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Hyperspectral data and machine learning offer great potential for identifying valuable open ecosystems. Due to the large volume of data, preprocessing of hyperspectral images must involve dimensionality reduction. The main goal of this study was to test the effectiveness of various types of feature reduction (feature selection and feature extraction) when performing classification using the Random Forest algorithm. A comparison was conducted between two ecosystems - heathlands and mires protected as Natura 2000 habitats. Two transformations of feature extraction were chosen, namely Minimum Noise Fraction (MNF) and Principal Component Analysis (PCA), while Linear Discriminant Analysis (LDA) was used as a feature selection method. It was proven that irrespective of the class type, accuracy is higher with the feature extraction method (mean F1 accuracy of 0.922) than with feature selection (mean F1 accuracy of 0.787). At the same time, no significant differences in accuracies were found between the MNF and PCA methods. Although LDA resulted in lower accuracies (0.816 for heathland and 0.750 for mires), the method could also be used due to relatively high F1 values. The effectiveness of the LDA method for feature reduction in open ecosystem identification was confirmed for the first time for open natural vegetation. [ABSTRACT FROM AUTHOR]

Introduction: Dysregulated androgen receptor (AR) activity is central to various diseases, particularly prostate cancer (PCa), in which it drives tumour initiation and progression. Consequently, antagonising AR activity via anti-androgens is an indispensable treatment option for metastatic PCa. However, despite initial tumour remission, drug resistance occurs. Therefore, the AR signalling pathway has been intensively investigated. However, the role of AR protein stability in AR signalling and therapy resistance has not yet been deciphered. Therefore, this study aimed to investigate the role of AR protein changes in transactivity and assess its mechanism as a possible target in PCa. Methods: LNCaP, C4-2, and 22Rv1 cells were treated with R1881, enzalutamide, cycloheximide, and Rocaglamide. Mass spectrometry analyses were performed on LNCaP cells to identify the pathways enriched by the treatments. Western blotting was performed to investigate AR protein levels and localisation changes. Changes in AR transactivity were determined by qPCR. Results: Mass spectrometry analyses were performed on LNCaP cells to decipher the molecular mechanisms underlying androgen- and antiandrogen-induced alterations in the AR protein. Pathway analysis revealed the enrichment of proteins involved in different pathways that regulate translation. Translational and proteasome inhibitor experiments revealed that these AR protein changes were attributable to modifications in translational activity. Interestingly, the effects on AR protein levels in castration-resistant PCa (CRPC) cells C4-2 or enzalutamide-resistant cells 22Rv1 were less prominent and non-existent. This outcome was similarly observed in the alteration of AR transactivation, which was suppressed in hormone-sensitive prostate cancer (HSPC) LNCaP cells by translational inhibition, akin to the effect of enzalutamide. In contrast, treatment-resistant cell lines showed only a slight change in AR transcription. Conclusion: This study suggests that in HSPC, AR activation triggers a signalling cascade that increases AR protein levels by enhancing its translation rate, thereby amplifying AR activity. However, this mechanism appears to be dysregulated in castration-resistant PCa cells. [ABSTRACT FROM AUTHOR]

In this research, women were diagnosed as pregnant based on blood serum samples Raman spectroscopy. Raman spectroscopy is a vibrational technique that provides information on the chemical composition of samples. The Raman techniques have significantly impacted the study of various degenerative diseases, particularly cancer detection, using less invasive methods such as the analysis of blood serum samples. Additionally, these techniques have been used to study the health status of patients, which is often difficult to monitor using conventional techniques. This study obtained serum samples from 12 women diagnosed as pregnant and 11 non-pregnant volunteers (controls). Spectra were collected using a LabRAM HR800 Raman Spectrometer (Horiba Jobin-Yvon) with an 830 nm wavelength laser. For each serum sample, 10 Raman spectra were obtained by focusing the laser, using a 100 × microscope objective, on different points of the sample with an exposure time of 40 seconds and an irradiation power of 17 mW. The raw spectra were processed using baseline correction and smoothing to remove noise, fluorescence, and shot noise. Subsequently, the spectra were normalized and analyzed using the multivariate statistical method of Principal Component Analysis (PCA). In these spectra, the characteristic bands of main blood serum biomolecules such as phenylalanine (Phe), tyrosine (Tyr), glutathione, amide III, phospholipid, carotene, and tryptophan (Trp) can be observed. Nevertheless, when analyzing the average spectra of pregnant and non-pregnant women, the main spectral differences were associated with variations in molecules like glutathione, tryptophan (Trp), tyrosine, and phenylalanine, which occur during the first trimesters of pregnancy. This aligns with findings previously reported by other studies. Furthermore, the serum samples from pregnant and non-pregnant patients can be effectively discriminated using PCA applied to the Raman spectra, revealing two distinct clusters in the PCA plot corresponding to each group's status. The results demonstrate that pregnancy can be determined based on blood serum samples Raman spectroscopy with 92.5 % sensitivity and 97.3 % specificity. Although there are very effective devices on the market to determine pregnancy based on the Human Chorionic Gonadotropin (HCG) hormone detection in urine samples, these preliminary results indicate an alternative method known as Raman spectroscopy. On the other hand, the results could also suggest the possibility of carrying out other gynecological or fetal-related studies in women using these Raman techniques. [ABSTRACT FROM AUTHOR]

Surface‐enhanced Raman spectroscopy (SERS) was utilized to measure low‐level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4‐methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0–339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm−1) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0–0.4 mM, medium = 0.4–14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of “blind” fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS. [ABSTRACT FROM AUTHOR]

Honeycomb-structured materials are extensively utilised in both commercial and military aircraft. The occurrence of manufacturing defects and operational damage has emerged as a significant safety concern, consequently elevating the necessity for non-destructive testing (NDT) to identify flaws and damage during aircraft operation and maintenance. In addition to merely detecting defects, it is crucial to accurately characterise or classify them. In this paper, the honeycomb sandwich samples were designed and manufactured from two CFRP face-sheets and Nomex core with thick resin containing nano clay 30B mixed with ultrasonic coverage. Defects were placed into carbon sheets, resin, and core materials. An NDT technique based on infrared thermography was applied, along with PCA image processing, which automatically categorises prevalent defects found in honeycomb materials. These defects encompass debonding, adhesive issues, and fractures within the honeycomb core. Thermography results showed that defects such as delamination, core fracture, lack of adhesion, and resin inhomogeneity can be identified, especially with high accuracy using PCA image processing. [ABSTRACT FROM AUTHOR]

