Classical Statistical Approach Research Articles

Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics.

The contribution of mica mining activities to fluoride (F-) contamination in groundwater has been chased in this study. For the purpose, groundwater samples (n = 40, replicated thrice) were collected during the post-monsoons (September-October) from a mica mining area in the Tisri block of Giridih district, Jharkhand. The study has employed a synergy of classical aquifer chemistry, statistical approaches, different indices, Self-Organising Maps (SOM), and Sobol sensitivity index (SSI) to unveil the underlying aquifer chemistry, identify the impacts of mining activities on groundwater quality and its associated health hazard. Fluoride levels varied from 0.34 to 2.8ppm, with 40% of samples exceeding the World Health Organization's permissible limit (1.5ppm). Physicochemical analysis revealed significant differences in electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH) and major ion concentrations (Na+, HCO3-, Ca2+) between fluoride-contaminated (FC) and fluoride-uncontaminated (FU) groups. Higher Na+ and HCO3- associated with F- contaminated samples, were indicative of silicate weathering and carbonate dissolution as primary geogenic sources for this ion. Health risk assessment (HRA) revealed hazard quotient (HQ) values exceeding unity, indicating non-carcinogenic risks, particularly for children in most samples from group FC. The mean Water Quality Index (WQI) of FC group (156.76 ± 7.30) was significantly higher (p < 0.05) than group FU indicating of its unsuitability. SOM could accurately (80%) predict presence of fluoride in water samples based on other major ions. Sobol sensitivity analysis successfully identified fluoride concentration and body weight as most impactful parameters affecting human health. The integration of advanced modelling techniques and geospatial analysis as Inverse Distance Weightage (IDW) maps has provided a robust framework for ongoing groundwater quality monitoring in mining-affected regions and can help proactive intervention in risk-prone areas. Overall, this comprehensive study takes us a step ahead towards ensuring safe drinking water access for the global community.

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients

BackgroundMachine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome. MethodsThis study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance. ResultsFrom a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8. ConclusionML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality.

Resampling approaches for the quantitative analysis of spatially distributed cells

ABSTRACT Image segmentation is a crucial step in various image analysis pipelines and constitutes one of the cutting-edge areas of digital pathology. The advent of quantitative analysis has enabled the evaluation of millions of individual cells in tissues, allowing for the combined assessment of morphological features, biomarker expression, and spatial context. The recorded cells can be described as a point pattern process. However, the classical statistical approaches to point pattern processes prove unreliable in this context due to the presence of multiple irregularly-shaped interstitial cell-devoid spaces in the domain, which correspond to anatomical features (e.g. vessels, lipid vacuoles, glandular lumina) or tissue artefacts (e.g. tissue fractures), and whose coordinates are unknown. These interstitial spaces impede the accurate calculation of the domain area, resulting in biased clustering measurements. Moreover, the mistaken inclusion of empty regions of the domain can directly impact the results of hypothesis testing. The literature currently lacks any introduced bias correction method to address interstitial cell-devoid spaces. To address this gap, we propose novel resampling methods for testing spatial randomness and evaluating relationships among different cell populations. Our methods obviate the need for domain area estimation and provide non-biased clustering measurements. We created the SpaceR software (https://github.com/GBertolazzi/SpaceR) to enhance the accessibility of our methodologies.

Determination of the Statistical Power of Fatigue Characteristics in Relation to the Number of Samples

The paper presents guidelines included in the ISO 12107:2003 standard regarding the number of samples for determining the fatigue characteristics in the high cycle range. The proposed normative values were compared with the classical statistical approach. Fatigue test results for S355J2+C steel for rotary bending were used for verification. In addition, the error and power of a statistical test were determined for characteristics with different sample sizes. It determined the number of specimens to estimate the fatigue curve, which required significant error and power of the statistical test.

Predicting dental anxiety in young adults: classical statistical modelling approach versus machine learning approach

