The efficiency of Raphia hookeri adsorbent in indigo carmine dye removal: Economy depth via chemometrics.

The remediation of dye pollutants remains a concern in contemporary water management practices. Hence, the need for efficient and cost-effective techniques for dye removal from wastewater. In this study, the epicarp of Raphia hookeri fruits was treated with orthophosphoric acid for enhanced porosity and efficiency in the uptake of Indigo carmine dye (ICD). Treated Raphia hookeri fruit waste (RHPW) presented morphologically distributed pores as well as high porosity with Branneur-Emmet-Teller (BET) surface area of 945.43 m2/g. RHPW displayed functional groups suitable for adsorption. The maximum ICD uptake was observed at pH 5 while the maximum uptake (qmax) was 20.41 mg/g in the concentration range of 2-10 mg/L. Freundlich isotherm and Pseudo-second order kinetics well-described equilibrium and kinetics data respectively. This indicated a multilayered adsorption. The Dubinin-Radushkecich model energy value was 40.82 kJ/mol, indicating chemical adsorption. The ridge regression, the Lasso and the Elastic net statistical models were used to establish a positive relationship between the various adsorption operational parameters studied. Lasso provided the best result based on the estimated mean squared error. The RHPW-ICD adsorption system was more favorable at room temperature, as the removal efficiency decreased with temperature rise. The findings established Raphia hookeri fruit epicarp as an economical and sustainable precursor for the preparation of potent adsorbent for Indigo carmine dye removal. This can find possible application in wastewater treatment.

External Validation of the International IgA Nephropathy Prediction Tool in Older Adult Patients.

Purpose: The International IgA Nephropathy Prediction Tool (IIgAN-PT) can predict the risk of End-stage renal disease (ESRD) or estimated glomerular filtration rate (eGFR) decline ≥ 50% for adult IgAN patients. Considering the differential progression between older adult and adult patients, this study aims to externally validate its performance in the older adult cohort. Patients and Methods: We analyzed 165 IgAN patients aged 60 and above from six medical centers, categorizing them by their predicted risk. The primary outcome was a ≥50% reduction in estimated glomerular filtration rate (eGFR) or kidney failure. Evaluation of both models involved concordance statistics (C-statistics), time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival curves, and calibration plots. Comparative reclassification was conducted using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The study included 165 Chinese patients (median age 64, 60% male), with a median follow-up of 5.1 years. Of these, 21% reached the primary outcome. Both models with or without race demonstrated good discrimination (C-statistics 0.788 and 0.790, respectively). Survival curves for risk groups were well-separated. The full model without race more accurately predicted 5-year risks, whereas the full model with race tended to overestimate risks after 3 years. No significant reclassification improvement was noted in the full model without race (NRI 0.09, 95% CI: -0.27 to 0.34; IDI 0.003, 95% CI: -0.009 to 0.019). Conclusion: : Both models exhibited excellent discrimination among older adult IgAN patients. The full model without race demonstrated superior calibration in predicting the 5-year risk.

Pathway for Development and Validation of Multi-domain Endpoints for Amyloid Light Chain (AL) Amyloidosis.

Immunoglobin light chain (AL) amyloidosis is a rare disease in which a plasma cell dyscrasia leads to deposition of insoluble amyloid fibrils in multiple organs. To facilitate development of new therapies for this heterogenous disease, a public-private partnership was formed between the nonprofit Amyloidosis Research Consortium and the US Food and Drug Administration Center for Drug Evaluation and Research. In 2020, the Amyloidosis Forum launched an initiative to identify clinical trial endpoints and analytic strategies across affected organ systems and life impacts via specialized working groups. This review summarizes the proceedings of the Statistical Group and proposes a pathway for development and validation of multi-domain endpoints (MDEs) for potential use in AL amyloidosis clinical trials. Specifically, drawing on candidate domain-specific endpoints recommended by each organ-specific working group, different approaches to constructing MDEs were considered. Future studies were identified to assess the validity, meaningfulness and performance of MDEs through use of natural history and clinical trial data. Ultimately, for drug development, the context of use in a regulatory evaluation, the specific patient population, and the investigational therapeutic mechanism should drive selection of appropriate endpoints. MDEs for AL amyloidosis, once developed and validated, will provide important options for advancing patient-focused drug development in this multi-system disease.

Toward a statistical validation of brain signatures as robust measures of behavioral substrates.

