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Diagnosing Alzheimer's disease (AD) remains a significant challenge, particularly in effectively identifying individuals in the early (EMCI) and late (LMCI) stages of Mild Cognitive Impairment (MCI) within the normal control subjects (CN). Leveraging the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and relevant datasets, our aim is to establish a 4-way framework for multi-class diagnosis. Linear Discriminant Analysis (LDA), often coupled with Principal Component Analysis (PCA), has conventionally served as a method for supervised classification. However, this paper introduces an alternative approach using Pearson's correlation coefficient (PCC) instead of PCA. We integrate the optimal LDA subspace with the PCC method, primarily to address the singularity issue that arises when dealing with an underdetermined dataset. Our methodology comprises three main steps. Firstly, we engage in the preprocessing of 237 Diffusion Tensor and Magnetic Resonance brain images to map brain connectivity and extract connections within and between hemispheres. Secondly, we calculate correlation coefficients between features and classes, subsequently constructing empirical cumulative distribution functions (ECDF). Features exceeding a predetermined percentile in the ECDF, guaranteeing the non-singularity of the within-class variance matrix, are subsequently chosen and assessed using a primary classifier. The top k features, linked to the highest classification accuracy, are then mapped into the LDA space through 100 iterations of five-fold Cross-Validation. Following each trial, we assess the performance of six machine learning algorithms, selecting the Logistic Regression classifier to gauge the reliability of our proposed method. As a result, we observed a significant improvement in average accuracy, achieving a performance of 65.46% ± 1.94%, compared to the commonly used PCA+LDA approach, which achieved 50.71% ± 2.1%. Notably, our work achieved 100% accuracy in diagnosing the LMCI class, surpassing other methods. Furthermore, in a separate experiment conducted within and between hemispheres datasets, we identified connectivity between hemispheres as a pivotal biomarker for disease diagnosis in a medical context. [ABSTRACT FROM AUTHOR]

Plant tissues harbor abundant endophytes, which are crucial for plant growth. Endophytes present in Alsophila spinulosa, which is enriched with medicinal components, have not been isolated and characterized yet. Here we employed meta-amplicon sequencing to identify endophytic species and examined their diversity in the leaves, petioles, roots and stems of A. spinulosa. Our findings revealed 1,247 operational taxonomic units (OTUs) for endophytic bacteria across 210 species and 476 OTUs for endophytic fungi across 222 species. Alpha diversity analysis showed the highest endophytic bacterial diversity in A. spinulosa roots, whereas fungal diversity was similar across the leaf, petiole and root tissues. Fungal diversity in the leaves and petioles was markedly higher than that in the stems. Furthermore, beta diversity analysis revealed similarities in the endophytic bacterial and fungal compositions between the leaves and petioles, whereas the compositions in roots and stems considerably differed from those in the leaves and petioles. At the genus level, the predominant endophytic bacteria were Methylobacterium-Methylorubrum and Pseudomonas, whereas the predominant endophytic fungi were Cutaneotrichosporon and Pseudofabraea. Linear discriminant analysis effect size revealed characteristic endophytic bacterial genera specific to each tissue type and characteristic endophytic fungal genera specifically in the leaves, petioles and roots. The co-occurrence network analysis indicated that the complexity of endophyte networks was the highest in the leaves and the lowest in the stems of A. spinulosa. Overall, this study elucidates the distribution patterns of endophytes in A. spinulosa across various tissues, offering valuable microbial resources for the development of natural products for medicinal application. [ABSTRACT FROM AUTHOR]

Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive. Near infrared (NIR) and ultraviolet–visible (UV–Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV–Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration. Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV–Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v. With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R2CV's up to 0.8799 and RMSECV's lower than 3 ml/100 ml for NIR spectra and R2CV's up to 0.81 and RMSECV's lower than 4 ml/100 ml for UV–Vis spectra. NIR spectra produced better models as compared to UV–Vis spectra. [ABSTRACT FROM AUTHOR]

Broadband Coherent Anti-Stokes Raman Scattering (BCARS) is a valuable spectroscopic imaging tool for visualizing cellular structures and lipid distributions in biomedical applications. However, the inevitable biological changes in the samples (cells/tissues/lipids) introduce spectral variations in BCARS data and make analysis challenging. In this work, we conducted a systematic study to estimate the biological variance in BCARS data of two commonly used cell lines (HEK293 and HepG2) in biomedical research. The BCARS data were acquired from two different experimental setups (Leibniz Institute of Photonics Technology (IPHT) in Jena and Politecnico di Milano (POLIMI) in Milano) to evaluate the reproducibility of results. Also, spontaneous Raman data were independently acquired at POLIMI to validate those results. First, Kramers–Kronig (KK) algorithm was utilized to retrieve Raman-like signals from the BCARS data, and a pre-processing pipeline was subsequently used to standardize the data. Principal component analysis – Linear discriminant analysis (PCA-LDA) was performed using two cross-validation (CV) methods: batch-out CV and 10-fold CV. Additionally, the analysis was repeated, considering different spectral regions of the data as input to the PCA-LDA. Finally, the classification accuracies of the two BCARS datasets were compared with the results of spontaneous Raman data. The results demonstrated that the CH band region (2770–3070 cm−1) and spectral data in the 1500–1800 cm−1 region have significantly contributed to the classification. A maximum of 100% balanced accuracies were obtained for the 10-fold CV for both BCARS setups. However, in the case of batch-out CV, it is 92.4% for the IPHT dataset and 98.8% for the POLIMI dataset. This study offers a comprehensive overview for estimating biological variance in biomedical applications. The insights gained from this analysis hold promise for improving the reliability of BCARS measurements in biomedical applications, paving the way for more accurate and meaningful spectroscopic analyses in the study of biological systems. [ABSTRACT FROM AUTHOR]