ObjectivesTo predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar results to the machine learning (ML) approach in predicting factors for dental anxiety.MethodsA cross-sectional study of Western Illinois University students. Three survey instruments (sociodemographic questionnaire, modified dental anxiety scale (MDAS), and dental concerns assessment tool (DCA)) were distributed via email to the students using survey monkey. The dependent variable was the mean MDAS scores, while the independent variables were the sociodemographic and dental concern assessment variables. Multivariable analysis was done by comparing the classical statistical model and the machine learning model. The classical statistical modelling technique was conducted using the multiple linear regression analysis and the final model was selected based on Akaike information Criteria (AIC) using the backward stepwise technique while the machine learining modelling was performed by comparing two ML models: LASSO regression and extreme gradient boosting machine (XGBOOST) under 5-fold cross-validation using the resampling technique. All statistical analyses were performed using R version 4.1.3.ResultsThe mean MDAS was 13.73 ± 5.51. After careful consideration of all possible fitted models and their interaction terms the classical statistical approach yielded a parsimonious model with 13 predictor variables with Akaike Information Criteria (AIC) of 2376.4. For the ML approach, the Lasso regression model was the best-performing model with a mean RMSE of 0.617, R2 of 0.615, and MAE of 0.483. Comparing the variable selection of ML versus the classical statistical model, both model types identified 12 similar variables (out of 13) as the most important predictors of dental anxiety in this study population.ConclusionThere is a high burden of dental anxiety within this study population. This study contributes to reducing the knowledge gap about the impact of clinical exposure variables on dental anxiety and the role of machine learningin the prediction of dental anxiety. The predictor variables identified can be used to inform public health interventions that are geared towards eliminating the individual clinical exposure triggers of dental anxiety are recommended.

Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach

ObjectivesThis study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting. MethodsInformation on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss). Prediction models were built on significant factors (single or combinatory) selected by the RuleFit algorithm, and these factors were further adopted by regression models. Model performance was evaluated by Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Associations between predictors and the tooth loss phenotype were also evaluated by classical statistical approaches to validate the performance of machine learning models. ResultsIn total, 7840 patients were included. The machine learning model predicting the tooth loss phenotype achieved AUROC of 0.71 and AUPRC of 0.66. Age, periodontal diagnosis, number of missing teeth at baseline, furcation involvement, and tooth mobility were associated with the tooth loss phenotype in both machine learning and classical statistical models. ConclusionsThe rule-based machine learning approach improves model explainability compared to classical statistical methods. However, the model's generalizability needs to be further validated by external datasets. Clinical SignificancePredictors identified by the current machine learning approach using the RuleFit algorithm had clinically relevant thresholds in predicting the tooth loss phenotype in a large and diverse periodontitis patient cohort. The results of this study will assist clinicians in performing risk assessment for periodontitis at the initial visit.

From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables.

Spatiotemporal modelling of PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {PM}_{2.5}$$\\end{document} concentrations in Lombardy (Italy): a comparative study

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {PM}_{2.5}$$\\end{document} concentrations in Lombardy (North Italy) from 2016 to 2020. Despite differing methodologies, all models demonstrate proficient capture of spatiotemporal patterns within air pollution data with similar out-of-sample performance. Furthermore, the study delves into station-specific analyses, revealing variable model performance contingent on localised conditions. Model interpretation, facilitated by parametric coefficient analysis and partial dependence plots, unveils consistent associations between predictor variables and PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {PM}_{2.5}$$\\end{document} concentrations. Despite nuanced variations in modelling spatiotemporal correlations, all models effectively accounted for the underlying dependence. In summary, this study underscores the efficacy of conventional techniques in modelling correlated spatiotemporal data, concurrently highlighting the complementary potential of Machine Learning and classical statistical approaches.

Experimental verification of a data-driven algorithm for drive-by bridge condition monitoring

As the world’s transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost ‘drive-by’ monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either ‘damaged’ or ‘healthy’. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration.

Microstructure and Properties Variation of High-Performance Grey Cast Iron via Small Boron Additions

A study was undertaken to investigate the effects of small boron additions on the solidification and microstructure of hypo-eutectic alloyed grey cast iron. The characteristic temperatures upon crystallisation of the treated metal melt were recorded, specifically those concerning small boron addition by using thermal analysis with the ATAS system. Additionally, a standardised wedge test was set to observe any changes in chill performance. The microstructures of thermal analysis samples were analysed using a light optical microscope (LOM) and field emission scanning electron microscopy (FE-SEM) equipped with energy dispersive spectroscopy (EDS), which reveal variations in graphite count number with the addition of boron within observed random and undercooled flake graphite. The effect of boron was estimated by the classical analytical and statistical approach. The solidification behaviour under equilibrium conditions was predicted by a thermodynamic approach using Thermo-Calc. Based on all gathered data, a response model was set with boron for given melt quality and melt treatment using the experimentally determined data. The study reveals that boron as a ferrite and carbide-promoting element under the experimental set shows weak nucleation potential in synergy with other heterogenic nuclei at increased solidification rates, but no considerable changes were observed by the TA samples solidified at slower cooling rates, indicating the loss of the overall inoculation effect. The potential presence of boron nitride as an inoculator for graphite precipitation for a given melt composition and melt treatment was not confirmed in this study. It seems that boron at increased solidification rates can contribute to overall inoculation, but at slower cooling rates these effects are gradually lost. In the last solidification range, an increased boron content could have a carbide forming nature, as is usually expected. The study suggests that boron in traces could affect the microstructure and properties of hypo-eutectic alloyed grey cast iron.

