High Concordance Correlation Research Articles

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

Sea surface temperature (SST) and salinity (SSS) are essential variables at the ocean and atmosphere interface when considering risk factors for disease in farmed and wild fish stocks. Ecological research has witnessed a recent trend in use of digital and satellite technologies, including remote-sensing tools. We explored spatial coverage of remotely-sensed SST and SSS data and compared them with in situ measurements of water temperatures and salinity, which led to suggested adjustments to the remotely-sensed data for its use in aquaculture research. The in situ data were from farms and wild surveillance sites in coastal British Columbia, Canada, from 2003 to 2016. Concurrent SST and SSS values were extracted from remotely-sensed products and compared with 20,513 and 20,038 in situ records for water temperature and salinity, respectively, from 232 different sites. Among nine SST products evaluated, the UKMO OSTIA SST (UK Meteorological Office) had the highest retrieval, and highest concordance correlation coefficient (0.86), highest index of agreement (0.93), fewest missing values, and smallest mean and SD values for bias, when compared to in situ measurements. A mixed linear regression model with UKMO OSTIA SST as the predictor for in situ measurements estimated an adjustment coefficient of 0.89 °C for UKMO OSTIA SST. None of the three SSS products evaluated provided appropriate corresponding values for in situ sites, suggesting that spatial coverage for the study area is currently lacking. This study demonstrates that, among SST products, UKMO OSTIA SST is currently best suited for aquaculture studies in coastal BC. The near real-time availability of these data with the estimated adjustment would allow their use in forecast models, surveillance of pathogens, and the creation of risk maps.

Stability Analysis of CT Radiomics Features With Respect to the Variation of Manual Segmentation in Oropharyngeal Cancer

Accurate segmentation of tumors and quantification of tumor features are important for cancer detection, diagnosis, monitoring, and planning therapeutic intervention. Due to inherent noise components in multiparametric imaging and inter-observer variations, it is common that various segmentation methods, including both manual segmentation and computer-aided segmentation methods, may produce large segmentation errors in tumor volumes and their associated radiomics features. Such errors may eventually lead to large prediction errors. The aim of this study is to carry out the stability analysis for various radiomics features with respect to segmentation results based on the contrast-enhanced CT axial images. All the 436 CT images were imported to a commercial software and the gross tumor volumes of primary disease (GTVp) were segmented by two expert radiation oncologists. In order to illustrate the variations of segmentation results, additional two segmentation results were set up via resizing the original segmented regions of interest (ROIs) based on their geometric information on the surface. For the three ROI image groups, we calculated 109 radiomics features, and wavelet-based features. Then, a logistic regression model was built to investigate the correlation between the radiomics features extracted from GTVp and the response to chemoradiation in terms of overall survival (OS). Finally, based on the prediction probabilities, we assessed the inter-rater reliability and reproducibility via calculating the intraclass correlation coefficients (ICC) and concordance correlation coefficients (CCC), respectively. One of the features, 25percentile, outperforms other features in terms of both ICC and CCC. Its ICC and CCC between the original segmentation group and resized segmentation groups are around 0.27. In terms of the prediction accuracy, top features with high AUC extracted from different segmentation results are different from each other. Another interesting finding is that gray-level co-occurrence matrix (GLCM) based features have low ICC and CCC (<0.3) in comparison between original segmentation group and each resized segmentation group, while have high ICC and CCC (>0.9) in comparison between two resized segmentation groups. Considering the prediction performance, these GLCM based features derived from original segmentation group perform well with the area under the curve (AUC) above 0.7, which means these traditional features are sensitive to segmentation results. The segmentation variability affects both the radiomics features and prediction accuracy. Moreover, it is of great meaning to discover radiomics features with robustness of segmentation variability in oropharyngeal cancer, which can be warranted for treatment monitoring and prognosis prediction.

