Cross-validated Classification Accuracy Research Articles

Optimizing Lung Cancer Diagnosis with Machine Learning and Feature Selection Methods

Lung cancer is a prevalent disease, with nearly 238,000 new cases diagnosed in 2023. This study utilizes clinical predictors from a Kaggle dataset containing 309 observations across 15 variables to aid in lung cancer diagnosis. The variables include swallowing difficulty, peer pressure, gender, allergy, yellow fingers, anxiety, wheezing, alcohol consumption, chronic disease, chest pain, coughing, fatigue, smoking, age, and shortness of breath. The research aims to develop and compare various supervised machine learning models for classifying and predicting lung cancer, while also identifying key clinical tests and parameters using unsupervised statistical models. The dataset was divided into training and test sets, balanced, and preprocessed for unbiased training. Feature selection and machine learning models were applied to identify crucial predictors. The study explored tree models, logistic regression, Naïve Bayes, support vector machine (SVM), ensemble, neural network, and kernel models. Among these, the linear SVM achieved the highest accuracy of 93.75% with 5-fold cross-validation. However, it showed overfitting, with a lower test accuracy of 82.55%. The Gaussian Naïve Bayes model emerged as the optimal choice, providing consistent performance between validation and test cases. It achieved the highest cross-validation classification accuracy of 82.81% using only 9 variables: swallowing difficulty, peer pressure, gender, allergy, yellow fingers, anxiety, wheezing, alcohol consumption, and chronic disease. This model allows for effective training with fewer predictors without compromising classification

Sex estimation using Magnetic Resonance Imaging measurements of hand and foot in Egyptian population

BackgroundThe identification of skeletal remains begins with a sex evaluation since other biological profile elements, such as size and age, are sex-specific. The pelvis is the favored element for sex assessment since it is very sexually dimorphic.Aim of the studyDelivering hand and foot bones sex estimate criteria for Egyptian population identification was the goal of this work.Subject and methodsEighty-two Egyptian adult subjects (41 males and 41 females) aged ≥ 21 years old were subjected to MRI scan on the right hand and foot to assess 9 measurements: hand length, hand width, four measurements of middle metacarpal bone (length, breadth, head breadth and base breadth), first metatarsal bone length, first metatarsal bone width and (first metatarsal bone length x first metatarsal bone width).ResultsAll the measured parameters in the right hand and foot were higher in males than females. First metatarsal bone length x first metatarsal bone width in the foot and middle metacarpal bone breadth in the hand were the most sexually dimorphic parameters, with accuracy of 86.6 percent and 84.1 percent, respectively. In stepwise discriminant analysis, three of nine measurements were selected: First metatarsal bone length x first metatarsal bone width, middle metacarpal bone breadth and hand width for the sex prediction equation in the Egyptian population, with a cross-validated sex classification accuracy of 90.2%.ConclusionSex can be assessed from hand and foot metric parameters measured by MRI with high accuracy.

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.

Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet. Specifically, DA-TSnet extracts temporal and spatial features of EEG, while it is jointly supervised by task loss and domain loss. During training, DA-TSnet aims to maximize the domain loss while simultaneously minimizing the task loss. We conduct an offline analysis, simulate online experiments on a self-collected dataset of 85 subjects, and real online experiments on 22 subjects. DA-TSnet achieves a leave-one-subject-out (LOSO) cross-validation (CV) classification accuracy of 89.40% ± 9.96%, outperforming several state-of-the-art attention EEG decoding methods. In simulated online experiments, DA-TSnet achieves an outstanding accuracy of 88.07% ± 11.22%. In real online experiments, it achieves an average accuracy surpassing 86%. An end-to-end network framework does not rely on elaborate preprocessing and feature extraction steps, which saves time and human workload. Moreover, our framework utilizes domain-adversarial training neural network (DANN) to tackle the challenge posed by the high interindividual variability in EEG signals, which has significant reference value for handling other EEG signal decoding issues. Last, the performance of the DA-TSnet framework in offline and online experiments underscores its potential to facilitate more reliable applications.