AbstractObjectivesPatients and methodsResultsConclusionNeuropsychological tests (NPTs) are standard tools for assessing cognitive function. These tools can evaluate the cognitive status of a subject, which can be time-consuming and expensive for interpretation. Therefore, this paper aimed to optimize the systematic NPTs by machine learning and develop new classification models for differentiating healthy controls (HC), mild cognitive impairment, and Alzheimer’s disease dementia (ADD) among groups of subjects.A total dataset of 14,926 subjects was obtained from the formal 46 NPTs based on the Seoul Neuropsychological Screening Battery (SNSB). The statistical values of the dataset included an age of 70.18 ± 7.13 with an education level of 8.18 ± 5.50 and a diagnosis group of three; HC, MCI, and ADD. The dataset was preprocessed and classified in two- and three-way machine-learning classification from scikit-learn (www.scikit-learn.org) to differentiate between HC versus MCI, HC versus ADD, HC versus Cognitive Impairment (CI) (MCI + ADD), and HC versus MCI versus ADD. We compared the performance of seven machine learning algorithms, including Naïve Bayes (NB), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, and linear discriminant analysis (LDA). The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predictive value (NPV), area under the curve (AUC), confusion matrixes, and receiver operating characteristic (ROC) were obtained from each model based on the test dataset.The trained models based on 29 best-selected NPT features were evaluated, the model with the RF algorithm yielded the best accuracy, sensitivity, specificity, PPV, NPV, and AUC in all four models: HC versus MCI was 98%, 98%, 97%, 98%, 97%, and 99%; HC versus ADD was 98%, 99%, 96%, 97%, 98%, and 99%; HC versus CI was 97%, 99%, 92%, 97%, 97%, and 99% and HC versus MCI versus ADD was 97%, 96%, 98%, 97%, 98%, and 99%, respectively, in predicting of cognitive impairment among subjects.According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses. [ABSTRACT FROM AUTHOR]

Background: The application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle's milk production into low ( 4500 kg), medium (4500–7500 kg), and high ( 7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD). Results: The comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA's 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA. Conclusion: The findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets. [ABSTRACT FROM AUTHOR]

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n =380) and colorectal adenomas (CRA, n =110) against generally healthy participants (n =2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (microbial taxa, functional genes, enzymatic reactions, and metabolic pathways) as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions, and metabolic pathways) associated with CRC were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies. [ABSTRACT FROM AUTHOR]

Background: Plant-associated microorganisms can be found in various plant niches and collectively comprise the plant microbiome. The plant microbiome assemblages have been extensively studied, primarily in model species. However, a deep understanding of the microbiome assembly associated with plant health is still needed. Ginger rhizome rot has been variously attributed to multiple individual causal agents. Due to its global relevance, we used ginger and rhizome rot as a model to elucidate the metabolome-driven microbiome assembly associated with plant health. Results: Our study thoroughly examined the biodiversity of soilborne and endophytic microbiota in healthy and diseased ginger plants, highlighting the impact of bacterial and fungal microbes on plant health and the specific metabolites contributing to a healthy microbial community. Metabarcoding allowed for an in-depth analysis of the associated microbial community. Dominant genera represented each microbial taxon at the niche level. According to linear discriminant analysis effect size, bacterial species belonging to Sphingomonas, Quadrisphaera, Methylobacterium-Methylorubrum, Bacillus, as well as the fungal genera Pseudaleuria, Lophotrichus, Pseudogymnoascus, Gymnoascus, Mortierella, and Eleutherascus were associated with plant health. Bacterial dysbiosis related to rhizome rot was due to the relative enrichment of Pectobacterium, Alcaligenes, Klebsiella, and Enterobacter. Similarly, an imbalance in the fungal community was caused by the enrichment of Gibellulopsis, Pyxidiophorales, and Plectosphaerella. Untargeted metabolomics analysis revealed several metabolites that drive microbiome assembly closely related to plant health in diverse microbial niches. At the same time, 6-({[3,4-dihydroxy-4-(hydroxymethyl)oxolan-2-yl]oxy}methyl)oxane-2,3,4,5-tetrol was present at the level of the entire healthy ginger plant. Lipids and lipid-like molecules were the most significant proportion of highly abundant metabolites associated with ginger plant health versus rhizome rot disease. Conclusions: Our research significantly improves our understanding of metabolome-driven microbiome structure to address crop protection impacts. The microbiome assembly rather than a particular microbe's occurrence drove ginger plant health. Most microbial species and metabolites have yet to be previously identified in ginger plants. The indigenous microbial communities and metabolites described can support future strategies to induce plant disease resistance. They provide a foundation for further exploring pathogens, biocontrol agents, and plant growth promoters associated with economically important crops. 2Spd16sRC6jnqdSe2SRbtQ Video Abstract [ABSTRACT FROM AUTHOR]

Background: The role of microbes in diseases, especially cancer, has garnered significant attention. However, research on the oral microbiota in oral potentially malignant disorders (OPMDs) remains limited. Our study investigates microbial communities in OPMDs. Materials and methods: Oral biopsies from19 oral leukoplakia (OLK) patients, 19 proliferative verrucous leukoplakia (PVL) patients, 19 oral lichen planus (OLP) patients, and 19 oral lichenoid lesions (OLL) patients were obtained. 15 SCC specimens were also collected from PVL patients. Healthy individuals served as controls, and DNA was extracted from their paraffin-embedded tissues. 2bRAD-M sequencing generated taxonomic profiles. Alpha and beta diversity analyses, along with Linear Discriminant Analysis effect size analysis, were conducted. Results: Our results showed the microbial richness and diversity were significantly different among groups, with PVL-SCC resembling controls, while OLK exhibited the highest richness. Each disease group displayed unique microbial compositions, with distinct dominant bacterial species. Noteworthy alterations during PVL-SCC progression included a decline in Fusobacterium periodonticum and an elevation in Prevotella oris. Conclusions: Different disease groups exhibited distinct dominant bacterial species and microbial compositions. These findings offer promise in elucidating the underlying mechanisms of this disease. [ABSTRACT FROM AUTHOR]

Controlling the quality and health of foodstuffs is of great importance. The quality of foods like garlic is strongly influenced by the conditions of processing. Fungal infection is one of the most common hazards of garlic productivity that can affect its processing as well. This research aimed to use the E-Nose to investigate the aroma of garlic as a quality control factor influenced by different treatments such as type of processing, type of fungal infection, and time elapsed since the date of inoculation. The data was investigated and categorized through different methods such as principal component analysis (PCA), linear discriminant analysis (LDA), Support vector machine (SVM), and backpropagation neural network (BPNN). The Index of deterioration toughness increased during the monitoring period. In the analysis of the data related to the unprocessed whole (UW), dried slices (DS), garlic powder (PO), and garlic tablet (TA), the PCA included 55%, 75%, 47%, and 53% of the data, respectively. The LDA was able to classify the aroma of UW, DS, PO, and TA samples based on the TFI treatment with an accuracy of 90%, 93.33%, 88.89%, and 60%, respectively. Also, the BPNN classified the aromas of UW, DS, PO, and TA samples based on the TEI treatment with an accuracy of 90%, 95.6%, 72.2%, and 82.2%, respectively. The results revealed that the aroma alteration can be used as a comprehensive factor in the quality control of processed products. As well, the type of processing had significant effects on the severity of decay caused by fungal infection. [ABSTRACT FROM AUTHOR]

