Search Results (81)

Background: Digital dental photography is increasingly essential for documentation and smile design. This study aimed to compare the linear measurement accuracy of various smartphones and a Digital Single-Lens Reflex (DSLR) camera against digital models obtained by intraoral and desktop scanners. Methods: Tooth height and width from six different casts were measured and compared using images acquired with a Canon EOS 250D DSLR, six smartphone models (iPhone 13, iPhone 15, Samsung Galaxy S22 Ultra, Samsung Galaxy S23 Ultra, Samsung Galaxy S24, and Vivo T2), and digital scans obtained from the Helios 500 intraoral scanner and the Ceramill Map 600 desktop scanner. All image measurements were performed using ImageJ software (National Institutes of Health, Bethesda, MD, USA), and statistical analysis was conducted using one-way analysis of variance (ANOVA) with Tukey’s post hoc test (α = 0.05). Results: The results showed no significant differences in measurements across most imaging methods (p > 0.05), except for the Vivo T2, which showed a significant deviation (p < 0.05). The other smartphones produced measurements comparable to those of the DSLR, even at distances as close as 16 cm. Conclusions: These findings preliminary support the clinical use of smartphones for accurate dental documentation and two-dimensional smile design, including the posterior areas, and challenge the previously recommended 24 cm minimum distance for mobile dental photography (MDP). This provides clinicians with a simplified and accessible alternative for high-accuracy dental imaging, advancing the everyday use of MDP in clinical practice. Full article

Academic performance is a factor of concern and interest in the educational context for the improvement of the educational and economic system of any country. Determining the factors influencing it has been the subject of multiple investigations. This study focused on analysing which dimensions of school resilience could act as determinants of academic performance in a sample of 609 Spanish secondary education students, aged between 11 and 17 years. The School Resilience Scale (SRS) was used as a data collection instrument. The data were analysed using analysis of variance and discriminant analysis based on a canonical function model, which suggested the existence of a direct and significant relationship between academic performance and all dimensions of resilience, with somewhat larger effect sizes for the Internal Resources and Identity–Self-Esteem dimensions, which allowed us to classify students with particularly high levels of performance. The results also show that the school year was significantly associated with academic performance, with the highest percentages of students at the highest level observed in the 2nd and 3rd years. Full article

Agriculture plays a dual role in shaping biodiversity, providing secondary habitats while posing significant threats to ecological systems through habitat fragmentation and land-use intensification. This study aims to assess the relationship between bird species composition and land-use types in Apulia, Italy. Specifically, we investigate how different agricultural and semi-natural landscapes influence avian biodiversity and which agricultural models can have a positive impact on biodiversity. Biodiversity indices were calculated for each bird community observed. The abundance curves showed a geometric series pattern for the AGR communities, indicative of ecosystems at an early stage of ecological succession, and a lognormal distribution for the MIX and NAT communities, typical of mature communities with a more even distribution of species. Analysis of variance showed significant differences in richness and diversity between AGR and NAT sites, but not between NAT and MIX, which had the highest values. Logistic regression estimated the probability of sites belonging to the three ecosystem categories as a function of biodiversity, confirming a strong similarity between NAT and MIX. Finally, linear discriminant analysis confirmed a clear separation from AGR areas, as evidenced by the canonical components. The results highlight the importance of integrating high-diversity landscape elements and appropriate agricultural practices to mitigate biodiversity loss. Even a small increase in the naturalness of agricultural land would be sufficient to convert it from the AGR to the MIX ecosystem category, with significant biodiversity benefits. Full article

In response to the growing market demand for Litopenaeus vannamei, a variety of single-species, high-density, intensive, and high-yield aquaculture modes have arisen. These aquacultural systems are teeming with microorganisms, which play roles in water quality and host health. To uncover the prokaryotic community composition across cultivation modes, we investigated the prokaryotic community composition at two fractionated sizes in the water of three culture modes of Litopenaeus vannamei, including high-level pond culture, biofloc technology (BFT), and pond culture. The 16S rRNA gene high-throughput sequencing results indicated that the taxa particularly enriched by high-level pond culture modes were mainly Deltaproteobacteria, while Alpha- and Gammaproteobacteria and Flavobacteria were enriched in the BFT culture modes. The pond culture enriched Bacteroidetes, Sphingobacteriia, Actinobacteria, and Cyanobacteria. PCoA analysis showed that for the pond samples, there were significant differences in the community composition compared with the samples from the other two modes. However, the high-level pond and biofloc samples showed similar community compositions. Furthermore, Canonical Correspondence Analysis (CCA) and Variance Partitioning Analysis (VPA) revealed that NH₄⁺-N, salinity (Sal), and NO₃⁻-N were key factors affecting the aquaculture communities. Full article

