Lin's Concordance Correlation Research Articles

Mapping Cation Exchange Capacity (CEC) Across Sugarcane Fields with Different Comparisons by Using DUALEM Data

The sugarcane growing soil in far-north Queensland is sandy, and infertile. To ensure productivity, nutrient guidelines recommend lime application rates based on soil cation exchange capacity (CEC). However, laboratory determination of CEC is expensive. Because CEC is often correlated with soil apparent electrical conductivity (ECa, mS/m) measured from electromagnetic induction (EM) instruments, ECa can be used to predict CEC. Using ECa may lead to uncertainty in prediction, but estimates of true electrical conductivity (σ, mS/m) generated from inversion of ECa, can be correlated with depth-specific CEC. In this study, we compared linear regression (LR) between ECa from a DUALEM-421S and CEC at specific depths ( i.e., topsoil [0–0.3 m], subsurface [0.3–0.6 m], subsoil [0.6–0.9 m] and deep subsoil [0.9–1.2 m]), with a LR of σ using a quasi-2d (q-2d) or quasi-3d (q-3d) inversion of DUALEM-421S ECa and CEC at all depths. The use of a multiple linear regression (MLR) to predict CEC, using σ with depth and location ( i.e., Easting and Northing) is also explored along with σ from the other EM products ( i.e., DUALEM-1S and DUALEM-21S). The minimum number of calibration sample locations ( i.e., n = 165, 150,…, 15) is also investigated. The LR between ECa ( e.g., 1mPcon) and CEC were very weak (R2 = 0.27) and weak (0.36) in the topsoil and subsurface, respectively, but moderate in the subsoil (0.57) and deep subsoil (0.67). The LR between σ, estimated from q-2d (R2 = 0.66) and q-3d (0.64) inversion of DUALEM-421S ECa, and CEC at all depths was moderate. In terms of prediction agreement, the Lin's concordance correlation coefficient (LCCC) was moderate for q-2d (0.79) and q-3d (0.75). Using a MLR with depth, coordinates and σ, led to an improvement in calibration using q-2d (R2 = 0.71) or q-3d (0.67), with prediction agreement substantial for q-2d (LCCC = 0.83) and moderate for q-3d (0.78), with comparable agreement from DUALEM-1S and DUALEM-2S (0.77) estimates of σ. The minimum number of calibration samples for a strong MLR R2 (>0.7) and substantial and good agreement was 15 for q-2d and 30 for q-3d, respectively. The final digital soil mapping of topsoil CEC developed using MLR and σ estimated from q-3d of DUALEM-421S ECa could be used to apply the Australian sugarcane industry lime application guidelines with areas with intermediate (3–6 cmol[+]/kg) and small (<3 cmol[+]/kg) topsoil CEC requiring 4 and 2.25 t/ha, respectively.

Body composition affects the accuracy of predictive equations to estimate resting energy expenditure in older adults: An exploratory study

To investigate the accuracy of ten different predictive equations to estimate resting energy expenditure (REE) in a sample of Brazilian older adults and develop a predictive equation for estimating REE based on body composition data. A cross-sectional study with thirty-eight Brazilian older adults aged 60-84 years, who had their REE measured by indirect calorimetry (IC) and BC assessed by dual-energy x-ray absorptiometry (DXA). REE was compared to the estimation of ten predictive equations, and the differences between BC and anthropometric-based equations were investigated using Bland-Altman plots and Lin's concordance correlation. Accuracy was evaluated considering ±10% of the ratio between estimated and measured REE. The sample was composed of 57.9% men, with a mean age of 68.1 (5.8) years, and a mean REE by IC of 1528 (451) kcal. The highest accuracy was 47.4% obtained by Luhrmann and Fredrix equations, and the lowest accuracy was 13.2% reached by Weigle equation. In general, the proportion of underestimation was higher than overestimation. All anthropometric-based equations presented a good agreement with REE from IC. For those equations derived from BC, however, three of them reached only a moderate agreement. In terms of accuracy, all equations presented lower than 50% of accurate prediction of REE. In this sample of older adults, previous predictive equations to estimate REE did not show good accuracy, and those based on BC presented even worse results, showing that changes in BC related to aging could impact the accuracy of these equations.

Assessing the reliability and cross-sectional and longitudinal validity of fifteen bioelectrical impedance analysis devices.