Sensitivity of Radiomic Features to Acquisition Noise and Respiratory Motion

The sensitivity of radiomics features to different imaging parameters is poorly understood. The purpose of this work was to investigate the effects of motion and noise regarding radiomics analysis of dynamic cancers. The sensitivity of 43 radiomic features to image noise and breathing amplitude was investigated based on simulation and patient data. Extracted features were classified into 1 of 4 categories: (1) Morphological, (2) Intensity, (3) Fine Texture, and (4) Coarse Texture. First, a series of simulations were used to study feature sensitivity within a controlled tumor environment. Using the Extended Cardiac-Torso code, 16 dynamic respiratory environments were simulated with breathing amplitudes ranging from 0 to 30 mm. Noise was sequentially added by convolving the tumor with various Gaussian kernels, producing 16 simulated images with SNR values ranging from 16 to 3. Features were extracted from each simulated scenario, and characterized by their change from baseline conditions. Next, 31 patients were retrospectively identified as meeting 3 data availability requirements: an existing free breathing (FB) CT, an existing 4DCT, and a known tumor histology. For each patient, 3 feature spaces were produced based on each FB image, Average Intensity Projection (AIP) image, and End of Exhalation (EOE) phase image. Paired sample t-tests and concordance correlation coefficients (CCC) were used to determine which features demonstrated significant variability between 3D and 4D image acquisition. Finally, a task-based approach was used to demonstrate how such feature variability can ultimately influence radiomics end points. Logistic regression algorithms were developed and independently trained to classify tumor histology based on the different feature spaces. Simulation results demonstrated strong linear dependences (P > 0.95) between respiratory motion and morphological features (5/6), as well as between SNR and texture features (9/36). Regarding patient data, 40% of features demonstrated high CCC agreement (CCC >0.80) when comparing FB to EOE, compared to only 32% between FB and AIP. Similarly, 32% of features demonstrated significant P-values when comparing FB to AIP, compared to only 21% between FB and EOE. Histology model performance was directly proportional to SNR, and inversely proportional to temporal resolution. Statistically significant AUC values were 0.67 and 0.63 and 0.52 for AIP, FB, and EOE feature spaces, respectively. SNR values were statistically significant between acquisition type, and appeared to be the primary factor in driving model performance. Radiomic feature sensitivity to motion and noise was investigated using both simulation and patient data. By training acquisition specific machine learning models, we demonstrated that such variability can greatly influence task-based radiomics end-points, such as histological classification.

Dynamic Measurement of Arterial Liver Perfusion With an Interventional C-Arm System.

Objective intraprocedural measurement of hepatic blood flow could provide a quantitative treatment end point for locoregional liver procedures. This study aims to validate the accuracy and reproducibility of cone-beam computed tomography perfusion (CBCTp) measurements of arterial liver perfusion (ALP) against clinically available computed tomography perfusion (CTp) measurements in a swine embolization model. Triplicate CBCTp measurements using a selective arterial contrast injection were performed before and after complete embolization of the left lobe of the liver in 5 swine. Two CBCTp protocols were evaluated that differed in sweep duration (3.3 vs 4.5 seconds) and the number of acquired projection images (166 vs 248). The mean ALP was measured within identical volumes of interest selected in the embolized and nonembolized regions of the perfusion map generated from each scan. Postembolization CBCTp values were also compared with CTp measurements. The 2 CBCTp protocols demonstrated high concordance correlation (0.90, P < 0.001). Both CBCTp protocols showed higher reproducibility than CTp in the nontarget lobe, with an intraclass correlation of 0.90 or greater for CBCTp and 0.83 for CTp (P < 0.001 for all correlations). The ALP in the embolized lobe was nearly zero and hence excluded for reproducibility. High concordance correlation was observed between the CTp and each CBCTp protocol, with the shorter CBCTp protocol reaching a concordance correlation of 0.75 and the longer achieving 0.87 (P < 0.001 for both correlations). Dynamic blood flow measurement using an angiographic C-arm system is feasible and produces quantitative results comparable to CTp.

Feasibility of Combined Doppler–ECG Assessment of Internal Jugular Veins

A standardized combined Doppler–electrocardiography technique was developed for measurement of the triphasic waveform characteristics in the internal jugular vein. Flow velocities at the A, X, V and Y peaks, the RR interval and the PA and RX times were measured. From these the venous impedance index ([X−A]/X) and the ratios PA/RR and RX/RR were calculated. Six measurements were performed at three different locations by two ultrasonographers in 21 randomly selected pregnant and non-pregnant women. Statistical models proved the feasibility and reproducibility of this technique, with the highest concordance correlation coefficients in the right distal internal vein. Bootstrapping revealed that repeating the measurements more than four times would not significantly enhance the precision of the estimated mean. Concordance correlation coefficients for the venous impedance index, PA time and PA/RR ratio were >0.63 for all three locations, proving their possible use in ongoing and future studies, analogous to previous studies in kidney and liver.

Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment

Accurate estimation of soil carbon is essential for accounting carbon cycling on the background of global environment change. However, previous studies made little contribution to the patterns and stocks of soil inorganic carbon (SIC) in large scales. In this study, we defined the structure of the soil depth function to fit vertical distribution of SIC based on pedogenic knowledge across various landscapes. Soil depth functions were constructed from a dataset of 99 soil profiles in the alpine area of the northeastern Tibetan Plateau. The parameters of depth functions were mapped from environmental covariates using random forest. Finally, SIC stocks at three depth intervals in the upper 1m depth were mapped across the entire study area by applying predicted soil depth functions at each location. The results showed that the soil depth functions were able to improve accuracy for fitting the vertical distribution of the SIC content, with a mean determination coefficient of R2=0.93. Overall accuracy for predicted SIC stocks was assessed on training samples. High Lin's concordance correlation coefficient values (0.84–0.86) indicate that predicted and observed values were in good agreement (RMSE: 1.52–1.67kgm−2 and ME: −0.33 to −0.29kgm−2). Variable importance showed that geographic position predictors (longitude, latitude) were key factors predicting the distribution of SIC. Terrain covariates were important variables influencing the three-dimensional distribution of SIC in mountain areas. By applying the proposed approach, the total SIC stock in this area is estimated at 75.41Tg in the upper 30cm, 113.15Tg in the upper 50cm and 190.30Tg in the upper 1m. We concluded that the methodology would be applicable for further prediction of SIC stocks in the Tibetan Plateau or other similar areas.

Discrepancies between two immunoassays for the determination of MPO and PR3 autoantibodies

BackgroundTesting for autoantibodies to myeloperoxidase (MPO) and proteinase 3 (PR3) is part of anti-neutrophil cytoplasmic antibodies (ANCA) test that aids the diagnosis of a number of autoimmune diseases including small-vessel vasculitis. We characterized the differences between two automated immunoassays at three facilities for measuring MPO- and PR3-ANCA autoantibodies. Methods117 serum samples were analyzed for MPO and PR3 autoantibodies. The INOVA QUANTA Lite® IgG assay (INOVA Diagnostics) were performed at two facilities and the Bio-Plex® 2200 Vasculitis Panel (Bio-Rad) were performed at a third reference lab. The results were compared both qualitatively (between INOVA QUANTA Lite® and Bio-Plex® methods) and quantitatively (between two sites performing INOVA QUANTA Lite® assays). ResultsComparison of the INOVA QUNATA Lite® assays at two different facilities (n=36) demonstrated high concordance (97.2% for MPO and 94.4% for PR3) and quantitative correlation (R2=0.973 for MPO and R2=0.935 for PR3). Conversely, INOVA QUNATA Lite® and Bio-Plex® methods showed poor concordance at 70.4% for MPO (n=81; 95% CI: 59.7% to 79.2%) and at 76.5% for PR3 (n=81; 95% CI: 66.2% to 84.4%). ConclusionThis study demonstrated low concordance between two methods for MPO-ANCA and PR3-ANCA measurements. Given the discrepancies, the performance of different autoantibody immunoassay methods should be taken into consideration when evaluating MPO-ANCA and PR3-ANCA results.

Comparison between Simple Radiographic and Computed Tomographic Three-Dimensional Reconstruction for Evaluation of the Distal Metatarsal Articular Angle

The hallux valgus is a pathologic condition in the forefoot that often causes pain and functional limitations. Assessing all deformity components, such as the distal metatarsal articular angle (DMAA) is essential to hallux valgus treatment. Nevertheless, interobserver agreement of DMAA is poor on plain radiographs. The present study compared computed tomographic (CT) 3-dimensional (3D) reconstruction with plain radiographic measurements for measuring the DMAA. We included 43 consecutive patients (77 feet), diagnosed with hallux valgus at the orthopedic outpatient clinic at Hospital de Clínicas de Porto Alegre (Porto Alegre, Brazil) from April 2014 to June 2015 in our prospective study. The DMAAs were measured by 2 observers using CT 3D reconstruction. The results were compared between observers and with the plain radiographic measurements using the concordance correlation coefficient and the Bland-Altman plot to determine the agreement between the 2 methods. The interobserver agreement of the CT 3D measurements was high (concordance correlation coefficient 0.90; p < .001). Significant agreement was found between the plain radiographic and CT measurements (concordance correlation coefficient 0.667; p < .001). The Bland-Altman plot showed that the difference between the plain radiographic and CT measurements increased when greater DMAA values were associated with the presence of metatarsal rotation. The interobserver agreement of DMAA measurements was greater on CT 3D reconstruction than on plain radiography. The poor interobserver agreement of the radiographic DMAA might have resulted from metatarsal rotation.