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm−1, 1013 cm−1, 1002 cm−1, 1004 cm−1, and 1011 cm−1 in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy. [ABSTRACT FROM AUTHOR]

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification. [ABSTRACT FROM AUTHOR]

The ability to differentiate species is not adequate for modern forage breeding programs. The measurement of persistence is currently a bottleneck in the breeding system that limits the throughput of cultivars to the marketplace and prevents it from being selected as a trait. The use of hyperspectral data obtained through remote sensing offers the potential to reduce guesswork by identifying the distribution of pasture species, but only if such data alone can distinguish the subtle differences within species, i.e., cultivars. The implementation of this technology faces many challenges due to the spectral and temporal variability of species. To understand the spectral variability between and within species groups, differentiation using hyperspectral data from monoculture plots of turf species was utilized. Spectral data were collected over a year using an ASD FieldSpec® and canopy pasture probe (CAPP). The plots consisted of monocultures of various species, and cultivars (a total of 10 plots). Linear discriminant analysis (LDA) was conducted on the full spectrum and reduced band data. This technique successfully differentiated the species with high accuracy (>98%). We demonstrate the potential of hyperspectral data and analysis techniques to accurately separate differences down to cultivar level. We also show that diurnal variation is measurable in the spectra but does not preclude differentiation. [ABSTRACT FROM AUTHOR]

Objective. To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression. Approach. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression. Main Results. Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment approaches available at the time. LDA scores were also able to track progression effectively, although a multifrequency reactance-based measure also performed very well in this context. Significance. EIM enhanced with ML promises to be effective for providing effective diagnosis and tracking children and adults with SMA treated with currently available therapies. The normative trends identified here may also inform future applications of the technology in very young children. The basic analyses applied here could also likely be applied to other neuromuscular disorders characterized by muscle atrophy. [ABSTRACT FROM AUTHOR]

The Eurasian Eagle-Owl (Bubo bubo) shows reversed sexual size dimorphism. We aimed to evaluate morphological size differences between the sexes of the smallest eagle-owl subspecies (B. b. kiautschensis), for which little morphometric information is available, and to develop a discriminant function for sex using a minimum number of morphometric parameters. We compared nine morphological measurements of 41 genetically or anatomically sexed eagle-owls admitted to a Korean wildlife rescue center and used discriminant function analysis of 29 birds (17 female, 12 male) with complete measurement data of nine biometrics. We developed four equations using three measurements (total length, head length, and weight) that had 86–96% accuracy for sexing Korean Eagle-Owls. The subspecies-specific morphometric data and discriminant functions we developed can help fill the current knowledge gap regarding the morphological characteristics of this little-known subspecies, benefiting researchers, wildlife managers, and veterinarians who work with or study this apex predator in the Korean forest ecosystem. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) and schizophrenia (SZ) share traits, especially in social skills and negative symptoms, and to a lesser degree positive symptoms. Differential diagnosis can be challenging and discerning expressive and experiential negative symptoms may provide knowledge with potential diagnostic and functional relevance that can guide treatment. Two exploratory factor analyses (EFA) were conducted to reveal the underlying dimensions of negative and positive symptoms using the Positive and Negative Syndrome Scale (PANSS), the Scale for the Assessment of Positive Symptoms & Negative Symptoms (SAPS/SANS) and the Autism Diagnostic Observation Schedule-Generic (ADOS-G). Three factors emerged from the negative symptom EFA (70.5 % variance): NF1) Expressive Negative; NF2) Experiential Negative; and NF3) Preoccupation, Absorption & Expressive Affective Flattening. Three positive factors emerged (68.6 % variance): PF1) Hallucinations-Delusions; PF2) Grandiosity; and PF3) Thought Disorder-ADOS positive Symptoms. SZ showed higher PF1 scores, and ASD had higher PF3 scores. No differences between groups were observed in the negative factors. Across groups, all negative factors were inversely associated with quality of life. Only NF1 and NF2 and PF1 were detrimentally related to social functioning. A discriminant function analysis using all factors classified correctly 84.4 % of participants, with PF1, NF1 followed by NF2 being the best predictors of diagnosis. Expressive negative followed by Experiential negative symptoms are of diagnostic value independent of and beyond SZ-related positive symptoms and are related with detrimental functioning. Findings confirm the need to distinctively target negative symptoms, and specific SZ-related and ASD-related positive symptoms, and especially the use of several assessment tools for diagnostic classification. [ABSTRACT FROM AUTHOR]

This study employs multivariate analysis within a multifactorial design to investigate the author-translator style of distinguished Chinese author Eileen Chang. Using principal component analysis (PCA) and linear discriminant analysis (LDA), it examines Chang's stylistic multiverse across three literary types: original writing, self-translation, and regular translation of others' works. The findings reveal diverse patterns of stylistic convergence and divergence among these literary types, analyzed using the metrics of the most frequent words: 200-MFW, 20-MFW, and 5-MFW. These patterns are shaped by the perspectives introduced by two explanatory factors, translation status (original versus translated) and translation type (self-translation versus regular translation), and influenced by Chang's literary ambition, translation philosophy, and patronage. The study contributes methodologically to author-translator style research by demonstrating the effectiveness of multifactorial and multivariate approaches, which not only provide profound insights into datasets but facilitate methodological triangulation. [ABSTRACT FROM AUTHOR]

Geometric morphometrics (GMM) have been applied to understand morphological variation in biological structures. However, research studying cortical bone through geometric histomorphometrics (GHMM) is scarce. This research aims to develop a landmark‐based GHMM protocol to depict osteonal shape variation in the femoral diaphysis, exploring the role of age and biomechanics in bone microstructure. Proximal, midshaft, and distal anatomical segments from the femoral diaphysis of six individuals were assessed, with 864 secondary intact osteons from eight periosteal sampling areas being manually landmarked. Observer error was tested using Procrustes ANOVA. Average osteonal shape and anatomical segment‐specific variation were explored using principal component analysis. Osteon shape differences between segments were examined using canonical variate analysis (CVA). Sex differences were assessed through Procrustes ANOVA and discriminant function analysis (DFA). The impact of osteonal size on osteonal shape was investigated. High repeatability and reproducibility in osteon shape landmarking were reported. The average osteon shape captured was an elliptical structure, with PC1 reflecting more circular osteons. Significant differences in osteon shape were observed between proximal and distal segments according to CVA. Osteon shape differed between males and females, with DFA showing 52% cross‐validation accuracies. No effect of size on shape was reported. Osteonal shape variation observed in this study might be explained by the elderly nature of the sample as well as biomechanical and physiological mechanisms playing different roles along the femoral diaphysis. Although a larger sample is needed to corroborate these findings, this study contributes to the best of our knowledge on human microanatomy, proposing a novel GHMM approach. [ABSTRACT FROM AUTHOR]

