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BACKGROUND: Spearmint and peppermint teas are widely consumed around the world for their flavor and therapeutic properties. Dynamic headspace sampling (HS) coupled to gas chromatography/mass spectrometry (GC–MS) with principal component analysis (PCA) of 'fingerprint' volatile profiles were used to investigate 27 spearmint and peppermint teas. Additionally, comparisons between mint teas were undertaken with an electronic nose (enose). RESULTS: Twenty compounds, all previously known in the literature, were identified using HS–GC–MS. PCA found distinct differences between the fingerprint volatile profiles of spearmint, peppermint and spearmint/peppermint combination teas. HS–GC–MS analysis performed with an achiral column allowed faster processing time and yielded tighter clustering of PCA tea groups than the analysis which used a chiral column. Two spearmint outliers were detected. One showed a high degree of variation in volatile composition and a second wholly overlapped with the peppermint PCA grouping. Enose analysis separated all treatments with no overlaps. CONCLUSION: Characterizing the volatile fingerprints of mint teas is critical to quality control for this valuable agricultural product. The results of this study show that fingerprint volatile profiles and enose analysis of mint teas are distinctive and could be used to rapidly identify unknown samples. With specific volatile profiles identified for each tea, samples could be tested in the laboratory, or potentially on a farm or along the supply chain, to confirm the provenance and authenticity of mint food or beverage commodities. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

BACKGROUND: The present study focuses on the aroma and secondary metabolites profiling of three Pelargonium graveolens cultivars, baladi (GRB), sondos (GRS) and shish (GRSH), grown in Egypt. Utilizing a multiplex approach combining high resolution‐ultraperformance liquid chromatography (HR‐UPLC)/tandem mass spectrometry (MS/MS) and gas chromatography (GC)‐MS coupled with chemometrics, the study aims to identify and profile various secondary metabolites and aroma compounds in these cultivars. RESULTS: HR‐UPLC/MS/MS analysis led to the annotation of 111 secondary metabolites, including phenolics, flavonoids, terpenes and fatty acids, with several compounds being reported for the first time in geranium. Multivariate data analysis identified vinylanisole, dimethoxy‐flavonol, and eicosadienoic acid as discriminatory metabolites among the cultivars, particularly distinguishing the GRS cultivar in its phenolics profile. In total, 34 aroma compounds were detected using headspace solid‐phase microextraction coupled with GC‐MS, including alcohols, esters, ketones, ethers and monoterpene hydrocarbons. The major metabolites contributing to aroma discrimination among the cultivars were β‐citronellol in GRB, α‐farnesene in GRS and isomenthone in GRSH. CONCLUSION: The study provides a comprehensive profiling of the secondary metabolites and aroma compounds in the three Pelargonium graveolens cultivars. The GRS cultivar was identified as particularly distinct in both its phenolics and aroma profiles, suggesting its potential as a premium variety for cultivation and use. Future studies should focus on isolating and investigating the newly detected metabolites and exploring the biological effects of these compounds in food applications and other uses. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: Pitaya fruit (Hylocereus spp.) is rich in bioactive compounds such as betanin. This study aimed to extract betanin‐rich pitaya fruit and encapsulate it in electrospun nanofibers produced with sweet potato starch. The influence of different concentrations of this bioactive compound on the morphology, functional groups, hydrophilicity, load capacity, color, thermal properties, and contact angle of the electrospun nanofibers with water and milk was assessed. The potential antioxidant and stability of nanofibers during gastrointestinal digestion in vitro were demonstrated. Results: The nanofibers presented average diameters ranging from 134 to 204 nm and displayed homogeneous morphology. The load capacity of the extract in the nanofibers was 43% to 83%. The encapsulation increased the thermal resistance of betanins (197–297 °C). The static contact angle with water and milk showed that these materials presented greater affinity with milk. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that the nanofibers with 5%, 25%, and 45% pitaya extract presented unique characteristics. They showed resistance in delivering betanins to the stomach, with 12% inhibition of the 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH˙) radical. However, only the 45% concentration reached the intestine with 9.83% inhibition of the DPPH˙ radical. Conclusions: Pattern recognition from multivariate analyses indicated that nanofibers containing 5%, 25%, and 45% of the extract presented distinct characteristics, with the ability to preserve betanins against thermal degradation and perform the controlled delivery of these bioactives in the stomach and intestine to produce antioxidant activity. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Baijiu is a traditional Chinese liquor produced from grains through fermentation, distillation, aging and blending. The flavor of Baijiu is influenced by factors such as raw materials, starter, processes and the environment, and since the relationship between these factors and the flavor of Baijiu is still being analyzed, the identification of different Baijiu is still somewhat difficult. In this paper, the volatile differential markers of 42 types of strong‐aroma Baijiu of different origin, alcohol content and grade were explored. RESULTS: A total of 24 volatile substances were detected by gas chromatography–electronic nose (GC‐E‐Nose) and 99 volatile substances were detected by gas chromatography–time‐of‐flight mass spectrometry (GC‐TOF MS). The peak areas of the substances obtained by GC‐E‐Nose were analyzed by the partial least squares (PLS) method, and the substances with variable importance in projection (VIP) >1 were screened out. Combined with the qualitative results of GC‐TOF MS, four substances (isobutyric acid, 2‐butanone, 2,3‐butanediol and 3‐methylbutyric acid) were selected as volatile differential markers for strong‐aroma Baijiu. An external standard curve was established to accurately quantify these four substances, and the Kruskal–Wallis test confirmed that the absolute contents of these four substances varied significantly among different samples (P < 0.01). Principal component analysis and hierarchical cluster analysis based on the absolute content of these four substances showed that different samples were prioritized for different alcohol contents. CONCLUSION: These four substances can be used as volatile differential markers of strong‐aroma Baijiu samples. This research provides theoretical support for the detection and improvement of Baijiu product quality. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In this study, thalli nitrogen (N) and metal ions contents in the two fruticose lichens (Ramalina intermedia and Usnea cornuta) were quantified, which were collected from a pristine Himalayan forest of Shey Phoksundo National Park of western Nepal. Besides, the probable impacts of N and metal ions in physicochemical responses such as electrical conductivity, chlorophyll contents, and chlorophyll degradation were studied. Our initial hypothesis was there are considerable amounts of thallus N and metal ions in lichens from the study area, and it has substantial impacts on physicochemical responses. In comparison, the thalli N concentration was observed to be the greatest in R. intermedia thalli (0.24%–1.15%). However, comparable amounts of metal ions in both lichen species were observed. Regression analysis revealed the least or no impacts of N and metal ions in physicochemical responses. The principal component analysis suggests the contribution of different environmental variable and their correlation with lichen's variables. This study provides evidence of the least concentration of N and metal ions and only minor impacts on the physicochemical integrity of the two sensitive lichens in our study area. This study is likely to contribute as field‐based reference information for further studies on the species‐specific N and metal ions accumulation in lichens from different Himalayan forest systems to monitor ecosystem health. [ABSTRACT FROM AUTHOR]