Classification of lung pathologies in neonates using dual-tree complex wavelet transform

IntroductionUndiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis.MethodsWe propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class.ResultsUsing DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%.ConclusionOur work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.

Classification of building complex for the large-scale construction of distributed photovoltaics in urban buildings

The large-scale development of urban building photovoltaics (PV) has become an important avenue for cities to achieve building energy conservation, emission reduction, and carbon neutrality. Assessing the solar energy potential of urban buildings plays a crucial role in the installation and overall planning of urban building PV systems. However, due to the vast number and diverse types of urban buildings, individual building calculations can be both labor-intensive and inefficient. Therefore, batch processing of buildings after classification is a feasible approach. Previous assessments of solar energy potential in building clusters mainly focused on typical clusters or standard geometric areas, but all urban building types were covered. Based on this, this paper proposes a method for defining Building Complexes with a Single Land Use Nature (BCSLUN) by combining geographical distribution and urban land use characteristics. Furthermore, population density, building density, compactness, and land use characteristics are selected as features, and a method that combines K-means clustering and random forest classification is proposed to complete the classification of building complexes. Taking the central urban area of Xi'an, China as an example, 5,804 building complexes are classified into four building types. The cross-validation classification accuracy is 0.99, and the Kappa coefficient is 0.94. Based on Rhino modeling, the global solar radiation on the surface of each building complex was calculated. The annual surface global radiation of the four building types is 11,442.73 MWh, 2,734.71 MWh, 13,923.81 MWh, and 67,215 MWh. The violin-box plot shows a significant classification effect, which proves the rationality of the proposed classification method. Finally, a potential map based on solar radiation from urban building surfaces is generated. Building complex classification can quickly and accurately estimate the solar energy potential of urban buildings, providing decision-making support for the large-scale construction of urban building PV. Moreover, this method can be easily applied to other cities.

Identification of Important Factors in the Diagnosis of Breast Cancer Cells Using Machine Learning Models and Principal Component Analysis

Breast cancer (BC) is now identified as a disease with a significant impact on morbidity and mortality that is growing and widespread worldwide. This study uses a publicly available clinical dataset of 699 patients from the University of Wisconsin with 9 variables: (1) clump thickness, (2) uniformity of cell size, (3) uniformity of cell shape, (4) marginal adhesion, (5) single epithelial cell size, (6) bare nuclei, (7) bland chromatin, (8) normal nucleoli, and (9) mitoses. This dataset has been used for many studies in the past to pinpoint critical factors in patient diagnosis. Here, we use this data to ensure its unbiasedness and accuracy. We then apply principal component analysis and machine learning models to identify factors in diagnosing a malignant or benign tumor. We investigate and compare the classification accuracy of different machine learning models, including tree, linear discriminant, quadratic discriminant, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble, neural network, and kernel. The best models that can achieve the highest accuracy are medium Gaussian SVM, coarse Gaussian SVM, and cosine KNN, with an accuracy of 96.5%. The principal component analysis method is then performed to identify crucial components and build an accurate model with fewer parameters. The medium Gaussian SVM has the highest cross-validation classification accuracy of 96.98% and requires only three predictors: normal nucleoli, bare nuclei, and cell size uniformity.

