Privacy preserving validation for multiomic prediction models.

Reproducibility of results obtained using ribonucleic acid (RNA) data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification, which inhibits the validation of predictors across labs. While current RNA correction algorithms reduce these differences, they require simultaneous access to patient-level data from all datasets, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Despite an inherent trade-off between privacy and performance, SpinAdapt outperforms current correction methods, like Seurat and ComBat, on publicly available cancer studies, including TCGA and ICGC. Furthermore, SpinAdapt can correct new samples, thereby enabling unbiased evaluation on validation cohorts. We expect this novel correction paradigm to enhance research reproducibility and to preserve patient privacy.

Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women.

While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models could be used to screen for patients at a high risk of fracture and osteoporosis. PURPOSE: Fracture risk assessment tool (FRAX) is useful in classifying the fracture risk level, and precise prediction can be achieved by estimating both clinical risk factors and bone mineral density (BMD) using dual X-ray absorptiometry (DXA). However, DXA is not frequently feasible because of its cost and accessibility. This study aimed to establish the reliability of deep learning (DL)-based alternative tools for screening patients at a high risk of fracture and osteoporosis. METHODS: Participants were enrolled from the National Bone Health Screening Project of Taiwan in this cross-sectional study. First, DL-based models were built to predict the lowest T-score value in either the lumbar spine, total hip, or femoral neck and their respective BMD values. The Bland-Altman analysis was used to compare the agreement between the models and DXA. Second, the predictive model to classify patients with a high fracture risk was built according to the estimated BMD from the first step and the FRAX score without BMD. The performance of the model was compared with the classification based on FRAX with BMD. RESULTS: Approximately 10,827 women (mean age, 65.4 ± 9.4 years) were enrolled. In the prediction of the lumbar spine BMD, total hip BMD, femoral neck BMD, and lowest T-score, the root-mean-square error (RMSE) was 0.099, 0.089, 0.076, and 0.68, respectively. The Bland-Altman analysis revealed a nonsignificant difference between the predictive models and DXA. The FRAX score with femoral neck BMD for major osteoporotic fracture risk was 9.7% ± 6.7%, whereas the risk for hip fracture was 3.3% ± 4.6%. Comparison between the classification of FRAX with and without BMD revealed the accuracy rate, positive predictive value (PPV), and negative predictive value (NPV) of 78.8%, 64.6%, and 89.9%, respectively. The area under the receiver operating characteristic curve (AUROC), accuracy rate, PPV, and NPV of the classification model were 0.913 (95% confidence interval: 0.904-0.922), 83.5%, 71.2%, and 92.2%, respectively. CONCLUSION: While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models could be used to screen for patients at a high risk of fracture and osteoporosis.

Calibration plots for multistate risk predictions models.

INTRODUCTION: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. METHODS: We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. RESULTS: The pseudo-value, BLR-IPCW, and MLR-IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low-density regions of predicted transition probability. CONCLUSIONS: We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN.

Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale.

In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how leave-one-out cross validation (LOO) can be used to compare Bayesian models for MRP. We investigate two approximate calculations of LOO: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO). Using two simulation designs, we examine how accurately these two criteria recover the correct ordering of model goodness at predicting population and small-area estimands. Focusing first on variable selection, we find that neither PSIS-LOO nor WTD-PSIS-LOO correctly recovers the models' order for an MRP population estimand, although both criteria correctly identify the best and worst model. When considering small-area estimation, the best model differs for different small areas, highlighting the complexity of MRP validation. When considering different priors, the models' order seems slightly better at smaller-area levels. These findings suggest that, while not terrible, PSIS-LOO-based ranking techniques may not be suitable to evaluate MRP as a method. We suggest this is due to the aggregation stage of MRP, where individual-level prediction errors average out. We validate these results by applying to the real world National Health and Nutrition Examination Survey (NHANES) data in the United States. Altogether, these results show that PSIS-LOO-based model validation tools need to be used with caution and might not convey the full story when validating MRP as a method.

Weighted metrics are required when evaluating the performance of prediction models in nested case-control studies.

