Concordance Correlation Research Articles

A Comparative Evaluation of Point-of-Care and Laboratory HbA1c Testing in Diabetes Care: An Indian Perspective.

This study aimed to assess the diagnostic performance of a point-of-care (POC) glycated hemoglobin (HbA1c) device in the Indian population against the standard laboratory (high-performance liquid chromatography {HPLC}) method for the effective management of diabetes in India. This study on the diagnostic accuracy of a POC HbA1c device involved 121 participants. These participants were categorized into two groups according to their HbA1c levels - one group with HbA1c values below 6.5% and another group with HbA1c values equal to or greater than 6.5%. The HbA1c levels in enrolled participants were estimated using both the POC device (HemoCueHbA1c 501; Ängelholm, Sweden: HemoCue AB) and the standard HPLC-based method. The level of agreement and concordance between the two test results were assessed by the Bland-Altman plot and Lin's concordance correlation coefficient. Sensitivity, specificity, diagnostic accuracy, positive likelihood, and negative likelihood ratios of POC-HbA1c device were assessed with a 6.5% HbA1c cut-off. The mean HbA1c values obtained by the two methods showed no statistically significant difference with a minimum effect size (Cohen's d = 0.035), indicating there was a negligible difference between these methods. The Bland-Altman plot revealed that most values were within acceptable limits (95% CI: -0.5 to 0.7) and Lin's concordance correlation coefficient showed strong agreement (p < 0.0001).The POC-HbA1c device demonstrated an area under the curve (AUC) of 0.991 (95% CI: 0.953-1.000) with sensitivity, specificity, diagnostic accuracy, positive likelihood, and negative likelihood ratios of 93.62%, 97.30%, 95.87%, 34.64 and 0.07, respectively, compared to the standard diagnostic assay. The diagnostic accuracy, sensitivity, and specificity demonstrated by the POC-HbA1c device to the standard HPLC method offers a viable and practical solution for diabetes management in India. Its ability to provide rapid and reliable results at the point of care can improve patient outcomes, reduce healthcare costs, and enhance access to diabetes care, especially in primary care, remote areas, and resource-limited settings of developing countries like India.

Noninvasive Method Using Mechanical Extensometer for the Estimation of Intracranial Compliance by Repeated Measures Agreement Analysis.

Intracranial compliance refers to the relationship between changes in volume and the resultant changes in intracranial pressure (ICP). This study aimed to assess the agreement of a noninvasive ICP waveform device for the estimation of compliance compared with invasive ICP monitoring employing three distinct methods. We conducted a retrospective analysis of ICP waveform morphology recorded through both invasive (external ventricular drain) and noninvasive (mechanical extensometer) methods in adult patients with acute brain injury admitted to a neurointensive care unit between August 2021 and August 2022. Compliance was calculated as the amplitude of the fundamental component of cerebral arterial blood volume (estimated with concurrent Transcranial Doppler [TCD] recordings), divided by the amplitude of the fundamental component of the invasive and noninvasive ICP waveforms. Subsequently, we assessed the agreement between invasive and noninvasive intracranial compliance by repeated measures correlation coefficient analysis using three methods: TCD-derived, P2/P1 ratio, and time-to-peak (TTP). A linear mixed-effects model was used to compute the concordance correlation coefficient, total deviation index, and coefficient of individual agreement. Coverage probability plot was calculated to estimate the percent of observations within different cut points for each of the three methods. A total of 21 patients were identified for this study. Repeated measures correlation analysis showed a strong correlation (R = 0.982, 95% confidence interval [0.980-0.984], p < 0.0001) between log-transformed noninvasive and invasive compliance. Agreement statistics for TCD, P2/P1 ratio, and TTP indicated that although the concordance correlation coefficient was highest for log(TCD) values, TTP and P2:P1 ratio measures had better agreement with total deviation index and coverage probability plot analyses. Repeated measures correlations suggest that ICP waveform analyses may offer a more accurate estimate of compliance than TCD-derived methods for noninvasive ICP monitoring. Further validations studies are warranted to confidently establish this method as an indicator of intracranial compliance.

External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal computed tomography images.