ABSTRACT Recently, optically pumped magnetometers (OPMs), which use the spin polarization of alkali metal atoms to measure magnetic fields, have attracted much attention. However, in order to realize high‐sensitivity biomagnetic field measurements using OPMs, it is necessary to reduce environmental magnetic noise and system noise. In this study, we investigate the effectiveness of the noise reduction of OPM signals when principal component analysis, independent component analysis, and empirical mode decomposition are used for signal processing. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION The relationship between oat grain composition and physical attributes as influenced by oat genotype and Canadian growing environments was investigated. Thirty Canadian oat (Avena sativa L.) genotypes, grown in three Canadian growing locations (Brandon, Manitoba; Portage la Prairie, Manitoba; and Lacombe, Alberta) over 2 consecutive years (2020–2021), were analyzed.Analysis of variance showed that the protein, total starch, and amylose content were significantly affected by genotype, environment, and their interaction. A principal component analysis bi‐plot illustrated that protein and total starch had an inverse relationship and were more affected by growing year, whereas amylose content had a negligible influence. The majority of genotypes were stable across environments but some genotypes, like CDC Morrison, were more influenced by different environments. Correlation analysis suggested that drought‐like conditions early in the growing season generated oat genotypes that favored the accumulation of protein, β‐glucan, and oil.The results provide detailed information regarding the relationship between important oat chemical and physical traits and different growing environments, which can assist breeders to improve characteristics to obtain high‐quality oat grains and thus high‐quality end products. © 2025 His Majesty the King in Right of Canada and The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Reproduced with the permission of the Minister of Agriculture and Agri‐Food Canada. [ABSTRACT FROM AUTHOR]

This study focuses on identifying distinct precipitation zones across Europe, Spain and Catalonia, and second, examining how various large‐ and small‐scale climatic patterns affect the precipitation in these zones. Previous research has focused primarily on the relationships between individual climatic indices and precipitation in specific regions but has often overlooked the combined influence of multiple climate signals on precipitation variability. To address these issues, this study proposes the use of principal component analysis (PCA) as a multivariate analysis framework to investigate the complex relationships amongst multiannual precipitation patterns at different spatial scales, specifically in Europe, Spain and Catalonia. Distinct correlations amongst total annual precipitation occur in European countries, Spanish provinces and small Catalonian regions. Europe and Spain have five precipitation zones, whereas Catalonia has four. The calculated trends indicate a total precipitation reduction in the Iberian Peninsula, western Mediterranean and southwestern Europe, with a projected further decrease. Conversely, northern and central Europe anticipate normal to high precipitation tendencies. A second PCA application explores time and spatial correlations between precipitation zones and local/global climatic indices. The Southern Annular Mode, key Pacific teleconnections (PNA, TNA, WHWP, PACWARM and BEST) and confirmed Atlantic patterns (EA, NAO and AO) emerged as influential. The WeMO and MO indices showed the expected relevance at local spatial resolutions. Multivariate data analysis methods for two‐ or multidimensional datasets, which span multiple years and various spatial units (countries/provinces/regions), can extend the use of multivariate data analysis tools for correlation analysis over time in diverse geographical areas, including other continents, with varying spatial and temporal resolutions. The inclusion of monthly average precipitation data as an additional dimension in datasets analysed by multivariate statistical methods, such as PCA, will improve the knowledge of spatiotemporal climate variability. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time‐dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO.This retrospective cohort study included 927 adults admitted to an ICU for ≥72 h. FO was defined as a positive fluid balance ≥7% of admission body weight. After reviewing medication administration record data in 3‐h periods, medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess their temporal association with FO.FO occurred in 127 (13.7%) of 927 included patients. Patients received a median (interquartile range) of 31(13–65) discrete intravenous medication administrations over the 72‐h period. Across all 47,803 intravenous medication administrations, 10 unique medication clusters, containing 121 to 130 medications per cluster, were identified. The mean number of Cluster 7 medications administered was significantly greater in the FO cohort compared with patients without FO (25.6 vs.10.9, p < 0.0001). A total of 51 (40.2%) of 127 unique Cluster 7 medications were administered in more than five different 3‐h periods during the 72‐h study window. The most common Cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of Cluster 7 medications to an FO prediction model including the Acute Physiologic and Chronic Health Evaluation (APACHE) II score and receipt of diuretics improved model predictiveness from an Area Under the Receiver Operation Characteristic (AUROC) curve of 0.719 to 0.741 (p = 0.027).Using machine learning approaches, a unique medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict FO compared to traditional prediction models. Integration of this approach into real‐time clinical applications may improve early detection of FO to facilitate timely intervention. [ABSTRACT FROM AUTHOR]

Summary Background Objectives Study design Methods Results Main limitations Conclusions Assuming that the ventricular myocardium of horses is subjected to exercise‐induced hypertrophy, we hypothesised that the mean electrical axis (MEA) of the heart would change.To define a longitudinal study to detect any changes in the direction of the MEA in Thoroughbred horses using ECG.ECGs were recorded on each horse in each training group at day 0 (T0), 1 month (T1) and 2 months (T2) of training.A total of 43 Thoroughbred horses in training in Italy were recruited. The horses were divided into three groups according to age. The ECGs were recorded by positioning the electrodes according to Dubois's method for measuring MEA in the frontal plane. Intervals with artefact‐free QRS complexes in both bipolar DI and augmented unipolar aVF leads were selected, and the vector obtained was identified as the MEA. The statistical analysis was performed via generalised linear mixed model (GLMM) and principal component analysis (PCA).A statistically significant effect of time passing between T0 and T2 (p < 0.001) and an interaction between time and sex on the MEA was found (p = 0.04). PCA revealed that the population studied had different patterns, with three horses showing higher variability in the MEA direction.There was no good sex balance in the age groups of the population studied, and there was no control group. The 1‐month sampling intervals of ECGs may have been too short. Confirmatory studies are needed.We believe that our results are the first to suggest that training may lead to changes in MEA orientation in horses. Sex and individuality were found to influence MEA orientation and may have contributed to the difficulty in detecting training‐dependent changes in MEA to date. [ABSTRACT FROM AUTHOR]

Fractures influence the mechanical strength of coal roof and floor, constraining the design of hydraulic fracturing for coalbed methane production. Currently, the predominant approach involves the integration of petrophysical logging with machine learning for fracture prediction. Nevertheless, challenges exist regarding the model's accuracy. In this study, we present a novel approach to predict fracture density. Our method optimises a back‐propagation (BP) neural network and utilises principal component analysis for feature extraction. We employ logging parameters (density, compensated neutron and acoustic time difference) obtained from Shouyang Block well SY‐1 and fracture density data from electrical imaging logging to construct the FVDC model's dataset. The BP neural network model is optimised using the Sparrow Search algorithm and Tent Chaotic Mapping. The results demonstrate a substantial enhancement over the BP neural network model, with reductions of 80.102% in mean absolute error, 94.182% in mean square error, 75.879% in root mean square error and 79.764% in mean absolute percentage error. When considering accuracy, the optimised model (97.098%) surpasses the support vector regression model (96.478%), the random forest model (94.404%) and the BP neural network model (85.657%). Scalability testing for the optimised model was conducted using data from well SY‐2, yielding a remarkable prediction accuracy of 96.775%. This performance exceeds that of the BP neural network (with an accuracy of 85.102%), as well as the random forest and support vector regression models (with accuracies of 91.234% and 90.384%, respectively). These results underscore the potential of well logging and machine learning in FVDC prediction. [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]