BACKGROUND: Nested case-control (NCC) designs are efficient for developing and validating prediction models that use expensive or difficult-to-obtain predictors, especially when the outcome is rare. Previous research has focused on how to develop prediction models in this sampling design, but little attention has been given to model validation in this context. We therefore aimed to systematically characterize the key elements for the correct evaluation of the performance of prediction models in NCC data. METHODS: We proposed how to correctly evaluate prediction models in NCC data, by adjusting performance metrics with sampling weights to account for the NCC sampling. We included in this study the C-index, threshold-based metrics, Observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis. We illustrated the proposed metrics with a validation of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in data from the population-based Rotterdam study. We compared the metrics obtained in the full cohort with those obtained in NCC datasets sampled from the Rotterdam study, with and without a matched design. RESULTS: Performance metrics without weight adjustment were biased: the unweighted C-index in NCC datasets was 0.61 (0.58-0.63) for the unmatched design, while the C-index in the full cohort and the weighted C-index in the NCC datasets were similar: 0.65 (0.62-0.69) and 0.65 (0.61-0.69), respectively. The unweighted O/E ratio was 18.38 (17.67-19.06) in the NCC datasets, while it was 1.69 (1.42-1.93) in the full cohort and its weighted version in the NCC datasets was 1.68 (1.53-1.84). Similarly, weighted adjustments of threshold-based metrics and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. In the matched design, the bias of the unweighted metrics was larger, but it could also be compensated by the weight adjustment. CONCLUSIONS: Nested case-control studies are an efficient solution for evaluating the performance of prediction models that use expensive or difficult-to-obtain biomarkers, especially when the outcome is rare, but the performance metrics need to be adjusted to the sampling procedure.

Initial interactions with the FDA on developing a validation dataset as a medical device development tool.

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study.

INTRODUCTION: Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models. MATERIAL AND METHODS: We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed. RESULTS: A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods. CONCLUSIONS: The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

Characterization of the Secretome, Transcriptome, and Proteome of Human ß Cell Line EndoC-ßH1.

Early diabetes research is hampered by limited availability, variable quality, and instability of human pancreatic islets in culture. Little is known about the human ß cell secretome, and recent studies question translatability of rodent ß cell secretory profiles. Here, we verify representativeness of EndoC-ßH1, one of the most widely used human ß cell lines, as a translational human ß cell model based on omics and characterize the EndoC-ßH1 secretome. We profiled EndoC-ßH1 cells using RNA-seq, data-independent acquisition, and tandem mass tag proteomics of cell lysate. Omics profiles of EndoC-ßH1 cells were compared to human ß cells and insulinomas. Secretome composition was assessed by data-independent acquisition proteomics. Agreement between EndoC-ßH1 cells and primary adult human ß cells was ∼90% for global omics profiles as well as for ß cell markers, transcription factors, and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-ßH1 cells compared to adult ß cells. Consistently, similarity was slightly higher with benign nonmetastatic insulinomas. EndoC-ßH1 secreted 783 proteins in untreated baseline state and 3135 proteins when stressed with nontargeting control siRNA, including known ß cell hormones INS, IAPP, and IGF2. Further, EndoC-ßH1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. EndoC-ßH1 secretome contained an unexpectedly high proportion of predicted extracellular vesicle proteins. We believe that secretion of extracellular vesicles and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies.

Validation of a fall rate prediction model for community-dwelling older adults: a combined analysis of three cohorts with 1850 participants.

BACKGROUND: Fragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction. METHODS: A combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage. RESULTS: The coefficient estimates for the prior number of falls were consistent among the three studies. Higgin's I2 as heterogeneity measure ranged from 0 to 55.39%. The overall coefficient estimates indicated that the expected fall rate increases with an increasing number of previous falls. External model validation revealed that the prediction errors for the data sets were independent of the model to which they were applied. CONCLUSION: This analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.

Validation and comparison of triage-based screening strategies for sepsis.