Body composition assessment using computed tomography (CT) images at the L3-level is increasingly applied in cancer research. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images. Expert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3,187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2,535 patients with abdominal cancer. DLNN performance was evaluated with (geometric) Dice Similarity (DS) and Lin's Concordance Correlation Coefficient. There was a strong concordance between automatic and manual segmentations with median DS for SM, VAT, and SAT of 0.97 (interquartile range, IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: SM 0.964 (0.959-0.968), VAT 0.998 (0.998-0.998), and SAT 0.992 (0.991-0.993). Bland-Altman metrics indicated only small and clinically insignificant systematic offsets; SM radiodensity: 0.23 hounsfield units (0.5%), SM: 1.26 cm2.m-2 (2.8%), VAT: -1.02 cm2.m-2 (1.7%), and SAT: 3.24 cm2.m-2 (4.6%). A robustly-performing and independently externally validated DLNN for automated body composition analysis was developed. CT-based body composition analysis is highly prognostic for long-term overall survival in oncology. This DLNN was succesfully trained and externally validated on several large patient cohorts and will therefore enable large scale population studies and implementation of body composition analysis into clinical practice.

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

Development of the ONC-5: A quantifiable assessment of spiritual concerns for adult oncology patients

Religious and spiritual (R/S) beliefs are crucial coping resources for cancer patients, yet there’s no standard spiritual assessment approach. We developed the ONC-5, an evidence-based model to quantify patients’ spiritual concerns in adult oncology. Seventy-one patients with diverse cancer diagnoses completed a survey and a semi-structured ONC-5 interview with a chaplain. Measures included the distress thermometer, PHQ-2, R/S struggle, and spiritual well-being. Thirty interviews were reviewed for inter-rater reliability. Spearman correlations assessed validity, and the concordance correlation coefficient (CCC) was calculated for inter-rater reliability. Results showed 20% of patients had moderate or severe spiritual concerns. ONC-5 scores correlated with R/S struggle (r = .247; p = .038) and spiritual well-being (r = −.247; p = .038). The CCC of 0.4411 indicated moderate agreement. The ONC-5 helps chaplains identify spiritual concerns, prioritize care, and measure improvements. More research is needed to test its validity and reliability in diverse populations.

PSXI-23 Development of sub-models to estimate protein requirements and supply of lactating dairy cows using machine learning algorithms

Abstract The objectives of this study were to develop sub-models for predicting protein requirements and supply, encompassing 1) net protein for maintenance (NPm), 2) lactation (NPl), 3) rumen undegradable protein (RUP), and 4) duodenal microbial nitrogen (MicN) from the feed protein. The dataset used in this study was constructed by integrating in vivo experimental data collected from open databases (the National Animal Nutrition Program) and articles (Journal of Dairy Science), which includes a total of 1,779 observations from 436 publications. In the development of the model, animal information and feed chemical components were used as candidate variables, and two types of machine learning algorithms, Random Forest Regression (RFR) and Support Vector Regression (SVR) were employed. After testing, the following predictors were selected for predicting: 1) NPm: body weight (BW) and dry matter intake (DMI), 2) NPl: BW, DMI, days in milk (DIM), and dietary organic matter (OM) and crude protein (CP) contents, 3) RUP: DIM, DMI, dietary DM content, and CP fraction intake (B and C), and 4) MicN: DIM, DMI, DM, dietary neutral detergent fiber (NDF) content, CP fraction intake (A, B, and C). The selected models were assessed using a cross-validation method with the following statistical metrics including the coefficient of determination (R2), root-mean-square error of prediction (RMSEP), residual analyses, and concordance correlation coefficient (CCC). For the RUP and MicN models, they were compared with the NASEM (2021) model. In predicting NPm, both SVR and RFR algorithms demonstrated increased precision (R2 = 0.965 vs. 0.969) and accuracy (RMSEP = 9.7 vs. 9.2 g/d); however, during residual analysis, the RFR model showed a statistically significant slope bias (P &amp;lt; 0.05). In the NPl prediction, the RFR algorithm showed slightly greater performance compared with SVR (R2 = 0.864 vs. 0.814 and RMSEP = 86.7 vs. 98.5 g/d). However, similar to the NPm prediction, the RFR model displayed a statistically significant slope bias (P &amp;lt; 0.05). In the supply model, the RFR model exhibited the greatest precision and accuracy in predicting RUP (R2 = 0.60, RMSEP = 0.326 kg/d, and CCC = 0.71) without any biases. This model achieved a 2.37-fold increase in R2 and a decrease of 0.111 kg/d in RMSEP compared with the NASEM model. As for MicN prediction, the SVR model performed the best (R2 = 0.76, RMSEP = 42.4 g/d, and CCC = 0.86) without biases. This model attained a 19-times improvement in R2 and a reduction of 38.7 g/d in RMSEP when compared with the NASEM model. In conclusion, the models developed using machine learning algorithms can be helpful for accurately and precisely predicting protein requirements and supply based on animal information and the chemical composition of feed.

Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study

BackgroundAccurate assessment of basal bone width is essential for distinguishing individuals with normal occlusion from patients with maxillary transverse deficiency who may require maxillary expansion. Herein, we evaluated the effectiveness of a deep learning (DL) model in measuring landmarks of basal bone width and assessed the consistency of automated measurements compared to manual measurements.MethodsBased on the U-Net algorithm, a coarse-to-fine DL model was developed and trained using 80 cone-beam computed tomography (CBCT) images. The model’s prediction capabilities were validated on 10 CBCT scans and tested on an additional 34. To evaluate the performance of the DL model, its measurements were compared with those taken manually by one junior orthodontist using the concordance correlation coefficient (CCC).ResultsIt took approximately 1.5 s for the DL model to perform the measurement task in only CBCT images. This framework showed a mean radial error of 1.22 ± 1.93 mm and achieved successful detection rates of 71.34%, 81.37%, 86.77%, and 91.18% in the 2.0-, 2.5-, 3.0-, and 4.0-mm ranges, respectively. The CCCs (95% confidence interval) of the maxillary basal bone width and mandibular basal bone width distance between the DL model and manual measurement for the 34 cases were 0.96 (0.94–0.97) and 0.98 (0.97–0.99), respectively.ConclusionThe novel DL framework developed in this study improved the diagnostic accuracy of the individual assessment of maxillary width. These results emphasize the potential applicability of this framework as a computer-aided diagnostic tool in orthodontic practice.

PSV-11 Significance of specialty protein ingredients in feeding programs for broiler chickens on nitrogen utilization and excretion- A meta-analysis

Abstract The utility of specialty protein ingredients (SPI) in feeding programs for broiler chickens is premised on poor utilization of conventional vegetable proteins (CVP). The SPI includes ingredients of animal origin such as processed animal proteins (PAP), insect proteins (IP) and further processed conventional vegetable proteins (PCVP) characterized with greater concentration of crude protein/amino acids, and low anti-nutritional factors. The objective of this study was to apply meta-analysis approach of published studies that incorporated SPI to provide insights into their effectiveness on nitrogen utilization and excretion in broiler chickens. The data were retrieved from Web of Science, PubMed, ScienceDirect, hand searching and cross-referencing, and resulted in 44 publications meeting inclusion criteria such as being a broiler study and treatments diets being isocaloric and isonitrogenous Model development was performed in PROC GLIMMIX of SAS and treated the study as a random effect. The SPI protein source (PAP, IP, and PCVP), bird age (starter 0 to 14 d, grower 14 to 24 d and finisher 25 to 42 d), nitrogen intake and level were the main X variables of interest, while outcome (Y) variables explored included nitrogen excretion, nitrogen retention, nitrogen efficiency, average daily gain (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR). To address heterogeneity in sample size and errors between treatment means from different studies, the WEIGHT statement in PROC GLIMIXX was employed to weight variables using the inverse of their squared variance. The inclusion or rejection of models developed was based on the evaluation of root mean square prediction error and concordance correlation coefficient. Comparatively, the slopes of PAP, IP and PCVP (soybean meal) linearly reduced and quadratically (P &amp;lt; 0.05) increased ADG during the starter phase against their respective controls. The extent ADG was greatest with intake of PCVP followed by PAP. During the grower phase, ADG was highest for PAP, followed by IP with PCVP being the least (P &amp;lt; 0.05). However, PCVP had the greatest ADG during the finisher phase (P &amp;lt; 0.05). On nitrogen excretion, provision of PAP during the stater phase increased the nitrogen excretion while it reduced the excretion during the grower and finisher phase (P &amp;lt; 0.05) relative to control. The intake of IP up to 10% inclusion reduced nitrogen excretion only during the starter and grower phase (P &amp;lt; 0.05) when compared with respective controls. The intake of PCVP reduced nitrogen excretion in all growth stages compared with respective controls (P &amp;lt; 0.05). In conclusion specialty proteins (PAP and PCVP) and insect protein at an inclusion level of &amp;lt; 10%, were effective ways of reducing nitrogen excretion while improving growth performance. Combining these strategies may have a synergistic effect.