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 2. Rumen degradable and undegradable protein

This work evaluated the National Research Council (NRC) dairy model (2001) predictions of rumen undegradable (RUP) and degradable (RDP) protein compared with measured postruminal non-ammonia, nonmicrobial (NANMN) and microbial N flows. Models were evaluated using the root mean squared prediction error (RMSPE) as a percent of the observed mean, mean and slope biases as percentages of mean squared prediction error (MSPE), and concordance correlation coefficient (CCC). The NRC (2001) over-estimated NANMN by 18% and under-estimated microbial N by 14%. Both responses had large mean biases (19% and 20% of MSPE, respectively), and NANMN had a slope bias (22% of MSPE). The NRC NANMN estimate had high RMSPE (46% of observed mean) and low CCC (0.37); updating feed library A, B, and C protein fractions and degradation rate (Kd) estimates with newer literature only marginally improved fit. The re-fit NRC models for NANMN and microbial N had CCC of 0.89 and 0.94, respectively. When compared with a prediction of NANMN as a static mean fraction of N intake, the re-derived NRC approach did not have improved fit. A protein system of intermediate complexity was derived in an attempt to estimate NANMN with improved fit compared with the static mean NANMN model. In this system, postruminal appearance of A, B, and C protein fractions were predicted in a feed-type specific manner rather than from estimated passage and degradation rates. In a comparison to independent data achieved through cross-validation, the new protein system improved RMSPE (34 vs. 36% of observed mean) and CCC (0.42 vs. 0.30) compared with the static mean NANMN model. When the NRC microbial N equation was re-derived, the RDP term dropped from the model. Consequently, 2 new microbial protein equations were formulated, both used a saturating (increasing at a decreasing rate) form: one saturated with respect to TDN and the other saturated over increasing intakes of rumen degraded starch and NDF. Both equations expressed maximal microbial N production as a linear function of RDP intake. The function relating microbial N to intake of rumen degradable carbohydrate improved RMSPE (24 vs. 28% of the observed mean) and CCC (0.63 vs 0.30) compared with the re-derived NRC model. The newly derived equations showed modest improvements in model fit and improved capacity to account for known biological effects; however, substantial variability in NANMN and microbial N estimates remained unexplained.

Reproducibility for Heart Rate Variability Analysis during 6-Min Walk Test in Patients with Heart Failure and Agreement between Devices.

Heart rate variability (HRV) analysis is a useful method to assess abnormal functioning in the autonomic nervous system and to predict cardiac events in patients with heart failure (HF). HRV measurements with heart rate monitors have been validated with an electrocardiograph in healthy subjects but not in patients with HF. We explored the reproducibility of HRV in two consecutive six-minute walk tests (6MW), 60-minute apart, using a heart rate monitor (PolarS810i) and a portable electrocardiograph (called Holter) in 50 HF patients (mean age 59 years, NYHA II, left ventricular ejection fraction ~35%). The reproducibility for each device was analysed using a paired t-test or the Wilcoxon signed-rank test. Additionally, we assessed the agreement between the two devices based on the HRV indices at rest, during the 6MW and during recovery using concordance correlation coefficients (CCC), 95% confidence intervals and Bland-Altman plots. The test-retest for the HRV analyses was reproducible using Holter and PolarS810i at rest but not during recovery. In the second 6MW, patients showed significant increases in rMSSD and walking distance. The PolarS810i measurements had remarkably high concordance correlation [0.86<CCC<0.99] based on Holter at rest, during 6MW and recovery. At higher rates, a small effect in increasing differences between Holter and Polar in R-R intervals was observed. In conclusion, our study showed good reproducibility of HRV at rest in two consecutive 6MW using Holter and PolarS810i. Additionally, PolarS810i produced good agreements in short-term HRV indices based on Holter simultaneous recordings at rest, during the 6MW and recovery in HF patients.