Global sensitivity analysis for studying hot-mix asphalt dynamic modulus parameters

The dynamic modulus (E*) of hot-mix asphalt mixtures is one of the most laborious and time-consuming material parameters to measure in the laboratory. It involves expensive, specialized equipment and expertize that are not readily available in most laboratories. Consequently, several efforts have been devoted to E* prediction models. Unfortunately, developing these prediction models is complex because of the numerous contributory factors and their non-linear influence on E* values. Moreover, such models are not able to prioritize or screen the major factors influencing the E* values. This study presents a new framework for analyzing the dynamic modulus influencing factors by adopting two modeling approaches. First, deep residual neural networks (DRNNs) for non-parametric approaches are used to improve the E* prediction capabilities and derive deep insight into the contributory parameters' effect on the E* value. Second, the well-known Witczak 1–40D prediction equation is used as a representative of the classical statistical modeling approach. In the validation of the models, a comprehensive laboratory database is utilized to account for all significant contributory parameters, such as binder characteristics, volumetric properties, mixture gradation, and testing circumstances parameters. Then, the performance is assessed using typical performance metrics. Lastly, intensive global sensitivity analysis (GSA) is undertaken with the assistance of Latin Hypercube Simulation. Three distinct GSA methods are used to emphasize the influence of each contributory factor on the value of E* in actual practice while reducing the possibility for result distortion owing to correlations between contributory variables. Performance metrics of the DRNNs and the Witczak 1–40D prediction models give the GSA conclusions high credibility. The GSA reveals that, among all possible inputs, the binder content, shear modulus, voids in the mineral aggregates, and temperature are the most significant factors in determining the E* value.

Parkinson’s Disease Non-Motor Subtypes Classification in a Group of Slovenian Patients: Actuarial vs. Data-Driven Approach

Background and purpose: The aim of this study was to examine the risk factors, prodromal symptoms, non-motor symptoms (NMS), and motor symptoms (MS) in different Parkinson’s disease (PD) non-motor subtypes, classified using newly established criteria and a data-driven approach. Methods: A total of 168 patients with idiopathic PD underwent comprehensive NMS and MS examinations. NMS were assessed by the Non-Motor Symptom Scale (NMSS), Montreal Cognitive Assessment (MoCA), Hamilton Depression Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Epworth Sleepiness Scale (ESS), Starkstein Apathy Scale (SAS) and Fatigue Severity Scale (FSS). Motor subtypes were classified based on Stebbins’ method. Patients were classified into groups of three NMS subtypes (cortical, limbic, and brainstem) based on the newly designed inclusion criteria. Further, data-driven clustering was performed as an alternative, statistical learning-based classification approach. The two classification approaches were compared for consistency. Results: We identified 38 (22.6%) patients with the cortical subtype, 48 (28.6%) with the limbic, and 82 (48.8%) patients with the brainstem NMS PD subtype. Using a data-driven approach, we identified five different clusters. Three corresponded to the cortical, limbic, and brainstem subtypes, while the two additional clusters may have represented patients with early and advanced PD. Pearson chi-square test of independence revealed that a priori classification and cluster membership were significantly related to one another with a large effect size (χ2(8) = 175.001, p &lt; 0.001, Cramer’s V = 0.722). The demographic and clinical profiles differed between NMS subtypes and clusters. Conclusion: Using the actuarial and clustering approach, marked differences between individual NMS subtypes were found. The newly established criteria have potential as a simplified tool for future clinical research of NMS subtypes of Parkinson’s disease.

A probabilistic-driven framework for enhanced corrosion estimation of ship structural components

The work proposes a probabilistic-driven framework for enhanced corrosion estimation of ship structural components using Bayesian inference and limited measurement data. The new approach for modelling measurement uncertainty is proposed based on the results of previous corrosion tests that incorporate the non-uniform character of the corroded surface of structural components. The proposed framework's basic features are outlined, and the detailed algorithm is presented. Further, the proposed framework is validated by comparison with the classical statistical approach and mass measurements, considering previous experimental work results. Notably, the impact of the number of measuring points is investigated, and the accuracy index is proposed to identify the optimum number of measurements. The developed framework has a significant advantage over the classical approach since measuring uncertainty is incorporated. Additionally, the confidence intervals of both mean value corrosion depth and standard deviation could be gathered due to the probabilistic character of the framework. Thus, the presented approach can potentially be used in the structural health monitoring of ship structural components and reliability analysis.