Application of a Real-Time Field-Programmable Gate Array-Based Image-Processing System for Crop Monitoring in Precision Agriculture

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection.

495 Comparison of two internal markers and Near Infrared Spectroscopy (NIRS) for predicting nutrient digestibility in beef cattle offered diets varying in forage quantity and quality

Abstract The objective of this study was to evaluate acid detergent lignin (ADL), and amylase-treated ash-corrected undigestible neutral detergent fiber after 240 h of ruminal in vitro incubation (uNDF), and near infrared spectroscopy (NIRS) scanning of feces as methods to estimate the digestibility of diets varying in forage quantity and quality when fed to beef cattle. Five total collection (TC) digestibility studies examined 17 different diets and provided individual fecal samples and the corresponding apparent total tract digestibility of nutrients (n = 229). Feed, orts, and fecal samples were analyzed for dry matter (DM), organic matter (OM), nitrogen (N), amylase-treated ash-corrected neutral detergent fiber (aNDFom), acid detergent fiber (ADF), ADL, and uNDF. Previously developed fecal NIRS digestibility calibrations were expanded with dried and ground samples using a FOSS D3F scanning monochromator (FOSS, Eden Prairie, MN). Marker estimated and NIRS predicted nutrient digestibility coefficients were regressed against those determined by TC and goodness-of-fit statistics were applied. Mean concentrations of ADL and uNDF in diets ranged from 23.4 to 96.4 g/kg DM and 67.7 to 200 g/kg DM, respectively, with mean fecal recoveries of 94.2% (SD ± 15.7%) for ADL and 87.5% (SD ± 11.1%) for uNDF. Regression fit statistics between NIRS and TC were not different from one (P &amp;gt; 0.05), with R2 &amp;gt; 0.83 except for ADF digestibility (R2 = 0.63). In comparison, regression statistics between internal markers and TC were poorer with R2 ranging from 0.21 to 0.78. Concordance correlation coefficients (CCC) between NIRS and TC were greater than 0.90 for DM, OM, N, and aNDFom digestibility, and 0.77 for ADF digestibility. The CCC between ADL and TC ranged between 0.63 and 0.82, and between uNDF and TC from 0.26 to 0.78. Correction bias (Cb) was high for all parameters (Cb &amp;gt; 0.76) except for ADF digestibility (Cb = 0.58) as estimated by uNDF. The mean square error of prediction (MSEP) for NIRS predicted digestibility were lower (&amp;lt; 10.7%) than those estimated by internal markers, and most of the error was attributed to random bias (&amp;gt; 97.9%). Random bias for ADL was also greater (&amp;gt;76.1%); however, for uNDF the error was more evenly partitioned into mean bias (46.4% to 49.4%) and random bias (30.4 to 41.9%). Digestibility predictions from NIRS scanning of feces were more accurate and precise than when estimated using the internal markers ADL and uNDF. In the absence of NIRS, ADL estimations appear to be more precise and accurate than uNDF for forage-based diets, with uNDF possibly of greater value for estimating the digestibility of high concentrate diets.