Antibiotics have played a crucial role in significantly reducing the incidence of tuberculosis (TB) infection worldwide. Even before the mid‐20th century, the mortality rate of TB onset within five years was around 50 %. So, the introduction of antibiotics has changed the scenario of TB from a serious threat to a manageable one. However, the emergence of resistance to anti‐TB drugs poses a significant challenge. So, to overcome this situation the therapeutic approaches and drug targets need to be reformed. This study focused on finding potential inhibitors by targeting Pantothenate Synthetase, a crucial enzyme for Mycobacterium tuberculosis (Mtb) survival, through computational drug discovery methods. Molecular docking and virtual screening were employed to identify potential inhibitors from Diverse‐lib. Four compounds, namely CID2813602, 24357538, CID753354, and CID4798023, exhibited strong binding energies and stable interaction with the target protein. Further assessment of these compounds through MD simulation and Post MD simulation showed significant dynamic stability. The minimum energy transition calculated using the free energy landscape analysis of these compounds when docked with Pantothenate Synthetase confirmed the stability of each complex due to its minimum energy production. The free binding energy calculation of each complex also showed the intramolecular interaction contributes to the strong binding affinity of the compounds within the enzyme's active site clarifying their mechanisms of action. This research showcases the effectiveness of computational methods in promptly identifying potential anti‐TB drugs, paving the way for future experimental validation and optimization. It holds promise for the development of new treatments targeting drug‐resistant TB strains. [ABSTRACT FROM AUTHOR]

China is one of the regions most frequently affected by landslides, which have significant socio-economic impacts. Traditional slope stability analysis methods, such as the limit equilibrium method, limit analysis method, and finite element method, often face limitations due to computational complexity and the need for extensive soil property data. This study proposes a novel approach that combines Principal Component Analysis (PCA), Sparrow Search Algorithm (SSA), and Support Vector Machine (SVM) to improve the accuracy of slope stability prediction. PCA effectively reduces data dimensionality while retaining critical information. SSA optimizes SVM parameters, addressing the limitations of traditional optimization methods. The integrated PCA-SSA-SVM model was applied to a dataset of 257 slope stability samples and validated using five-fold cross-validation to ensure the model's generalization capability. The results show that the model exhibits superior performance in prediction accuracy, precision, recall, and F1-score, with the test set achieving an accuracy of 84.6%, a recall of 84.7%, a precision of 83.1%, and an F1-score of 84.6%. The model's robustness was further validated using slope data from the LongLian Expressway, demonstrating high consistency with the actual stability status. These findings indicate that the PCA-SSA-SVM-based slope stability prediction model has significant potential for practical engineering applications, providing a reliable and efficient tool for slope stability forecasting. Classify the training samples through cross-validation, using the accuracy of cross-validation as the fitness of the sparrow individual. Retain the optimal fitness value and position information. [ABSTRACT FROM AUTHOR]

Biochemical recurrence (BCR) is common in prostate cancer (PCa), and patients with BCR usually have a poor prognosis. Cuproptosis is a unique type of cell death, and copper homeostasis is crucial to the occurrence and development of malignancies. The present study aimed to explore the prognostic value of cuproptosis-related long non-coding RNAs (lncRNAs; CRLs) in PCa and to develop a predictive signature for forecasting BCR in patients with PCa. Using The Cancer Genome Atlas database, transcriptomic, mutation and clinical data were collected from patients with PCa. A total of 121 CRLs were identified using Pearson's correlation coefficient. Subsequently, a 6-CRL signature consisting of AC087276.2, CNNM3-DT, AC090198.1, AC138207.5, METTL14-DT and LINC01515 was created to predict the BCR of patients with PCa through Cox and least absolute shrinkage and selection operator regression analyses. Kaplan-Meier curve analysis demonstrated that high-risk patients had a low BCR-free survival rate. In addition, there was a substantial difference between the high- and low-risk groups in the immune microenvironment, immune therapy, drug sensitivity and tumor mutational burden. A nomogram integrating the Gleason score, 6-CRL signature and clinical T-stage was established and evaluated. Finally, the expression of signature lncRNAs in PCa cells was verified through reverse transcription-quantitative PCR. In conclusion, the 6-CRL signature may be a potential tool for making predictions regarding BCR in patients with PCa, and the prognostic nomogram may be considered a practical tool for clinical decision-making. [ABSTRACT FROM AUTHOR]

Understanding consumers' emotional responses to wine is essential for improving marketing strategies and product development. Emotions play a pivotal role in shaping consumer preferences. This study investigates the emotional reactions elicited by different types of Douro wines (white, red, and Port) through facial expression analysis using FaceReader software, version 9.0 (Noldus Information Technology, Wageningen, The Netherlands). A total of 80 participants tasted six wine samples, and their facial expressions were recorded and analyzed. FaceReader quantified the intensity of emotions such as happiness, sadness, anger, surprise, fear, and disgust. Arousal levels were also assessed. The results were analyzed through principal component analysis (PCA) to identify patterns and groupings based on emotional responses. White wines evoked more sadness due to their acidity, while red wines were associated with lower levels of sadness and greater comfort. Port wines elicited surprise, probably due to their sweet and fortified nature. Additionally, female participants showed consistently higher arousal levels than males across all wine types. The study highlights distinct emotional profiles for each type of wine and suggests that demographic factors, such as gender, influence emotional responses. These insights can inform targeted marketing and enhance the consumer experience through better alignment of wine characteristics with emotional engagement. [ABSTRACT FROM AUTHOR]

Predicting survival outcomes in critical accidents has been a focal point in machine learning research. This study addresses several limitations of existing methods, including insufficient management of data imbalance, lack of emphasis on hyperparameter tuning, and proneness to overfitting. Many existing models struggle to generalize effectively on imbalanced datasets or depend on default hyperparameter settings, resulting in biased predictions. By integrating Principal Component Analysis (PCA), hyperparameter optimization, and resampling methods, as well as combining Edited Nearest Neighbors (ENN) with the Synthetic Minority Oversampling Technique (SMOTE), the model significantly improves predictive accuracy and model generalization. An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. Stochastic Weighted Averaging (SWA) was applied to mitigate overfitting and enhance generalization. The accuracy increased from 91.97% to 94.89% after SWA was applied in this specific scenario. The combination of PCA-based dimensionality reduction, hyperparameter tuning, and resampling techniques (ENN + SMOTE) ensured the model handled data imbalance and optimized predictive accuracy. The final model demonstrated excellent performance, with Area Under the Curve (AUC) and Average Precision (AP) values both reaching 0.98, indicating high accuracy and precision. These improvements were validated using the Titanic dataset in a binary classification problem of predicting passenger survival. The results emphasize that ensemble learning, enhanced by SWA, offers a powerful framework for handling imbalanced and complex datasets, providing significant advancements in predictive modeling accuracy. This study provides insights into how machine learning techniques can be effectively combined to solve classification challenges in real-world scenarios. [ABSTRACT FROM AUTHOR]

In the current investigation, a novel methodology was employed to assess iron powder as a recyclable and sustainable energy carrier. Concurrently, an examination of the modeling of iron powder ignition and the ensuing heat output from the burner was undertaken. The flame temperature was determined by examining the light intensity emitted by the particles as they melted, which is directly related to the particle's cross-sectional area. An account of the characterization of the experimental procedure, validation, and calibration is presented. Through measurements, distinct one-to-one correlations have been established between the scales of flame combustion and the temperatures of particles of varying sizes of iron. Additionally, a theoretical model for the combustion of expanding particles, particularly iron, within the diffusion-limited regime has been rigorously developed. This model delves into the spectra acquired from particle flames within the burner, utilizing Partial Least Squares Regression (PLSR) and Principal Component Analysis (PCA). This study investigates the use of optical fiber spectroscopy to predict flame temperature and assess iron powder size. The aim was to investigate how different sizes of iron powder affect flame temperature and to create calibration models for non-destructive prediction. The study shows that smaller particles had an average temperature of 1381 °C while larger particles reach up to 1842 °C, demonstrating the significant impact of particle size on combustion efficiency. The results were confirmed using advanced statistical methods, including PLSR and PCA, with PCA effectively differentiating between particle sizes and PLSR achieving an R2 value of 0.90 for the 30 µm particles. [ABSTRACT FROM AUTHOR]