People have beliefs about the unacceptability of the expression and experience of negative emotions. These beliefs affect psychological health and can have a negative effect on the treatment and symptoms of people with mental disorders and medically unexplained symptoms. This study aimed to develop a Japanese version of the Beliefs about Emotions Scale (BES‐J) and evaluate its reliability and validity. In an online survey, participants with fibromyalgia (n = 226) and healthy controls (n = 184) completed the BES‐J and questionnaires concerning perfectionism, dysfunctional attitudes, depression, anxiety, pain, disability, well‐being, interdependent happiness, and loneliness. The results of the principal component analysis showed that the BES‐J comprised a one‐factor structure, identical to the original. The BES‐J had good internal consistency (.89) and showed a significant correlation with the questionnaires. The BES‐J showed good internal reliability, concurrent validity, and test–retest reliability. The present study suggests that the Japanese version of the BES is appropriate for use with Japanese speakers. [ABSTRACT FROM AUTHOR]

Comparisons between contemporary and historic surveys are useful in assessing how fish assemblages respond to anthropogenic stressors. When these stressors degrade aquatic environments, assemblages often experience biotic homogenization. The Strawberry River flows through the Ozarks of northern Arkansas and has been subject to both pasture land use conversion and constructed waterbodies which can degrade aquatic environments and alter fish assemblages. We investigated how fish assemblages in the Strawberry River have changed over a 35‐year time span in response to pasture land use and constructed waterbodies. We found evidence of both taxonomic and functional homogenization of fish assemblages from the mid‐1980s to 2019. This shift towards homogenization was primarily driven by increases in both site occurrence and abundance of generalist centrarchid species (associated with land use practices) and headwater specialist species (likely related to increased intermittency upstream). We created a composite variable using principal component analysis that represented pasture land use and constructed water body metrics because of their close relationship with each other. We found evidence of early functional differentiation associated with this composite variable; however, we found that over time fish assemblages ultimately experienced greater levels of homogenization associated with this same variable. This pattern of biotic homogenization associated with species additions suggests the Strawberry River is at a tipping point along a subsidy stress gradient, and in the future, we expect to see losses of specialist endemic species if conservation actions are not taken. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) captures characteristic oscillatory shifts in infant brain rhythms over the first year of life, offering unique insights into early functional brain development and potential markers for detecting neural differences associated with autism. This study used functional principal component analysis (FPCA) to derive dynamic markers of spectral maturation from task‐free EEG recordings collected at 3, 6, 9, and 12 months from 87 infants, 51 of whom were at higher likelihood of developing autism due to an older sibling diagnosed with the condition. FPCA revealed three principal components explaining over 96% of the variance in infant power spectra, with power increases between 6 and 9 Hz (FPC1) representing the most significant age‐related trend, accounting for more than 71% of the variance. Notably, this oscillatory change occurred at a faster rate in infants later diagnosed with autism, indicated by a steeper trajectory of FPC1 scores between 3 and 12 months (p < 0.001). Age‐related spectral changes were consistent regardless of familial likelihood status, suggesting that differences in oscillatory timing are associated with autism outcomes rather than genetic predisposition. These findings indicate that while the typical sequence of oscillatory maturation is preserved in autism, the timing of these changes is altered, underscoring the critical role of timing in autism pathophysiology and the development of potential screening tools. [ABSTRACT FROM AUTHOR]

Explaining the decisions made by population‐based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in areas that increasingly require end‐user input and confirmation, the need to explain the internal decisions being made has grown. In this article, we present our approach to the extraction of explanation supporting features using trajectory mining. This is achieved through the application of principal components analysis techniques to identify new methods of tracking population diversity changes post‐runtime. The algorithm search trajectories were generated by solving a set of benchmark problems with a genetic algorithm and a univariate estimation of distribution algorithm and retaining all visited candidate solutions which were then projected to a lower dimensional sub‐space. We also varied the selection pressure placed on high fitness solutions by altering the selection operators. Our results show that metrics derived from the projected sub‐space algorithm search trajectories are capable of capturing key learning steps and how solution variable patterns that explain the fitness function may be captured in the principal component coefficients. A comparative study of variable importance rankings derived from a surrogate model built on the same dataset was also performed. The results show that both approaches are capable of identifying key features regarding variable interactions and their influence on fitness in a complimentary fashion. [ABSTRACT FROM AUTHOR]

Online shopping has become an essential part of modern life, with food delivery being one of the most popular services. In this study, we investigate the factors influencing customers' preferences in online food ordering, such as easy payment, customization, and fast delivery. Analysing customer ratings and reviews is crucial in ensuring the quality of food services and promoting a healthy lifestyle. However, the trustworthiness of online reviews remains a challenge. Based on their rating, new customers can get help from online shoppers for their preferences or demands. Data are collected from customer reviews through online platforms to improve food service quality in online shopping. This work aims to predict the preferences of customers in online shopping based on optimized feature extraction using Principal Component Analysis with a Social Spider Optimization (PCA‐SSO) algorithm. Despite existing algorithms for analysing customer preferences, most may have inaccurate predictions or take time to analyse data. To overcome these challenges, this model of PCA‐SSO is proposed to predict customers' more accurate online shopping preferences. This model provides valuable implications for online shopping owners regarding customer satisfaction. Compared to the accuracy rates of KNN (78.22%), LDA (83.16%), and PCA (88.56%) algorithms, our proposed algorithm of PCA‐SSO achieved a higher accuracy of 93.54%. [ABSTRACT FROM AUTHOR]

Lavender, widely cultivated in the Mediterranean region, produces essential oil known for its significant biological activities and is a key component of the perfume industry due to its high levels of Linalool and Linalyl acetate, along with low Camphor content, which contributes to its high cost. However, the market is plagued by adulterated lavender oil, often mixed with cheaper alternatives such as eucalyptus and rosemary. Current detection methods, primarily gas chromatography, are expensive, time‐consuming and often fail to detect low levels of adulteration. To address these limitations, this study examines the use of mid‐infrared spectroscopy for the detection and prediction of adulteration levels. A set of 105 samples, comprising pure lavender oil and adulterated lavender oil, was prepared in the laboratory. Principal component analysis (PCA), hierarchical clustering ascending (HCA) and K‐means clustering were applied to the FT‐MIR results for qualitative analysis to effectively discriminate between authentic and adulterated essential oils. For quantitative analysis, partial least squares regression (PLSR) was used to develop accurate calibration models for predicting the percentage of adulteration. The results from PCA, HCA and K‐means demonstrated the efficacy of these techniques in detecting adulteration, even at low levels (2%). Calibration models were developed using the PLSR method with different spectral preprocessing techniques to predict the percentage of adulteration, with results indicating that models generated on the raw data and those using MSC (multiplicative signal correction) pre‐processing are optimal. In addition, the use of interval‐partial least squares (IPLS) variable selection techniques (Forward, Backward) improved the predictive accuracy of the models developed by reducing the number of wavelengths used. [ABSTRACT FROM AUTHOR]