This study delves into the fascinating morphological diversity and population groups of the Crucifix crab (Charybdis feriatus), a species steeped in the cultural and spiritual significance of recreational fisheries across East and Southeast Asia. It is known in the West as the “Crucifix crab” due to the distinct cross pattern on its shell. In this research, we collected 759 specimens from seven estuarine locations: Kyushu (Japan), Shanghai, Xiamen, Hong Kong (China), Yilan, Kaohsiung (Taiwan), and Singapore. Using advanced statistical methods, including canonical variate analysis (CVA) and hierarchical clustering, we identified three distinct population groups: the Northeast Asian group (NAG), the Kuroshio tributary group (KTG), and the Southeast Asian group (SAG). Significant morphological differences were found between these populations, suggesting that the crab’s adaptation to varying sea environments is as unique as its symbolic cross-shaped marking. The canonical variate analysis revealed that the first two eigenvalues explained 88% of the total variance (61% and 27%, respectively) in females and 80% in males (62% and 18%, respectively). The key morphometric traits CP1 (frontal teeth) and CP4 (posterior margin) showed the highest variability (correlation coefficients ranging from 0.76 to 0.82, p < 0.001). Interestingly, the traits CP1 (frontal teeth) and CP4 (posterior margin) emerged as key drivers of allometric growth variation, further enriching our understanding of this species. Full article

Today’s advancements in neuroimaging have been pivotal in enhancing our understanding of brain development and function using various MRI techniques. This study utilizes images from T1-weighted imaging and diffusion-weighted imaging to identify gray matter and white matter coherent growth patterns within 2 years from 9–10-year-old participants in the Adolescent Brain Cognitive Development (ABCD) Study. The motivation behind this investigation lies in the need to comprehend the intricate processes of brain development during adolescence, a critical period characterized by significant cognitive maturation and behavioral change. While traditional methods like canonical correlation analysis (CCA) capture the linear interactions of brain regions, a deep canonical correlation analysis with an autoencoder (DCCAE) nonlinearly extracts brain patterns. The study involves a comparative analysis of changes in gray and white matter over two years, exploring their interrelation based on correlation scores, extracting significant features using both CCA and DCCAE methodologies, and finding an association between the extracted features with cognition and the Child Behavior Checklist. The results show that both CCA and DCCAE components identified similar brain regions associated with cognition and behavior, indicating that brain growth patterns over this two-year period are linear. The variance explained by CCA and DCCAE components for cognition and behavior suggests that brain growth patterns better account for cognitive maturation compared to behavioral changes. This research advances our understanding of neuroimaging analysis and provides valuable insights into the nuanced dynamics of brain development during adolescence. Full article

The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article

Co-varying disturbance and environmental gradients can shape vegetation dynamics and increase the diversity of plant communities and their features. Pinyon–juniper woodlands are widespread in semi-arid climates of western North America, encompassing extensive environmental gradients, and a knowledge gap is how the diversity in features of these communities changes across co-varying gradients in fire history and soil. In pinyon–juniper communities spanning soil parent materials (basalt, limestone) and recent fire histories (0–4 prescribed fires or managed wildfires and 5–43 years since fire) in Grand Canyon-Parashant National Monument (Arizona, USA), we examined variation at 25 sites in three categories of plant community features including fuels, tree structure, and understory vegetation. Based on ordinations, canonical correlation analysis, and permutation tests, plant community features varied primarily with the number of fires, soil coarseness and chemistry, and additionally with tree structure for understory vegetation. Fire and soil variables accounted for 33% of the variance in fuels and tree structure, and together with tree structure, 56% of the variance in understories. The cover of the non-native annual Bromus tectorum was higher where fires had occurred more recently. In turn, B. tectorum was positively associated with the percentage of dead trees and negatively associated with native forb species richness. Based on a dendroecological analysis of 127 Pinus monophylla and Juniperus osteosperma trees, only 18% of trees presently around our study sites originated before the 1870s (Euro-American settlement) and <2% originated before the 1820s. Increasing contemporary fire activity facilitated by the National Park Service since the 1980s corresponded with increasing tree mortality and open-structured stands, apparently more closely resembling pre-settlement conditions. Using physical geography, such as soil parent material, as a landscape template shows promise for (i) incorporating diversity in long-term community change serving as a baseline for vegetation management, (ii) customizing applying treatments to unique conditions on different soil types, and (iii) benchmarking monitoring metrics of vegetation management effectiveness to levels scaled to biophysical variation across the landscape. Full article