In the present study, the influence of heating on the evolution of oxidative indices, antioxidant activity, phenolic and volatile compounds in monovarietal extra virgin olive oils (EVOOs) obtained from Leccino, Istarska bjelica, and Buža cultivars was investigated. The samples were submitted to heating in an air oven (180 °C and 220 °C), simulating usual roasting conditions typical for Mediterranean cuisine. The decreases in the oxidative indicators, phenolic and volatile compounds were more pronounced at higher heating temperatures, underlining the temperature dependency of the oxidative degradation during heating conditions. Despite this, it must be emphasized that a significant amount of phenolic compounds and antioxidative activity remained preserved after the heating treatment. Each oil cultivar showed some specificity during the course of the thermal degradation. Hydroxytyrosol acetate among phenolic compounds and octanal, (E)-2-octenal, hexanal, 3-pentanone, and 1-penten-3-one among the volatiles were underlined as possible markers of thermal oxidation. Principal component analysis revealed that the content of volatile compounds in monovarietal EVOO samples distinguished samples primarily by the heating temperature, while the changes in the phenolic compounds were cultivar-dependent aside from being influenced by the temperature of heating. [ABSTRACT FROM AUTHOR]

The mulberry fruit is edible and medicinal, and it is commonly referred to as the "best health product of the 21st century". The purpose of this study was to find out whether different drying methods affect the quality of mulberry fruits and the main nature of the volatile organic compounds (VOCs) they contain. This study used vacuum freeze-drying (VFD), vacuum drying (VD), sun drying (SD), hot-air drying (HAD), and microwave drying (MD) to treat fresh mulberry fruits. Gas-phase ion mobility spectrometry (GC-IMS) was used to detect and analyze the VOCs in mulberry fruit samples treated with the different drying methods. There were 47 VOCs detected, with aldehydes and alcohols dominating. The obtained data were subjected to principal component analysis (PCA), cluster analysis (CA), nearest neighbor fingerprint analysis, and partial least-squares regression analysis (PLS-DA). The conclusion was drawn that fresh mulberry fruits contain abundant VOCs, and mulberry fruits after VD contain many aldehydes; thus, VD promoted the synthesis of phellandrene and other compounds widely used in the preparation of cosmetics such as perfume and soap. HAD promoted the synthesis of esters commonly used in the preparation of fruit flavor and wine essence. The higher (E)-2-heptenal content with SD was conducive to the Maillard reaction. MD promoted the synthesis of heptanal and valeraldehyde with aroma characteristics such as fatty, green, fruity, grassy, and floral. According to the VIP results, VOCs (E)-2-heptenal, pentanal D, cyclohexanone, and 2-hexanone D influenced the VOCs in most of the mulberry fruit samples. The findings of this study provide an important reference for drying mulberry fruits, which, in turn, will help to ensure the safety and effectiveness of processed mulberry fruit products. [ABSTRACT FROM AUTHOR]

Wine vinegar, valued for its ancient origins and culinary versatility, has garnered scientific interest due to its complex composition and potential health benefits. This study aims to explore the nutritional and bioactive properties of different wine vinegars, focusing on their amino acid content, particularly tryptophan-derived molecules such as serotonin and melatonin. White wine vinegar, red wine vinegar, port wine vinegar, and balsamic vinegar from the Douro and Rioja regions were analyzed using high-performance liquid chromatography and solid-phase extraction (HPLC-SPE). The study examined the amino acid profiles and the presence of serotonin and melatonin across the samples. The analysis revealed the presence of significant bioactive amino acids, including arginine (found in sample 059 at 61.21 mmol/L), alanine (in a concentration of 30.33 mmol/L in sample 209), and threonine (sample 336 presented the highest concentration—71.47 mmol/L), which have been linked to cardiovascular health, immune system support, and mucosal regulation. The amino acid content varied among the vinegar types, with slower acetification and prolonged aging reducing their concentrations. Tryptophan was mainly found in sample 059 (30.54 mmol/L). These findings, with their potential to influence the scientific community's understanding of the health-promoting properties of wine vinegar, particularly its amino acid content and the potential influence of production processes on bioactive molecules, are of great interest. [ABSTRACT FROM AUTHOR]

The body conformations of 262 adult Ganjam goats were subjected to principal component analysis (PCA) with 11 morphometric variables. The results were then used to predict the mature body weight of the goats. Most of the traits were positively correlated, and the correlations were statistically significant. The three main components that the PCA recovered explained 76.12% of the variation in body morphometry overall. The first component accounted for approximately 54.74% of the overall variation and described almost all the traits except ear length and tail length, as indicated by high component loadings. The second component accounted for approximately 11.48% of the variation, mostly accounting for the variation in tail length. The principal component accounted for 9.89% and mostly explained the variation in ear length. The communalities ranged between 0.557 (horn length) and 0.848 (chest circumference) for the first three extracted components. The highest percentage of variability in chest girth was explained by the first three principal components, whereas it was the lowest for the horn length. In the context of predicting body weight through stepwise regression analysis, nine primary variables accounted for 57.3% of the total variance in body weight. Conversely, utilizing the first principal component alongside six additional principal components as independent variables resulted in capturing 56.3% of the variation in the adult live weight of goats while maintaining model comparability with other pertinent parameters. PCA was used efficiently for body weight prediction from major morphometric traits of Ganjam goats addressing the multicollinearity issue. [ABSTRACT FROM AUTHOR]

Designed experiments were conducted to investigate the influence of ohmic heating (OH) at varying electric field strength (EFS) and holding time on the recovery of oil from mustard (Brassica juncea) seeds during mechanical expression. Hyperspectral imaging (HSI) in the visible-near infrared (Vis–NIR, 399–1003 nm) and short-wave infrared (SWIR, 895–1712 nm) ranges was used to visualize the change in oil distribution induced by OH on the mustard seeds. OH treatment led to an increase in expression of oil content by 25% as compared to control samples. Chemometric techniques, including partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR), were employed to analyze spectral data and develop models for predicting the enhancement in expressible oil due to OH treatment and its quality in terms of free fatty acids thereof. PLS-DA differentiated OH treated seeds from the control sample for by Vis–NIR and SWIR HSI at 93.0 and 95.8% accuracy, respectively. The variable selection method (iPLS) identified crucial wavelengths with minimal performance loss for accurate prediction. The PLSR model using SWIR HSI data accurately predicted oil content and fatty acid composition (R2 > 0.92), while Vis–NIR predictions exhibited a lower accuracy (R2 > 0.73). [ABSTRACT FROM AUTHOR]