The exponential growth of IoT devices and their interdependency makes the technology more vulnerable to network attacks like Distributed Denial of Service (DDoS) that interrupt network resources. The prevalence of these attacks necessitates the development of robust and effective defense mechanisms. In recent years, many machine learning defense methodologies have been developed to address the ubiquitous growth of DDoS attacks on IoT, and the majority of them suffer from detection time delay issues. Thus, the paper presents an approach focusing on dimensionality reduction and feature selection techniques to minimize long-time detection without compromising accuracy. The proposed approach uses Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Factor Analysis and Recursive Feature Elimination with Cross Validation (RFECV) as the dimensionality reduction and feature selection techniques and Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), AdaBoost, and Logistic Regression (LR) as machine learning models to classify the malicious traffic. The approach provides a reliable DDoS detection model that effectively enhances the detection time delay with the combination of GNB with LDA and achieves 99.98% accuracy in 0.582 s of detection time. [ABSTRACT FROM AUTHOR]

Protecting patient privacy has become a top priority with the introduction of Healthcare 5.0 and the growth of the Internet of Things. This study provides a revolutionary strategy that makes use of blockchain technology, information fusion, and federated illness prediction and deep extreme machine learning to meet the difficulties with regard to healthcare privacy. The suggested framework integrates several innovative technologies to make healthcare systems safe and privacy-preserving. The framework leverages the blockchain system, a distributed and unchangeable ledger, to secure the integrity, traceability and openness of private medical information. Patient privacy is better protected as a result, and there is less chance of data breaches or unauthorized access. The system makes use of the Linear Discriminant Analysis (LDA), Decision Tree, Extra Tree Classifier, AdaBoost, and Federated Deep Extreme Machine Learning algorithms to increase the accuracy and efficacy of illness prediction. This method allows for collaborative learning across many healthcare organizations without disclosing raw data, protecting privacy. The system obtains a thorough awareness of patient health, allowing for the early diagnosis of diseases and the development of individualized treatment suggestions. To further detect and reduce possible security risks in the IoMT environment, the framework also includes intrusion detection methods. Protecting patient data and infrastructure, the system can quickly identify and react to unauthorized actions or threats. High accuracy and privacy protection are shown by the results, making it appropriate for Healthcare 5.0 applications. The findings have important ramifications for researchers, politicians, and healthcare professionals who are seeking to develop safe and privacy-conscious healthcare systems. [ABSTRACT FROM AUTHOR]

Objective: This study investigated the effects of Apidaecin Api-PR19 as feed additive on growth performance, intestinal health, and small intestinal microbiota of broilers. Methods: A total of 360 1-d-old Arbor Acres broilers were randomly assigned to 3 groups with 6 replicates including control group with basal diet (CON), antibiotic growth promotor group with basal plus 10 mg/kg colistin sulfate and 50 mg/kg roxarsone (AGP), and antibacterial peptide group with basal diet plus 330 mg/kg Apidaecin Api-PR19 (ABP). The trial lasted 35 d. Results: Results showed that dietary Api-PR19 addition increased (p<0.05) the average daily feed intake, average daily gain and decreased (p<0.05) feed conversion ratio (FCR) during 1 to 21 d compared with the CON group. The digestibility of dry matter and crude protein were higher in AGP and ABP groups (p<0.05) where greater trypsin activity was detected in duodenum (p<0.05). The ratio of villus height to crypt depth (V/C) in duodenum and jejunum was increased at 35 d when broilers were given diets with ABP or AGP (p<0.05). Besides, ABP treatments up-regulated (p<0.05) the mRNA expression of EAAT3, GLUT2, ZO-1, and Claudin-1 in duodenum of broilers at 35 d of age. The results of immunohistochemistry showed that ABP treatment significantly increased (p<0.05) duodenal secretory immunoglobulin A (sIgA) content. In addition, 16S rRNA gene sequencing revealed that there were differences in the intestinal microbiota diversity and composition among three groups. Notably, the linear discriminant analysis effect size showed that p_Firmicutes, g_Enterococcus, g_Carnobacterium, g_Kitasatospora, and g_Acidaminococcus were dominant in ABP group. Redundancy analysis showed that these changes in gut microbiota in ABP group had correlation with growth performance, intestinal morphology, and content of sIgA. Conclusion: In general, these results indicated that dietary 330 mg/kg Apidaecin ApiPR19 supplementation promoted growth performance of broilers by improving intestinal development, nutrients absorption, immune function and modulating intestinal microbiota. [ABSTRACT FROM AUTHOR]

This article discusses a study that used machine learning to identify and validate plant peroxisome targeting signal type 1 (PTS1) tripeptides. Peroxisomes are organelles in eukaryotic cells that are involved in metabolic pathways. The researchers trained machine learning models using a dataset of functional PTS1 sequences and non-PTS1 sequences, achieving high accuracy and recall rates in predicting peroxisome targeting. They then used the models to predict novel PTS1 tripeptides and confirmed their peroxisome targeting through experimental validation. The study expands our understanding of peroxisomes in plants and has implications for predicting peroxisomal proteins and metabolic pathways in different plant species. However, it is important to consider that the presence of a PTS1 tripeptide does not guarantee peroxisome targeting, as other factors may also influence protein localization. [Extracted from the article]

