Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

The chiroptical activity of various semiconductor inorganic nanocrystalline materials has typically been tested using circular dichroism or circularly polarized luminescence. Herein, we report on a high-throughput screening method for identifying and differentiating chiroptically active quantum-sized ZnO crystals using Raman spectroscopy combined with principal component analysis. ZnO quantum dots (QDs) coated by structurally diverse homo- and heterochiral aminoalcoholate ligands (cis- and trans-1-amino-2-indanolate, 2-amino-1-phenylethanolate, and diphenyl-2-pyrrolidinemethanolate) were prepared using the one-pot self-supporting organometallic procedure and then extensively studied toward the identification of specific Raman fingerprints and spectral variations. The direct comparison between the spectra demonstrates that it is very difficult to make definite recognition and identification between QDs coated with enantiomers based only on the differences in the respective Raman bands\' position shifts and their intensities. However, the applied approach involving the principal component analysis performed on the Raman spectra allows the simultaneous differentiation and identification of the studied QDs. The first and second principal components explain 98, 97, 97, and 87% of the variability among the studied families of QDs and demonstrate the possibility of using the presented method as a qualitative assay. Thus, the reported multivariate approach paves the way for simultaneous differentiation and identification of chirotopically active semiconductor nanocrystals.

Triclosan (TCS), an antibacterial biocide, pervades water and sediment matrices globally, posing a threat to aquatic life. In densely populated cities like Mumbai, rivers and coastal bodies demand baseline TCS data for ecotoxicological assessment due to the excessive use of personal care products comprising TCS. This pioneering study compares spatiotemporal TCS variations and risks in freshwater and marine ecosystems employing multivariate analysis of physicochemical parameters. Over five months (January to May 2022), Mithi River exhibited higher TCS concentrations (water: 1.68 μg/L, sediment: 3.19 μg/kg) than Versova Creek (water: 0.49 μg/L, sediment: 0.69 μg/kg). Principal component analysis revealed positive correlations between TCS and physicochemical parameters. High-risk quotients (>1) underscore TCS threats in both water bodies. This study furnishes crucial baseline data, emphasizing the need for effective treatment plans for TCS in effluent waters released into the adjacent aquatic systems.

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

BACKGROUND: Alkaloids, important secondary metabolites produced by plants, play a crucial role in responding to environmental stress. Heuchera micrantha, a well-known plant used in landscaping, has the ability to purify air, and absorb toxic and radioactive substances, showing strong environmental adaptability. However, there is still limited understanding of the accumulation characteristics and metabolic mechanism of alkaloids in H. micrantha.
RESULTS: In this study, four distinct varieties of H. micrantha were used to investigate the accumulation and metabolic traits of alkaloids in its leaves. We conducted a combined analysis of the plant\'s metabolome and transcriptome. Our analysis identified 44 alkaloids metabolites in the leaves of the four H. micrantha varieties, with 26 showing different levels of accumulation among the groups. The HT and JQ varieties exhibited higher accumulation of differential alkaloid metabolites compared to YH and HY. We annotated the differential alkaloid metabolites to 22 metabolic pathways, including several alkaloid metabolism. Transcriptome data revealed 5064 differentially expressed genes involved in these metabolic pathways. Multivariate analysis showed that four key metabolites (N-hydroxytryptamine, L-tyramine, tryptamine, and 2-phenylethylamine) and three candidate genes (Cluster-15488.116815, Cluster-15488.146268, and Cluster-15488.173297) that merit further investigation.
CONCLUSIONS: This study provided preliminarily insight into the molecular mechanism of the biosynthesis of alkaloids in H. micrantha. However, further analysis is required to elucidate the specific regulatory mechanisms of the candidate gene involved in the synthesis of key alkaloid metabolites. In summary, our findings provide important information about how alkaloid metabolites build up and the metabolic pathways involved in H. micrantha varieties. This gives us a good starting point for future research on the regulation mechanism, and development, and utilization of alkaloids in H. micrantha.