493 Predicting pellet quality using multiple linear regression with Principal Component Analysis (PCA)

Abstract Pellet quality is a crucial key performance indicator (KPI) for commercial feed manufacturing, which influences both the efficiency of the feed mill and downstream performance of animals fed these diets. However, due to the complexity of feed manufacturing and the large number of factors involved in the manufacturing process, controlling pellet quality is an ongoing challenge for the feed industry. Previous studies have mainly explored the impact of a few factors on pellet quality under experimental settings, and empirical equations have been seldomly developed to reflect the relationship between the factors and pellet quality under the commercial feed mill settings. This study aimed to establish a relationship between pellet quality and factors collected under the settings of a commercial feed mill. The data were collected from Trouw Nutrition Canada’s feed mill located in St. Marys, Ontario (Plant 2), between December 15, 2021, and December 6, 2022. During this period, 2,691 observations were collected, with each observation representing an individual batch of pelleted feed. A total of 75 factors were recorded, including 4 factors associated with the general information of each batch, 10 manufacturing parameters, 41 feed ingredients, 8 factors regarding the nutrient composition of each diet, and 12 environmental factors. Pellet Durability Index (PDI), which was the response variable, was determined for each batch using the Holmen method. The data were randomly split into an 80% subset for training and a 20% subset for testing. The training subset was used to construct the model via a 5-fold cross-validation, while the testing subset was withheld as an independent dataset to evaluate the generalization performance of the model. The response variable (PDI) was transformed (tPDI) using the Box-Cox method to meet a normal distribution assumption. To avoid multicollinearity, Principal Component Analysis (PCA) was used to reduce the dimensionality of the numeric factors before building the multiple linear regression model. The model prediction performance was evaluated on both the training subset (using 5-fold cross-validation) and the testing subset, and the prediction performance metrics were consistent between the two subsets (Mean Absolute Error = 1.94 ± 0.102 vs. 2.02; Root Mean Square Prediction Error = 2.47 ± 0.111 vs. 2.58; Mean Square Prediction Error = 6.12 ± 0.538 vs. 6.68; Concordance Correlation Coefficient = 0.538 ± 0.0231 vs. 0.490; Pearson Correlation Coefficient = 0.606 ± 0.0247 vs. 0.553, respectively). Most feed ingredients and nutrient compositions showed either positive or negative loadings on Component 1 (17.87% of total variance), and outdoor/indoor environmental factors were positively loaded on Component 2 (14.21% of total variance). The model developed in this study could help commercial feed mills better understand how various factors impact pellet quality and optimize the manufacturing processes of pelleted feeds.

194 Evaluation of a method to optimize diets of individual dairy cows to maximize income over feed cost

Abstract Recent optimization methods have demonstrated the potential to maximize marginal profits in beef (Marques et al., 2020) and dairy (Campos et al., 2023) by optimizing diet formulation. These methods present an opportunity to explore how diets could be optimized for individual dairy cows, such as in a closed-loop precision feeding system. Therefore, our objectives were to 1) compare individual income over feed costs (IOFC), calculated with observed dry matter intake (DMI), milk yield (MY), milk protein and milk fat, with a predicted IOFC for the same diet and cows using the NASEM (2021) model, and 2) optimize the inclusion rate of ingredients in the diet to maximize predicted IOFC. Previously, 20 multiparous (lactation 2 through 4) and 9 primiparous cows, ranging from 22 to 472 d in milk (DIM), were housed in a free-stall pen at the Ontario Dairy Research Centre (Canada) equipped with sensors to record DMI, body weight (BW; walk-over-weight) and body condition score (BCS; DeLavel 3D camera), in addition to daily MY, weekly milk component tests, and 4 X/d respiratory gas exchange measurements (GreenFeed, C-Lock Inc, USA) for estimation of daily energy flows (Kedzierski, 2020). Weekly means for each cow were used to evaluate IOFC in this study (n = 116). Each IOFC was calculated as milk revenue minus total feed cost. Milk revenue was calculated from milk fat, protein and other solids, consistent with pricing on Canadian dairy farms. The observed IOFC was calculated using the observed MY, milk components and DMI, and the same diet formulation and costs for all cows, whereas the predicted IOFC estimated these values using the NASEM (2021) model with all available observed animal inputs (e.g., BW, BCS, DIM). However, the observed DMI and MY had to be provided initially for the model, as the DMI prediction equation requires a target MY, but the prediction of MY requires DMI. Final predicted DMI and MY values were used in the calculation of predicted IOFC. The differential evolution algorithm from the ‘SciPy’ package (v 1.11.4) in python (v 3.12.0) was used to maximize IOFC by adjusting the inclusion of wheat straw, alfalfa silage, corn silage and high moisture corn grain, while fixing minerals to their original proportions. Overall, the concordance correlation coefficient (CCC) indicated moderate agreement (0.62) between the predicted IOFC and observed IOFC and good agreement (0.99) between the predicted IOFC and the optimized IOFC, suggesting the optimization method was able to select diets to meet a similar objective. Results indicate that a strategy is needed to minimize the residual error between predicted and observed values for individual cows, such as algebraic reparameterization of the NASEM model, before using an optimization method to automatically formulate diets.