Domain adaptation is a subfield of statistical learning theory that takes into account the shift between the distribution of training and test data, typically known as source and target domains, respectively. In this context, this paper presents an incremental approach to tackle the intricate challenge of unsupervised domain adaptation, where labeled data within the target domain is unavailable. The proposed approach, OTP-DA, endeavors to learn a sequence of joint subspaces from both the source and target domains using Linear Discriminant Analysis (LDA), such that the projected data into these subspaces are domain-invariant and well-separated. Nonetheless, the necessity of labeled data for LDA to derive the projection matrix presents a substantial impediment, given the absence of labels within the target domain in the setting of unsupervised domain adaptation. To circumvent this limitation, we introduce a selective label propagation technique grounded on optimal transport (OTP), to generate pseudo-labels for target data, which serve as surrogates for the unknown labels. We anticipate that the process of inferring labels for target data will be substantially streamlined within the acquired latent subspaces, thereby facilitating a self-training mechanism. Furthermore, our paper provides a rigorous theoretical analysis of OTP-DA, underpinned by the concept of weak domain adaptation learners, thereby elucidating the requisite conditions for the proposed approach to solve the problem of unsupervised domain adaptation efficiently. Experimentation across a spectrum of visual domain adaptation problems suggests that OTP-DA exhibits promising efficacy and robustness, positioning it favorably compared to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objectives: This study tests whether (1) premolar topography of extant "prosimians" (strepsirrhines and tarsiers) successfully predicts diet and (2) whether the combination of molar and premolar topography yields higher classification accuracy than using either tooth position in isolation. Materials and Methods: Dental topographic metrics (ariaDNE, relief index, and orientation patch count rotated) were calculated for 118 individual matched‐pairs of mandibular fourth premolars (P4) and second molars (M2). The sample represents 7 families and 22 genera. Tooth variables were analyzed in isolation (P4 only; M2 only), together (P4 and M2), and combined (PC1 scores of bivariate principal component analyses of P4 and M2 for each metric). Discriminant function analyses were conducted with and without a measure of size (two‐dimensional surface area). Results: When using topography only, "prosimian" P4 shape predicts diet with a success rate that is slightly higher than that of M2 shape. When absolute size is included, premolars and molars perform comparably well. Including both premolar and molar topography (separately or combined) improves classification accuracy for every analysis beyond considering either in isolation. Classification accuracy is highest when premolar and molar topography and size are included. Discussion: Our findings indicate that molar teeth incompletely summarize the functional requirements of oral food breakdown for a given diet, and that the mechanism selecting for premolar form is more varied than what is expressed by molar teeth. Finally, our findings suggest that fossil P4s (in isolation or with the M2) can be used for meaningful dietary reconstruction of extinct primates. Research Highlights: "Prosimian" premolar shape predicts diet with similar accuracy to molarsCombining premolar and molar variables improves dietary classificationPremolar shape reflects functional demands that may contrast with those on molars shape [ABSTRACT FROM AUTHOR]

Summary: Intrustion Detection System (IDS) refers to the gear or software that monitors a network or system for malicious activity or policy violations. Periodically, the system records any intrusion action or breach, which frequently modifies the administrator. Cyber Physical System (CPS) is particularly called as networked connected system, in which the system components are spatially distributed and integrated via the communication network. The control mechanism ensures computation significance; however, the system does affect attacks. Researchers are trying to handle this issue via the existing anomaly datasets. In this way, this paper follows an intrusion detection system under three major stages including extraction of features, selection of feature, and detection. The primary stage is the extraction of Statistical features like standard deviation, mean, mode, variance, and median, as well as higher‐order statistical features like moment, percentile, improved correlation, kurtosis, mutual information, skewness, flow‐based features, and information gain‐based features. The curse of dimensionality becomes a significant problem in this scenario, so it is crucial to choose the right features. Improved Linear Discriminant Analysis (LDA) is utilized to choose the right features. The selected features are subjected to a Hybrid classifier for final detection. Here, models like CNN (Convolutional Neural Network) and Bi‐GRU (Bidirectional Gated Recurrent Unit) are combined. A new Bernoulli Map Estimated Arithmetic Optimization Algorithm (BMEAOA) is added to train the system by adjusting the ideal weights of the two classifiers, leading to improved detection outcomes. Ultimately, the effectiveness is assessed in comparison to the other traditional techniques. [ABSTRACT FROM AUTHOR]

Calyx cavity is a common physiological disorder in many persimmon cultivars. It is characterised by a separation between the calyx and the surrounding flesh. The presence of calyx cavity in export fruit can lead to phytosanitary risks and reduced storage potential due to more rapid softening and increased chilling injury. Manual detection of calyx cavity at packing is very time-consuming and uneconomic. This work investigated the ability of non-destructive evaluation, combined with modelling, to identify calyx cavity in 'Fuyu' persimmons. Fruit were evaluated at harvest, followed by a period of nine-week storage in modified atmosphere packaging at 0°C. The presence and severity of calyx cavity was related with higher red colouration and fresh weight before storage. Using colour index data and weight data, binary classification of calyx cavity by linear discriminant analysis (LDA) resulted 74.1% correct segregation. The model correctly identified 70.5% of calyx cavity fruit (29.5% false negatives) and 77.4% of no cavity fruit (22.6% false positives). The use of non-destructive calyx cavity classification based on the evaluation of quality parameters is a starting point in providing the ability to segregate healthy fruit before packaging and storage. Further work is necessary to improve model performance before implementation is viable. [ABSTRACT FROM AUTHOR]

In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, and portability. Additionally, they enable non-destructive testing and real-time monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis through multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA). The application of machine learning (ML) algorithms resulted in a good outcome, achieving high accuracies in identifying fruits contaminated with pesticides and accurately determining the concentrations of those pesticides. This level of precision underscores the robustness and reliability of the methodologies employed, highlighting their potential as alternative tools for pesticide residue detection in agricultural products. [ABSTRACT FROM AUTHOR]

Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT. [ABSTRACT FROM AUTHOR]

This study primarily investigated the variations in stable isotope ratios and elemental fingerprints in tea leaves, which are influenced by multiple factors such as tea cultivars, plant parts, geographical areas, and harvest times. Statistical analyses revealed significant differences in isotope and elemental profiles among tea cultivars. By employing advanced statistical methods, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and linear discriminant analysis (LDA), we achieved 100% accuracy in classifying tea cultivars. Additionally, different parts of the tea plant and geographical areas exhibited substantial impacts on the concentrations of stable isotopes and elements. The variation in harvest times further indicated the regulation of isotope fractionation and element accumulation. The variations in carbon (C), nitrogen (N), and oxygen (O) isotopes among different tea tree parts illuminated the pathways of nutrient transport within the plant. Overall, plant parts were identified as the most crucial factor affecting tea quality, followed by geographical areas and tea cultivars. These findings provide scientific evidence for tea classification and quality control, while also enhancing the understanding of the nutritional metabolism mechanisms in tea plants. [ABSTRACT FROM AUTHOR]