A generalized approach to compute neutrosophic k-factor analysis of variance

The application of classical statistical methods is not feasible given the presence of imprecise, fuzzy, uncertain, or undetermined observations in the underlying dataset. This is due to the existence of uncertainties pervading every aspect of real-life situations, which cannot always be accurately addressed by classical statistical approaches. In order to tackle this problem, a new methodology known as neutrosophic analysis of variance (NANOVA) has been developed as an extension of classical approaches to analyze datasets with uncertainty. The proposed approach can be applied regardless of the number of factors and replications. Moreover, NANOVA introduces a novel matrix-based approach to derive the F_N-test in an uncertain environment. To assess the effectiveness of NANOVA, various real datasets have been employed, and research findings on single- and two-factor NANOVAs with measures of indeterminacy have been presented. According to our comparisons, NANOVA provides a more informative, efficient, flexible, and reliable approach to deal with uncertainties than classical statistical methods. Therefore, there is a need to go beyond conventional statistical techniques and adopt advanced methodologies that can effectively handle uncertainties.

Morphological, physicochemical, and bioactive characteristics of Hypericum perforatum genotypes, and their classification by GT-Biplot approach

Thirty-three different Hypericum perforatum genotypes were characterized in terms of morphological (plant height, herbage yields), physicochemical (ash, cellulose, protein, and colour), and bioactive properties. The antioxidant and antibacterial capacity were also assessed. All genotype samples were characterized as flower part and whole plant (aerial part). It was observed that the plant height ranged between 21.6 and 96.6 cm and whole plant drog yield was in the range of 11–146 g. Bioactivity showed wide distribution for the samples and all bioactive parameter values were measured as lower for the whole plant compared to the flower part. Total phenolic content ranged between 60.39-110.54 and 49.08–89.53 mg GAE/g for the flower part and whole plant, respectively. Flower extracts also showed good inhibition activity against some Gram (+) bacteria. The GT-Biplot statistical mathematical classification approach was applied to reveal the promising genotypes.

An intercontinental machine learning analysis of factors explaining consumer awareness of food risk

Food safety is a common concern at the household level, with important variations across different countries and cultures. Nevertheless, identifying the factors that best explain similarities and differences in consumer awareness pertaining to this topic is not straightforward. Starting from a questionnaire administered in seven countries from four continents (Argentina, Brazil, Colombia, Ghana, India, Peru, and the United Kingdom), we present an analysis of the answers related to food safety concerns, aimed at identifying possible explanatory factors. As classical statistical approaches can be limited when dealing with complex datasets, we propose an analysis with machine learning techniques, that can take into account both categorical and numerical values. With the questionnaire as a base, we task a machine learning algorithm, Random Forest, with predicting consumers’ answers to the target questions using information from all other answers. Once the algorithm is trained, it becomes possible to obtain a ranking of the questions considered the most important for the prediction, with the top-ranked questions likely representing explanatory factors. Top-ranked questions are then analyzed using a Random Forest regression algorithm, to test possible correlations. The results show that the most significant explanatory variables of safety concerns seem to be estimates of carbon footprints and calories associated with food products, and primarily with beef and chicken meat. These results tend to indicate that people who are most concerned about food safety are also those who are highly aware of environmental and nutritional impacts of food, hinting at differences in food education as a possible underlying explanation for the data.

Identifying predictors of tooth loss using a rule-based machine learning approach: A retrospective study at university-setting clinics.

This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach. Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root-mean-squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model. In total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models. The two-step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule-based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.

DQPB: software for calculating disequilibrium U–Pb ages

Abstract. Initial radioactive disequilibrium amongst intermediate nuclides of the U decay chains can have a significant impact on the accuracy of U–Pb ages, especially in young samples. For samples that can reasonably be assumed to have attained radioactive equilibrium at the time of analysis, a relatively straightforward correction may be applied. However, in younger materials where this assumption is unreasonable, it is necessary to replace the familiar U–Pb age equations with more complete expressions that account for growth and decay of intermediate nuclides through time. DQPB is software for calculating U–Pb ages while accounting for the effects of radioactive disequilibrium among intermediate nuclides of the U decay chains. The software is written in Python and distributed as both a pure Python package and a stand-alone graphical user interface (GUI) application that integrates with standard Microsoft Excel spreadsheets. The software implements disequilibrium U–Pb equations to compute ages using various approaches, including concordia intercept ages on a Tera–Wasserburg diagram, U–Pb isochron ages, Pb*/U ages based on single aliquots, and 207Pb-corrected ages. While these age-calculation approaches are tailored toward young samples that cannot reasonably be assumed to have attained radioactive equilibrium at the time of analysis, they may also be applied to older materials where disequilibrium is no longer analytically resolvable. The software allows users to implement a variety of regression algorithms based on both classical and robust statistical approaches, compute weighted average ages and construct customisable, publication-ready plots of U–Pb age data. The regression and weighted average algorithms implemented in DQPB may also be applicable to other (i.e. non-U–Pb) geochronological datasets.

Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia

Machine learning (ML) algorithms perform better than classical statistical approaches to explore hidden nonlinear relationships. In this context, the goal of this research is to predict wheat yield utilizing remote sensing and climatic data in southern part of Pakistan. Four remote sensing indices, viz.., Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI) are integrated with five climatic variables, i.e., Maximum Temperature (Tmax), Minimum Temperature (Tmin), Rainfall (R), Relative humidity (RH) and windspeed (WS) and one drought index, i.e., Standardized Precipitation Evapotranspiration Index (SPEI). Eight model combinations are built within two scenarios of wheat season, i.e., Whole Seasonal mean (WSM) (SC1), and Peak of Seasonal Mean (POSM) (SC2). Two nonlinear ML algorithms, i.e., Random Forest (RF), and Support Vector Machines (SVM), and one linear model, i.e., LASSO is being employed for wheat yield prediction to find the best combination and ML algorithm in two scenarios. Results revealed that in SC1, RF regression for the model combination (GNDVI +Tmax+ Tmin + R + RH + WS) outperformed other models (R2 = 0.71, RMSE = 2.365). Similarly, in SC2 RF regression outperformed SVM with model combination (GNDVI + Tmax+ Tmin + R + RH + WS) performed highest with R2 = 0.78, and lowest RMSE = 2.07, followed by (GNDVI + SPEI + RH + WS; R2 = 0.75). Interestingly, linear LASSSO also performed equally with RF with R2 = 0.77–0.73 in both scenarios. However, the output of this research recommends using SC2 for yield prediction in ML models. Overall, this research reveals the significance and potential of ML techniques for timely prediction of crop yield in different stages of crop growth that provide a solid foundation for food security in the region.

Mitochondria and cytochrome components released into the plasma of severe COVID-19 and ICU acute respiratory distress syndrome patients

IntroductionProteomic analysis of human plasma by LC–ESI–MS/MS has discovered a limited number of new cellular protein biomarkers that may be confirmed by independent biochemical methods. Analysis of COVID-19 plasma has indicated the re-purposing of known biomarkers that might be used as prognostic markers of COVID-19 infection. However, multiple molecular approaches have previously indicated that the SARS-COV2 infection cycle is linked to the biology of mitochondria and that the response to infections may involve the action of heme containing oxidative enzymes.MethodsHuman plasma from COVID-19 and ICU-ARDS was analyzed by classical analytical biochemistry techniques and classical frequency-based statistical approaches to look for prognostic markers of severe COVID-19 lung damage. Plasma proteins from COVID-19 and ICU-ARDS were identified and enumerated versus the controls of normal human plasma (NHP) by LC–ESI–MS/MS. The observation frequency of proteins detected in COVID-19 and ICU-ARDS patients were compared to normal human plasma, alongside random and noise MS/MS spectra controls, using the Chi Square (χ2) distribution.ResultsPCR showed the presence of MT-ND1 DNA in the plasma of COVID-19, ICU-ARDS, as well as normal human plasma. Mitochondrial proteins such as MRPL, L2HGDH, ATP, CYB, CYTB, CYP, NDUF and others, were increased in COVID-19 and ICU-ARDS plasma. The apparent activity of the cytochrome components were tested alongside NHP by dot blotting on PVDF against a purified cytochrome c standard preparation for H2O2 dependent reaction with luminol as measured by enhanced chemiluminescence (ECL) that showed increased activity in COVID-19 and ICU-ARDS patients.DiscussionThe results from PCR, LC–ESI–MS/MS of tryptic peptides, and cytochrome ECL assays confirmed that mitochondrial components were present in the plasma, in agreement with the established central role of the mitochondria in SARS-COV-2 biology. The cytochrome activity assay showed that there was the equivalent of at least nanogram amounts of cytochrome(s) in the plasma sample that should be clearly detectable by LC–ESI–MS/MS. The release of the luminol oxidase activity from cells into plasma forms the basis of a simple and rapid test for the severity of cell damage and lung injury in COVID-19 infection and ICU-ARDS.Graphical