This study aimed to identify differences in the composition of whole blood of patients with chronic kidney disease (CKD), before and after a hemodialysis session (HDS), and possible differences in blood composition between stages and between genders using Raman spectroscopy and principal component analysis (PCA). Whole blood samples were collected from 40 patients (20 women and 20 men), before and after a HDS. Raman spectra were obtained and the spectra were evaluated by PCA and partial least squares (PLS) regression. Mean spectra and difference spectrum between the groups were calculated: stages Before and After HDS, and gender Women and Men, which had their most intense peaks identified. Stage: mean spectra and difference spectrum indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin in the group Before HDS. There was no statistically significant difference by PCA. Gender: mean spectra and difference spectrum Before HDS indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin with greater intensity in the group Women, and negative peaks to white blood cells and serum, with greater intensity in the group Men. There was statistically significant difference by PCA, which also identified the peaks assigned to white blood cells, serum and porphyrin for Women and red blood cells and amino acids (tryptophan) for Men. PLS model was able to classify the spectra of the gender with 83.7% accuracy considering the classification per patient. The Raman technique highlighted gender differences in pacients with CKD.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts\' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

The Yamuna River in India and the Mississippi River in the United States hold significant commercial, cultural, and ecological importance. This preliminary survey compares the bacterial communities sampled in surface waters at 11 sites (Yamuna headwaters, Mississippi headwaters, Yamuna River Yamunotri Town, Mississippi River at Winona, Tons River, Yamuna River at Paonta Sahib, Yamuna River Delhi-1, Yamuna River Delhi-2, Yamuna River before Sangam, Sangam, Ganga River before Sangam). Bacterial 16S rDNA analyses demonstrate dominance of Proteobacteria and Bacteroidetes phyla. Actinobacteria were also dominant at sites near Sangam in India and sites in Minnesota. A dominance of Epsilonbacteraeota were found in Delhi, India. Principal component analysis (PCA) using unique operational taxonomic units (OTUs) resulted in the identification of 3 groups that included the Yamuna River locations in Delhi (Delhi locations), Yamuna headwaters and Yamuna River at Yamunotri (Yamuna River locations below the Glacier) and Mississippi, Ganga, Tons, and other Yamuna River locations. Diversity indices were significantly higher at the Yamuna River locations below the Glacier (Simpson D = 0.986 and Shannon H = 5.06) as compared (p value <0.001) to the Delhi locations (D = 0.951 and H = 4.23) and as compared (p value < 0.001) to Mississippi, Ganga, Tons, and other Yamuna River locations (D = 0.943 and H = 3.96). To our knowledge, this is the first survey to compare Mississippi and Yamuna River bacterial communities. We demonstrate higher diversity in the bacterial communities below the Yamunotri glacier in India.

Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in South Carolina.

At the top of many ecosystems, raptors, also known as birds of prey, hold major influence. They shape their surroundings through their powerful hunting skills and complex interactions with their environment. This study investigates the beak morphology of four prominent raptor species, Golden eagle (Aquila chrysaetos), Common buzzard (Buteo buteo), Peregrine falcon (Falco peregrinus) and Common kestrel (Falco tinnunculus), found in Türkiye. By employing geometric morphometric methods, we investigate shape variations in the beaks of these species to unravel the adaptive significance of their cranial structures. This analysis reveals distinct beak morphologies among the studied raptors, reflecting adaptations to their feeding habits, hunting techniques and ecological niches. The results from Principal component analysis and Canonical variate analysis demonstrate significant differences in beak morphology between the Falconiformes and Accipitriformes clades, as well as among all three groups. The overall mean beak shapes of Golden Eagles are quite similar to Common Buzzards, with both species having longer beaks. In contrast, Falcons exhibit a distinctly different beak morphology, characterized by wider and shorter beaks. Changes in beak shape can lead to changes depending on the skull. It is thought that skull shape variations among predator families may have an impact on beak shape. These findings highlight the importance of integrating morphometric analyses with ecological insights to enhance our understanding of the evolutionary processes shaping raptor beak morphology.