The general increase in awareness of environmental pollutants and typical sources reflects the application of sustainability and development goals. Energy-Dispersive X-Ray Fluorescence spectroscopy analysis has been used to analyse sand samples collected from five different beaches located on the east and north-eastern coasts of Malta and Gozo during two summers and two winters. Samples were collected along linear transects perpendicular to the shoreline at three different depths. Chemometrics were used to discriminate between four latent variables, including season, location, depth, and distance from shoreline. The highest concentrations were attributed to Fe₂O₃, Al₂O₃, SrO, and SnO₂. Principal Components Analysis and Factor Analysis classified distributions of Fe₂O₃, CoO, As₂O₃, MnO, SrO, SeO₂, and CaCO₃ under Principal Component 1. However, since no loading value dominance was observed, such distributions most likely represent a combination of lithogenic and anthropogenic natures. Discrimination using Stepwise Linear Canonical Discriminant Analysis (SLC-DA) and Partial Least Squares Discriminant Analysis (PLS-DA) using Leave-One-Out-Cross-Validation with Variance Importance Plots proved highly effective in classifying data by location, followed by seasonal variability. It follows that concentrations are not affected by depth and distance from shoreline variability, proving that accumulation and anthropogenic effects from land are not concentrated in specific zones but are spatially spread out along the bays and do not increase with depth. Full article

Tomato (Solanum lycopersicum L.) is one of the most important cultivated vegetables in the world. However, in some countries such as Mexico the lack of cultivars adapted to different environmental production conditions is a limitation. Moreover, recent studies have indicated that breeding aimed at increasing yield has led to a loss of genetic diversity. Therefore, it is necessary to explore and characterize new sources of germplasms. This study aimed to characterize new sources of germplasm and identify the most transcendental traits for distinguishing tomato types and lines that are useful for the genetic improvement of the species. Sixty characters were evaluated in 16 advanced lines of native tomatoes from Central and Southern Mexico during the fall–winter cycles 2023–2024 at the Bajío Experimental Station, Celaya, Guanajuato, Mexico, based on the guidelines of the International Union for the Protection of New Varieties of Plants (UPOV) and the International Plant Genetic Resources Institute (IPGRI). The data were analyzed using descriptive statistics, analysis of variance and post hoc tests, canonical discriminant analysis, and the Eigenanalysis selection index method (ESIM). Morphological variation showed that five qualitative traits were determinant factors in distinguishing tomato types and lines, whereas agronomic discriminant traits were the equatorial and polar diameters of the fruit and its ratio, number of locules, pedicel length, stem length, and internode distance. In addition, significant positive correlations were found between leaf length and width, equatorial diameter of the fruit, and polar diameter of the fruit. Lines JCM-17, JMC-10, and JCM-01 were the most selectable lines according to the ESIM values. The morphological variation found and the characteristics with higher selection values identified may be valuable for optimizing the tomato genetic improvement process in general. Full article

Different cropping systems and nutrient management techniques impact the microbiological characteristics of soil and nutrient availability for plants. This study assessed four cropping systems—rice–wheat, cotton–wheat, pearl millet–wheat, and pearl millet–mustard in Hisar district, Haryana, using 80 soil samples (20 from each system) collected in April 2022 after the Rabi crop harvest. The cotton–wheat system had the highest accessible nitrogen (N) at 155.9 kg ha⁻¹, while both the cotton–wheat (59.3 kg ha⁻¹) and rice–wheat (54.0 kg ha⁻¹) systems had higher available sulfur (S) levels compared to pearl millet–wheat (41.2 kg ha⁻¹). Pearl millet–wheat also showed 12.4% higher potassium (K) levels than rice–wheat. The rice–wheat system exhibited the highest phosphorus (P) concentration at 54.3 kg ha⁻¹ and greater DTPA-extractable micronutrients. Soils from the rice–wheat system had higher DTPA-extractable micronutrients (Zn, Fe, Mn, Cu) and superior microbial biomass nitrogen (MBN, 54.7 mg kg⁻¹), urease (37.9 µg NH₄⁺-N g⁻¹ h⁻¹), and alkaline phosphatase activity (APA, 269.7 µg PNP g⁻¹ h⁻¹) compared to other systems. Canonical discriminant functions explained 88.1% of the variability among cropping systems, while principal component analysis identified available P, DTPA-extractable Zn, and Cu as key soil quality indicators, accounting for 66.9% of the variance. These insights can inform policymakers on promoting effective cropping systems and sustainable soil health in northwestern India. Full article