OBJECTIVE: This study sought to externally validate and compare proposed methods for stratifying sepsis risk at emergency department (ED) triage. METHODS: This nested case/control study enrolled ED patients from four hospitals in Utah and evaluated the performance of previously-published sepsis risk scores amenable to use at ED triage based on their area under the precision-recall curve (AUPRC, which balances positive predictive value and sensitivity) and area under the receiver operator characteristic curve (AUROC, which balances sensitivity and specificity). Score performance for predicting whether patients met Sepsis-3 criteria in the ED was compared to patients' assigned ED triage score (Canadian Triage Acuity Score [CTAS]) with adjustment for multiple comparisons. RESULTS: Among 2000 case/control patients, 981 met Sepsis-3 criteria on final adjudication. The best performing sepsis risk scores were the Predict Sepsis version #3 (AUPRC 0.183, 95 % CI 0.148-0.256; AUROC 0.859, 95 % CI 0.843-0.875) and Borelli scores (AUPRC 0.127, 95 % CI 0.107-0.160, AUROC 0.845, 95 % CI 0.829-0.862), which significantly outperformed CTAS (AUPRC 0.038, 95 % CI 0.035-0.042, AUROC 0.650, 95 % CI 0.628-0.671, p < 0.001 for all AUPRC and AUROC comparisons). The Predict Sepsis and Borelli scores exhibited sensitivity of 0.670 and 0.678 and specificity of 0.902 and 0.834, respectively, at their recommended cutoff values and outperformed Systemic Inflammatory Response Syndrome (SIRS) criteria (AUPRC 0.083, 95 % CI 0.070-0.102, p = 0.052 and p = 0.078, respectively; AUROC 0.775, 95 % CI 0.756-0.795, p < 0.001 for both scores). CONCLUSIONS: The Predict Sepsis and Borelli scores exhibited improved performance including increased specificity and positive predictive values for sepsis identification at ED triage compared to CTAS and SIRS criteria.

Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.

Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification. We created an algorithm to predict cMRI-quantified PRF from echocardiography using machine learning (ML). We retrospectively performed echocardiographic measurements paired to cMRI within 3 months in patients with ≥ mild PR from 2009 to 2022. Model inputs were vena contracta ratio, PR index, PR pressure half-time, main and branch pulmonary artery diastolic flow reversal (BPAFR), and transannular patch repair. A gradient boosted trees ML algorithm was trained using k-fold cross-validation to predict cMRI PRF by phase contrast imaging as a continuous number and at > mild (PRF ≥ 20%) and severe (PRF ≥ 40%) thresholds. Regression performance was evaluated with mean absolute error (MAE), and at clinical thresholds with area-under-the-receiver-operating-characteristic curve (AUROC). Prediction accuracy was compared to historical clinician accuracy. We externally validated prior reported studies for comparison. We included 243 subjects (median age 21 years, 58% repaired tetralogy of Fallot). The regression MAE = 7.0%. For prediction of > mild PR, AUROC = 0.96, but BPAFR alone outperformed the ML model (sensitivity 94%, specificity 97%). The ML model detection of severe PR had AUROC = 0.86, but in the subgroup with BPAFR, performance dropped (AUROC = 0.73). Accuracy between clinicians and the ML model was similar (70% vs. 69%). There was decrement in performance of prior reported algorithms on external validation in our dataset. A novel ML model for echocardiographic quantification of PRF outperforms prior studies and has comparable overall accuracy to clinicians. BPAFR is an excellent marker for > mild PRF, and has moderate capacity to detect severe PR, but more work is required to distinguish moderate from severe PR. Poor external validation of prior works highlights reproducibility challenges.

A systematic review of cardiac surgery clinical prediction models that include intra-operative variables.

BACKGROUND: Most cardiac surgery clinical prediction models (CPMs) are developed using pre-operative variables to predict post-operative outcomes. Some CPMs are developed with intra-operative variables, but none are widely used. The objective of this systematic review was to identify CPMs with intra-operative variables that predict short-term outcomes following adult cardiac surgery. METHODS: Ovid MEDLINE and EMBASE databases were searched from inception to December 2022, for studies developing a CPM with at least one intra-operative variable. Data were extracted using a critical appraisal framework and bias assessment tool. Model performance was analysed using discrimination and calibration measures. RESULTS: A total of 24 models were identified. Frequent predicted outcomes were acute kidney injury (9/24 studies) and peri-operative mortality (6/24 studies). Frequent pre-operative variables were age (18/24 studies) and creatinine/eGFR (18/24 studies). Common intra-operative variables were cardiopulmonary bypass time (16/24 studies) and transfusion (13/24 studies). Model discrimination was acceptable for all internally validated models (AUC 0.69-0.91). Calibration was poor (15/24 studies) or unreported (8/24 studies). Most CPMs were at a high or indeterminate risk of bias (23/24 models). The added value of intra-operative variables was assessed in six studies with statistically significantly improved discrimination demonstrated in two. CONCLUSION: Weak reporting and methodological limitations may restrict wider applicability and adoption of existing CPMs that include intra-operative variables. There is some evidence that CPM discrimination is improved with the addition of intra-operative variables. Further work is required to understand the role of intra-operative CPMs in the management of cardiac surgery patients.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