Kudoa thyrsites infection of marine fish typically results in myoliquefaction, which is only apparent 24 to 56 h post‐mortem. The traditional methods for the detection of K. thyrsites infected fish are time‐consuming and destructive, reducing its marketability. This poses a challenge for the fish industry to remove infected fish before it reaches the market or further processing activities. This study investigated the use of near‐infrared (NIR) spectroscopy, in combination with soft independent modelling of class analogy (SIMCA) and partial least square discriminant analysis (PLS‐DA), for discriminating K. thyrsites infected fish from uninfected fish. Performance of the classification models was evaluated by calculating the sensitivity, specificity and precision. A total of 334 fish samples (200 sardine, 64 hake and 70 kingklip) were used for this study. Infection of K. thyrsites was determined with the use of qPCR assays. Ninety per cent (90%) of the sardine samples, 78% of the hake samples and 37% of the kingklip samples were infected. Class groups of infected and uninfected fish samples were created for the purpose of generating SIMCA and PLS‐DA classification models for each species of fish, as well as for a species independent data set. Principal component analysis (PCA) of NIR spectra did not show any clustering for infected and uninfected samples. Calibration and test sample sets were generated for the purpose of building and testing the SIMCA and PLD‐DA classification models. SIMCA and PLS‐DA were unable to classify test samples correctly into the two classes. The number of misclassifications (NMC) was higher for the SIMCA models than for the PLS‐DA models, with more than 60% incorrectly classified. SIMCA classified most of the test samples into both classes. The precision for PLS‐DA were 89% for sardine, 81% for hake, 0% for kingklip and 87% for species independent models, however, most samples were classified at infected. The use of NIR spectroscopy and classification models such as SIMCA and PLS‐DA showed limited use as a method to distinguish between K. thyrsites infected and uninfected fish samples. Textural and chemical changes during extended frozen storage of the fish samples may have masked the effects associated with K. thyrsites infection. Further studies are suggested where NIR spectroscopy is used in combination with texture analysis and image spectroscopy. [ABSTRACT FROM AUTHOR]

Statistical shape modeling (SSM) offers the potential to describe the morphological differences in similar shapes using a compact number of variables. Its application in orthopedics is rapidly growing. In this study, an SSM of the intramedullary canal of the proximal femur was built, with the aim to better understanding the complexity of its shape which may, in turn, enhance the preoperative planning of total hip arthroplasty (THA). This includes the prediction of the prosthetic femoral version (PFV) which is known to be highly variable amongst patients who have undergone THA. The model was built on three dimensional (3D) models of 64 femoral canals which were generated from pelvic computed tomography images including the proximal femur in the field of view. Principal component analysis (PCA) was performed on the mean shape derived from the model and each segmented canal. Five prominent modes of variations representing approximately 84% of the total 3D variations in the population of shapes were found to capture variability in size, proximal torsion, intramedullary femoral anteversion, varus/valgus orientation, and distal femoral shaft twist/torsion, respectively. It was established that the intramedullary femoral canal is highly variable in its size, shape, and orientation between different subjects. PCA‐driven SSM is beneficial for identifying patterns and extracting valuable features of the femoral canal. [ABSTRACT FROM AUTHOR]

In the first part of the study, we sampled and investigated the composition of commercial glasses in the UK market from 2022 to 2023, as well as property data provided by various models. In this part, we utilize principal component analysis (PCA) to conduct a comparative analysis, integrating these data with the composition of commercial glass documented in previous literature. The widely held belief that the composition of commercial soda–lime–silica glasses has remained essentially unchanged over the past 30+ years is challenged by this research. The differences in composition of current commercial glass compositions compared to glasses from 30 to 40 years ago have been quantified. This not only sheds light on the direction of travel and reasons for adjustments to UK glass compositions over recent decades, but it also provides insights and predictions into the future trends. [ABSTRACT FROM AUTHOR]

People have beliefs about the unacceptability of the expression and experience of negative emotions. These beliefs affect psychological health and can have a negative effect on the treatment and symptoms of people with mental disorders and medically unexplained symptoms. This study aimed to develop a Japanese version of the Beliefs about Emotions Scale (BES‐J) and evaluate its reliability and validity. In an online survey, participants with fibromyalgia (n = 226) and healthy controls (n = 184) completed the BES‐J and questionnaires concerning perfectionism, dysfunctional attitudes, depression, anxiety, pain, disability, well‐being, interdependent happiness, and loneliness. The results of the principal component analysis showed that the BES‐J comprised a one‐factor structure, identical to the original. The BES‐J had good internal consistency (.89) and showed a significant correlation with the questionnaires. The BES‐J showed good internal reliability, concurrent validity, and test–retest reliability. The present study suggests that the Japanese version of the BES is appropriate for use with Japanese speakers. [ABSTRACT FROM AUTHOR]

Articular cartilage (AC) is a specialized connective tissue that covers the ends of long bones and facilitates the load‐bearing of joints. It consists of chondrocytes distributed throughout an extracellular matrix and organized into three zones: superficial, middle, and deep. Nuclear magnetic resonance (NMR) techniques can be used to characterize this layered structure. In this study, devoted to a better understanding of the NMR response of this complex tissue, 20 specimens excised from femoral and tibial cartilage of bovine samples were analyzed by the low‐field single‐sided NMR‐MOUSE‐PM10. A multiparametric depth‐wise analysis was performed to characterize the laminar structure of AC and investigate the origin of the NMR dependence on depth. The depth dependence of the single parameters T1, T2, and D has been described in literature, but their simultaneous measurement has not been fully exploited yet, as well as the extent of their variability. A novel parameter, α, evaluated by applying a double‐quantum‐like sequence, has been measured. The significant decrease in T1, T2, and D from the middle to the deep zone is consistent with depth‐dependent composition and structure changes of the complex matrix of fibers confining and interacting with water. The α parameter appears to be a robust marker of the layered structure with a well‐reproducible monotonic trend across the zones. The discrimination of cartilage zones was reinforced by a multivariate principal component analysis statistical analysis. The large number of samples allowed for the identification of the smallest number of parameters or their combination able to classify samples. The first two components clustered the data according to the different zones, highlighting the sensitivity of the NMR parameters to the structural and compositional variations of AC. Using two parameters, the best result was obtained by considering T1 and α. Single‐sided NMR devices, portable and low‐cost, provide information on NMR parameters related to tissue composition and structure. [ABSTRACT FROM AUTHOR]