Chlorophyll b quantities of raw (control) and roasted rapeseed samples were specified to be higher than chlorophyll a. Additionally, total phenol, total flavonoid quantities and antioxidant activity (DPPH and FRAP assays) values of roasted powdered and whole rapeseed samples were observed to increase compared to the control. K232 and K270 values ​​of conventional oven roasted, ground and whole rapeseeds were slightly higher than the results in the microwave. The highest K232 value was observed in oil provided from the sample that was powdered and heat treated in conventional oven. The phenolic constituents of ground and whole rapeseeds were partially higher than those of their oils. The dominant fatty acids of the oils extracted from unroasted and roasted rapeseeds were oleic, linoleic, linolenic and palmitic acids in decreasing order. Oleic acid quantities of the oil provided from powdered and whole rapeseeds roasted in microwave and oven were defined as 62.00 and 61.97% to 61.48 and 62.06%, respectively. The main variables of PC1 for rapeseed were dedected as catechin (0.912), coumaric acid (0.840) and ferulic acid (0.733). [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) attributes to intelligent sensors and actuators for better manufacturing and industrial operations. At the same time, IIoT devices must be secured from the potentially catastrophic effects of eventual attacks, and this necessitates real-time prediction and preventive strategies for cyber-attack vectors. Due to this, the objective of this investigation is to obtain a high-accuracy intrusion detection technique with a minimum payload. As the experimental process, we have utilized the IIoT network security dataset, namely WUSTL-IIOT-2021. The feature selection technique Particle Swarm Optimization (PSO) and feature reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are applied. Additionally, the Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS) are used to detect payloads that can interfere with the normal operation of an application. Both PSO and PCA combined with MARS have produced predictive results with an exceptional accuracy of 100%. Yet, the trained Machine Learning (ML) model is quantized with 4-bit and 8-bit, and it is deployed on Azure IoT Edge to simulate edge devices. Experimental results show that the latency of the model was reduced by 25% on quantization. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify the manufacturer of Hanji, Korean handmade paper widely used in conservation science. To achieve this, machine learning models utilizing attenuated total reflectance–infrared spectroscopy (ATR–IR) were developed to assess the robustness and effectiveness of the computed models. Principal component analysis (PCA), partial least squares–discriminant analysis (PLS–DA), decision tree (DT), and k-NN models were constructed using IR spectral data, with the spectral region between 1800 and 1500 cm⁻1 identified as the critical input variable through Variable Importance in Projection (VIP) scores. The transformation of the obtained spectra into second derivative spectra proved beneficial in this key spectral region, leading to significant improvements in model performance. Additionally, the application of DBSCAN for outlier detection was effective in refining the dataset, further enhancing the performance of the models. Specifically, the k-NN model, when applied to the selected variables and preprocessed with the second derivative transformation, achieved an F1 score of 0.92. These findings underscore the importance of focusing on the 1800–1500 cm⁻1 spectral range and applying outlier detection techniques, such as DBSCAN, to enhance the robustness and accuracy of the Hanji classification models by eliminating the influence of atypical data points. [ABSTRACT FROM AUTHOR]

Siliceous rocks served as raw materials in the production of stone tools from the Middle Paleolithic onwards. Due to migration, the provenance of archaeological artefacts can differ from their natural outcrop location. The aim of this work was the application of 1D and 2D low-field nuclear magnetic resonance (LF-NMR) relaxometry to distinguish cherts by their original source. Herein, bedded cherts and accompanying nodular cherts coming from three different outcrops of Kraków-Częstochowa Upland were investigated. 1D and 2D (T1-T2) experiments of water-saturated and dry rock sample states delivered T1, T2 times and T1/T2 ratios of distinct hydrogen populations – parameters sensitive to pore size, surface properties, and hydrogen bonding length. In-depth analysis of NMR data showed substantial differences in the porosity, pore surface and pore structure properties of investigated chert samples tested in the three different saturation levels (100% water-saturated, dried and differential). Finally, principal component analysis (PCA) was performed to reduce the number of correlations obtained and highlight the most important NMR properties specific to the particular outcrop localization."Please check captured corresponding author email if correct.""The email is correct" [ABSTRACT FROM AUTHOR]

Objective. To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications. Approach. Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting. Main results. It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation. Significance. This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU). [ABSTRACT FROM AUTHOR]

The 35 wooden coffin paintings from the Tubo period of the Tang Dynasty, housed in the collection of Qinghai Tibetan Medicine Cultural Museum, are valuable materials for studying Tubo culture. Research has shown that the wood used for coffin paintings was cypress. The adhesives contain bovine collagen and chicken ovalbumin. The pigments used include carbon black, azurite, cinnabar, orpiment, minium, and lead white. This article provides a statistical analysis of the types and areas of diseases that appear in wooden coffin paintings. Through the application of descriptive statistics, correlation coefficient analysis, principal component analysis, and cluster analysis, it was determined that deterioration phenomena such as rotten, crack, pulverization, and discoloration disease were particularly pronounced in coffin paintings. Key variables influencing the disease of wooden coffin paintings include crystal salt, drop, and pulverization. The dataset was categorized into three distinct clusters, each exhibiting significant differences. This study offers valuable insights and foundational support for the future conservation and restoration of coffin paintings. [ABSTRACT FROM AUTHOR]

The worm Caenorhabditis elegans, with its short lifecycle and well-known genetic and metabolic pathways, stands as an exemplary model organism for biological research. Its simplicity and genetic tractability make it an ideal system for investigating the effects of different conditions on its metabolism. The chemical analysis of this nematode was performed to identify specific metabolites produced by the worms when fed with either self- or nonself-DNA. A standard diet with OP50 feeding was used as a control. Different development stages were sampled, and their chemical composition was assessed by liquid chromatography–mass spectrometry combined with chemometrics, including both principal component analysis and orthogonal partial least squares discriminant analysis tools. The obtained data demonstrated that self-DNA-treated larvae, when arrested in their cycle, showed significant decreases in dynorphin, an appetite regulator of the nematode, and in N-formyl glycine, a known longevity promoter in C. elegans. Moreover, a substantial decrease was also recorded in the self-DNA-fed adults for the FMRF amide neuropeptide, an embryogenesis regulator, and for a dopamine derivative modulating nematode locomotion. In conclusion, this study allowed for the identification of key metabolites affected by the self-DNA diet, providing interesting hints on the main molecular pathways involved in its biological inhibitory effects. [ABSTRACT FROM AUTHOR]

Natural compounds are important source of desired biological activity which helps to improve nutritional status and brings many health benefits. Ilex paraguariensis St. Hill. which belongs to the family Aquifoliaceae is a plant rich in bioactive substances (polyphenols, saponins, alkaloids) with therapeutic potential including hepatic and digestive disorders, arthritis, rheumatism, and other inflammatory diseases, obesity, hypertension, hypercholesterolemia. In terms of phytochemical research I. paraguariensis has been the subject of most intensive investigations among Ilex species. Therefore, we concentrated on other available Ilex varieties and focused on the content of fatty acids of these shrubs. The fatty acid compounds present in Ilex sp. samples were analyzed by GC-MS. 27 different fatty acids were identified in the extracts. The results showed that many constituents with significant commercial or medicinal importance were present in high concentrations. The primary component in all samples was α linolenic acid(18:3 Δ9,12,15). Differences of this component concentration were observed between cultivars and extensively analyzed by PCA, one- way ANOVA and Kruskal-Wallis ANOVA. Significant correlations between compound concentrations were reported. [ABSTRACT FROM AUTHOR]