Prediction of portal and hepatic blood flow from intake level data in cattle.

Interest is growing in developing integrated postabsorptive metabolism models for dairy cattle. An integral part of linking a multi-organ postabsorptive model is the prediction of nutrient fluxes between organs, and thus blood flow. The purpose of this paper was to use a multivariate meta-analysis approach to model portal blood flow (PORBF) and hepatic venous blood flow (HEPBF) simultaneously, with evaluation of hepatic arterial blood flow (ARTBF; ARTBF=HEPBF - PORBF) and PORBF/HEPBF (%) as calculated values. The database used to develop equations consisted of 296 individual animal observations (lactating and dry dairy cows and beef cattle) and 55 treatments from 17 studies, and a separate evaluation database consisted of 34 treatment means (lactating dairy cows and beef cattle) from 9 studies obtained from the literature. Both databases had information on dry matter intake (DMI), metabolizable energy intake (MEI), body weight, and a basic description of the diet including crude protein intake and forage proportion of the diet (FP; %). Blood flow (L/h or L/kg of BW0.75/h) and either DMI or MEI (g or MJ/d or g or MJ/kg of BW0.75/d) were examined with linear and quadratic fits. Equations were developed using cow within experiment and experiment as random effects, and blood flow location as a repeated effect. Upon evaluation with the evaluation database, equations based on DMI typically resulted in lower root mean square prediction errors, expressed as a % of the observed mean (rMSPE%) and higher concordance correlation coefficient (CCC) values than equations based on MEI. Quadratic equation terms were frequently nonsignificant, and the quadratic equations did not outperform their linear counterparts. The best performing blood flow equations were PORBF (L/h)=202 (±45.6) + 83.6 (±3.11) × DMI (kg/d) and HEPBF (L/h)=186 (±45.4) + 103.8 (±3.10) × DMI (kg/d), with rMSPE% values of 17.5 and 16.6 and CCC values of 0.93 and 0.94, respectively. The residuals (predicted - observed) for PORBF/HEPBF were significantly related to the forage % of the diet, and thus equations for PORBF and HEPBF based on forage and concentrate DMI were developed: PORBF (L/h)=210 (±51.0) + 82.9 (±6.43) × forage (kg of DM/d) + 82.9 (±6.04) × concentrate (kg of DM/d), and HEPBF (L/h)=184 (±50.6) + 92.6 (±6.28) × forage (kg of DM/d) + 114.2 (±5.88) × concentrate (kg of DM/d), where rMSPE% values were 17.5 and 17.6 and CCC values were 0.93 and 0.94, respectively. Division of DMI into forage and concentrate fractions improved the joint Bayesian information criterion value for PORBF and HEPBF (Bayesian information criterion=6,512 vs. 7,303), as well as slightly improved the rMSPE and CCC for ARTBF and PORBF/HEPBF. This was despite minimal changes in PORBF and HEPBF predictions. Developed equations predicted blood flow well and can easily be used within a postabsorptive model of nutrient metabolism. Results also suggest different sensitivity of PORBF and HEPBF to the composition of DMI, and accounting for this difference resulted in improved ARTBF predictions.

Shorter sampling periods and accurate estimates of milk volume and components are possible for pasture based dairy herds milked with automated milking systems.

Dairy cows grazing pasture and milked using automated milking systems (AMS) have lower milking frequencies than indoor fed cows milked using AMS. Therefore, milk recording intervals used for herd testing indoor fed cows may not be suitable for cows on pasture based farms. We hypothesised that accurate standardised 24 h estimates could be determined for AMS herds with milk recording intervals of less than the Gold Standard (48 hs), but that the optimum milk recording interval would depend on the herd average for milking frequency. The Gold Standard protocol was applied on five commercial dairy farms with AMS, between December 2011 and February 2013. From 12 milk recording test periods, involving 2211 cow-test days and 8049 cow milkings, standardised 24 h estimates for milk volume and milk composition were calculated for the Gold Standard protocol and compared with those collected during nine alternative sampling scenarios, including six shorter sampling periods and three in which a fixed number of milk samples per cow were collected. Results infer a 48 h milk recording protocol is unnecessarily long for collecting accurate estimates during milk recording on pasture based AMS farms. Collection of two milk samples only per cow was optimal in terms of high concordance correlation coefficients for milk volume and components and a low proportion of missed cow-test days. Further research is required to determine the effects of diurnal variations in milk composition on standardised 24 h estimates for milk volume and components, before a protocol based on a fixed number of samples could be considered. Based on the results of this study New Zealand have adopted a split protocol for herd testing based on the average milking frequency for the herd (NZ Herd Test Standard 8100:2015).