Ensemble Learning for Alcoholism Classification Using EEG Signals

Excessive drinking is a major risk factor that leads to many health complications. The diagnosis of alcoholism is challenging, especially when the standard diagnostic tests rely on blood tests and questionnaires that are subjective to the patient and the examiner. The study’s major goal is to find new EEG classification methods to improve past findings and construct a robust EEG classification algorithm to generate accurate predictions with explainable results. The EEG records were examined from two different perspectives and combined with an ensemble of classification models. The first approach was temporal data, and the second was images derived from the original signals. Using fast Fourier transform (FFT) and independent component analysis (ICA), we convert 64-channel temporal data into images along with applying the Symbolic Aggregate approXimation (SAX) technique. Our model combines input data in tabular, temporal, and image formats with an ensemble of linear neural networks, long short-term memory (LSTM), and efficient-net classification models. We have evaluated our method using a publicly available dataset for EEG classification of alcoholic and non-alcoholic subjects. Overall, our algorithm’s highest cross-validation classification accuracy is 85.52% compared to the state-of-the-art EEG-NET’s accuracy of 81.19%.

A Model for EEG-Based Emotion Recognition: CNN-Bi-LSTM with Attention Mechanism

Emotion analysis is the key technology in human–computer emotional interaction and has gradually become a research hotspot in the field of artificial intelligence. The key problems of emotion analysis based on EEG are feature extraction and classifier design. The existing methods of emotion analysis mainly use machine learning and rely on manually extracted features. As an end-to-end method, deep learning can automatically extract EEG features and classify them. However, most of the deep learning models of emotion recognition based on EEG still need manual screening and data pre-processing, and the accuracy and convenience are not high enough. Therefore, this paper proposes a CNN-Bi-LSTM-Attention model to automatically extract the features and classify emotions based on EEG signals. The original EEG data are used as input, a CNN and a Bi-LSTM network are used for feature extraction and fusion, and then the electrode channel weights are balanced through the attention mechanism layer. Finally, the EEG signals are classified to different kinds of emotions. An emotion classification experiment based on EEG is conducted on the SEED dataset to evaluate the performance of the proposed model. The experimental results show that the method proposed in this paper can effectively classify EEG emotions. The method was assessed on two distinctive classification tasks, one with three and one with four target classes. The average ten-fold cross-validation classification accuracy of this method is 99.55% and 99.79%, respectively, corresponding to three and four classification tasks, which is significantly better than the other methods. It can be concluded that our method is superior to the existing methods in emotion recognition, which can be widely used in many fields, including modern neuroscience, psychology, neural engineering, and computer science as well.

Data fusion and multivariate analysis for food authenticity analysis

A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.

Adversarial Learning Improves Vision-Based Perception from Drones with Imbalanced Datasets

This work proposes a vision-based perception algorithm that combines image-processing-based detection and tracking of aerial objects with convolutional neural networks (CNNs) integrated for classification of general aviation aircraft, multirotor small uncrewed aerial systems (SUAS), fixed-wing SUAS, and birds to enable improved onboard avoidance algorithm decision making. Furthermore, we integrate adversarial learning during the training of the CNNs and evaluate performance with class balanced and imbalanced datasets because this maximizes the utility of resource-expensive flight experiments to collect aviation datasets. We compare our proposed CNN with adversarial learning (CNN+ADVL) model with a state-of-the-art CNN as well as a you only look once (YOLO, v4) model retrained (YOLO v4 aircraft) on the same data. The CNN+ADVL trained on the imbalanced dataset achieves the highest 10-fold cross-validation classification accuracy of 76.2% for aircraft and birds for all ranges while achieving 87.0% aircraft classification accuracy, meeting proposed self-assurance separation distances derived from Federal Aviation Administration (FAA) guidelines. In comparison, the CNNs achieved 74.4% 10-fold cross-validation classification accuracy for aircraft and birds as well as 83.4% accuracy for the aircraft, meeting proposed self-assurance separation distances derived from FAA guidelines. Furthermore, we demonstrate that the integration of adversarial learning improves the classification performance for the perception of aerial objects using a class imbalanced dataset.

Predicting online problem gambling treatment discontinuation: New evidence from cross-validated models.