Stingless bee honeys are appreciated in the world market due to their nutritional and biological properties. In Argentina, the most commercially exploited species of stingless bee is Tetragonisca fiebrigi whose honey is the only one of its kind incorporated into the Argentine Food Code. The value of stingless bee honeys has been increasing in recent years, making them more prone to adulteration. However, there are no methodologies available to perform adequate controls to verify their authenticity. Therefore, the objective of this work was to develop and validate a UV–Vis spectroscopy and chemometric combined methodology to detect and quantify adulterations of Tetragonisca fiebrigi honeys produced in Argentina. The adulterants analyzed included Apis mellifera honey, three commercial sugar syrups (glucose, extra glucose, maltodex40) and three laboratory-formulated sugar syrups (sucrose, fructose and high fructose). The results showed that through principal component analysis it is possible to differentiate pure honey from adulterated honey. Through linear discriminant analysis and partial least squares regression, unknown mixtures could be classified by determining the degree of adulteration and identifying the type of adulterant used. The method allows detecting the addition of up to 64% of Apis mellifera honey, while the maximum amount of detectable added syrup varies between 51 and 70%, depending on the type of syrup. Highlights: The adulteration of stingless bees honeys has increased due to its economic value. The Argentine Food Code does not define methodology for detection. In Argentina, Tetragonisca fiebrigi is the most widespread species of stingless bee. A method was developed to detect and quantify adulterations. The results demonstrate the effectiveness of the method to determine adulteration. [ABSTRACT FROM AUTHOR]

This study investigates the intricate relationship between natural disasters and regional entrepreneurship in Türkiye, a developing nation highly vulnerable to earthquakes, floods, and other hazards. The study extends Hierarchical Multiple Regression (HMR) analysis (1990–2020, 81 NUTS-III regions), beyond existing literature focused on physical damage. Our findings reveal a counterintuitive effect: while the number of disasters and total property damage correlate positively with regional entrepreneurship, human life loss has a negative impact. This suggests a complex interplay between destruction and entrepreneurial spirit. Further analyses using Discriminant Function Analysis (DFA) and MANOVA-ANOVA for robustness check explores regional characteristics that contribute to entrepreneurial resilience against natural disasters. By comparing regions categorized by entrepreneurial performance and disaster exposure, the study identifies key differentiators. Regions with supportive business environments demonstrate greater resilience, highlighting the crucial role of fostering a strong entrepreneurial ecosystem in mitigating disaster effects. This research fills a critical gap by examining the understudied relationship between natural disasters and regional entrepreneurship, going beyond individual firm-level studies. By analysing interregional disparities and resilience factors, this paper provides valuable insights for both regional development and entrepreneurship & resilience literature. It emphasizes the importance of human capital alongside disaster preparedness and institutional quality in building regional resilience. [ABSTRACT FROM AUTHOR]

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Linear discriminant analysis (LDA) is widely used for dimensionality reduction under supervised learning settings. Traditional LDA objective aims to minimize the ratio of the squared Euclidean distances that may not perform optimally on noisy datasets. Multiple robust LDA objectives have been proposed to address this problem, but their implementations have two major limitations. One is that their mean calculations use the squared \(\ell_{2}\) -norm distance to center the data, which is not valid when the objective depends on other distance functions. The second problem is that there is no generalized optimization algorithm to solve different robust LDA objectives. In addition, most existing algorithms can only guarantee that the solution is locally optimal rather than globally optimal. In this article, we review multiple robust loss functions and propose a new and generalized robust objective for LDA. Besides, to remove the mean value within data better, our objective uses an optimal way to center the data through learning. As one important algorithmic contribution, we derive an efficient iterative algorithm to optimize the resulting non-smooth and non-convex objective function. We theoretically prove that our solution algorithm guarantees that both the objective and the solution sequences converge to globally optimal solutions at a sub-linear convergence rate. The results of comprehensive experimental evaluations demonstrate the effectiveness of our new method, achieving significant improvements compared to the other competing methods. [ABSTRACT FROM AUTHOR]

Introduction: Excess body fat elevates colorectal cancer risk. While bariatric surgery (BRS) induces significant weight loss, its effects on the fecal stream and colon biology are poorly understood. Specifically, limited data exist on the impact of bariatric surgery (BRS) on fecal secondary bile acids (BA), including lithocholic acid (LCA), a putative promotor of colorectal carcinogenesis. Methods: This cross-sectional case–control study included 44 patients with obesity; 15 pre-BRS (controls) vs. 29 at a median of 24.1 months post-BRS. We examined the fecal concentrations of 11 BA by liquid chromatography and gene abundance of BA-metabolizing bacterial enzymes through fecal metagenomic sequencing. Differences were quantified using non-parametric tests for BA levels and linear discriminant analysis (LDA) effect size (LEfSe) for genes encoding BA-metabolizing enzymes. Results: Total fecal secondary BA concentrations trended towards lower levels post- vs. pre-BRS controls (p = 0.07). Individually, fecal LCA concentrations were significantly lower post- vs. pre-BRS (8477.0 vs. 11,914.0 uM/mg, p < 0.008). Consistent with this finding, fecal bacterial genes encoding BA-metabolizing enzymes, specifically 3-betahydroxycholanate-3-dehydrogenase (EC 1.1.1.391) and 3-alpha-hydroxycholanate dehydrogenase (EC 1.1.1.52), were also lower post- vs. pre-BRS controls (LDA of − 3.32 and − 2.64, respectively, adjusted p < 0.0001). Post-BRS fecal BA concentrations showed significant inverse correlations with weight loss, a healthy diet quality, and increased physical activity. Conclusions: Concentrations of LCA, a secondary BA, and bacterial genes needed for BA metabolism are lower post-BRS. These changes can impact health and modulate the colorectal cancer cascade. Further research is warranted to examine how surgical alterations and the associated dietary changes impact bile acid metabolism. [ABSTRACT FROM AUTHOR]

Brain tumors pose significant threats within neurological disorders, demanding accurate classification for effective diagnosis and treatment. This study explores brain tumor classification employing Classical Local Binary Patterns (CLBP) and Convolutional Neural Networks (CNN), alongside texture feature extraction from MRI images using classical LBP and HOG (Histogram of Oriented Gradients). These methods adeptly capture both local and global texture patterns crucial for tumor identification. Our proposed framework encompasses three pivotal steps: image pre-processing, feature extraction via CLBP, and classification utilizing CNN. Evaluation on a publicly available brain tumor dataset showcased an impressive 95.6% accuracy in tumor classification, affirming the efficacy of the CLBP+CNN approach. This method bears promising implications for enhancing clinical diagnosis and treatment planning. Furthermore, we propose future extensions including CLBPs such as DLBP and LBP. DLBP introduces a parameter, 'D', dictating pixel distance, while LBP varies pixel values across specified ranges. Additionally, tumor classification was explored employing ANN, AlDE, and LDA classification methods, with future prospects of incorporating DLBP, LBP, and CLBP extractions from MRI images within the dataset [ABSTRACT FROM AUTHOR]

