of well-suited similarity variables, the nonlinear coupled Ordinary Differential Equations obtained from the mathematical model governing the fluid flow are solved with MATLAB in-built bvp4c software package following shooting approach in conjunction with 4th order Runge–Kutta formula. In addition, a statistical tool called slope of linear regression through data point is introduced. A good agreement was established when the limiting case of the system of equations is compared with previous report in the literature. The results are presented in graphs and tables. It was discovered that enhancement in the viscous variation parameter boosts the velocity gradient and heat transfer rate but lowers the rate of mass transfer and density of motile microorganisms at different levels of R d and K r . More so, increase in chemical reaction lowers the concentration distribution but rises with thermal radiation.

133-LB: Relationship of HbA1c and Glucose by Ethnicity in UK Biobank

The UK Biobank recruited 502,481 people aged 40 to 70 years who were invited to participate by post so not necessarily representative of the general population. The 405,363 participants with random glucose, HbA1c and the necessary demographic/clinical data were aged 56.5 (8.09) years, mean SD, with 54% female, and ethnicity 95.0% White W, 1.9 South Asian SA, 1.3% Black B, 0.9% Other O, 0.6% Mixed Background MB, and 0.3% Chinese C. Glucose was measured by hexokinase and HbA1c by BioRad VARIANT II TURBO analysers, reference range 20-42 mmol/mol in a dedicated central laboratory. Cohen's D statistic was used to compare glucose/HbA1c by ethnicity versus the largest group W. Mean glucose ranged from 5.04 to 5.41 mmol/L with HbA1c40.7 mmol/mol for SAversus36.0 for W (D=0.78 medium effect M) , B 39.3 (D=0.51 M) , O 38.4 (D=0.37 small S) , C 37.1 (D=0.17 very small V) &amp; MB 36.6 (D=0.09 V) , all p values &amp;lt;0.001. There were large differences by ethnicity in linear regression slopes for the glucose/HbA1c relationship when compared to W with correlation coefficient comparison Z-statistics for SA 16.74, B 8.69, O 10.94, C 8.96, all p&amp;lt;0.001, except for MB 2.56, p=0.01. Such ethnic differences in HbA1c have been reported previously and questions raised as whether different cut-offs should be used for diagnosis of diabetes. However, more information on the prevalence of microvascular complications and differences in haematological profiles is required for such modifications. Disclosure J. A. Williams: None. S. E. Manley: n / a. A. Karwath: None. R. A. Round: None. I. M. Stratton: None. S. Ghosh: Other Relationship; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk. S. Mostafa: None. G. Roberts: None. J. Webber: None. G. Gkoutos: None. Funding Medical Research Council (MR/S003991/1) , MRC HDR UK (HDRUK/CFC/01) , NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032) , MAESTRIA (965286)

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning.

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R2) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R2 of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

Long-term body weight change assessed by non-contact load cells under the bed in older people with and without eating assistance: a preliminary study

Utilizing automatic daily body weight (BW) measurements may be helpful for assessing nutritional status and detecting underlying diseases particularly in older people who require nursing care. This preliminary study aimed to verify effectiveness of eating assistance for maintaining BW in older people using a contact-free load cells under the bed (Bed Sensor System: BSS). BW was measured every night for 3 months in eight nursing home older people with severe cognitive and physical dysfunctions. Body composition of the subject's trunk and each limb was measured using a segmented multi-frequency bioelectrical impedance analyzer (BIA). A monthly BW loss was estimated as a slope of linear regression of the daily BW plot. BSS successfully measured daily BW for the study period in all participants. The 4 residents with eating assistance gained slightly more weight, while the 4 residents without eating assistance lost weight. There was a significant difference between the two groups in the monthly BW change (− 0.79 ± 0.51 kg/month versus 0.20 ± 0.49 kg/month, P = 0.030). None of the BIA-derived parameters was associated with the monthly BW change. BSS revealed effectiveness of eating assistance to maintain BW in nursing home residents with severe cognitive and physical dysfunctions.