There are tens of millions of problem gamblers in the world, many of whom either do not seek treatment or fail to commit to it. Dropout rates are high, and not enough is known about factors predicting treatment adherence. We focus on an online cognitive behavioral therapy program for severe problem gambling to determine the likelihood of treatment discontinuation at three different treatment phases: pretreatment, before halfway, and before the end of the program. Participants were Finnish adults (N = 1,139, 670 males, Mage = 34.5) registered in the program between 2019 and 2021. Using logistic regression and five-fold cross-validated naïve Bayes classification, we predicted discontinuation with demographic-, psychometric-, and other gambling-related variables, including the quality of one's social relations, time spent on the waiting list, and experienced readiness to behavioral change. The models had acceptable predictive ability (area under the curve [AUC] values from .69 to .745; cross-validated balanced classification accuracy = 63.2%). In logistic regressions, treatment discontinuation was prominently associated with younger age (p = .008), lower education (p < .001), not being ready to change gambling behavior (p < .001), problem gambling severity (p < .0001), longer time spent on the treatment waiting list (p < .0001), and fewer close social relationships (p < .001). We found significant new real-world evidence on factors statistically predicting treatment discontinuation, which is crucial when existing programs are modified to better serve those in need. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

48. MICRO - The Microsurgical Index for Complication Risk and Outcomes

PURPOSE: Free tissue transfer (FTT) is determined by a multitude of factors. However, no tool exists to quantify risk for complications following FTT. This study developed the Microsurgical Index for Complication Risk and Outcomes (MICRO) to address this. METHODS: Patients were queried from the 2003-2015 MarketScan Databases with CPT codes for FTT requiring microsurgical anastomosis. ICD9 codes were used to query comorbidity and 90-day postoperative complication data for each patient. The Charlson and Elixhauser Comorbidity Indexes were constructed for each patient. The MICRO was then constructed with forward stepwise selection from Elixhauser comorbidities and domain expert input. Indexes were used as covariates in multivariate logistic regression models with patient age, sex, and flap tissue type to predict complications following FTT. The area under the receiver operating curve (AUC) and five-fold cross validated classification accuracy were determined. RESULTS: 5,595 patients were included. The final MICRO consists of seven variables (Charlson - nineteen; Elixhauser - thirty). It had the highest AUC (0.60) and accuracy (60.4%) when predicting complications. CONCLUSION: The MICRO outperforms available comorbidity indexes at predicting complications following FTT with far fewer variables. Future studies could augment the MICRO with surgeon, donor-site, and recipient-site data to create a sharper risk tool for the plastic surgeon.

Genetics Information with Functional Brain Networks for Dementia Classification

Mild cognitive impairment (MCI) precedes the Alzheimer’s disease (AD) continuum, making it crucial for therapeutic care to identify patients with MCI at risk of progression. We aim to create generalized models to identify patients with MCI who advance to AD using high-dimensional-data resting state functional magnetic resonance imaging (rs-fMRI) brain networks and gene expression. Studies that integrate genetic traits with brain imaging for clinical examination are limited, compared with most current research methodologies, employing separate or multi-imaging features for disease prognosis. Healthy controls (HCs) and the two phases of MCI (convertible and stable MCI) along with AD can be effectively diagnosed using genetic markers. The rs-fMRI-based brain functional connectome provides various information regarding brain networks and is utilized in combination with genetic factors to distinguish people with AD from HCs. The most discriminating network nodes are identified using the least absolute shrinkage and selection operator (LASSO). The most common brain areas for nodal detection in patients with AD are the middle temporal, inferior temporal, lingual, hippocampus, amygdala, and middle frontal gyri. The highest degree of discriminative power is demonstrated by the nodal graph metrics. Similarly, we propose an ensemble feature-ranking algorithm for high-dimensional genetic information. We use a multiple-kernel learning support vector machine to efficiently merge multipattern data. Using the suggested technique to distinguish AD from HCs produced combined features with a leave-one-out cross-validation (LOOCV) classification accuracy of 93.07% and area under the curve (AUC) of 95.13%, making it the most state-of-the-art technique in terms of diagnostic accuracy. Therefore, our proposed approach has high accuracy and is clinically relevant and efficient for identifying AD.