Comment on Martínez-Delgado et al. Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions. Sensors 2021, 21, 5273.

The paper "Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions" (Sensors2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model's root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors' code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones.

Predicting Goal Attainment in Process-Oriented Behavioral Interventions Using a Data-Driven System Identification Approach.

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk, thus emphasizing the importance of a personalized approach for optimized behavioral interventions.

Wood volume is overestimated in the Brazilian Amazon: Why not use generic volume prediction methods in tropical forest management?

The Amazon has a range of species with high potential for sustainable timber harvesting, but for them to be utilized globally, the merchantable wood volume must be accurately quantified. However, since the 1950s, inadequate methods for estimating merchantable timber volumes have been employed in the Amazon, and Brazilian Government agencies still require some of them. The natural variability of the Amazon Forest provides an abundance of species of different sizes and shapes, conferring several peculiarities, which makes it necessary to use up-to-date and precise methods for timber quantification in Amazon Forest management. Given the employment of insufficient estimation methods for wood volume, this study scrutinizes the disparities between the actual harvested merchantable wood volume and the volume estimated by the forest inventory during the harvesting phase across five distinct public forest areas operating under sustainable forest management concessions. We used mixed-effect models to evaluate the relationships between inventory and harvested volume for genera and forest regions. We performed an equivalence test to assess the similarity between the volumes obtained during the pre-and post-harvest phases. We calculated root mean square error and percentage bias for merchantable volume as accuracy metrics. There was a strong tendency for the 100% forest inventory to overestimate merchantable wood volume, regardless of genus and managed area. There was a significant discrepancy between the volumes inventoried and harvested in different regions intended for sustainable forest management, in which only 22% of the groups evaluated were equivalent. The methods currently practiced by forest companies for determining pre-harvest merchantable volume are inaccurate enough to support sustainable forest management in the Amazon. They may even facilitate the region's illegal timber extraction and organized crime.

Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies.

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings.

Improving groundwater vulnerability assessment in structurally controlled hard rock aquifer: insight from lineament density and land use/land cover pattern.

A comprehensive seasonal assessment of groundwater vulnerability was conducted in the weathered hard rock aquifer of the upper Swarnrekha watershed in Ranchi district, India. Lineament density (Ld) and land use/land cover (LULC) were integrated into the conventional DRASTIC and Pesticide DRASTIC (P-DRASTIC) models and were extensively compared with six modified models, viz. DRASTIC-Ld, DRASTIC-Lu, DRASTIC-LdLu, P-DRASTIC-Ld, P-DRASTIC-Lu, and P-DRASTIC-LdLu, to identify the most optimal model for vulnerability mapping in hard rock terrain of the region. Findings were geochemically validated using NO3- concentrations of 68 wells during pre-monsoon (Pre-M) and post-monsoon (Post-M) 2022. Irrespective of the applied model, groundwater vulnerability shows significant seasonal variation, with > 45% of the region classified as high to very high vulnerability in the pre-M, increasing to Ì´67% in post-M season, highlighting the importance of seasonal vulnerability assessments. Agriculture and industries' dominant southern region showed higher vulnerability, followed by regions with high Ld and thin weathered zone. Incorporating Ld and LULC parameters into DRASTIC-LdLu and P-DRASTIC-LdLu models increases the 'Very High' vulnerability zones to 17.4% and 17.6% for pre-M and 29.4% and 27.9% for post-M, respectively. Similarly, 'High' vulnerable zones increase from 32.5% and 25% in pre-M to 33.8% and 35.3% in post-M for respective models. Model output comparisons suggest that modified DRASTIC-LdLu and P-DRASTIC-LdLu perform better, with accurate estimations of 83.8% and 89.7% for pre-M and post-M, respectively. However, results of geochemical validation suggest that among all the applied modified models, DRASTIC-LdLu performs best, with accurate estimations of 34.4% and 20.6% for pre-M and post-M, respectively.