The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.

Statistical Validation of Risk Alleles in Genetic Addiction Risk Severity (GARS) Test: Early Identification of Risk for Alcohol Use Disorder (AUD) in 74,566 Case-Control Subjects.

Since 1990, when our laboratory published the association of the DRD2 Taq A1 allele and severe alcoholism in JAMA, there has been an explosion of genetic candidate association studies, including GWAS. To develop an accurate test to help identify those at risk for at least Alcohol Use Disorder (AUD), Blum's group developed the Genetic Addiction Risk Severity (GARS) test, consisting of ten genes and eleven associated risk alleles. In order to statistically validate the selection of these risk alleles measured by GARS, we applied strict analysis to studies that investigated the association of each polymorphism with AUD or AUD-related conditions published from 1990 until 2021. This analysis calculated the Hardy-Weinberg Equilibrium of each polymorphism in cases and controls. If available, the Pearson's χ2 test or Fisher's exact test was applied to comparisons of the gender, genotype, and allele distribution. The statistical analyses found the OR, 95% CI for OR, and a post-risk for 8% estimation of the population's alcoholism prevalence revealed a significant detection. The OR results showed significance for DRD2, DRD3, DRD4, DAT1, COMT, OPRM1, and 5HTT at 5%. While most of the research related to GARS is derived from our laboratory, we are encouraging more independent research to confirm our findings.

Predicting defects in imbalanced data using resampling methods: an empirical investigation.

The development of correct and effective software defect prediction (SDP) models is one of the utmost needs of the software industry. Statistics of many defect-related open-source data sets depict the class imbalance problem in object-oriented projects. Models trained on imbalanced data leads to inaccurate future predictions owing to biased learning and ineffective defect prediction. In addition to this large number of software metrics degrades the model performance. This study aims at (1) identification of useful metrics in the software using correlation feature selection, (2) extensive comparative analysis of 10 resampling methods to generate effective machine learning models for imbalanced data, (3) inclusion of stable performance evaluators-AUC, GMean, and Balance and (4) integration of statistical validation of results. The impact of 10 resampling methods is analyzed on selected features of 12 object-oriented Apache datasets using 15 machine learning techniques. The performances of developed models are analyzed using AUC, GMean, Balance, and sensitivity. Statistical results advocate the use of resampling methods to improve SDP. Random oversampling portrays the best predictive capability of developed defect prediction models. The study provides a guideline for identifying metrics that are influential for SDP. The performances of oversampling methods are superior to undersampling methods.

Comparing classification models-a practical tutorial.

While machine learning models have become a mainstay in Cheminformatics, the field has yet to agree on standards for model evaluation and comparison. In many cases, authors compare methods by performing multiple folds of cross-validation and reporting the mean value for an evaluation metric such as the area under the receiver operating characteristic. These comparisons of mean values often lack statistical rigor and can lead to inaccurate conclusions. In the interest of encouraging best practices, this tutorial provides an example of how multiple methods can be compared in a statistically rigorous fashion.

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data.

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the "Internet of Medical Things" (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.

Coming to Terms with the Black Box Problem: How to Justify AI Systems in Health Care.

The use of opaque, uninterpretable artificial intelligence systems in health care can be medically beneficial, but it is often viewed as potentially morally problematic on account of this opacity-because the systems are black boxes. Alex John London has recently argued that opacity is not generally problematic, given that many standard therapies are explanatorily opaque and that we can rely on statistical validation of the systems in deciding whether to implement them. But is statistical validation sufficient to justify implementation of these AI systems in health care, or is it merely one of the necessary criteria? I argue that accountability, which holds an important role in preserving the patient-physician trust that allows the institution of medicine to function, contributes further to an account of AI system justification. Hence, I endorse the vanishing accountability principle: accountability in medicine, in addition to statistical validation, must be preserved. AI systems that introduce problematic gaps in accountability should not be implemented.

Comparison between Thin-Layer Models and Non-Traditional Methods in the Modelling of Drying Kinetics of Crustacean Wastes

Abstract This research aims to compare the classical thin-layer models, stepwise fit regression method (SRG) and artificial neural networks (ANN) in the modelling of drying kinetics of shrimp shell and crab exoskeleton. Thus, drying curves were obtained using a convective dryer (3.0 m/s) at temperatures of 30.45 and 60oC. The results showed a decreasing tendency for the drying time as the temperature increased for both materials. Drying curves modelling of both materials showed fitted results with R 2 adj >0.998 and MRE<13.128% for some thin-layer models. On the other hand, by SRG a simple model could be obtained as a function of time and temperature, with the greatest accuracy being found in the modelling of experimental data of crab exoskeleton, with MRE<10.149%. Finally, the ANNs were employed successfully in the modelling of drying kinetics, showing high prediction quality with the trained recurrent ANN models.