Mechanistic modelling of in vitro fermentation and methane production by rumen microbiota

Existing mechanistic models of the rumen ecosystem have proven to be useful to better understand and represent rumen fermentation. Opportunities for improving rumen fermentation models include a better representation of the microbiota, hydrogen dynamics and a mechanistic description of pH. The objective of this work was to include such aspects in the development of a mathematical model of rumen fermentation under in vitro conditions. The developed model integrates microbial metabolism, acid–base reactions and liquid–gas transfer. Model construction was based on an aggregated representation of the hydrolysis of carbohydrates and proteins, and the further fermentation of soluble monomers. The model is a differential algebraic equation model with 18 compartments. One of the main contributions of the model developed here resides in the mechanistic description of pH, the use of biochemical reactions and partition rules to define the stoichiometry of fermentation, the representation of hydrogen metabolism, and the representation of the rumen microbiota into functional groups associated with the utilization of hexoses, amino acids and hydrogen. The model was calibrated with published data from a 2×2 factorial experiment devoted to assessing the relative importance of the type of inoculum and substrate on the fermentation pattern. The treatments were the level of concentrate in the substrate (low concentrate vs. high concentrate), and the inocula type (obtained from goats fed at low or high concentrate). The model was implemented in Matlab. The code is available on request for academic purposes. Model evaluation was performed by regression analysis and the calculation of statistical indicators using the model predicted values and observed values. The model was capable to represent in a satisfactory fashion the dynamics of the fermentation, that is the pH, the individual volatile fatty acids and the gas compounds, namely methane, hydrogen and carbon dioxide. The model predictions exhibited high concordance correlation coefficients (CCC). For the pH and the CH4, the CCC was of 0.91 and 0.93 respectively. For the other variables CCC >0.96. The model developed was instrumental to quantify the differences of the fermentation pattern between the treatment combinations. These differences were mainly captured by parameters related to the flux distribution and were found to be dependent mainly on the type of inoculum. For instance, the flux towards butyrate production from sugars utilization for the microbiota of the inoculum adapted to high concentrate was about 30% higher than that for the inoculum adapted to low concentrate. This result, however, requires further validation with new data. Further developments are needed to incorporate physiological in vivo factors into our model. Nevertheless, the structure developed here appears to be a promising approach for enhancing the mechanistic description of the rumen microbial ecosystem.

Calibration and validation of optical chlorophyll‐measuring devices for use in predicting crude protein concentration in tropical grass herbage

AbstractThe objective of this experiment was to evaluate the Fieldscout CM 1000 NDVI and Yara N‐Tester as easy‐to‐use and cost‐effective tools for predicting foliar chlorophylls (a, b and total) and crude protein (CP) concentrations in herbage from three tropical grass species. Optical chlorophyll measurements were taken at three stages (4, 8 and 12 weeks) of regrowth maturity in Guinea grass (Panicum maximum) and Mulato II (Brachiaria hybrid) and at 6 and 12 weeks maturity in Paspalum spp (Paspalum atratum). Grass samples were harvested subsequent to optical measurements for laboratory analysis to determine CP and solvent‐extractable chlorophylls (a, b and total) concentrations. Optical chlorophyll measurements and CP concentrations were highly correlated (Yara N‐Tester: r2 = 0·77–0·89; Fieldscout CM 1000 NDVI: r2 = 0·52–0·84). Crude protein prediction models from the Yara N‐Tester and Fieldscout CM 1000 NDVI accounted for 70–89% and 44–73% CP variability, respectively, in Mulato II and Guinea grass. The Yara N‐tester produced more accurate and reliable CP estimates based on very high concordance correlation coefficient [CCC (0·73–0·91)] and low rMSPE, mean and regression bias. It is concluded that the Yara N‐Tester produces more accurate and reliable CP estimates of tropical pastures.