The purpose of this investigation was to expand upon the limited existing research examining the test-retest reliability, cross-sectional validity and longitudinal validity of a sample of bioelectrical impedance analysis (BIA) devices as compared with a laboratory four-compartment (4C) model. Seventy-three healthy participants aged 19-50 years were assessed by each of fifteen BIA devices, with resulting body fat percentage estimates compared with a 4C model utilising air displacement plethysmography, dual-energy X-ray absorptiometry and bioimpedance spectroscopy. A subset of thirty-seven participants returned for a second visit 12-16 weeks later and were included in an analysis of longitudinal validity. The sample of devices included fourteen consumer-grade and one research-grade model in a variety of configurations: hand-to-hand, foot-to-foot and bilateral hand-to-foot (octapolar). BIA devices demonstrated high reliability, with precision error ranging from 0·0 to 0·49 %. Cross-sectional validity varied, with constant error relative to the 4C model ranging from -3·5 (sd 4·1) % to 11·7 (sd 4·7) %, standard error of the estimate values of 3·1-7·5 % and Lin's concordance correlation coefficients (CCC) of 0·48-0·94. For longitudinal validity, constant error ranged from -0·4 (sd 2·1) % to 1·3 (sd 2·7) %, with standard error of the estimate values of 1·7-2·6 % and Lin's CCC of 0·37-0·78. While performance varied widely across the sample investigated, select models of BIA devices (particularly octapolar and select foot-to-foot devices) may hold potential utility for the tracking of body composition over time, particularly in contexts in which the purchase or use of a research-grade device is infeasible.

A comparative analysis of body composition assessment by BIA and DXA in children with type II and III spinal muscular atrophy.

Body composition analysis is a valuable tool for assessing and monitoring the nutritional status of children with spinal muscular atrophy (SMA). This study was designed to compare the consistency of bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), as the gold standard method for assessing body composition in clinical practice when treating children with type II and III SMA. From 2019 to 2021, we performed a retrospective analysis of body composition by DXA and BIA measurement methods in patients with type II and III SMA treated at a Chinese tertiary children's hospital. Fat mass (FM), muscle mass (MM), bone mineral content (BMC), and visceral fat area (VFA) were compared using paired sample t-tests. We calculated Lin's concordance correlation coefficient (CCC) and Spearman correlation coefficient to verify the correlation between DXA and BIA measurements. Bland-Altman analysis was used to assess the consistency of the two methods. Fifty-seven children with type II and III SMA were recruited. Compared with body composition measured by DXA, the average FM measured by BIA is significantly lower (P <0.001), whereas the average MM, BMC, and VFA measured by BIA are significantly higher (P < 0.001) in children with SMA. Overall, the difference between MM (Delta [BIA-DAX] = 1.6 kg) and FM (Delta [BIA-DAX] = -1.6 kg) measured by DXA and BIA was minor, whereas the difference of VFA (Delta [BIA-DAX] = -43.5 cm) was significantly large. Correlation analysis indicated a substantial correlation of MM (CCC = 0.96 [95% confidence interval (CI) = 0.93-0.98], r = 0.967 [P < 0.0001]) and FM (CCC = 0.95 [95% CI = 0.92-0.97], r = 0.953 [P < 0.0001]), and poor correlation of BMC (CCC = 0.61 [95% CI = 0.42-0.75], r = 0.612 [P < 0.0001]) and VFA (CCC = 0.54 [95% CI = 0.33-0.70], r = 0.689 [P < 0.0001]) measurements between the two methods. The Bland-Altman analysis suggests that the majority of participants were within LOA. In addition, differences in MM and VFA measurements between BIA and DAX increased according to patients' increasing height, whereas differences in FM and BMC did not differ with height. BIA overestimates MM and underestimates the FM, BMC, and VFA in children with SMA compared with DXA measurements. Overall, the non-invasive, easy-to-use, and repeatable BIA measurements were found to be in good agreement with DXA measurements, especially for FM and MM, which are essential parameters for the nutritional evaluation of children with SMA.