ABSTRACT Gastric cancer (GC) is a highly lethal malignancy, seriously threatening people's physical health. Accurate screening of gastric cancer could improve the survival rate of patients. Therefore, exploring noninvasive and efficient cancer screening methods for gastric cancer is of great significance. In the past few years, exosomes have received much attention for their potential in disease diagnosis and treatment. Here, the aim of this study was to explore the detection of serum exosomes via surface‐enhanced Raman spectroscopy (SERS) technique based on TiN‐Ag@Ag sol composite substrate, and its potential application in gastric cancer diagnosis is evaluated. Exosomes were extracted from the serum of 31 GC patients and 31 healthy controls (HC) using an exosome kit. This study used various machine learning algorithms such as principal component analysis linear discriminant analysis (PCA‐LDA), partial least squares discriminant analysis (PLS‐DA), support vector machine (SVM), and k‐nearest neighbor (KNN) algorithm to analyze SERS spectra, in order to distinguish between HC and GC. The results show that the k‐nearest neighbor algorithm performs the best in HC and GC classification. These results indicate that the combination of SERS and machine learning methods provides a new technological approach for gastric cancer screening. This study offers a new proposal for the universal applicability of analysis and identification with SERS of serum exosomes samples in clinical diagnosis. [ABSTRACT FROM AUTHOR]

In studying the association between clinical measurements and time‐to‐event outcomes within a cure model, utilizing repeated observations rather than solely baseline values may lead to more accurate estimation. However, there are two main challenges in this context. First, longitudinal measurements are usually observed at discrete time points and second, for diseases that respond well to treatment, a high censoring proportion may occur by the end of the trial. In this article, we propose a joint modelling approach to simultaneously study the longitudinal observations and time‐to‐event outcome with an assumed cure fraction. We employ the functional principal components analysis (FPCA) to model the longitudinal data, offering flexibility by not assuming a specific form for the longitudinal curve. We used a Cox's proportional hazards mixture cure model to study the survival outcome. To investigate the longitudinal binary observations, we adopt a quasi‐likelihood method which builds pseudo normal distribution for the binary data and use the E‐M algorithm to estimate the parameters. The tuning parameters are selected using the Akaike information criterion. Our proposed method is evaluated through extensive simulation studies and applied to a clinical trial data to study the relationship between the longitudinal prostate specific antigen (PSA) measurements and overall survival in men with metastatic prostate cancer. [ABSTRACT FROM AUTHOR]

Background: The success of a Mendelian randomization (MR) study critically depends on the validity of the assumptions underlying MR. We focus on detecting heterogeneity (also known as horizontal pleiotropy) in two‐sample summary‐data MR. A popular approach is to apply Cochran's Q$$ Q $$ statistic method, developed for meta‐analysis. However, Cochran's Q$$ Q $$ statistic, including its modifications, is known to lack power when its degrees of freedom are large. Furthermore, there is no theoretical justification for the claimed null distribution of the minimum of the modified Cochran's Q$$ Q $$ statistic with exact weighting (Qmin$$ {Q}_{\mathrm{min}} $$), although it seems to perform well in simulation studies. Method: The principle of our proposed method is straightforward: if a set of variables are valid instruments, then any linear combination of these variables is still a valid instrument. Specifically, this principle holds when these linear combinations are formed using eigenvectors derived from a variance matrix. Each linear combination follows a known normal distribution from which a p$$ p $$ value can be calculated. We use the minimum p$$ p $$ value for these eigenvector‐based linear combinations as the test statistic. Additionally, we explore a modification of the modified Cochran's Q$$ Q $$ statistic by replacing the weighting matrix with a truncated singular value decomposition. Results: Extensive simulation studies reveal that the proposed methods outperform Cochran's Q$$ Q $$ statistic, including those with modified weights, and MR‐PRESSO, another popular method for detecting heterogeneity, in cases where the number of instruments is not large or the Wald ratios take two values. We also demonstrate these methods using empirical examples. Furthermore, we show that Qmin$$ {Q}_{\mathrm{min}} $$ does not follow, but is dominated by, the claimed null chi‐square distribution. The proposed methods are implemented in an R package iGasso. Conclusions: Dimension reduction techniques are useful for generating powerful tests of heterogeneity in MR. [ABSTRACT FROM AUTHOR]

Sangihe nutmeg is an important crop because of its usefulness in the pharmacology, spices and cosmetics industries. Sangihe is the oldest active subduction zone island in the Indonesia–Philippines region, where frequent tectonic earthquakes and the geographic and reproductive isolation of Sangihe nutmeg occur. This isolation results in adaptation and speciation because of increasing variability. Using morphological and molecular approaches, we aimed to assess the genetic variety of Sangihe nutmeg based on morphological and intersimple sequence repeat (ISSR) markers. In total, 31 morphological characteristics were examined, and molecular data of ISSR markers using five primers were analysed based on numerical taxonomy using the unweighted pair group method with arithmetic mean (UPGMA) and principal component analysis (PCA) methods. Results showed five major groups of Sangihe nutmeg based on seed variation, that is, thick round, thin round, thick oval, thin oval, and twin seeds, whereas the presence of variation in molecular characters was indicated by DNA polymorphisms between 0% and 33.33%. The phenetic relationships within Sangihe nutmeg, based on morphological and ISSR markers, exhibit two groups with different member compositions. The major morphological characteristics influencing the phenetic relationship pattern were tree shape, leaf shape, fruit shape, number of fractions when ripe, fruit size index, number of fruit indentations, indentation, aryl density, seed shell, and number of seeds. [ABSTRACT FROM AUTHOR]

In this study, we leverage 25 years of observations from spaceborne radars, along with coincident reanalysis data, to determine how the depth and width of precipitating convective storms are related to the large‐scale environments in which they are observed. We find that the deepest convective features are observed in environments markedly different from the environments of other convective features, including organized convection. Deep storms co‐occur with relatively dry, unstable conditions, while wide storms are observed in moist, relatively stable environments. We identify eight large‐scale environmental variables that serve to distinguish between storm modes, and then show that principal component analysis can be used to condense this information into just two scalar variables. The methodology presented offers a succinct way to describe a storm's environment and will allow us to better relate a storm's initial environment to its dynamical characteristics. Plain Language Summary: Convective storms are important to understand because of the many ways in which they impact the atmosphere and life on Earth. Our study uses data from satellite missions to look at how large‐scale environmental factors relate to storm characteristics. We match radar observations of storm structures with environmental data like temperature, moisture, and winds. Using a statistical method called principal component analysis, we can then represent these complex environmental patterns in a simplified manner using two main variables. This helps us understand how different types of storms relate to their surroundings. We find that deep storms are often associated with relatively drier, warmer conditions, while wide storms are observed in moister, cooler environments. Using the analytical tools developed here, future research can use new satellite mission observations to better understand storm dynamics and intensity, and their relationship with the environment. Key Points: Eight large‐scale environmental variables are identified that are useful for distinguishing between global convective storm modesPrincipal component analysis can collapse most of the information in these variables into just two dimensions that separate deep storm environments from other storm environmentsThis framework will be useful in the future to connect storm dynamics to storm environments, and to track environmental changes over the lifetime of a convective system [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION There is limited information on the effect of potassium (K) on the taste quality of rice. Field experiments with five K fertilizer application rates (0, 60, 120, 180, and 240 kg K2O ha−1) were conducted in 2019 and 2020 using two cultivars (Xiadao No. 1 and Shenliangyou 5814) to study the effects of K fertilization on grain yield, taste quality, starch components, and protein components in grains.Compared with a no‐potassium treatment (K0), K fertilization increased the grain yield significantly: by 57.29% for Xiadao No. 1 and 33.51% for Shenliangyou 5814. It did this mainly by increasing the number of rice spikelets per panicle and improving the seed‐setting rate. K fertilization improved the taste value by 12.64% for Xiadao No. 1 and 10.83% for Shenliangyou 5814. Starch components, protein components, and viscosity parameters were also influenced by K fertilization. With increased K application, the amylose content and the ratio of amylose content to amylopectin content tended to increase. K fertilization increased the peak viscosity and cool paste viscosity, while decreasing the prolamin content and pasting temperature. Principal component analysis highlighted the importance of amylose content and the ratio of amylose content to amylopectin content in determining taste value, texture, and appearance.Applying 180 kg K2O ha−1 of K fertilizer ensured high rice yields and taste quality, providing a basis for the production of high‐yield and high‐quality rice. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