Modelling cross-species feed intake responses to thermal stress

SUMMARYThe objectives of the current study were to compare and model feed intake responses to ambient temperature across species and to assess opportunities to use cross-species (CS) data to parameterize models when species-specific (SS) data were limited. Literature searches were conducted to identify studies reporting intake during thermal stress compared with thermoneutral (TN) conditions. The resulting data set comprised 614 treatment means from 108 studies on livestock responses to thermal stress. An analysis of variance was conducted with the CS data set to identify the effects of species, temperature and species by temperature interactions on intake as (fractional feed intake; FFI). Four models were derived from the CS data set and root mean squared prediction error (RMSPE) and concordance correlation coefficients (CCC) of these models were compared with models of the same form derived from SS data sets. Models used explanatory variables for (1) duration of exposure; (2) mean temperature; (3) minimum and maximum temperatures; or (4) difference between minimum and maximum temperatures. An additional model accounting for temperature and stage of production was derived from the SS data. Analysis of variance demonstrated that the species by temperature interaction did not have a significant effect on FFI. Across species, intake decreased with temperature. Notably, all species demonstrated a constant decrease in intake across the TN zone indicating the previous assumption of constant intake during thermoneutrality may be not fully valid. When compared on a SS basis, SS-derived models had marginally lower RMSPE and higher CCC when compared with models derived from the CS data sets. The model fit with production data had the lowest RMSPE and highest CCC within the study. When compared over temperature ranges with minimal data available in some species (e.g., cold stress), using CS models often resulted in decreased RMSPE and improved CCC when compared with SS models. Although fitting models based on SS data allows for incorporating unique covariates, like level of production, fitting responses based on CS data can help to improve model estimates when knowledge gaps exist.

Geostatistics-based spatial distribution of soil moisture and temperature regime classes in Mazandaran province, northern Iran

Soil moisture regime (SMR) and soil temperature regime (STR) classes as soil classification criterions are required by US Soil Taxonomy because they affect genesis, use, and management of soils. The lack of sufficient soil moisture and temperature data requires the characterization of the pedoclimate on the basis of climatic data processed by simulation models. This research was conducted to consider the new approach for SMR and STR mapping. The objectives of this study were to compare the four interpolation schemes including ordinary kriging (OK), cokriging (Co-K), inverse distance weighting, and conditional simulation for interpolating the monthly mean total precipitation (MMTP) and monthly mean air temperature (MMAT) and to apply the Java Newhall simulation model for the MMTP and MMAT predictive values at each node of 1 km2 grids across the Mazandaran province, northern Iran, for delineating the SMR and STR classes. The semivariogram analyses showed moderate to strong spatial dependence of data sets. The accuracy of interpolators varied within months for both MMTP and MMAT data sets. In most cases, OK and Co-K methods had the highest accuracy with lower mean error, root mean square error, and higher concordance correlation coefficient. The predictive maps show high diversity of SMR classes including Aridic, Ustic, Udic, and Xeric. The STR classes comprise Mesic, Thermic, and Cryic regimes. Results herein indicated that geostatistical approaches can potentially provide the opportunity for mapping of SMR and STR classes in data scarce regions.

Comparison between echocardiographic and non-ECG-gated CT measurements in dogs

The aim of this study was to compare echocardiographic measurements with non-ECG-gated contrast-enhanced cardiac CT measurements in dogs. Fifty-seven dogs were included in the study. The following echocardiographic parameters were...

Quantification of hepatic steatosis: A comparison of the accuracy among multiple magnetic resonance techniques