A quantitative model based on grey theory for sea surface temperature prediction

In order to predict sea surface temperature (SST), combined with the genetic algorithm and the least-squares method, a GM(1,1|sin) power model prediction method based on similarity deviation is proposed. We first combined the data of two consecutive years into a new time series, analyzed the similarity of the data of the previous year, and obtained the most similar year and the corresponding new time series. Then, we established a GM(1,1|sin) power model to predict SST. In model validation, we predicted the monthly average SST from 2016 to 2020 with the data from 1985 to 2015, 2016, 2017, 2018, and 2019. The validation results showed that the maximum mean relative error (MRE) was 13.28%, the minimum MRE was 5.54%, and the average MRE and the root mean square error (RMSE) were 9.81% and 1.0627, respectively. All of evaluation metrics of Lin’s concordance correlation coefficient (LCCC) and the ratio of performance to deviation (RPD) were excellent. We iteratively predicted the monthly average SST from 2016 to 2020 with the data from 1985 to 2015, the maximum MRE was 13.91%, the minimum was 7.80%, and the average MRE, RMSE, LCCC and RPD are 11.07% 1.0603, 0.9894, and 7.497, respectively. Compared with GM(1,1), GM(1,1|sin + cos), and GM(1,1|sin) models, the proposed model outperformed these models with at least 50% in the MRE. It proves that the proposed model can be regarded as a better solution to predicting SST.

Comparing the Movement Assessment Battery for Children with the Bruininks-Oseretsky Test of Motor Proficiency in adolescents with and without schizophrenia.

As motor impairments have implications for health and functioning, they need to be addressed early, not only in childhood but also in adolescence, the period in which mental disorders as schizophrenia, typically develops. Further, the possible prognostic value of motor impairments in schizophrenia highlights theimportance. The Bruininks-Oseretsky Test of Motor Proficiency (BOT-2) and the Movement Assessment Battery for Children (MABC-2) assess adolescent motor performance. However, MABC-2 is not valid past age 16 and has no sex-norms. Further, while the concurrent validity between the tests and their ability to distinguish between clinical and non-clinical groups has been established in children, however they have not been established in adolescence. To compare the ability of MABC-2 and BOT-2 to distinguish between adolescents with and without schizophrenia, to examine the concurrent validity between tests, to examine the agreement between tests in classifying adolescents "at risk" or scoring "below average" and to examine the influence of age and sex on MABC-2. Motor performance assessed by BOT-2 and MABC-2 was compared in 25 adolescents with schizophrenia (14-18) and age- and sex-matched controls using t-test, Cohen's D and false discovery rate's q-value. The associations between tests were assessed using Pearson's correlation and Lin's concordance correlation coefficient. The Kappa coefficient was used to assess the agreement between tests in classifying "risk/below average" and linear regression was adopted to assess the influence of age/sex on MABC-2. MABC-2 and BOT-2 significantly distinguished adolescents with schizophrenia from controls with large effect size. A strong association (p>0.001) was found between the tests. The tests revealed moderate agreement in identifying "risk" or scoring "below average". Only sex influenced MABC-2 scores. MABC-2 and BOT-2 are both useful for assessing motor performance and distinguishing between adolescents with and without schizophrenia, although BOT-2 provides a more detailed picture of the challenges in adolescent with schizophrenia.

The nursing critical thinking in clinical practice questionnaire for nursing students: A psychometric evaluation study

AimTo adapt and validate the N-CT-4 Practice for use with Spanish nursing students. BackgroundPromoting critical thinking is one of the primary objectives of nursing education programs all over the world. Using reliable and valid instruments to measure critical thinking is essential. The Nursing Critical Thinking in Clinical Practice Questionnaire (N-CT-4 Practice) is used internationally to assess critical thinking in nursing practice. However, little is known about the possible applications of this instrument in the context of nursing education. MethodsTwo-phase study: Phase I, adaptation of the N-CT-4 Practice for use with nursing students and verification of its content validity; phase 2, cross-sectional study to validate its psychometric properties in a sample of 331 nursing students from three university nursing schools. A subsample of 34 students completed the questionnaire on two occasions, with a time interval of two weeks. The construct validity and reliability of the instrument were tested using confirmatory factor analysis, convergent validity, internal consistency reliability and test-retest reliability. The STROBE guidelines and the COSMIN checklist were followed. ResultsAll items obtained optimal content validity values. The model fit indices obtained from the confirmatory factor analysis supported the hypothesis of the four-dimensional structure on which the original questionnaire was based. Cronbach's alpha of 0.96 indicated high reliability. The correlations between the total score and the scores for the dimensions were statistically significant, positive and high, with values above r = 0.78 (p < 0.05). Lin's concordance correlation coefficient was 0.6 (p < 0.001). ConclusionsThe adapted version of the N-CT-4 Practice is suitable for measuring critical thinking skills in the educational context of the degree course in nursing in Spain. Its psychometric validation yielded satisfactory results.