External validation of the PAGE-B score for HCC risk prediction in people living with HIV/HBV coinfection.

BACKGROUND & AIMS: HBV coinfection is common among people living with HIV (PLWH) and is the most important cause of hepatocellular carcinoma (HCC). While risk prediction tools for HCC have been validated in patients with HBV monoinfection, they have not been evaluated in PLWH. Thus, we performed an external validation of PAGE-B in people with HIV/HBV coinfection. METHODS: We included data on PLWH from four European cohorts who were positive for HBsAg and did not have HCC before starting tenofovir. We estimated the predictive performance of PAGE-B for HCC occurrence over 15 years in patients receiving tenofovir-containing antiretroviral therapy. Model discrimination was assessed after multiple imputation using Cox regression with the prognostic index as a covariate, and by calculating Harrell's c-index. Calibration was assessed by comparing our cumulative incidence with the PAGE-B derivation study using Kaplan-Meier curves. RESULTS: In total, 2,963 individuals with HIV/HBV coinfection on tenofovir-containing antiretroviral therapy were included. PAGE-B was <10 in 26.5%, 10-17 in 57.7%, and ≥18 in 15.7% of patients. Within a median follow-up of 9.6 years, HCC occurred in 68 individuals (2.58/1,000 patient-years, 95% CI 2.03-3.27). The regression slope of the prognostic index for developing HCC within 15 years was 0.93 (95% CI 0.61-1.25), and the pooled c-index was 0.77 (range 0.73-0.80), both indicating good model discrimination. The cumulative incidence of HCC was lower in our study compared to the derivation study. A PAGE-B cut-off of <10 had a negative predictive value of 99.4% for the development of HCC within 5 years. Restricting efforts to individuals with a PAGE-B of ≥10 would spare unnecessary HCC screening in 27% of individuals. CONCLUSIONS: For individuals with HIV/HBV coinfection, PAGE-B is a valid tool to determine the need for HCC screening. IMPACT AND IMPLICATIONS: Chronic HBV infection is the most important cause of hepatocellular carcinoma (HCC) among people living with HIV. Valid risk prediction may enable better targeting of HCC screening efforts to high-risk individuals. We aimed to validate PAGE-B, a risk prediction tool that is based on age, sex, and platelets, in 2,963 individuals with HIV/HBV coinfection who received tenofovir-containing antiretroviral therapy. In the present study, PAGE-B showed good discrimination, adequate calibration, and a cut-off of <10 had a negative predictive value of 99.4% for the development of HCC within 5 years. These results indicate that PAGE-B is a simple and valid risk prediction tool to determine the need for HCC screening among people living with HIV and HBV.

Quantifying metal-binding specificity of CcNikZ-II from Clostridium carboxidivorans in the presence of competing metal ions.

Many proteins bind transition metal ions as cofactors to carry out their biological functions. Despite binding affinities for divalent transition metal ions being predominantly dictated by the Irving-Williams series for wild-type proteins, in vivo metal ion binding specificity is ensured by intracellular mechanisms that regulate free metal ion concentrations. However, a growing area of biotechnology research considers the use of metal-binding proteins in vitro to purify specific metal ions from wastewater, where specificity is dictated by the protein's metal binding affinities. A goal of metalloprotein engineering is to modulate these affinities to improve a protein's specificity towards a particular metal; however, the quantitative relationship between the affinities and the equilibrium metal-bound protein fractions depends on the underlying binding mechanisms. Here we demonstrate a high-throughput intrinsic tryptophan fluorescence quenching method to validate binding models in multi-metal solutions for CcNikZ-II, a nickel-binding protein from Clostridium carboxidivorans. Using our validated models, we quantify the relationship between binding affinity and specificity in different classes of metal-binding models for CcNikZ-II. We further illustrate the potential relevance of data-informed models to predicting engineering targets for improved specificity.