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite's all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology. [ABSTRACT FROM AUTHOR]

In this study, we evaluated 16 strains of Sarcomyxa edulis from the Changbai Mountains, China through antagonism tests and ISSR molecular marker technique for cytological and molecular biological evaluation. Q-type and R-type cluster analyses were utilised to evaluate and classify agronomic traits according to the test guidelines for distinctness, uniformity, and stability. The tested strains showed antagonistic reactions. The differences in antagonistic reactions among different germplasm resources are helpful for evaluating genetic diversity and provide a basis for better development and utilisation of fungal germplasm resources. A total of 330 bands were amplified by 21 ISSR primers, including 321 polymorphic bands. The percentage of polymorphic bands observed was 97.07%. Using PopGen32 software, the effective number of alleles (Ne), Nei's gene diversity index (H), and Shannon's diversity index (I) were calculated, yielding average values of 1.6638, 0.3796, and 0.5480, respectively. In addition, the genetic similarity coefficient of 16 strains ranged from 0.4306–0.7778. According to the Q-type clustering, the tested strains were classified into two categories based on the Euclidean distance of 8.52. While R-type cluster analysis revealed a significant correlation between the traits. At the Euclidean distance of 6.298, the traits could be classified into three distinct categories. According to the findings, all 16 strains demonstrated high genetic diversity. The ISSR clustering results revealed that strains with shorter genetic distances were more similar in specific traits. However, there were notable differences between the results of comprehensive trait clustering (Q-type clustering) and ISSR clustering. [ABSTRACT FROM AUTHOR]

This research paper proposes a framework utilizing multicriteria tools for optimal site selection of photovoltaic solar farms. A comparative analysis was conducted using three quantitative methods—CRITIC (criteria importance through intercriteria correlation), PCA (principal component analysis), and entropy—to obtain the weights for the selection process. The evaluation considered environmental, demographic, financial, meteorological, and performance system criteria. TOPSIS (technique for order preference by similarity to ideal solution) was employed to rank the alternatives based on their proximity to the ideal positive solution and distance from the ideal negative solution. The capital cities of the seven departments in the Colombian Caribbean region were selected for the assessment, characterized by high annual solar radiation, to evaluate the suitability of the proposed decision-making framework. The results demonstrated that Barranquilla consistently ranked in the top two across all methods, indicating its strong performance. Cartagena, for instance, fluctuated between first and third place, showing some stability but still influenced by the method used. In contrast, Sincelejo consistently ranked among the lowest positions. A sensitivity analysis with equal weight distribution confirmed the top-performing cities, though it also highlighted that the weight assignment method impacted the final rankings. Choosing the appropriate method for weight calculation depended on factors such as the diversity and interdependence of criteria, the availability of reliable data, and the desired sensitivity of the results. For instance, CRITIC captured inter-criteria correlation, while PCA focused on reducing dimensionality, and entropy emphasized the variability of information. [ABSTRACT FROM AUTHOR]

Antibiotics can be effectively removed from wastewater using constructed wetlands (C.W.s). However, little is known about using attractive garden plants in C.W.s to eliminate antibiotics. Thus, the current study aims to treat amoxicillin (AMX)-contaminated wastewater through a Rhapis excelsa-based bioretention system (BS). The investigation was done at 15 days hydraulic retention time (HRT) under two conditions: set-1, varied AMX 5 to 25 ppm with constant NPK (nitrogen, phosphorus, potassium) source; and set-2, varied NPK sources with constant AMX (25 ppm). During the study, it was observed that in the set-1 condition with increasing AMX concentration, the removal of AMX through BS decreased; however, in the set-2 experiment, with enhancing NPK source, the performance of the BS treating 25-ppm AMX-contaminated wastewater increased. AMX removal of 2.3%, 66.3%, 60.6%, 52.2%, 46.7%, and 44.9% was achieved for control, BS-1, BS-2, BS-3, BS-4, and BS-5, respectively, during set-1 experiment. However, in the set-2 experiment, 23.4% (control), 43.3% (BS-1), 60.3% (BS-2), 75.9% (BS-3), 88.8% (BS-4), and 99% (BS-5) AMX removal were achieved. Removing pollutants like AMX, COD, PO43−-P, NO3−-N, and NH4+-N followed first-order kinetics. A positive correlation of COD with AMX was observed through principal component analysis and correlation matrix. The microbial community study was also covered to prioritize the role of microbes in treating AMX through BSs. The AMX treatment through Rhapis excelsa-based BS supported plant growth and development with increasing chlorophyll content, fresh weight, and C, H, N value. [ABSTRACT FROM AUTHOR]

Drought is one of the abiotic stresses that can reduce crop yields. It has a major impact on crop yield reduction. For crops under stress, organic modifiers such as biochar can be useful. Guar (Cyamopsis tetragonoloba L.), an annual legume from the Fabaceae Family, is highly adaptable to arid and semi-arid regions, with many applications in various industries. Field experiments were carried out in a randomized complete block design with three replications using a split-split plots arrangement. The aim was to evaluate the influence of irrigation levels (Ir1 = 10, Ir2 = 14, and Ir3 = 17 days irrigation cycle) and biochar (B1 = 0, B2 = 5, and B3 = 10 tons ha−1) application on physiological traits [(chlorophyll a and b, chlorophyll index (SPAD), relative leaf water content (RWC), electrolyte leakage (EL), canopy temperature, leaf area, water use efficiency (WUE)], morphological parameters (length and diameter of the stem, pod length, fresh weight of root and plant, root length), yield components (seed yield, number of branch plant−1, number of clusters plant−1, pod plant−1, seed pod−1, seed plant−1, 1000-seed weight, and gum contents), and leaf nutrient uptake (Ca, Mg, P, Na, and K) of two commercial and local cultivars (cv1 = RGC-936 and cv2 = Saravan) of the guar plant. It was observed that the Ir3 irrigation treatment produced the highest seed yield (1921.8 kg ha−1) in terms of water stress. However, the maximum pod plant−1 (75.5), seed plant−1 (454.2), seed yield (1871.1 kg ha−1), leaf area (861.8 mm2), SPAD (92.2), Mg (49.8 mg g−1), Na (43.3 mg g−1) and P (0.49 mg g−1) were observed in RGC-936. The results also revealed that biochar was more effective than cultivars in terms of morphological traits. While yield and yield components were affected by cultivar, irrigation at different levels also had a significant effect on functional traits, physiology, and morphology. The addition of biochar appeared to have a positive effect on water stress alleviation and guar growth and leaf nutrient uptake. According to Pearson's correlation analysis, plant weight and length, root weight and length, stem diameter, seed pod−1, branches plant−1, and 1000-seed weight are moderately correlated with seed yield, while pod plant−1 and seed plant−1 are strongly associated with seed yield. On the other hand, the pod length, branches plant−1, and gum content showed a positive but not significant relationship. [ABSTRACT FROM AUTHOR]