We developed a single cell amine analysis approach utilizing isobarically multiplexed samples of 6 individual cells along with analyte abundant carrier. This methodology was applied for absolute quantitation of amino acids and untargeted relative quantitation of amines in a total of 108 individual cells using nanoflow LC with high‐resolution mass spectrometry. Together with individually determined cell sizes, this provides accessible quantification of intracellular amino acid concentrations within individual cells. The targeted method was partially validated for 10 amino acids with limits of detection in low attomoles, linear calibration range covering analyte amounts typically from 30 amol to 120 fmol, and correlation coefficients (R) above 0.99. This was applied with cell sizes recorded during dispensing to determine millimolar intracellular amino acid concentrations. The untargeted approach yielded 249 features that were detected in at least 25 % of the single cells, providing modest cell type separation on principal component analysis. Using Greedy forward selection with regularized least squares, a sub‐selection of 100 features explaining most of the difference was determined. These features were annotated using MS2 from analyte standards and accurate mass with library search. The approach provides accessible, sensitive, and high‐throughput method with the potential to be expanded also to other forms of ultrasensitive analysis. [ABSTRACT FROM AUTHOR]

Alternaria leaf blight (ALB), caused by Alternaria brassicicola, poses a significant threat to radish cultivation, particularly in the agroclimatic conditions of Jammu and Kashmir, India. This study investigates the epidemiology of ALB during 2019/2020 and 2020/2021 growing seasons, focusing on the impact of meteorological factors on disease progression. Disease severity was monitored across various radish‐growing regions, revealing considerable spatial and temporal variability. The analysis identified maximum temperature (Tmax) as the most critical factor influencing percent disease index (PDI), with a strong positive correlation observed in both seasons, particularly in 2020/2021. Conversely, maximum relative humidity (Max RH) showed a negative correlation with PDI, suggesting complex interactions between temperature and humidity in disease dynamics. Principal component analysis further highlighted the distinct weather patterns between the two seasons, highlighting the role of environmental variability in disease progression. The study employed an advanced time series model, seasonal autoregressive integrated moving average (SARIMA), to forecast disease progression based on historical PDI data and corresponding weather parameters. The SARIMA model, by incorporating seasonal components, was found to be the best fit for predicting disease outbreaks. The findings of this study highlight the importance of integrating meteorological data into disease forecasting models to provide early warnings and guide timely interventions. By enhancing the understanding of ALB epidemiology, this research offers valuable insights for the development of sustainable disease management strategies in radish cultivation, reducing reliance on chemical fungicides and mitigating yield losses. [ABSTRACT FROM AUTHOR]

ABSTRACT Targeting alpha‐glucosidase (maltase‐glucoamylase [MGAM] and sucrase‐isomaltase [SI]) under diabetes conditions is important to overcome hyperglycemia. Moreover, it is necessary to mitigate hyperglycemia‐mediated oxidative stress to evade the progression of diabetes‐associated secondary complications. Hence, in the present study, under‐explored Nyctanthes arbor‐tristis flowers (NAFs) were studied for inhibition of alpha‐glucosidase activities. The NAF methanolic extract (NAFME) was prepared. Through liquid chromatography/electrospray ionization tandem mass spectrometry (LC–ESI/MS/MS) analysis, various phytocompounds belonging to different classes—flavonoids, iridoid glycosides, proanthocyanidin, anthocyanin, polyphenol, phenolic acid, fatty acid ester, and carotenoid—were identified. NAFME showed in vitro antioxidant activity. NAFME inhibited maltase, sucrase, glucoamylase, and isomaltase in mixed mode with Ki values of 179.93, 176.38, 126.03, and 201.56 µg/mL, respectively. In silico screening of phytocompounds identified in NAFME indicated that hinokiflavone (HKF), pelargonidin‐3‐O‐glucoside (PG), isorhamnetin‐3‐glucoside‐7‐rhamnoside (IGR), and petunidin‐3‐rutinoside (PR) showed better interactions with different subunits of human alpha‐glucosidase, namely, N‐terminal (Nt‐MGAM and Nt‐SI) and C‐terminal (Ct‐MGAM and Ct‐SI). Molecular dynamics (MD) simulation, binding free energy study (molecular mechanics–generalized Born surface area [MM/GBSA]), and post‐MD simulation studies (principal component analysis [PCA] and dynamic cross‐correlation matrix [DCCM]) provided an in‐depth understanding of these ligands’ interactions with proteins. The overall efficacy of NAFME against oxidative stress and alpha‐glucosidase in vitro is understood. Moreover, in silico analysis has shown the possible potential of HKF, PG, IGR, and PR to act as alpha‐glucosidase inhibitors. Further studies on the antidiabetic potential of NAFME, HKF, PG, IGR, and PR in in vivo conditions are required to fully unveil the applicability of NAFME in the management of T2DM as a complementary medicine. [ABSTRACT FROM AUTHOR]

The present work reports on the preparation, characterization, and evaluation of a set of novel triphenyl‐modified silica‐based stationary phases without and with embedded ion‐exchange sites for mixed‐mode liquid chromatography. The three synthesized triphenyl phases differed in additionally incorporated ion‐exchange sites. In one embodiment, allyltriphenylsilane was bonded to thiol‐modified silica by thiol‐ene click reaction, leading to particles with no ion‐exchange sites. A second stationary phase was obtained by thiol‐yne click reaction of thiol silica with 2‐propinyl‐triphenylphosphonium bromide, yielding a strong anion‐exchanger (SAX). A third stationary phase was obtained from this SAX phase by the oxidation of residual thiols to sulfonic acid moieties, leading to a zwitterionic surface. All synthesized materials were subjected to elemental analysis, 13C and 29Si solid‐state cross‐polarization/magic angle spinning nuclear magnetic resonance (CP/MAS NMR) spectroscopy analysis, and pH‐dependent ζ‐potential determinations via electrophoretic light scattering. The prepared stationary phases were chromatographically evaluated under classical reversed‐phase, ion‐exchange, and hydrophilic interaction chromatography conditions and classified within a set of commercially available columns by principal component analysis of retention factors. Finally, the obtained stationary phases were applied for biomolecule separations (e.g., teicoplanin and siRNA patisiran). These LC tests proved the orthogonality of the three prepared stationary phases and indicated possible fields of application. [ABSTRACT FROM AUTHOR]