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are important diagnostic tools for the non-invasive assessment of hepatic steatosis (HS). This study was conducted to compare different magnetic resonance (MR) techniques and correlate the MR findings with histological and intracellular lipid density findings. In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant prospective study, 60 patients scheduled for liver resection were included in this study. Fat fraction in the non-tumorous liver parenchyma was estimated using double-echo MRI, triple-echo MRI (TE-MRI), and MRS. HS was defined by the histologic steatosis percentage (HSP), and intrahepatocellular triglyceride density (IHTGD) of the surgical specimen used as the reference standard. Imaging quantification results were evaluated using Pearson's correlation. Lin's concordance coefficient and Bland-Altman 95% limits of agreement were used to evaluate the concordance of IHTGDs estimated by the three MR techniques. The diagnostic performance was compared using receiver operating characteristic curve analysis. HS assessed by TE-MRI and MRS had a stronger relationship with HS assessed by HSP and IHTGD. The TE-MRI method had the highest concordance correlation coefficients (ρ = 0.881) and percentage (95%, 57/60) within the Bland-Altman 95% limits of agreement. Receiver operating characteristic curve analysis for diagnosing > 5% HSP showed significantly larger area under the curve (0.9783) for TE-MRI than for double-echo MRI (0.8773, P = 0.0121). Among the three MR techniques, TE-MRI and MRS may be the preferred techniques for non-invasive assessment of HS.

Reproducibility of diabetes quality of care indicators as reported by patients and physicians

Self-report of diabetes care has moderate validity and is prone to under- and over-reporting. We assessed reproducibility of a range of processes and outcomes of diabetes care as reported by patients and physicians. In a Swiss community-based survey, patients with diabetes and physicians independently reported past 12 months processes of care (HbA1c, lipids, microalbuminuria, blood pressure, weight, foot and eye examinations) and last measured values of HbA1c, height, weight and blood pressure. For dichotomous variables, we assessed reliability by Cohen's kappa and agreement by uniform kappa. For continuous measures, we used Lin's concordance correlation coefficient and limits of agreement, respectively. Mean age of the 210 patients was 65 years; 40% were women, and 51% had diabetes for >10 years. Agreement was good for recommended processes of care such as blood pressure (uniform kappa = 0.94), HbA1c (0.93), weight (0.88) and lipid (0.78), but lower for microalbuminuria, foot and eye examinations (all <0.50). Cohen's kappa values were all low (<0.25). Comparisons of reported continuous variables showed large limits of agreement for height (±6 cm) and weight (8-10 kg) despite high concordance correlation coefficients (0.93 and 0.97). Concordance correlation coefficients were smaller for HbA1c (0.72) and blood pressure (0.5-0.6), with large limits of agreement (±2% and ±25 mmHg). While agreement of routine processes of care was good, agreement was less satisfactory for microalbuminuria, foot and eye examinations. Reports of continuous outcomes yielded good reliability but too wide limits of agreement. Quality of care evaluation relying on self-report only should be made cautiously.

Comparison of the performance of two spontaneous sedimentation techniques for the diagnosis of human intestinal parasites in the absence of a gold standard

Performance evaluation of diagnostic tests is critical in the search for accurate diagnoses. A gold standard test is usually absent in parasitology, thus rendering satisfactory assessment of diagnostic accuracy difficult. Moreover, reliability (assessed by the study of repeatability) is a rarely studied characteristic of diagnostic tests. This study compared and evaluated the performance (repeatability, concordance and accuracy) of the spontaneous sedimentation technique (SST) and the Paratest for the diagnosis of Giardia lamblia, Entamoeba histolytica complex, Blastocystis spp., Ascaris lumbricoides, hookworm, Trichuris trichiura and Calodium hepaticum. Fecal samples of 143 individuals were separated into three replicates for each test. Concordance and homogeneity of the results between replicates of each test and between tests were evaluated. Proportions of positives, sensitivity and specificity were estimated using a Bayesian Latent Class Model. High repeatability of both tests was found for the detection of intestinal parasites, except for Blastocystis spp. and hookworm. Concordance between tests was generally high (concordance correlation coefficient, 0.72–0.88), except for Blastocystis spp., hookworm and T. trichiura. The Paratest detected more cases of Blastocystis spp. and fewer of hookworm than the SST. The tests were quite discordant in the detection of T. trichiura. A low sensitivity (39.4–49.2% for SST, 35.8–53.8% for Paratest) and a high specificity (93.2–97.2%) were found for both tests. The Paratest presented a slightly higher sensitivity for the diagnosis of Blastocystis spp. (53.8%), and SST did so for hookworm (49.2%). This is the first study on repeatability and accuracy (using a Bayesian approach) of two spontaneous sedimentation techniques. These results suggest underdiagnosis of little dense parasitic forms due to technical limitations in both tests. We conclude that the combined study of repeatability, concordance and accuracy is a key strategy for better evaluation of the performance of tests and is also useful for the identification of technical limitations.