Organic farming takes on particular importance in the context of implementing the sustainable development concept as it combines environmentally safe farming methods with (as a general assumption) producing pollution-free food. Hence, environmental conditions might play a role in determining the development pace of that type of farming. The key objective of this paper is therefore to identify the scope and direction of multidimensional relationships between the development level of organic farming and environmental conditions. This was performed with the canonical analysis. The research process included the structuring of the authors’ own synthetic metrics used in assessing the condition of the environment and the development level of organic farming. The study covered European Union countries and all 380 Polish districts (Poland is one of the very few Union members where organic farming development is currently inconsistent with the expected trends adopted under the Common Agricultural Policy). It follows from the analyses that when the variables relating to environmental conditions are known, they can explain only less than 10% of variance in the set of variables used in describing the development level of Polish organic farming. In turn, the analysis at Union level suggests that a positive—but not stronger than moderate—correlation exists between the two phenomena. Full article

Linear latent variable models such as principal component analysis (PCA), independent component analysis (ICA), canonical correlation analysis (CCA), and factor analysis (FA) identify latent directions (or loadings) either ordered or unordered. These data are then projected onto the latent directions to obtain their projected representations (or scores). For example, PCA solvers usually rank principal directions by explaining the most variance to the least variance. In contrast, ICA solvers usually return independent directions unordered and often with single sources spread across multiple directions as multiple sub-sources, severely diminishing their usability and interpretability. This paper proposes a general framework to enhance latent space representations to improve the interpretability of linear latent spaces. Although the concepts in this paper are programming language agnostic, the framework is written in Python. This framework simplifies the process of clustering and ranking of latent vectors to enhance latent information per latent vector and the interpretation of latent vectors. Several innovative enhancements are incorporated, including latent ranking (LR), latent scaling (LS), latent clustering (LC), and latent condensing (LCON). LR ranks latent directions according to a specified scalar metric. LS scales latent directions according to a specified metric. LC automatically clusters latent directions into a specified number of clusters. Lastly, LCON automatically determines the appropriate number of clusters to condense the latent directions for a given metric to enable optimal latent discovery. Additional functionality of the framework includes single-channel and multi-channel data sources and data pre-processing strategies such as Hankelisation to seamlessly expand the applicability of linear latent variable models (LLVMs) to a wider variety of data. The effectiveness of LR, LS, LC, and LCON is shown in two foundational problems crafted with two applied latent variable models, namely, PCA and ICA. Full article

Modern industrial control systems (ICSs), which consist of sensor nodes, actuators, and buses, contribute significantly to the enhancement of production efficiency. Massive node arrangements, security vulnerabilities, and complex operating status characterize ICSs, which lead to a threat to the industrial processes’ stability. In this work, a condition-monitoring method for ICSs based on canonical variate analysis with probabilistic principal component analysis is proposed. This method considers the essential information of the operating data. Firstly, the one-way analysis of variance method is utilized to select the major variables that affect the operating performance. Then, a concurrent monitoring model based on probabilistic principal component analysis is established on both the serially correlated canonical subspace and its residual subspace, which is divided by canonical variate analysis. After that, monitoring statistics and control limits are constructed. Finally, the effectiveness and superiority of the proposed method are validated through comparisons with actual drilling operations. The method has better sensitivity than traditional monitoring methods. The experimental result reveals that the proposed method can effectively monitor the operating performance in a drilling process with its highest accuracy of 92.31% and a minimum monitoring delay of 11 s. The proposed method achieves much better effectiveness through real-world process scenarios due to its distributed structural division and the characteristic canonical analysis conducted in this paper. Full article

Both chronic hepatitis B (CHB) and stroke contribute to a high burden of disease in the majority of low- and middle-income countries. Epidemiological studies yield conflicting results on the association between CHB and stroke, and the causal relationship remains inconclusive. This study aimed to assess the causal effects of CHB on stroke and its subtypes in East Asians by Mendelian randomization (MR) analysis. Variants associated with CHB were obtained from a genome-wide association study (GWAS) of Chinese samples as instrumental variables. The summary statistics for stroke in East Asians were derived from the largest published GWAS to date. Two-sample MR analyses were implemented to evaluate the causal effects of CHB on stroke and its subtypes by using the canonical inverse variance weighting method and other supplementary approaches. We observed an association between genetic predisposition to CHB and a decreased risk of large-artery atherosclerotic stroke (odds ratio = 0.872, 95% confidence interval = 0.786–0.967, p = 0.010). The causal effects of CHB on other stroke outcomes were not statistically significant. Evidence for heterogeneity and horizontal pleiotropy were not found in our analyses. This study provides genetic evidence for a negative association between CHB and stroke in East Asians, which helps improve our understanding of the etiology of stroke. Full article