Next-generation smart watches to estimate whole-body composition using bioimpedance analysis: accuracy and precision in a diverse, multiethnic sample

Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of these devices are unknown. This study evaluated smart watches with integrated bioelectrical impedance analysis (BIA) sensors for their ability to measure and monitor changes in body composition. Participants recruited across BMIs received duplicate body composition measures using 2 wearable bioelectrical impedance analysis (W-BIA) model smart watches in sitting and standing positions, and multiple versions of each watch were used to evaluate inter- and intramodel precision. Duplicate laboratory-grade octapolar bioelectrical impedance analysis (8-BIA) and criterion DXA scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor changes in body composition. Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (P < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA; P > 0.05; Lin's concordance correlation coefficient = 0.97). FFM was less precise on the watches than DXA {CV, 0.7% [root mean square error (RMSE) = 0.4 kg] versus 1.3% (RMSE = 0.7 kg) for W-BIA}, requiring more repeat measures to equal the same confidence in body composition changes over time as DXA. After systematic correction, smart-watch BIA devices are capable of stable, reliable, and accurate body composition measurements, with precision comparable to but lower than that of laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems, such as homes, training centers, and geographically remote locations.

A deep learning model for CT-based kidney volume determination in dogs and normal reference definition.

Kidney volume is associated with renal function and the severity of renal diseases, thus accurate assessment of the kidney is important. Although the voxel count method is reported to be more accurate than several methods, its laborious and time-consuming process is considered as a main limitation. In need of a new technology that is fast and as accurate as the manual voxel count method, the aim of this study was to develop the first deep learning model for automatic kidney detection and volume estimation from computed tomography (CT) images of dogs. A total of 182,974 image slices from 386 CT scans of 211 dogs were used to develop this deep learning model. Owing to the variance of kidney size and location in dogs compared to humans, several processing methods and an architecture based on UNEt Transformers which is known to show promising results for various medical image segmentation tasks including this study. Combined loss function and data augmentation were applied to elevate the performance of the model. The Dice similarity coefficient (DSC) which shows the similarity between manual segmentation and automated segmentation by deep-learning model was 0.915 ± 0.054 (mean ± SD) with post-processing. Kidney volume agreement analysis assessing the similarity between the kidney volume estimated by manual voxel count method and the deep-learning model was r = 0.960 (p < 0.001), 0.95 from Lin's concordance correlation coefficient (CCC), and 0.975 from the intraclass correlation coefficient (ICC). Kidney volume was positively correlated with body weight (BW), and insignificantly correlated with body conditions score (BCS), age, and sex. The correlations between BW, BCS, and kidney volume were as follows: kidney volume = 3.701 × BW + 11.962 (R 2 = 0.74, p < 0.001) and kidney volume = 19.823 × BW/BCS index + 10.705 (R 2 = 0.72, p < 0.001). The deep learning model developed in this study is useful for the automatic estimation of kidney volume. Furthermore, a reference range established in this study for CT-based normal kidney volume considering BW and BCS can be helpful in assessment of kidney in dogs.

Soil total and organic carbon mapping and uncertainty analysis using machine learning techniques

• We conducted spatial prediction and uncertainty analysis of SOC and STC. • RFRK gave best predictions for SOC and STC. • RFRK produced higher local accuracy than RF. • Land use types, NDVI and elevation were key factors for SOC and STC predictions. Soil carbon is the largest terrestrial carbon pool. Reliable mapping of soil organic carbon (SOC) and soil total carbon content (STC) is essential for agricultural ecosystem management and carbon accounting under global warming conditions. This study was conducted at a fine scale and aimed to perform spatial distribution prediction and uncertainty mapping of SOC and STC and quantify the contribution of environmental variables affecting the variability of SOC and STC. Three machine learning models, namely, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Random Forest plus residuals Kriging (RFRK), were developed using 4345 agricultural topsoil samples and 16 environmental covariates. Mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE), and Lin’s concordance correlation coefficient (LCCC) were used to evaluate the prediction global accuracy. Accuracy plot was used to quantify the uncertainty (i.e. local accuracy) of SOC and STC predictions. RFRK performed best with MAE, RMSE, NSE, and LCCC of 20.76%, 27.61%, 0.39, and 0.56 for SOC and 27.20%, 38.59%, 0.35, and 0.53 for STC, respectively. RF outperformed XGBoost in terms of NSE (0.33 vs 0.29 for SOC and 0.36 vs 0.32 for STC). Accuracy plots showed that RFRK produced higher local accuracy than RF both in quantifying the prediction uncertainty of SOC and STC. XGBoost performed excellently in the uncertainty estimation of SOC. Land use types, mean annual Normalized Difference Vegetation Index, and elevation were the top three important indicators in determining the spatial variability of SOC and STC. These results could provide inspiration and support for monitoring soil carbon in complex terrain areas.