Perceived emotional intelligence assessment - the Portuguese validation of Schutte's Emotional Intelligence Scale (SEIS-P) / Avaliação da inteligência emocional percebida - validação portuguesa da Escala de Inteligência Emocional de Schutte (EIES-P) / Evaluación de la inteligencia emocional percibida - versión portuguesa de la Escala de Inteligencia Emocional de Schutte (EIES-P)

The aim of this study was to expand the (perceived) emotional intelligence scientific assessment domain and to validate the Schutte Emotional Intelligence Scale (SEIS) with Portuguese speaking adults. The research sample was composed of 2,380 subjects, all Portuguese speakers, with a mean age of 34.91 years. The factorial validity analysis produced four factors, similar to the international reference studies with this scale, which explained 45.56% of the total variance. In accordance with the theoretical and instrumental background, the four factors were named: Recognition of others' emotions; Recognition and communication of one's own emotions; Management of one's own emotions and Use of emotions. The adapted instrument presented valid psychometric characteristics for the assessment of perceived emotional intelligence, suggesting that the Portuguese version of the SEIS maintains the content validity and a regular structure, when compared to previously adapted and explored versions.(AU)

O objetivo deste estudo foi expandir o domínio de avaliação científica da inteligência emocional (percebida) e validar a Escala de Inteligência Emocional de Schutte (SEIS) para adultos falantes de língua portuguesa. A amostra pesquisada foi formada por 2.380 indivíduos, todos falantes de português, com idade média de 34,91 anos. A análise da validade fatorial mostrou quatro fatores, semelhante aos estudos internacionais de referência com essa escala, que explicaram 45,56% da variância total. Em consonância com os antecedentes teóricos e instrumentais, os quatro fatores foram denominados: Reconhecimento das emoções dos outros; Reconhecimento e comunicação de suas próprias emoções; Gerenciamento de suas próprias emoções e Uso de emoções. O instrumento adaptado demonstrou características psicométricas válidas para avaliação da inteligência emocional percebida, sugerindo que a versão em português da SEIS mantém a validade do conteúdo e uma estrutura regular, em comparação com versões previamente adaptadas e exploradas.(AU)

El objetivo de este artículo fue expandir el dominio de evaluación científica de la inteligencia emocional (percibida) y validar la Escala de Inteligencia Emocional de Schutte (SEIS) para adultos hablantes de lengua portuguesa. La muestra investigada fue formada por 2380 individuos con una edad media de 34.91 años. El análisis de la validez factorial presentó cuatro factores, equiparable a los estudios internacionales de referencia con esta escala, que resultó el 45.56% de la varianza total. En conformidad con los antecedentes teóricos e instrumentales, los cuatro factores fueron denominados: Percepción emocional de los demás; Reconocimiento y comunicación de sus propias emociones; Gestión de sus propias emociones; y Utilización de emociones. El instrumento adaptado mostró características psicométricas válidas para evaluar la inteligencia emocional percibida, sugiriendo que la versión en portugués de la SEIS mantiene la validez del contenido y una estructura regular, en comparación con versiones previamente adaptadas y exploradas.(AU)

Validation and Improvement of a Bicycle Crank Arm Based in Numerical Simulation and Uncertainty Quantification.

In this study, a finite element model of a bicycle crank arm are compared to experimental results. The structural integrity of the crank arm was analyzed in a universal dynamic test bench. The instrumentation used has allowed us to know the fatigue behavior of the component tested. For this, the prototype was instrumented with three rectangular strain gauge rosettes bonded in areas where failure was expected. With the measurements made by strain gauges and the forces registers from the load cell used, it has been possible to determine the state of the stresses for different loads and boundary conditions, which has subsequently been compared with a finite element model. The simulations show a good agreement with the experimental results, when the potential sources of uncertainties are considered in the validation process. This analysis allowed us to improve the original design, reducing its weight by 15%. The study allows us to identify the manufacturing process that requires the best metrological control to avoid premature crank failure. Finally, the numerical fatigue analysis carried out allows us to conclude that the new crank arm can satisfy the structural performance demanded by the international bicycle standard. Additionally, it can be suggested to the standard to include the verification that no permanent deformations have occurred in the crank arm during the fatigue test. It has been observed that, in some cases this bicycle component fulfils the minimum safety requirements, but presents areas with plastic strains, which if not taken into account can increase the risk of injury for the cyclist due to unexpected failure of the component.