Evaluation of performance of metal oxide electronic nose for detection of aflatoxin in artificially and naturally contaminated maize

Aflatoxins are of great concern for food safety and security due to their impact on human health and the agriculture economy in developing countries. This study aimed to evaluate the potential use of a field portable metal oxide sensors based electronic nose to detect aflatoxin contamination in Kenyan maize varieties that were artificially and naturally infected with Aspergillus flavus. Mutual information was used to select features from the electronic nose sensor signals for classification of the samples. The effectiveness of selected features to discriminate between the different classes of samples was evaluated by support vector machines and k-nearest neighbour with leave-one-out cross-validation. External validation was also conducted by analysing samples naturally contaminated with A. flavus using the classification model generated with samples that had been artificially inoculated with the aflatoxigenic A. flavus. Cross-validated classification accuracies ranged from 72% to 88% for maize samples artificially inoculated with A. flavus and 61–86% for samples naturally infected with A. flavus. Classification accuracies achieved with external validation for maize samples naturally contaminated with aflatoxins ranged from 58% to 78% and were relatively consistent with accuracies obtained from internal validation. Results suggest that the electronic nose could be a promising cost-effective screening method to detect aflatoxin contamination in maize.

Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features

Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer’s disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADNI database to investigate prediction of a trajectory towards AD in a group of patients defined as MCI at a baseline examination. One group remained stable over time (sMCI, n = 357) and one converted to AD (cAD, n = 321). By running two independent classification methods within a machine learning framework, with cognitive function, hippocampal volume and genetic APOE status as features, we obtained a cross-validation classification accuracy of about 70%. This level of accuracy was confirmed across different classification methods and validation procedures. Moreover, the sets of misclassified subjects had a large overlap between the two models. Impaired memory function was consistently found to be one of the core symptoms of MCI patients on a trajectory towards AD. The prediction above chance level shown in the present study should inspire further work to develop tools that can aid clinicians in making prognostic decisions.

On the reliability of deep learning-based classification for Alzheimer's disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation.

Structural changes in the brain due to Alzheimer’s disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer’s Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.

Seizure-related differences in biosignal 24-h modulation patterns

A seizure likelihood biomarker could improve seizure monitoring and facilitate adjustment of treatments based on seizure risk. Here, we tested differences in patient-specific 24-h-modulation patterns of electrodermal activity (EDA), peripheral body temperature (TEMP), and heart rate (HR) between patients with and without seizures. We enrolled patients who underwent continuous video-EEG monitoring at Boston Children’s Hospital to wear a biosensor. We divided patients into two groups: those with no seizures and those with at least one seizure during the recording period. We assessed the 24-h modulation level and amplitude of EDA, TEMP, and HR. We performed machine learning including physiological and clinical variables. Subsequently, we determined classifier performance by cross-validated machine learning. Patients with seizures (n = 49) had lower EDA levels (p = 0.031), EDA amplitudes (p = 0.045), and trended toward lower HR levels (p = 0.060) compared to patients without seizures (n = 68). Averaged cross-validated classification accuracy was 69% (AUC-ROC: 0.75). Our results show the potential to monitor and forecast risk for epileptic seizures based on changes in 24-h patterns in wearable recordings in combination with clinical variables. Such biomarkers might be applicable to inform care, such as treatment or seizure injury risk during specific periods, scheduling diagnostic tests, such as admission to the epilepsy monitoring unit, and potentially other neurological and chronic conditions.