Water constraint is a crucial factor in determining the productivity and production of linseed or flaxseed globally. The root system of flaxseed consists of a single taproot mainly confined to the topsoil; thus, the structure of the root system significantly impacts the uptake of water from the soil. This research conducted root–shoot phenotyping on a diverse range of linseed germplasm under PEG‐induced water deficit stress conditions to unravel the potential of genebank germplasm for drought tolerance. Varied responses to stress and substantial diversity in genotypic response were observed among the linseed accessions for all growth parameters under both normal and stress conditions. The application of PEG led to a decrease (ranging from 7.39% to 62.02%) in all parameters except chlorophyll content, which exhibited an increase of 13.76% in response to stress conditions. Principal component analysis revealed that the first four principal components (PCs) with Eigenvalue >1 explained 74.23% of the total variance, with the first PC alone accounting for 42.15% of the total variance contributed by various traits such as leaf width, shoot length, root–shoot biomass, root length, surface area, and volume. Assessing the comparative performance based on the stress susceptibility index (SSI) for shoot–root length and root surface area, a subset of 12 drought tolerant (SSI ≤ 0.50) and 5 susceptible (SSI > 1.00), genotypes was constituted for validation at the adult plant stage. The accessions IC0096648, IC0523799, IC0249015, IC0096587, IC0385336, IC0498744, IC0499170, EC0041481, IC0526017, IC0623723, IC0113110, and IC0621685 exhibiting tolerance to PEG‐induced water stress during the initial (seedling stage) growth maintained physiological efficiency and yield at the adult plant stage. The elite drought‐tolerant genotypes identified in the present study will provide access to genetically diverse material in breeding to enhance drought tolerance in linseed. [ABSTRACT FROM AUTHOR]

In the context of the valorisation of edible mushrooms from the province of Taza, Morocco, the present study, carried out for the first time, aims to evaluate the mineral composition, phenolic content, antibacterial activity and self‐preservation potential of wild Morchella esculenta (L.) Pers. from Taza. Inductively coupled plasma atomic emission spectrometry (ICP‐AES) determined the mineral salt content. We performed aqueous extractions using decoction, infusion and maceration, as well as hot and cold organic extractions with ethanol, acetone and diethyl ether. We then carried out a qualitative and quantitative analyses of the chemical composition. The antibacterial activity of the aqueous and organic extracts was studied using the disc diffusion method and by determining minimum inhibitory concentrations (MICs) and bactericidal concentrations (MBCs). Principal component analysis (PCA) was used to analyse the correlation between the chemical composition and antibacterial activity. Mineral analysis showed that the mushroom is rich in minerals, especially K (3578.4), Fe (2969.16), P (1818.66) and Mg (183.657) mg/kg dry matter. Quantitative analysis showed that the diethyl ether extract had the highest flavonoid content (846.07 ± 1.87 mgEQ/gE), the aqueous macerated extract was the richest in polyphenols (20.51 ± 0.45 mg EAG/gE) and the acetone extract had the highest catechic tannin content (17.13 ± 1.10 mgEC/gE). A study on antibacterial activity demonstrated that the decocted was bactericidal against Proteus mirabilis, while the aqueous macerated extract was bactericidal against Bacillus subtilis. The macerated diethyl ether extract was bacteriostatic against Proteus mirabilis, whereas the ethanolic extract was bactericidal against Staphylococcus aureus. This study showed that this mushroom is abundant in minerals and phenolic compounds, demonstrating its nutritional and therapeutic interest. In addition, this mushroom has antibacterial potential, which provides it the ability to protect itself against infection by pathogenic bacteria during storage and commercialization. PCA correlated the richest extracts in phenolic compounds and antibacterial activity. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates the therapeutic and nutritional potential of fenugreek sprouts from 30 diverse genotypes sourced from various regions. The aim was to characterize and compare their therapeutic attributes, including antioxidant capacity, antidiabetic, and anti‐cholinesterase activities, along with their nutritional compositions, particularly minerals, and protein content. Results revealed significant variations among the genotypes in terms of their therapeutic properties. China genotypes exhibited notable α‐amylase inhibition 64.57%, suggesting potential antidiabetic properties, while South Sudan genotypes demonstrated significant acetylcholinesterase (14.44%) and butyrylcholinesterase inhibitions, indicating possible cognitive health benefits. The Morocco and Konya/Türkiye genotypes exhibited noteworthy antioxidant effects, with showing DPPH• scavenging activities of 7.79% and 7.23%, and ABTS•+ activities of 27.87% and 27.31%, respectively. Mineral analysis revealed considerable differences across genotypes. Israel genotypes had the highest iron content (43.18 mg/100 g), Sivas/Türkiye genotype had the highest potassium levels (2259.87 mg/100 g), and Kayseri/Türkiye genotype had the highest sodium content (616.91 mg/100 g). Ukraine genotypes contained the most magnesium (266.61 mg/100 g), while Israel genotypes also had the highest zinc content (54.44 mg/100 g). The protein content of the fenugreek sprouts varied significantly, with Corum/Türkiye showing the highest protein content at 5.75/100 g. Principal component analysis (PCA) highlighted the relationships among the mineral nutrients and protein content, revealing distinct groupings of genotypes based on their mineral compositions. Correlation analysis further elucidated the associations between various minerals and protein content. In conclusion, this study underscores the potential therapeutic and nutritional significance of fenugreek sprouts. [ABSTRACT FROM AUTHOR]

The global prevalence of diabetes mellitus was 537 million in 2021 and is projected to increase to 783 million by 2045. Diabetes progression is also associated with histone modifications. Previous studies have reported that Acalypha hispida leaves are rich in flavonoids and phenolic acids, which are associated with antihyperglycemic activity. Recently, we produced a nanoextract of A. hispida leaf that contains bioactive compounds with antidiabetic effects. However, there were no studies reported the antidiabetic efficacy of A. hispida extract and nanoextract. Hence, we aimed to evaluate the antidiabetic effects of A. hispida extract and nanoextract on pancreatic β‐cells, insulin secretion, glycogen content, and histone modifications in streptozotocin‐induced diabetic rats. Diabetic rats received oral doses of the extract (300 mg/kg bw) and nanoextract (30 and 60 mg/kg bw) daily for 28 days. Bodyweight, blood glucose levels, insulin levels, islets of Langerhans and pancreatic β‐cells, glycogen content, acetylation of lysine 9 on histone 3 (H3K9Ac) level, hematology, and lipid profile were assessed. Analyses of variance, principal component analysis (PCA), and partial least squares were performed. The nanoextract of A. hispida was successfully produced at a nanosize of 733 nm. The extract (300 mg/kg bw) and nanoextract (60 mg/kg bw) exhibited antidiabetic effects based on the following parameters: decreased blood glucose levels, increased pancreatic β‐cell number and density, increased insulin levels, decreased H3K9Ac level in the islets of Langerhans and liver, enhanced glycogen levels in the liver, and improved lipid metabolism. Moreover, PCA indicated that the nanoextract (60 mg/kg bw) showed the strongest antidiabetic effect based on these parameters. The increased β‐cell number and density led to decreased blood glucose levels. In conclusion,​ A. hispida extract and nanoextract have antidiabetic effects by restoring β‐cell number and density and decreasing H3K9 acetylation in the islets of Langerhans in streptozotocin‐induced diabetic rats. [ABSTRACT FROM AUTHOR]