Influence of Technological Innovations on Industrial Production: A Motif Analysis on the Multilayer Network.

In this work we aim at identifying combinations of technological advancements that reveal the presence of local capabilities for a given industrial production. To this end, we generated a multilayer network using country-level patent and trade data, and performed motif-based analysis on this network using a statistical-validation approach derived from maximum-entropy arguments. We show that in many cases the signal far exceeds the noise, providing robust evidence of synergies between different technologies that can lead to a competitive advantage in specific markets. Our results can be highly useful for policymakers to inform industrial and innovation policies.

Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes.

Validation of multivariate models is of current importance for a wide range of chemical applications. Although important, it is neglected. The common practice is to use a single external validation set for evaluation. This approach is deficient and may mislead investigators with results that are specific to the single validation set of data. In addition, no statistics are available regarding the precision of a derived figure of merit (FOM). A statistical approach using bootstrapped Latin partitions is advocated. This validation method makes an efficient use of the data because each object is used once for validation. It was reviewed a decade earlier but primarily for the optimization of chemometric models this review presents the reasons it should be used for generalized statistical validation. Average FOMs with confidence intervals are reported and powerful, matched-sample statistics may be applied for comparing models and methods. Examples demonstrate the problems with single validation sets.

Statistical validation of 1H NMR protocol vs standard biochemical assay in quality control of RBC packed units.

BACKGROUND: Time dependent quantification of endogenous metabolites in biological samples (blood, urine, biological tissues extracts) in normal and pathological conditions as well as following therapeutic protocols is well established. In the clinical practice, such a dynamic flux of information allows the physician to identify and appreciate alterations associated to biochemical pathways of specific organs. In the years, many biochemical assays have been developed to detect, selectively, this vast array of molecules. METHODS: The Proton Nuclear Magnetic Resonance (1H NMR) spectrum allows the identification and quantification of more than 30 RBC-associated metabolites with minimum manipulation of the sample. To validate the use of 1H NMR spectroscopy for quality control purposes in transfusion medicine, a series of statistical tools have been employed to analyse and compare accuracy and precision of the 1H NMR results with respect to the ones obtained by standard biochemical assays. RESULTS: Among the many metabolites that can be detected and quantified by 1H NMR spectroscopy we selected creatinine and lactate, since they are routinely quantified by standard biochemical assays and because they are characterized by a wide concentration dynamic range. We show that 1D 1H NMR spectroscopy is an accurate a precise method for metabolite quantification. CONCLUSION: These results validate the use of 1H NMR spectroscopy in transfusion medicine as a method to evaluate the quality of RBC packed units and to develop novel and more efficient RBCs storage protocols.

Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations

ABSTRACT Dry tropical forests are a key component in the global carbon cycle and their biomass estimates depend almost exclusively of fitted equations for multi-species or individual species data. Therefore, a systematic evaluation of statistical models through validation of estimates of aboveground biomass stocks is justifiable. In this study was analyzed the capacity of generic and specific equations obtained from different locations in Mexico and Brazil, to estimate aboveground biomass at multi-species levels and for four different species. Generic equations developed in Mexico and Brazil performed better in estimating tree biomass for multi-species data. For Poincianella bracteosa and Mimosa ophthalmocentra, only the Sampaio and Silva (2005) generic equation was the most recommended. These equations indicate lower tendency and lower bias, and biomass estimates for these equations are similar. For the species Mimosa tenuiflora, Aspidosperma pyrifolium and for the genus Croton the specific regional equations are more recommended, although the generic equation of Sampaio and Silva (2005) is not discarded for biomass estimates. Models considering gender, families, successional groups, climatic variables and wood specific gravity should be adjusted, tested and the resulting equations should be validated at both local and regional levels as well as on the scales of tropics with dry forest dominance.

False Discovery Rate Estimation in Proteomics.