Novel weight estimation equation for children with cerebral palsy in low-resource settings: Validation in a population-based cohort.

To validate a novel equation to estimate weight from mid-upper arm circumference (MUAC) among children with cerebral palsy (CP) in rural Bangladesh. Children with CP aged 2 to 18 years registered in the Bangladesh CP Register were randomly selected. Data on sociodemographics, Gross Motor Function Classification System level, and anthropometric measurements were extracted. Bland-Altman plots with a 95% agreement limit and Lin's concordance correlation coefficient with 95% confidence intervals (CI) were reported to measure agreement between observed and estimated weight. Percentage error was used to determinate the method's accuracy. There were 497 participants with a mean age at assessment of 9 years (SD 4 years 11 months) (47.7% female). Lin's concordance correlation coefficient between the observed and estimated weights was 0.90 (95% CI 0.89-0.92). Bland-Altman plots showed a reasonable accuracy of the equation in the study cohort. The mean percentage error of the equation was 5.04%. The average difference between observed and estimated weights was -1.02 kg (SD 5.1). The differences between observed and estimated weights were significantly greater among children with weight-for-age, height-for-age, or BMI-for-age z-scores less than or equal to -4. It is possible to predict the weight of children with CP from MUAC with sufficient accuracy. The equation can be used for populations in low-resources and low- and middle-income countries. The equations predict the weight of children with cerebral palsy from their mid-upper arm circumference reasonably accurately. The difference between observed and estimated weights ranged between 0 kg and ± 5 kg in 81.5% of children. Sex and Gross Motor Function Classification System level did not affect the accuracy of the equations. The equations were less accurate for estimating the weight of severely undernourished children.

Measurement of Stricture Dimensions Using a Visual Comparative Estimation Method With Biopsy Forceps During Endoscopy.

Estimation of the dimensions of endoscopic findings such as stricture diameter is largely subjective. Accurate assessment of stricture dimensions has multiple benefits including facilitating the choice of appropriately sized endoscopic therapies for treating stricture, properly tracking response to endoscopic therapies between procedures, and potentially even predicting outcomes of endoscopic therapy. Endoscopies performed in children with repaired esophageal atresia between August 2019 and August 2021 for which both (1) an endoscopic estimate of esophageal stricture diameter obtained by visual comparison with the known dimensions of the biopsy forceps and (2) an intraoperative esophageal fluoroscopy study were performed were included for analysis. Fluoroscopic stricture diameter measurements were manually obtained using a software ruler tool calibrated to the known dimensions of the intraluminal endoscope. Statistical concordance was calculated between the visual diameter estimates and the standard fluoroscopic stricture measurements. One hundred ninety-one endoscopies were included for analysis. Lin's concordance correlation coefficient was 0.92 (95% confidence interval: 0.89-0.94) between the visual diameter estimates and the fluoroscopic stricture measurements. Correlation was strongest for smaller to mid-sized stricture diameters. Use of the biopsy forceps as a visual reference of known dimensions enables accurate visual estimation of esophageal stricture diameter during endoscopy using commonly available tools, with high concordance with standard fluoroscopic measurement techniques.

Cytohistologic immunohistochemical correlation of epithelial tubo-ovarian neoplasms: Can cell blocks substitute for tissue?