This work deals with the problem of multilinear principal component analysis (MPCA) and multilinear discriminant analysis (MDA), that solve for a tensor to tensor projection (TTP) using Einstein product. MPCA and MDA are considered as a higher-order extension of principal component analysis (PCA) and linear discriminant analysis (LDA), respectively. MPCA seeks to find a low-dimensional representation that captures most of the variation present in the original data tensor. Whereas MDA seeks to find discriminative features that maximize the separation between classes, while preserving the multilinear structure. Specifically, we are interested in finding a projective tensor that maps the original data tensor onto a new lower-dimensional subspace. In this paper, we propose to solve the MPCA problem by employing the global Lanczos procedure via Einstein product for a fourth-order tensor, while solving the MDA problem by combining Newton method and global tensorial Lanczos method. The numerical experiments illustrate the use of these algorithms for face recognition problems, compression and classification. [ABSTRACT FROM AUTHOR]

AbstractForestiera includes woody plants distributed from the United States to Ecuador, with a wide distribution in Mexico. However, basic knowledge about its biology and taxonomy is still limited. In the present study, a leaf morphometric analysis of the 14 Mexican Forestiera species, including F. neomexicana, was carried out to determine if leaf characters allow classifying the species and to evaluate if the inclusion of qualitative features in statistical analyses improves the fit of used methods. Four quantitative and four qualitative variables from herbarium specimens, were evaluated. A matrix was generated which was submitted to multivariate analysis, including Linear Discriminant Analysis and Random Forest. Results revealed that the species are correctly classified, with Random Forest being the method with the best classifying capacity. The most important variables to differentiate the species are the shape of the base, width and margin of the leaves. All species were classified as different entities including F. pubescens and F. neomexicana, currently considered synonyms, were classified by Random Forest as different entities. [ABSTRACT FROM AUTHOR]

Although gastroesophageal reflux has been recognized as one of the risk factors of nontuberculous mycobacterial pulmonary disease (NTM-PD) progression, the effect of reflux on the lower respiratory tract microbiota has not been studied in detail. We investigated the composition of the lower respiratory tract microbiota in patients with clinically suspected NTM-PD, comparing them based on the presence of reflux. Forty-seven patients suspected of having NTM-PD were enrolled and assigned according to presence of reflux (n = 22) and non- reflux (n = 25). We performed a pepsin ELISA assay to identify the presence of reflux and 16S ribosomal RNA gene amplicon sequencing to evaluate the microbiota in bronchoalveolar lavage fluid. There were no significant differences in the diversity or composition of the lower respiratory microbiota between the NTM-PD and non-NTM-PD groups. Bacterial richness was observed in the non-reflux group than in the reflux group [P = 0.03] and a cluster in the reflux group was observed. The reflux group showed a predominance for Pseudomonas aeruginosa or Staphylococcus aureus among the NTM-PD group and for P. aeruginosa, Haemophilus influenzae, Klebsiella pneumoniae, or Eikenella species among the non-NTM-PD group. The non-reflux groups presented diverse patterns. A linear discriminant analysis and volcano plot demonstrated that P. aeruginosa, H. haemolyticus, Selenomonas artemidis, and Dolosigranulum pigrum were specifically associated with the NTM-PD reflux group, while P. aeruginosa was specifically associated with the non-NTM-PD reflux group. These observations confirm that the lower respiratory microbiota is consistently altered by reflux but not in NTM-PD. [ABSTRACT FROM AUTHOR]

The Silurian system in Tazhong area is characterized by extensive, low-abundance lithological reservoirs with strong diagenesis, resulting in significant heterogeneity. The complex pore structure in this area significantly impacts fluid control, making accurate characterization and classification of pore structures crucial for understanding reservoir properties and their influence on oil and gas distribution. Based on 314 Mercury Injection Capillary Pressure (MICP) samples in combination with core slices and thin casting slices observation, a pipeline of characterization and classification scheme by data-mining analytics of strong diagenesis sandstone pore structure types in the study zone is established, and the characteristics of different pore structures are clarified. According to the pore structure parameter abstracted by MICP data compression and variable analysis based on hierarchical clustering and principal component analysis (PCA) analysis, the variables are reasonably evaluated and screened, and the screened variables can be divided into three groups: mean pore throat radius-maximum pore throat radius-median pore throat radius-pore throat diameter mean variable group, microscopic mean coefficient variable group, and median pressure displacement pressure-relative sorting coefficient variable group. The combination of classification schemes analysed by decision tree model and linear discriminant analysis (LDA) model was determined. In the two-dimensional projection diagram of LDA model, a relatively obvious distribution of low displacement pressure, middle displacement pressure and high displacement pressure was obtained, and three distribution lines were nearly parallel. Based on the relevant information, 6 combined classification schemes suitable for final pore structure modelling were determined verified by microscopic observation. The correct characterization and classification of pore structure can be applied to the prediction of pore type, which can be used to improve the prediction of oil and gas distribution and oil and gas recovery in the future. [ABSTRACT FROM AUTHOR]

This document is a compilation of various research articles and papers related to the assessment of performance validity in neuropsychological testing. The articles cover topics such as the use of computerized assessments, the detection of feigned cognitive impairment, the development of performance validity tests, and the use of embedded validity indicators. The research explores different methods and measures for assessing performance validity and highlights the importance of accurate and reliable testing in various populations, including individuals with traumatic brain injury and children with neurological conditions. The references provide a range of perspectives and approaches to performance validity testing, and may be useful for researchers and practitioners in the field of neuropsychology. [Extracted from the article]

Statistician C. R. Rao made many contributions to multivariate analysis over the span of his career. Some of his earliest contributions continue to be used and built upon almost 80 years later, while his more recent contributions spur new avenues of research. The present article discusses these contributions, how they helped shape multivariate analysis as we see it today, and what we may learn from reviewing his works. Topics include his extension of linear discriminant analysis, Rao's perimeter test, Rao's U statistic, his asymptotic expansion of Wilks' Λ$$ \Lambda $$ statistic, canonical factor analysis, functional principal component analysis, redundancy analysis, canonical coordinates, and correspondence analysis. The examination of his works shows that interdisciplinary collaboration and the utilization of real datasets were crucial in almost all of Rao's impactful contributions. [ABSTRACT FROM AUTHOR]