In the context of hyperspectral image (HSI) analysis, a widely used feature extraction method, Principal Components Analysis (PCA) suffers from limitations such as wavelength bias and a lack of consideration for local spectral information. While various segmentation based PCA methods attempt to address these issues by incorporating local relationships, they still overlook band similarity beyond immediate neighbours. To address these challenges, this paper introduces a novel approach called dependency based segmented PCA (dPCA). This method employs hierarchical clustering-driven mutual information-based segmentation, facilitating more comprehensive feature extraction from HSI data. By utilizing this dependency-based segmentation, both global and local structures are effectively captured, providing enhanced details for classification tasks. The proposed dPCA is evaluated on four prominent HSI datasets in remote sensing for land use classification, and the experimental results underscore its superiority over conventional PCA, and other segmentation based PCA methods in terms of classification performance. [ABSTRACT FROM AUTHOR]

AbstractLaurus nobilis L. is a polypragmatic plant with high added value. Leaves collected from different regions in the natural state without breeding have different economic values. In this study, the Geographic Origin and Altitude Effects of Laurus nobilis L. leave samples collected from provinces close to each other were investigated. For this purpose, SEM–EDS was used for morphological characterization of leaves, XRD was used for the characterization of inorganic components and FTIR-ATR was used for the identification of functional groups. By SEM analysis in the samples from Zonguldak region, significant differences were observed in the density of trichomes and glandular plumes more dense and also morphological cell sizes were found to be more large then Kastamonu region. In XRD plot, besides the amorphous state, it has seen that the crystal structure is also present in the structure. The effect of different altitudes on the FTIR spectra of the samples collected according to the provinces was also examined, and no significant difference was observed in the peak positions and intensities in the spectra according to the altitudes. Score plot on ranges of 2800–3000 cm-1 and 750–1800 cm−1 allowed origin of the Laurus nobilis L. discrimination. In order to analysis and classify them according to their geographical origin, FTIR data were combined with chemometric methods using PCA and discriminant analysis. PCA analysis explained 88.7% of the variance in the model. When the results of the discriminant analysis were evaluated, 56 of the 72 samples were classified correctly and 77.8% success was achieved. Both analysis showed that there are differences in the response of the same plant species to their presence in different origins. As a result, FTIR analysis allowed possibility the discrimination of Laurus nobilis L. leaves and in this way it is thought to discriminate of examples for other areas. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has transformed our world by connecting smart devices and enabling seamless interactions. This reliance, however, has led to new security issues and types of attacks. It is of the utmost importance to safeguard the security of IoT networks, with network intrusion detection systems (NIDS) having a significant impact. This paper proposes a novel approach integrating Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Convolutional Neural Network (CNN) to overcome these security issues. Our innovative method reduces data dimensionality and selects highly correlated features using PCC and PCA, addressing overfitting and improving model performance while maintaining high computational speed and low costs. Our approach uniquely distinguishes between benign and threat packets by employing 1D-CNN, 2D-CNN, and 3D-CNN algorithms trained on Edge-IIoTset and NSL-KDD benchmark datasets. The findings from our experiments indicate that the proposed framework significantly enhances accuracy, precision, recall, and F1-score compared to existing models for both binary and multiclass classifications. Our binary classification models achieved exceptional performance, with an average accuracy of 99.76%, 99.79% precision, 99.89% recall, and 99.85% F1-score on the Edge-IIoTset dataset. On the NSL-KDD dataset, the models attained 99.20% accuracy, 98.07% precision, 97.95% recall, and 97.71% F1-score. For multiclass classification, the proposed model demonstrated an average accuracy of 99.41%, precision of 98.61%, recall of 98.49%, and an F1-score of 98.56% on the Edge-IIoTset dataset. On the NSL-KDD dataset, the model achieved 92.43% accuracy, 93.21% precision, 93.60% recall, and a 93.7% F1-score. Our research introduces a significant advancement that substantially improves NIDS capabilities, making IoT networks safer and more connected. [ABSTRACT FROM AUTHOR]

Academic writing assessment is pivotal in university career. Some teachers tend to assign holistic marks to written compositions, ignoring assessment criteria. However, this paper highlights the importance of systematicity in assessment using scoring rubrics that are designed according to the context and population they are directed to. This paper suggests a paragraph scoring rubric after performing exploratory Principal Components Analysis (PCA). A set of 19 evaluation criteria is suggested and piloted on 51 paragraphs written by first-year university students from the Department of English at Tebessa University. After setting oblique rotation, PCA indicates that the variables are correlated (sig. > 0.001 in Bartlett's test), and are extracted into five factors that are correlated as well (r > 0.32). The PCA pattern matrix indicates that some variables have cross-loadings, so they should be deleted. Therefore, the initial variables are extracted into four factors, named academic writing style, paragraph structure, lexis, and writing mechanics. These components constitute the holistic scoring rubric, which is recommended in scoring paragraphs, and which is tested to validate its groupings and psychometric properties. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess volatile component profiles and the antioxidant activity of propolis samples from eight different locations in Türkiye. α‐Pinene, β‐pinene, 3‐carene, limonene, 2‐acetylfuran, benzaldehyde, acetic acid, benzoic acid, longifolene, isopentyl acetate, m‐cymene, styrene, and δ‐cadinene were the most common volatile components found in the most of propolis samples. Principal component analysis (PCA) and hierarchical cluster analysis were performed on the gas chromatography–mass spectrometry (GC–MS) data to identify trends and clusters in the propolis samples. As a result of the PCA, the common components in all propolis were m‐cymene, decanal, α‐pinene, limonene, pinocarvone, benzaldehyde, and butanoic acid. The samples had 2,2‐diphenyl‐1‐picrylhydrazyl radical (DPPH) inhibition activity ranging from 19.2% to 92.5% and 2,2′‐azinobis(3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS) values ranging from 480 to 1370 μM trolox/g extract. The total phenolic content (TPC) ranged from 10,900 to 34,033 mg GAE/100 g. Compared with butylated hydroxytoluene (BHT) (control), all but one of the propolis samples exhibited higher DPPH activity. In addition, ABTS levels of propolis extracts were higher than those of BHT. These results unequivocally show that Turkish propolis has remarkable antioxidant qualities, which makes it a viable option for addition to food and medicine products. [ABSTRACT FROM AUTHOR]