Cytologic specimens often represent the initial diagnostic material for tubo-ovarian neoplasms resulting from therapeutic paracentesis for patients presenting with high-volume ascites. However, subtyping and immunohistochemical (IHC) characterization, which have implications in preoperative management and downstream ancillary testing, are not routinely performed in many institutions. This study aims to perform cytohistologic correlation of commonly used IHC stains to establish their reliability in peritoneal fluids/washing specimens. A retrospective search of the laboratory information systems was performed to identify peritoneal fluid/washing specimens involved by borderline or malignant epithelial tubo-ovarian neoplasms and concurrent/subsequent surgical resection specimens. Cell blocks and tissue were stained for PAX8, WT-1, p53, p16, Napsin-A, estrogen receptor, and progesterone receptor, and staining between cytological and surgical specimens was compared. A total of 56 case pairs were included, with the following final diagnoses on histological examination: 37 high-grade serous carcinomas, eight clear cell carcinomas, one endometrioid adenocarcinoma, two low-grade serous carcinomas, and eight serous borderline tumors. There was perfect cytohistologic correlation for PAX8 (Lin's concordance correlation coefficient [LINCCC]=1.00) and WT-1 (LINCCC=1.00), substantial/good correlation for p53 (LINCCC=0.96), p16 (LINCCC=0.93), napsin-A (LINCCC=0.91) and ER (LINCCC=0.77), and moderate correlation for PR (LINCCC=0.54). Immunohistochemical correlation between peritoneal fluid and surgical resection specimens for tubo-ovarian neoplasms is high. Common subtypes of tubo-ovarian carcinomas can be reliably distinguished on fluids using IHC.

Validation of the NIH Toolbox-Cognition Battery against legacy neurocognitive measures in adults with cognitive impairments: An exploratory analysis.

The purpose of this exploratory study was to describe associations between NIH Toolbox-Cognition Battery subtests and legacy measures of neurocognitive function in two samples with neurological conditions (stroke and sickle cell disease (SCD)). This exploratory secondary analysis uses data from two studies that assessed cognition at one time point using the NIH Toolbox-Cognition Battery, the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and subtests from the Delis-Kaplan Executive Functions System (DKEFS). People with stroke (n = 26) and SCD (n = 64) were included. Associations between the NIH Toolbox-Cognition Battery subtests and corresponding legacy measures were examined using linear correlations, Bland-Altman analysis, and Lin's Concordance Correlation Coefficient. Linear correlations and Lin's Concordance Correlation Coefficient were poor to strong in both samples on NIH Toolbox-CB subtests: Flanker Inhibitory Control and Attention (r = .35 to .48, Lin CCC = .27 to .37), Pattern Comparison Processing Speed (r = .40 to .65, Lin CCC = .37 to .62), Picture Sequence Memory (r = .19 to .55, Lin CCC = .18 to .48), Dimensional Change Card Sort (r = .39 to .77, Lin CCC = .38 to .63), Fluid Cognition Composite (r = .88 to .90, Lin CCC = .60 to .79), and Total Cognition Composite (r = .64 to .83, Lin CCC = .60 to .78). Bland-Altman analyses demonstrated wide limits of agreement across all subtests (-3.17 to 3.78). The NIH Toolbox-Cognition Battery subtests may behave similarly to legacy measures as an overall assessment of cognition across samples at risk for neurological impairment. Findings should be replicated across additional clinical samples.

Assessment of global, national and regional‐level digital soil mapping products at different spatial supports

AbstractDigital soil mapping provides estimates of soil properties and, in turn, an understanding of the spatial variation in soil across landscapes. In recent years, several digital soil mapping products have been released across the world. An end user in New South Wales (NSW), Australia, could choose between a global product (SoilGrids), a national product (Soil and Landscape Grid of Australia) and a state product (NSW digital soil maps). All predict at grid resolutions of 250 m or less, which approaches the required detail for within‐field site‐specific management such as variable‐rate application of agricultural inputs. In this study, the quality of clay and soil organic carbon (SOC) products (0–30 cm) from these three publicly available digital soil maps (DSM) were validated with an external dataset of 394 soil observations, collected from 14 farms across eastern Australia. Each farm was sampled using stratified random sampling, allowing for unbiased estimates of prediction quality at three spatial supports: point (soil core), strata (management zone) and farm. For the clay attribute, this study observed Lin's concordance correlation coefficient (LCCC) values of 0.22–0.37 at the point spatial support, far below reported values. This improved at the strata spatial support with LCCC values of 0.20–0.60 and the farm spatial support with values of 0.07–0.54. For SOC, LCCC values ranged from 0.25 to 0.45 for points, 0.31–0.55 for strata and 0.31–0.60 for farms. The grid resolution of DSMs depends largely on the resolution of the covariates used, and current efforts look to incorporate more recent, finer resolution covariates to create higher‐resolution soil maps. However, the results of this study suggest that both the grid resolution and spatial support used in DSMs need further consideration. Rather than letting the resolution of covariates determine the resolution of DSMs, this should also be determined by the number of soil observations available for modelling, their distribution in geographic and attribute space and the prediction quality.Highlights DSM products do not meet published accuracies when assessed with an independent dataset The large‐extent DSM products were unable to represent within‐farm variability Aggregation of the DSM products over large spatial supports improved accuracies DSM products should be created at different spatial supports, each with an associated uncertainty