Gas chromatography-ion mobility spectrometric discrimination of trunk borer infested Platycladus orientalis using a novel topographic segmentation strategy

Gas chromatography coupled with ion mobility spectrometry (GC-IMS), a volatile analysis technique, has been widely used in the agricultural field over the past decade. However, complex three-dimensional (3D) fingerprint, including millions of data elements, causes significant challenges for its data process. This study proposed a novel topographic segmentation-based strategy, which could automatically extract the maxima and volume features from individual peaks. Compared with the manual marker selection approach, its efficiency was identified using different classification and prediction models based on trunk borer infested Platycladus orientalis samples. The grid search-support vector machine (GS-SVM) classifier combined with a topographic segmentation strategy could correctly classify at least 93.67% of P. orientalis samples into their corresponding infestation stages. The volume feature performed best, and its cross-validation classification accuracy could reach 97.05%. The PLSR prediction model based on the volume feature got the most satisfactory result, whose Rc2 = 0.9624 and RMSEC = 6.328 in calibration set and Rp2 = 0.9056 and RMSEP = 9.926 in validation set. In a word, our proposed topographic segmentation strategy had enough capability to extract local maxima and volume features of peaks from GC-IMS fingerprints. The GC-IMS-based approach combined with chemometric methods had the potential to discriminate the infestation stages of trunk borer damaged P. orientalis plants.

Assessment of variations in metal concentrations of the Ganges River water by using multivariate statistical techniques

Worldwide, metal pollution of river waters is a societal problem as human civilization thrives on the bank of rivers, warrants identification of sources and evaluation of possible toxicity to formulate strategies for pollution abatement and sustainable management of water resources. The present study was conducted to document the metal concentrations of surface water samples along the Ganges River using multivariate statistics and to generate information for comparison of water quality. Ba, Cu, Fe, Li, Na and Sr showed significant variations (P < 0.05) both at spatial and temporal scales. Contamination factor and metal index demonstrated that the water in the middle segment stretching from Kanpur to Varanasi is more contaminated and vulnerable to anthropogenic stress. Principal component analysis (PCA) generated four principal components (PCs) with eigenvalues > 1 and these PCs explain the 87.4% of variation in metal concentration. The first two PCs accounted for 52.2% of the total variance and showed a strong correlation with Fe, Li, Mn, Na and Mg. The hierarchical cluster analysis (HCA) shows three clusters based on seasonal sampling at the four locations along the Ganges River. The first two discriminant functions (DFs) explained 99.7% of the variance in metal concentrations among Narora, Kanpur, Varanasi and Bhagalpur sampling locations. Mn, Sr and Na were most significant in the distinction of water samples to their original location with a cross-validation classification accuracy of 63.9%. In addition to long-term monitoring programs, the information generated on the variations of metal concentrations can be used to solve the problems of metal pollution of the Ganges River water.

Population-Inclusive Assigned-Sex-at-Birth Estimation from Skull Computed Tomography Scans

Methods for estimating assigned, binary sex at birth from skeletonized remains have primarily been developed for specific population groups in the U.S. (e.g., African American, European American, Hispanic) and, thus, inherently rely on ancestry estimation as a foundational component for constructing the biological profile. However, ongoing discussions in forensic anthropology highlight pressing issues with ancestry estimation practices. Therefore, this research provides population-inclusive assigned-sex estimation models for cases where ancestry is not estimated or is truly unknown. The study sample (n = 431) includes 3D volume-rendered skull computed tomography scans from the novel New Mexico Decedent Image Database of African, Asian, European, Latin, and Native Americans. Five standard nonmetric traits were scored, and eighteen standard measurements were obtained. Binary logistic regressions and discriminant function analyses were employed to produce models and classification accuracies, and intraobserver reliability was assessed. The population-inclusive nonmetric and metric models produced cross-validated classification accuracies of 81.0–87.0% and 86.7–87.0%, respectively, which did not differ significantly from the accuracy of most population-specific models. Moreover, combined nonmetric and metric models increased accuracy to 88.8–91.6%. This study indicates that population-inclusive assigned-sex estimation models can be used instead of population-specific models in cases where ancestry is intentionally not estimated, given current concerns with ancestry estimation.