ABSTRACT Here, we demonstrate mid‐field 1H NMR spectroscopy combined with chemometrics to be powerful in the classification and authentication of motor oils (MOs). The 1H NMR data were processed with a new algorithm for simultaneous phase and baseline correction, which, for crowded spectra such as those of the refinery products, allowed for more accurate estimation of phase parameters than other literature approaches tested. A principal component analysis (PCA) model based on the unbinned CH3 fingerprint region (0.6–1.0 ppm) enabled the differentiation of hydrocracked and poly‐α‐olefin‐based MOs and was effective in resolving mixtures of these base stocks with conventional base oils. PCA analysis of the 1.0‐ to 1.14‐ppm region enabled the detection of poly (isobutylene) additive and was useful for differentiating between single‐grade and multigrade MOs. Non‐equidistantly binned 1H NMR data were used to detect the addition of esters and to establish discriminant models for classifying MOs by viscosity grade and by major categories of synthetic, semisynthetic, and mineral oils. The performances of four classifiers (linear discriminant analysis [LDA], quadratic discriminant analysis [QDA], naïve Bayes classifier [NBC], and support vector machine [SVM]) with and without PCA dimensionality reduction were compared. In both tasks, SVM showed the best efficiency, with average error rates of ~2.3% and 8.15% for predicting major MO categories and viscosity grades, respectively. The potential to merge spectra collected from different NMR instruments is discussed for models based on spectral binning. It is also shown that small errors in phase parameters are not detrimental to binning‐based PCA models. [ABSTRACT FROM AUTHOR]

The development of sex estimation standards, especially in locations with high murder case loads like South Africa, is important to aid in establishing the biological profile of victims. There is a lack of information on the utility of the measurements around the metatarsal diaphyseal nutrient foramina (NF) in sex estimation in South Africans of Mixed ancestry (SAMA). Five measurements around the NF were taken from a total of 248 metatarsals (first to fifth) from 28 males and 23 females of SAMA population. Measurements subjected to direct and stepwise discriminant function (DFA) and logistic regression (LRA) analyses included total length, distance from proximal end to NF, circumference, and mediolateral and dorsoplantar diameters at the level of the NF. The univariable functions produced 60.8–80% accuracy rates. The original classification accuracies for the stepwise and direct DFA multivariable functions ranged from 75.5–80% and 75–81.3%, respectively. The original classification accuracies for the stepwise and direct LRA multivariable functions ranged from 75.5%–80% and 75%–83.7%, respectively. The cross-validation classifications showed a drop of 0–4% for DFA and 0.7–5.4% for LRA. The multivariable DFA and LRA functions produced high average classification accuracies appropriate for use in forensic settings. [ABSTRACT FROM AUTHOR]

Background: Emerging zoonotic diseases arise from cross-species transmission events between wild or domesticated animals and humans, with bats being one of the major reservoirs of zoonotic viruses. Viral metagenomics has led to the discovery of many viruses, but efforts have mainly been focused on some areas of the world and on certain viral families. Methods: We set out to describe full-length genomes of new picorna-like viruses by collecting feces from hundreds of bats captured in different regions of Spain. Viral sequences were obtained by high-throughput Illumina sequencing and analyzed phylogenetically to classify them in the context of known viruses. Linear discriminant analysis (LDA) was performed to infer likely hosts based on genome composition. Results: We found five complete or nearly complete genomes belonging to the family Picornaviridae, including a new species of the subfamily Ensavirinae. LDA suggested that these were true vertebrate viruses, rather than viruses from the bat diet. Some of these viruses were related to picornaviruses previously found in other bat species from distant geographical regions. We also found a calhevirus genome that most likely belongs to a proposed new family within the order Picornavirales, and for which genome composition analysis suggested a plant host. Conclusions: Our findings describe new picorna-like viral species and variants circulating in the Iberian Peninsula, illustrate the wide geographical distribution and interspecies transmissibility of picornaviruses, and suggest new hosts for calheviruses. [ABSTRACT FROM AUTHOR]

Knowledge transfer in engineering design activity involves effectively applying prior practical experience in novel contexts. While related works fostering knowledge transfer focus on educational practices and experience generalisation, recent advancements enable us to analyze its complex cognitive processes using neuroimaging technology. This paper proposes a multimodal approach for facilitating knowledge transfer in engineering design, utilising functional near-infrared spectroscopy (fNIRS) and eye-tracking technologies based on neurocognitive profiling. We conducted knowledge transfer experiments using mixed and extended design tasks. Meanwhile, The analysis focused on gaze duration, gaze counts, brain region activation levels, functional connectivity, and effective connectivity of the participants. Finally, we employed stepwise linear regression to identify significant and potential associated factors of knowledge transfer. By combining eye-tracking predictors and brain network predictors, a multivariate linear discriminant analysis (LDA) significantly differentiated between participants in near-transferred and far-transferred states, achieving an accuracy of 91.16% using leave-one-out cross-validation. The ‘basic-supplementary-target task’ method effectively improved the knowledge transfer performance by 27.6%. Our findings demonstrate the efficacy of the proposed multimodal approach in facilitating knowledge transfer in engineering design activities. [ABSTRACT FROM AUTHOR]

Fisher discriminant analysis (FDA) is a widely used dimensionality reduction tool in pattern recognition. However, FDA cannot obtain an optimal subspace for classification without sufficient labeled samples. Thus, semi-supervised discriminant analysis has attracted great attention in recent years. In this paper, the proposed method employs the exponential-adjusted geometric distance as the measure of similarity, which modifies the exponential function and the scaling factor. The distance not only satisfies the global and local consistency requirements, but also the similarity matrix obtained is more consistent with the real data distribution, thus improves the dimensionality reduction performance. First, in order to deal with the nonlinear separated data, the kernel function is used to map the original data into the high-dimensional feature space. Then, both labeled and unlabeled data in feature space are used to capture the consistence assumption of geometrical structure based on exponential-adjusted geometric distance, which are incorporated into the objection function of local Fisher discriminant analysis as a regularization term. Eventually, the optimal projection matrix is obtained by maximizing the objective function. Experiments on artificial datasets, UCI benchmark datasets, and high-dimensional recognition problems indicate that the presented technique has a significantly improvement in discriminant performance compared with the-state-of-art dimensionality reduction techniques. [ABSTRACT FROM AUTHOR]