Agreement Between Different Body Composition Methods And Dual-energy X-ray Absorptiometry In Adults

The assessment of body composition is often used in clinical practice for nutritional assessment and monitoring. The gold standard method, dual energy X-ray absorptiometry (DXA), is hardly feasible in routine clinical practice. The bioelectrical impedance analysis (BIA) method is a common applicable alternative because it is accessible and easy to use. In addition, methods that use artificial intelligence to assess body composition through photos (AI-Photo) began to emerge as cheaper alternatives than BIA. However, few studies have evaluated the agreement of different methods with DXA. PURPOSE: Evaluate the agreement between one IA-Photo method, three BIA devices and DXA for estimating body fat percent (BF%) in adults. METHODS: A total of 215 Brazilian adults, aged between 18 and 65 years, BMI from 18 to 35 kg/m2 and sedentary and physically active. Volunteers were evaluated by the IA-Photo method (Shaped, Rio de Janeiro, Brasil), bioimpedance scales of the inBody 270 (Biospace, California, USA), inBCA F-500 (Shenzhen, China), Omron HBF-514 (Kyoto, Japan) models and DXA (GE, Madison, USA) at an interval of 1 minute between the methods. The method of Bland-Altman (BA) was used to evaluate the identify the 95% limits of agreement and BIAS between IA-Photo method and BIA devices with DXA. Lin's Concordance Correlation Coefficient (CCC) was utilized to evaluate concordance between each method with DXA. RESULTS: Confidence interval values are represented by the dotted line and the BIAS by the dashed line (Figure 1). The BIAS value of the %error between methods and DXA ranged from 3.33 (Shaped) to 12.2 (inBody-270). CCC was 0.96; 0.93; 0.91 and 0.45 for the Shaped, Omron, inBody-270 and inBCA F-500 methods. The inBody and Omron model BIAs presented a significant p value for the linear regression in the BA plot, indicating a proportion bias in relation to the BF%. CONCLUSIONS: Shaped and Omron had greater agreement with the DXA compared to inBody-270 and inBCA F-500 for estimating BF%.

Validation of Presurgical Simulation of White Matter Damage Using Diffusion Tensor Imaging

The understanding of white matter (WM) was revolutionized by the emergence of tractography based on diffusion tensor imaging (DTI). Currently, DTI simulations are implemented in preoperative planning to optimize surgical approaches. The reliability of these simulations has been questioned and investigated seeking for correlation between neurological performance and anomalies in DTI parameters. However, the ability of preoperative WM simulations to predict a surgical injury has not been thoroughly evaluated. Our objective was to assess the reliability of preoperatively simulated WM injuries for conventional neurosurgical procedures. WM surgical damage was preoperatively simulated by creating a 3-dimensional volume representing the endoscope or the surgical trajectory. This volume was used as an additional region of interest in the fascicle reconstruction to be subtracted from the original fascicle. Simulated, injured fascicles were compared in terms of the number of fibers and volume to those created from postoperative DTI studies. Reliability was assimilated into the correlation between the simulation and the postoperative reconstruction; evaluated using the intraclass correlation coefficient or Lin's Concordance correlation coefficient (CCC), and represented on Bland-Altman plots. The preoperative and postoperative DTI studies of 30 patients undergoing various neurosurgical approaches were processed. The correlation between simulated injuries and postoperative studies was high in terms of fibers (Concordance correlation coefficient= Rho.C=0.989 [95% confidence interval= 0.979-0.995]) and volume (intraclass correlation coefficient=0.95 [95% CI= 0.89-0.97]). Bland-Altman plots demonstrated that the great majority of cases fell within the mean±2 Standard deviations. Presurgical simulation of WM fascicles based on DTI is consistent with postoperative DTI studies. These findings require further validation by neurophysiological and clinical correlation.

Reproducibility of high-resolution manometry among healthy and constipated persons.