The mass of nutrients accumulated in the vegetative parts of winter wheat (WW) in the period from the beginning of booting to the full flowering stage (Critical Cereal Window, CCW) allows for the reliable prediction of the grain yield (GY) and its components, and the grain protein content (GPC) and its yield. This hypothesis was verified in a one-factor field experiment carried out in the 2013/2014, 2014/2015, and 2015/2016 growing seasons. The field experiment included seven nitrogen-fertilized variants: 0, 40, 80, 120, 160, 200, and 240 kg N ha−1. The N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu content in wheat vegetative parts (leaves, stems) was determined in two growth stages: (i) beginning of booting (BBCH 40) and (ii) full flowering (BBCH 65). We examined the response of eight WW traits (ear biomass at BBCH 65, EAB; grain yield, GY; grain protein content, GPC; grain protein yield, GPY; canopy ear density, CED; number of grains per ear, GE; number of grains per m−2—canopy grain density, CGD; and thousand grain weight, TGW) to the amount of a given nutrient accumulated in the given vegetative part of WW before flowering. The average GY was very high and ranged from 7.2 t ha−1 in 2016 to 11.3 t ha−1 in 2015. The mass of ears in the full flowering stage was highest in 2016, a year with the lowest GY. The highest N mass in leaves was also recorded in 2016. Only the biomass of the stems at the BBCH 65 stage was the highest in 2015, the year with the highest yield. Despite this variability, 99% of GY variability was explained by the interaction of CGD and TGW. Based on the analyses performed, it can be concluded that in the case of large yields of winter wheat, GE is a critical yield component that determines the CGD, and in consequence the GY. The leaf nutrient mass at the BBCH 40 stage was a reliable predictor of the GPC (R2 = 0.93), GPY (0.92), GE (0.84), and CED (0.76). The prediction of the GY (0.89), CGD (0.90), and TGW (0.89) was most reliable based on the leaf nutrient mass at the BBCH 65 stage. The best EAB prediction was obtained based on the mass of nutrients in WW stems at the BBCH 65 stage. The magnesium accumulated in WW parts turned out to be, with the exception of TGW, a key predictor of the examined traits. In the case of the TGW, the main predictor was Ca. The effect of Mg on the tested WW traits most often occurred in cooperation with other nutrients. Its presence in the developed stepwise regression models varied depending on the plant part and the WW trait. The most common nutrients accompanying Mg were micronutrients, while Zn, Fe, Mn, and Ca were the most common macronutrients accompanying Mg. Despite the apparently small impact of N, its yield-forming role was indirect. Excessive N accumulation in leaves in relation to its mass in stems, which appeared in the full flowering phase, positively impacted the EAB and GPC, but negatively affected the GE. Increasing the LE/ST ratio for both Mg and Ca resulted in a better formation of the yield components, which, consequently, led to a higher yield. This study clearly showed that nutritional control of WW during the CCW should focus on nutrients controlling N action. [ABSTRACT FROM AUTHOR]

In order to determine the diversity in local seedling origin mango genotypes from different five districts (Navsari, Valsad, Dang, Tapi and Surat) of South Gujarat region during 2021–2023, which have been found in orchards or backyards by using IPGRI mango descriptor. The exploration of genetic resources was carried out to find alternative genotype against prevailing varieties in the state and it sustain mango production in future. A total of 113 mango seedlings were identified and characterized on basis of 7 qualitative, 12 quantitative and 6 biochemical parameters using Completely Randomized Design with three replications of quantitative characteristics. High variation among genotypes was observed with respect qualitative, quantitative and biochemical parameters. Ten superior genotypes were identified based on five commercially important traits viz., fruit weight (> 200 g), pulp percentage (> 60.00%), TSS (> 20 °Brix), shelf life (> 7 days) and overall acceptability (> 7 point). The genetic variability revealed that higher heritability coupled with higher genetic advance as per cent of mean for 12 quantitative characteristics namely mature fruit weight, fruit length, fruit width, pulp weight, peel weight, stone weight, TSS, acidity, ascorbic acid, total sugars, reducing sugars and non-reducing sugars. The First three Principal component contributed 70.21% of total variation and characters such as mature fruit weight, fruit length, fruit width, pulp weight, peel weight and stone weight most contributing towards diversity in germplasm. Hierarchical cluster analysis resulted the genotypes was grouped into three clusters and genotypes present in cluster III (20) and cluster I (39) had found most divers from each other. [ABSTRACT FROM AUTHOR]

The degree to which landscape genetics findings can be extrapolated to different areas of a species range is poorly understood. Here, we used a broadly distributed ectothermic lizard (Sceloporus occidentalis, Western Fence lizard) as a model species to evaluate the full role of topography, climate, vegetation, and roads on dispersal and genetic differentiation. We conducted landscape genetics analyses with a total of 119 individuals in five areas within the Sierra Nevada mountain range. Genetic distances calculated from thousands of ddRAD markers were used to optimize landscape resistance surfaces and infer the effects of landscape and topographic features on genetic connectivity. Across study areas, we found a great deal of consistency in the primary environmental gradients impacting genetic connectivity, along with some site‐specific differences, and a range in the proportion of genetic variance explained by environmental factors across study sites. High‐elevation colder areas were consistently found to be barriers to gene flow, as were areas of high ruggedness and slope. High temperature seasonality and high precipitation during the winter wet season also presented a substantial barrier to gene flow in a majority of study areas. The effect of other landscape variables on genetic differentiation was more idiosyncratic and depended on specific attributes at each site. Across study areas, canyon valleys were always implicated as facilitators to dispersal and key features linking populations and maintaining genetic connectivity, though the relative importance varied in different areas. We emphasize that spatial data layers are complex and multidimensional, and careful consideration of spatial data correlation structure and robust analytic frameworks will be critical to our continued understanding of spatial genetics processes. [ABSTRACT FROM AUTHOR]

Quantification of X‐ray photoelectron spectroscopy (XPS) data is often limited by the heterogeneous nature of the material surface. However, it is often the case that heterogeneous material contains areas within the analyzed area that are effectively homogeneous. In this Insight note, concepts, and methods used to analyze both XPS data are presented to extract both spatial and spectral information from heterogeneous surfaces. These concepts and methods are applied to a specific material surface that contains three chemical compounds separated spatially. The analysis entails converting XPS image data to spectral data and is designed to highlight the potential of XPS imaging in revealing compositional information correlation with spatial information. Properties of algorithms used to evaluate XPS images and spectra are described to outline their application to image data. A case study of an imaging XPS data set is presented that demonstrates how poor signal‐to‐noise images, where the signal is recorded for 4 s per image, are still open to analysis yielding useful information. Ultimately, the methods presented here will aid in interpreting complex XPS data obtained from spatially complex materials often obtained during extensive cycling, such as conventional or electrocatalysts. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC‐MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi‐toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute‐and‐shoot LC‐MS/MS‐based multi‐analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. RESULTS: We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi‐mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. CONCLUSION: The results presented in this study underline the added value of combining targeted LC‐MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Summary: Three different Acacia seeds (A. retinodes, A. provincialis and A. tenuissima) harvested from different locations and in different years were analysed for their variations in proximate and mineral composition. Results showed no one species had the highest content across all proximate and mineral analyses, for example, A. retinodes Harmans 2020 had the highest ash (3.7%), A. retinodes Harmans 2022 had the highest protein content (31.3%), A. tenuissima Hindmarsh 2020 had the highest fat content (18.5%) and A. provincialis Tarrington 2022 had the highest magnesium content (469 mg/100 g). Principle component analysis was carried out to determine the effect of species, harvest locations and years on the chemical composition. A biplot of the first two principal components with a total of 60.5% variation showed clustering based on harvest years. The compositions of the Acacia seeds were determined to be affected by species, harvest location and year differences. However, a complete gene–environment interaction study is needed to validate this. [ABSTRACT FROM AUTHOR]