Performance of patient-collected dried blood spot specimens for HIV-1 viral load testing: evidence from the DO ART Study in South Africa.

Evaluate the clinical utility of patient-collected dried blood spots (DBS) in measuring HIV-1 viral load (VL) for monitoring antiretroviral therapy (ART) compared to provider-collected DBS and blood plasma. In a randomized trial of community-based delivery of ART in South Africa, we assessed performance of: (1) DBS specimens compared to plasma, and (2) participant-collected versus staff-collected DBS specimens, to measure HIV-1 VL. The bioMérieux NucliSENS EasyQ HIV-1 v2.0 assay was used for VL measurement. From October 2017 to November 2019, we collected 996 pairs of plasma/DBS specimens from 760 participants and 315 pairs of staff-/participant-collected DBS cards from 261 participants. We assessed DBS test sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using the WHO failure threshold of 1000 copies/mL. Log-transformed VL was compared using concordance correlation coefficients (CCC) and mean differences from linear mixed models. In a population with 13% detectable VL, DBS VL compared with plasma VL had 91% (95% CI: 86-95) sensitivity, 99% (98-100) specificity, 94% (90-98) PPV, and 99% (98-99) NPV. We observed high agreement between staff-collected DBS VL and plasma VL (CCC: 0.94), and between participant-collected DBS VL and plasma VL (CCC: 0.92). Correlation between participant- and staff-collected DBS was very high (CCC: 0.97; mean difference for those with a detectable result: -0.10 log 10 copies/mL [-0.21-0.02]). VL results from participant-collected DBS are clinically comparable with those collected by clinical staff and using blood plasma. Self-collected DBS has potential for use for ART monitoring outside the clinic.

Impact of Previous Abdominal Surgery and Timing of Peritoneal Metastases on Accuracy of Imaging in Predicting the Surgical PCI: A Report From the PRECINCT Study.

In this report from Phase 1 of the prospective, observational, PRECINCT (Pattern of peritoneal dissemination and REsponse to systemic Chemotherapy IN Common and uncommon peritoneal Tumours) study, a correlation was performed between the radiological PCI (peritoneal cancer index; rPCI) and surgical PCI (sPCI). The impact of timing of peritoneal malignancy (PM) and previous abdominal surgery was also studied. The rPCI and sPCI were considered the 'same' if they differed by ≤ 3 points. The agreement was assessed using Bland-Altman analysis and the strength of the agreement was assessed using the concordance correlation coefficient (CCC). The extent of prior surgery was classified according to prior surgical score (PSS). In 707 (79.4%) patients, rPCI and sPCI concurred in 280 (39.6%). In the Bland-Altman analysis, < 40% patients were in the ±3 PCI points limit of acceptable difference. The average difference between the two scores was 4.5 points (95% CI- -5.16 to -3.92). The CCC- was 0.59 for the whole cohort ('moderate' concordance) and was not influenced by imaging modality, timing of PM or PSS. The rPCI underestimated sPCI by an average of 4.5 points. The role of peritoneal MRI in patients undergoing iterative procedures and the performance of imaging according to sites of recurrence need further evaluation.

Some limitations of the concordance correlation coefficient to characterise model accuracy

Perusal of the environmental modelling literature reveals that the Lin's concordance correlation coefficient is a popular validation statistic to characterise model or map quality. In this communication, we illustrate with synthetic examples three undesirable statistical properties of this coefficient. We argue that ignorance of these properties have led to a frequent misuse of this coefficient in modelling and mapping studies. The stand-alone use of the concordance correlation coefficient is insufficient because i) it does not inform on the relative contribution of bias and correlation, ii) the values cannot be compared across different datasets or studies and iii) it is prone to the same problems as other linear correlation statistics. The concordance coefficient was, in fact, thought initially for evaluating reproducibility studies over repeated trials of the same variable, not for characterising model accuracy. For the validation of models and maps, we recommend calculating statistics that, combined with the concordance correlation coefficient, represent various aspects of the model or map quality, which can be visualised together in a single figure with a Taylor or solar diagram.