Although high-resolution manometry (HR_ARM) is commonly used to diagnose defecatory disorders, its intraindividual day-to-day reproducibility is unknown. Since these measurements entail voluntary effort, this study aimed to evaluate the intraindividual day-to-day reproducibility of HR_ARM. In 22 healthy women (35 years [14] mean [SD]) and 53 constipated patients(42 years [13], 46 women), the rectal balloon expulsion time (BET), and anorectal pressures were measured with HR_ARM on 2 days separated by a median (interquartile range) of 15 days (11-53) in healthy and 40 days (8-75) in constipated patients. Anorectal pressures were evaluated at rest (20 s), during squeeze, and duringevacuation. Rectal sensation was also evaluated in constipated patients. The agreement between both measurements was evaluated with Lin's concordance correlation coefficient (CCC) and other tests. Among constipated patients, the anal resting (mean CCC [95%CI] 0.62 [0.43-0.76]) and squeeze pressures (CCC 0.65 [0.47-0.79]), rectoanal gradient during evacuation (CCC 0.42 [0.17-0.62]), and rectal sensory thresholds (CCC for urgency 0.72 [0.52-0.84]) were concordant. Between days 1 and 2, one or more outcomes, characterized as normal or abnormal, differed in 79% of constipated patients, i.e., anal resting and squeeze pressures, the gradient, and the rectal BET differed in, respectively, 25%, 19%, 36%, and 10% of constipated patients. The rectal BET was prolonged in three healthy persons (both days) and either on 1 day (six patients) or 2 days (33 patients) in constipated patients. While HR_ARM variables and the BET are reproducible within healthy and constipated persons, the outcome on repeat testing differed in 79% of constipated patients.

The spatial variability of temporal changes in soil pH affected by topography and fertilization

Understanding the spatial–temporal variation of soil pH is essential for agricultural production. However, studies on the spatial–temporal variation of soil pH in arable soils in areas of complex terrain are still limited. In this work, 143 and 232 topsoil samples (0 ∼ 20 cm) were collected in 2012 and 2017, respectively, in a typical hilly and mountainous area (235.35 km2) of southwestern China. Descriptive statistics, semivariance analysis, and boosted regression trees (BRT) model were used to analyze the spatial–temporal distribution characteristics of soil pH and their relationships with environmental factors (topography, parent material, and soil type) and fertilization. Samples were randomly divided into calibration (80%) and validation (20%) sets. Model performance was assessed by mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin’s concordance correlation coefficient (LCCC). Results indicated that BRT models could explain approximately 75% and 51% of soil pH variability in 2012 and 2017, respectively, with topography significantly influencing the spatial variation in soil pH. Spatial autocorrelation analysis showed a more homogeneous spatial distribution of soil pH in 2012 (range = 16180 m) than in 2017 (range = 588.9 m). Additionally, the BRT model was used to analyze the relationship between pH changes (2017–2012) and environmental factors and fertilization. The values of MAE, RMSE, R2, and LCCC of BRT for pH changes were 0.36, 0.44, 0.61, and 0.73, respectively. This model explained 61% of the spatial variation in soil pH changes. Elevation, organic fertilizer, slope, valley depth, and slopelengthand slope steepnessfactor were the most important factors affecting the spatial variability of soil pH changes. From 2012 to 2017, the average soil pH increased significantly by 0.63 (p < 0.001) with an increase in the northern and northeastern parts and a decrease in the southwestern areas due to the differences in organic fertilizer application and topography.

Evaluation of the Iranian panel reactive antibody calculator and potential usefulness: A retrospective study.

There are several cPRA websites based on large enough samples in Eurotransplant, the United Network for Organ Sharing (UNOS), and the Canadian Transplant Registry (CTR). On the other hand, those calculators can differ based on the ethnicity to which they are applied. We developed the Iranian PRA calculator and compared it with UNOS and CTR calculators. The allele and haplotype frequencies of the Iranian donor pool were estimated using the HLA typing of 523 deceased Iranian kidney donors. The Organ Procurement and Transplantation Network formula was used to generate cPRA (cPRA frequency). We also used a computer script to compare the undesirable antigens of patients with the human leukocyte antigen (HLA) phenotype of donors (cPRA filtering). A total of 100 anti-HLA antibody profiles were determined in 100 sensitized individuals on the waiting list, and cPRA was estimated using various PRA calculators. Variable allelic frequencies were obtained from population heterogeneity in each calculator's donor panel. However, no significant changes in cPRA were identified between the Iranian calculator, UNOS, and the Canadian online calculators. Lin's concordance correlation coefficient of .98 showed that cPRA (freq) and cPRA (filter) values had almost perfect agreement. The cPRA values from the Iranian calculator are comparable to those from UNOS and CTR calculators. The donor filtering method was more useful because of factors like cost and flexibility. It also makes it easier to update cPRA on a regular basis.