Improving the low productivity levels of native cattle breeds in smallholder farming systems is a pressing concern in Pakistan. Crossbreeding high milk-yielding holstein friesian (HF) breed with the adaptability and heat tolerance of Sahiwal cattle has resulted in offspring that are well-suited to local conditions and exhibit improved milk yield. The exploration of how desirable traits in crossbred dairy cattle are selected has not yet been investigated. This study aims to provide the first overview of the selective pressures on the genome of crossbred dairy cattle in Pakistan. A total of eighty-one crossbred, thirty-two HF and twenty-four Sahiwal cattle were genotyped, and additional SNP genotype data for HF and Sahiwal were collected from a public database to equate the sample size in each group. Within-breed selection signatures in crossbreds were investigated using the integrated haplotype score. Crossbreds were also compared to each of their parental breeds to discover between-population signatures of selection using two approaches: cross-population extended haplotype homozygosity and fixation index. We identified several overlapping genes associated with production, immunity, and adaptation traits, including U6, TMEM41B, B4GALT7, 5S_rRNA, RBM27, POU4F3, NSD1, PRELID1, RGS14, SLC34A1, TMED9, B4GALT7, OR2AK3, OR2T16, OR2T60, OR2L3, and CTNNA1. Our results suggest that regions responsible for milk traits have generally experienced stronger selective pressure than others. [ABSTRACT FROM AUTHOR]

Summary: Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. [ABSTRACT FROM AUTHOR]

We consider the problem of large‐scale finite‐sum minimization on Riemannian manifold. We develop a sub‐sampled adaptive trust region method on Riemannian manifolds. Based on inexact information, we adopt adaptive techniques to flexibly adjust the trust region radius in our method. We present the iteration complexity is Omax{εg−2εH−1,εH−3}$$ O\left(\max \left\{{\varepsilon}_g^{-2}{\varepsilon}_H^{-1},{\varepsilon}_H^{-3}\right\}\right) $$ when the algorithm attains an (εg,εH)$$ \left({\varepsilon}_g,{\varepsilon}_H\right) $$‐second‐order stationary point, which matches the result on trust region method. Numerical results for PCA on Grassmann manifold and low‐rank matrix completion are reported to demonstrate the effectiveness of the proposed Riemannian method. [ABSTRACT FROM AUTHOR]

Study was conducted to determine the impact of current velocity on density and abundance of benthic macroinvertebrate fauna in an Indian Himalayan River, the Ramganga. The samples were collected at 8 different stations from headwater to mouth (S1 to S8) during October 2014 to March 2015. One-time intensive sampling (20 quadrates per station) was performed in the dry-period. The benthic macroinvertebrate samples were collected to all possible current velocities at each station from 7 cm to 55 cm depth. and they were categorized in to 5 categories of current velocity; extreme low (type I, 0–15 cm s–1), low (type II, 15–30 cm s–1), mild (type III, 30–45 cm s–1), moderate (type IV, 45–60 cm s–1) and high (type V, >60 cm s–1). The standard methods were used to collect invertebrate samples and identify at family level. The total mean density of invertebrate fauna decreased from type I (616 ind. × m–2) to type II (358.87 ind. × m–2), and increased to type III (925.25 ind. × m–2), but again decreased to type IV (539.37 ind. × m–2) and type V (85.25 ind. × m–2). The mild velocity (type III) was most important for providing maximum density and richness. The total density (ind. × m–2) between every two current velocity types was not found significantly different except current velocity type V-I. Total 34 taxa were observed among all the stations, maximum number (22) taxa were found in type III, while lowest number (8) in type IV. However, out of 34 taxa, Glossoscolecidae was most abundant in type I velocity, Simulidae in type II, Leptoceridae in type III and V, while Psephenidae in type IV. Similarly, PCA also suggested the characteristics taxa at each velocity type. [ABSTRACT FROM AUTHOR]

Sunflower (Helianthus annuus L.), known for its adaptability and high yield potential, is vital in global edible oil production. Estimating genetic diversity is a key pre-breeding activity in crop breeding. The current study comprised of 48 genotypes which were assessed for their biometrical traits at department of Oilseeds, Tamil Nadu Agricultural University, during the rainy season of 2022. The lines were subsequently characterised using 103 simple sequence repeat (SSR) markers for molecular diversity analysis. The results indicated that the net nucleotide distances indicated varying genetic divergence, with subpopulations II and V showing the highest (0.056) and I and IV the lowest (0.014). Subpopulation IV exhibited the highest heterozygosity (0.352), while subpopulation III had the lowest heterozygosity and a low Fst (0.173). Principal components analysis (PCA) and hierarchical cluster analysis were employed for assessing the morphological diversity, facilitating genotype grouping and parent selection for breeding programs. The first four components cumulatively accounted for 86.72% of the total variation. Cluster Analysis grouped 48 sunflower genotypes into three clusters based on genetic diversity. COSF 13B stands out for its high head diameter, oil content, seed yield, and oil yield based on mean performance of morphological data. Principal coordinate analysis (PCoA) mirrored the groupings from the Neighbor Joining method, with the first three components explaining 27.24% of the total variation. Molecular data analysis identified five distinct clusters among the germplasm. By integrating morphological and molecular marker data with genetic distance analysis, substantial diversity was revealed with the genotypes RHA 273 and GMU 325 consistently demonstrated high oil yield per plant. The genotypes GMU 477, GMU 450, COSF 13B, RHA 102, CMS 1103B, and RHA GPR 58 have been identified as suitable parents for enhancing oil content in sunflower breeding programs. These findings also aid in selecting SSR markers for genotype characterization and in choosing diverse parents for breeding programs. [ABSTRACT FROM AUTHOR]

In the present study, descriptive and multivariate statistical techniques (principal component analysis) were used to investigate groundwater salinity in the area adjacent to Lake Dayet Erroumi. Nine groundwater samples were collected during September 2019 and analyzed for the following physicochemical variables: pH, EC, DO, Ca2+, Mg2+, Na+, K+, HCO3 -, Cl-, SO4 2- and NO3 - . On the basis of on concentration averages, cation abundance is Na+ > Ca2+ > Mg2+ > K+ and anion abundance is Cl- > HCO3 - > SO4 2- > NO3 - . Two principal components were selected on the basis of eigenvalue, which explains 71.39% of the total variance. The first principal component (F1) accounts for 52.37% of total variance and indicates water salinization, which depends on abiotic factors. The second principal component (F2) explains 19.01% of the information and indicates parameters dependent on biotic factors (DO and pH). Projection of the observations revealed two groups of wells: the first group comprises the wells characterized by very high salinity, and the second group comprises the wells with lower salinity. These results show that the wells on the southern shore of the lake are more highly mineralized than other wells. The high mineralization of the groundwater is of natural origin, due to the leaching of Triassic evaporitic rocks. [ABSTRACT FROM AUTHOR]

An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal ("ideal") optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400–1100 nm) and near-infrared (900–1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context. [ABSTRACT FROM AUTHOR]