Enhancing the temporal resolution of water levels from altimetry using D-InSAR: A case study of 10 Swedish Lakes

Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC >0.8) and moderate agreement in two lakes (CCC >0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.

Single vs. multi-slice assessments of in vivo placental T2∗ measurements

IntroductionPlacental health is vital for maternal and fetal well-being, and placental T2∗ has been suggested to identify in vivo placental dysfunction prior to delivery. However, ideal regions of interest to best inform functional assessments of the placenta remain unknown. The aim of this study is to compare global and slice-wise measures of in-vivo placental T2∗ assessments. MethodsThis prospective study recruited pregnant people with singleton pregnancies between December 2017 and February 2022.3D multi-echo RF-spoiled gradient echo sequences were acquired, and placental T2∗ values were derived from global and slice-wise approaches. Statistical analyses included Pearson correlation coefficients, concordance correlation coefficients (CCC), intraclass correlation coefficients (ICC), and Bland-Altman analyses. ResultsOf 115 participants (mean gestational age, 29.25 ± 5.05 weeks), 68 were healthy controls, and 47 were high-risk pregnancies. Global and slice-wise placental T2∗ assessments for the entire cohort showed no significant difference nor for individual subgroups (healthy controls or high-risk). Pearson correlation values ranged between 0.88 and 0.99 for mean global and slice-wise placental T2∗. CCC analyses ranged from 0.88 to 0.99 for mean T2∗, and ICC analyses ranged between 0.88 and 0.99 for mean T2∗, showing a strong agreement between measurements. Bland-Altman analyses depicted T2∗ differences across coverage methods, and groups resided within the 95 % limits of agreement. DiscussionSingle-slice placental assessments offer robust, comparable T2∗ values to global assessments, with the added benefit of reducing post-processing time and SAR exposure. This supports slice-wise approaches as valid alternatives for assessing placental health in various pregnancies.

Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea

IntroductionContext Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effectiveness, DDMs can be complemented by inputs derived from mechanistic models (MMs).MethodsThis study investigated enhancing the predictive quality of DDMs by using as features a combination of MMs outputs, specifically biomass and soil moisture, with conventional data sources like satellite imagery, weather, and soil information. Four experiments were performed with different datasets being used for prediction: Experiment 1 combined MM outputs with conventional data; Experiment 2 excluded MM outputs; Experiment 3 was the same as Experiment 1 but all conventional temporal data were omitted; Experiment 4 utilised solely MM outputs. The research encompassed ten field-years of wheat and chickpea yield data, applying the eXtreme Gradient Boosting (XGBOOST) algorithm for model fitting. Performance was evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC).Results and conclusionsThe validation results showed that the XGBOOST model had similar predictive power for both crops in Experiments 1, 2, and 3. For chickpeas, the CCC ranged from 0.89 to 0.91 and the RMSE from 0.23 to 0.25 t ha−1. For wheat, the CCC ranged from 0.87 to 0.92 and the RMSE from 0.29 to 0.35 t ha−1. However, Experiment 4 significantly reduced the model's accuracy, with CCCs dropping to 0.47 for chickpeas and 0.36 for wheat, and RMSEs increasing to 0.46 and 0.65 t ha−1, respectively. Ultimately, Experiments 1, 2, and 3 demonstrated comparable effectiveness, but Experiment 3 is recommended for achieving similar predictive quality with a simpler, more interpretable model using biomass and soil moisture alongside non-temporal conventional features.

Intraindividual variability of semen quality, proteome, and sncRNA profiles in a healthy cohort of young adults.