With the advancement in proteomics separation techniques and improvements in mass analyzers, the data generated in a mass-spectrometry based proteomics experiment is rising exponentially. Such voluminous datasets necessitate automated computational tools for high-throughput data analysis and appropriate statistical control. The data is searched using one or more of the several popular database search algorithms. The matches assigned by these tools can have false positives and statistical validation of these false matches is necessary before making any biological interpretations. Without such procedures, the biological inferences do not hold true and may be outright misleading. There is a considerable overlap between true and false positives. To control the false positives amongst a set of accepted matches, there is a need for some statistical estimate that can reflect the amount of false positives present in the data processed. False discovery rate (FDR) is the metric for global confidence assessment of a large-scale proteomics dataset. This chapter covers the basics of FDR, its application in proteomics, and methods to estimate FDR.

Seizure prediction for therapeutic devices: A review.

Research in seizure prediction has come a long way since its debut almost 4 decades ago. Early studies suffered methodological caveats leading to overoptimistic results and lack of statistical significance. The publication of guidelines addressing mainly the question of performance evaluation and statistical validation in seizure prediction helped revising the status of the field. While many studies failed to prove that above chance prediction is possible by applying these guidelines, other studies were successful. Methods based on EEG analysis using linear and nonlinear measures were reportedly successful in detecting preictal changes and using them to predict seizures above chance. In this review, we present a selection of studies in seizure prediction published in the last decade. The studies were selected based on the validity of the methods and the statistical significance of performance results. These results varied between studies and many showed acceptable levels of sensitivity and specificity that could be appealing for therapeutic devices. The relatively large prediction horizon and early preictal changes reported in most studies suggest that seizure prediction may work better in closed loop seizure control devices rather than as seizure advisory devices. The emergence of a large database of annotated long-term EEG recordings should help prospective assessment of prediction methods. Some questions remain to be addressed before large clinical trials involving seizure prediction can be carried out.

Statistical Validation of a Web-Based GIS Application and Its Applicability to Cardiovascular-Related Studies.

PURPOSE: There is abundant evidence that neighborhood characteristics are significantly linked to the health of the inhabitants of a given space within a given time frame. This study is to statistically validate a web-based GIS application designed to support cardiovascular-related research developed by the NIH funded Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN) Data Coordinating Center (DCC) and discuss its applicability to cardiovascular studies. METHODS: Geo-referencing, geocoding and geospatial analyses were conducted for 500 randomly selected home addresses in a U.S. southeastern Metropolitan area. The correlation coefficient, factor analysis and Cronbach's alpha (α) were estimated to quantify measures of the internal consistency, reliability and construct/criterion/discriminant validity of the cardiovascular-related geospatial variables (walk score, number of hospitals, fast food restaurants, parks and sidewalks). RESULTS: Cronbach's α for CVD GEOSPATIAL variables was 95.5%, implying successful internal consistency. Walk scores were significantly correlated with number of hospitals (r = 0.715; p < 0.0001), fast food restaurants (r = 0.729; p < 0.0001), parks (r = 0.773; p < 0.0001) and sidewalks (r = 0.648; p < 0.0001) within a mile from homes. It was also significantly associated with diversity index (r = 0.138, p = 0.0023), median household incomes (r = -0.181; p < 0.0001), and owner occupied rates (r = -0.440; p < 0.0001). However, its non-significant correlation was found with median age, vulnerability, unemployment rate, labor force, and population growth rate. CONCLUSION: Our data demonstrates that geospatial data generated by the web-based application were internally consistent and demonstrated satisfactory validity. Therefore, the GIS application may be useful to apply to cardiovascular-related studies aimed to investigate potential impact of geospatial factors on diseases and/or the long-term effect of clinical trials.

What a validation strategy means for the quantitation of cocaine and heroin?

A method of separation by gas chromatography with a flame ionisation detector was developed for quantifying cocaine and heroin in powders seized by law enforcement. The method was validated by studying parameters of calibration, trueness, precision based on trueness error (or systematic bias) and random error. Total error, which is the combination of these errors, verified its adequacy with the objectives fixed by the analyst. Accuracy profile proved to be an efficient decision tool for that purpose. Results obtained with weighted regression model were analysed and allowed to conclude that the method enables quantitation of heroin and cocaine in powders on 2-100% concentration (w/w) range with acceptance limits fixed at 10% and a risk at 5%. The possible sources of uncertainty were evaluated and measurement of their contribution was integrated. The combined standard uncertainty and expanded uncertainty were determined.