Within-subject variability of semen parameters and molecular components of ejaculates in young men remains poorly understood. To investigate intraindividual variability (IIV) of semen parameters and molecular markers in repeated ejaculates from young men. Semen parameters were assessed in samples collected 6-8 days apart from 164 18-19-year old participants of the Russian Children's Study, a prospective cohort. Subsets of paired samples were used for label-free quantitation and targeted mass-spectrometry of proteins in seminal plasma (SP) and seminal extracellular vesicles (EVs), and for small non-coding RNA (sncRNA) profiling in EVs and spermatozoa using RNA-seq. The mean difference between two ejaculates, within-subject variation, intraclass correlation, and concordance correlation were used to assess IIV for all parameters. Low variability with high reproducibility and high reliability was considered if CVw<15% and ICC>0.90, respectively. Analytical variability was low for all investigated parameters in technical replicates. IIV was assessed for basic semen parameters and proteins in SPs and EVs: 319 and 777 proteins, respectively, using untargeted analysis; 9 and 10 proteins using targeted quantification. We also described the IIV for sncRNA, including microRNA, piwi-interacting RNA, tRNA, and tRNA-derived small RNA (tsRNA) in EVs (409 sncRNA and 78 tsRNA) and in spermatozoa (265 sncRNA and 15 tsRNA). We identified 22 and 27 non-overlapping proteins in SP and EVs, respectively, and 46 and 9 sncRNA, including 5 and 0 tsRNA in seminal EVs and spermatozoa, respectively, with low variability. The fatty acid synthase (FAS) had the lowest IIV in both media in targeted protein quantification. We identified a number of proteins and sncRNA with low variability among 111 proteins, 176 sncRNA, and 12 tsRNA which were previously suggested as biomarkers of male fertility and reproductive outcomes: lactotransferrin, cysteine-rich secretory protein 3, alpha-1-antichymotrypsin, epididymal sperm-binding protein 1, glutathione S-transferase Mu 3, alpha-1-acid glycoprotein 2, serum amyloid P-component, aminopeptidase N, neprilysin, FAS, and miR-10b-3p, miR-122-5p, miR-205-5p, miR-222-3p, miR-34c-5p, miR-509-3-5p, miR-888-5p, miR-892a, miR-363-3p, miR-941, miR-146a-5p, miR-744-5p. These molecules have low IIV and may be promising candidate biomarkers of male fertility and reproductive health.

Multiparametric MRI Scoring System of the Pancreas for the Diagnosis of Chronic Pancreatitis.

Ductal features alone may not offer high diagnostic sensitivity or most accurate disease severity of chronic pancreatitis (CP). Diagnose CP based on multiparametric MRI and MRCP features. Prospective. Between February 2019 and May 2021, 46 control (23 males, 49.3 ± 14.1 years), 45 suspected (20 males, 48.7 ± 12.5 years), and 46 definite (20 males, 53.7 ± 14.6 years) CP patients were enrolled at seven hospitals enrolled in the MINIMAP study. CP classification was based on imaging findings and clinical presentation. 1.5 T. T1-weighted (T1W) spoiled gradient echo, T1 map with variable flip angle, dual-echo Dixon, secretin-enhanced MRCP before and after secretin infusion. Dual-echo fat fraction (FF), T1 relaxation time, extracellular volume (ECV), T1 signal intensity ratio of the pancreas to the spleen (T1 score), arterial-to-venous enhancement ratio (AVR), pancreatic tail diameter (PTD), pancreas volume, late gadolinium enhancement, pancreatic ductal elasticity (PDE), and duodenal filling grade of secretin-enhanced MRCP were measured. Logistic regression analysis generated CP-MRI and secretin-enhanced CP-SMRI scores. Receiver operating characteristics analysis was used to differentiate definite CP from control. Interobserver agreement was assessed using Lin's concordance correlation coefficient. Compared to control, definite CP cohort showed significantly higher dual-echo FF (7% vs. 11%), lower AVR (1.35 vs. 0.85), smaller PTD (2.5 cm vs. 1.95 cm), higher ECV (28% vs. 38%), and higher incidence of PDE loss (6.5% vs. 50%). With the cut-off of >2.5 CP-MRI score (dual-echo FF, AVR, and PTD) and CP-SMRI score (dual-echo FF, AVR, PTD, and PDE) had cross-validated area under the curves of 0.84 (sensitivity 87%, specificity 68%) and 0.86 (sensitivity 89%, specificity 67%), respectively. Interobserver agreement for both CP-MRI and CP-SMRI scores was 0.74. The CP-MRI and CP-SMRI scores yielded acceptable performance and interobserver agreement for the diagnosis of CP. 1 TECHNICAL EFFICACY: Stage 2.