ABSTRACT Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi‐omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole‐genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web‐based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to facilitate their exploration and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue‐specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure, and so on. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION While low‐fat yogurt offers numerous health benefits, its texture and sensory qualities are poor. This study aimed to investigate the effects of γ‐polyglutamic acid (γ‐PGA) on the rheological, microstructural and sensory properties of low‐fat yogurt using rheological tests, scanning electron microscopy (SEM) and sensory evaluation.The results showed that the syneresis of low‐fat yogurt added with 0.15% γ‐PGA was significantly (P < 0.05) reduced to 23.03%, compared to that of low‐fat yogurt without γ‐PGA (42.87%). An improvement in storage and loss moduli (G', G") and apparent viscosity was also observed. The power law model fitted to rheological data indicated that γ‐PGA enhanced the viscoelasticity and strength of the gel network though interaction with casein gel, which might lead to changes in the moduli of yogurts. Microstructural observation via SEM confirmed the enhanced crosslinking between casein micelles and the filling effect of γ‐PGA. Sensory liking scores were strongly correlated with rheological properties and apparent viscosity. The results of principal component analysis (PCA) confirmed the positive effects of γ‐PGA on quality and rheological attributes of low‐fat yogurt.The results of this study provided valuable references for the potential use of γ‐PGA as a fat replacer to stabilize the sensory quality of low‐fat yogurt. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Mobile health studies often collect multiple within‐day self‐reported assessments of participants' behavior and well‐being on different scales such as physical activity (continuous scale), pain levels (truncated scale), mood states (ordinal scale), and the occurrence of daily life events (binary scale). These assessments, when indexed by time of day, can be treated and analyzed as functional data corresponding to their respective types: continuous, truncated, ordinal, and binary. Motivated by these examples, we develop a functional principal component analysis that deals with all four types of functional data in a unified manner. It employs a semiparametric Gaussian copula model, assuming a generalized latent non‐paranormal process as the underlying generating mechanism for these four types of functional data. We specify latent temporal dependence using a covariance estimated through Kendall's τ$$ \tau $$ bridging method, incorporating smoothness in the bridging process. The approach is then extended with methods for handling both dense and sparse sampling designs, calculating subject‐specific latent representations of observed data, latent principal components and principal component scores. Simulation studies demonstrate the method's competitive performance under both dense and sparse sampling designs. The method is applied to data from 497 participants in the National Institute of Mental Health Family Study of Mood Spectrum Disorders to characterize differences in within‐day temporal patterns of mood in individuals with the major mood disorder subtypes, including Major Depressive Disorder and Type 1 and 2 Bipolar Disorder. Software implementation of the proposed method is provided in an R‐package. [ABSTRACT FROM AUTHOR]

ABSTRACT Canine lymphoma represents a biologically and metabolically heterogeneous group of neoplasms that arise from malignant transformation of lymphoid cells. An accurate diagnosis is crucial because of its impact on survival. Current diagnostic methods include clinical laboratory tests and imaging, most of which are invasive and lack sensitivity and specificity. Interestingly, recent work in cancer patients focuses on the search for biomarkers for diagnosis, investigation of treatment response mechanisms, treatment efficacy and prognosis and the discovery of tumour metabolic pathways using metabolomic analysis. In this study, we compare the metabolite profiles in serum from 37 dogs with multicentric lymphoma (22 B‐cell lymphomas/LB, 9 CD45+ T‐cell lymphomas/LTCD45+, 6 CD45‐ T‐cell lymphomas/LTCD45‐) and 25 healthy dogs using 1H nuclear magnetic resonance spectroscopy (NMR). 1H NMR‐based metabolite profiling analysis recognised lipids and 22 metabolites, with 16 of them altered, and was shown to be an effective approach for differentiating samples from dogs with lymphoma and healthy controls based on principal component analysis of the NMR data. We also investigated variations in the serum metabolome between immunophenotypes and the control group through pairwise comparisons of the healthy against the LB, LTCD45+ and LTCD45‐ groups, respectively which showed similar metabolomic profiles. In addition, there were significant differences in the levels of five individual metabolites based on the univariate statistical analysis. Our results showed alterations in energy, protein and lipid metabolism, suggesting glucose, lactate, N‐acetyl glycoproteins (NAGs), scyllo‐inositol and choline as possible new candidate biomarkers in canine multicentric lymphoma. [ABSTRACT FROM AUTHOR]

This study aimed to explore whether the three primary sensory attributes—fruitiness, bitterness, and pungency—could be used to discriminate among seven Greek olive oil varieties. Sensory data from 110 extra virgin olive oil samples, collected over 9 years by the Kalamata Olive Oil Taste Laboratory, were analyzed using principal component analysis (PCA) and learning vector quantization (LVQ). PCA revealed that varieties with overlapping sensory profiles formed three distinct sensory groups: Koroneiki‐Lianolia‐Chalkidikis, Makri‐Manaki‐Megaritiki, and Athenolia. Consequently, the research focus shifted to evaluating the ability of these sensory attributes to differentiate between groups of varieties rather than individual varieties. LVQ, validated through leave‐one‐out cross‐validation (LOOCV), classified the olive oil samples into these sensory groups with 80% accuracy. The results demonstrate that the intensity levels of fruitiness, bitterness, and pungency provide valuable information for distinguishing groups of varieties with similar sensory profiles. While the study benefits from a robust nine‐year dataset, limitations include a relatively small sample size, potentially limiting generalizability, and a selection criterion that excludes samples with lower fruitiness scores, possibly introducing a degree of bias. Practical Applications: The study's findings provide practical applications in various areas. For panel training, the identified sensory profiles can serve as benchmarks, helping tasters recognize and classify olive oil varieties. Producers can utilize these sensory profiles for consistent quality control and strategic blending, ensuring their products meet desired sensory standards. Finally, these profiles can be used in culinary education, enabling chefs and food enthusiasts to select and pair olive oils more effectively with different dishes. [ABSTRACT FROM AUTHOR]

This paper describes an optimised scattering power decomposition method suitable for ship detection in polarimetric synthetic aperture radar imagery. Based on the principal component analysis technique for dimension reduction and the scattering power decompositions with strong physical interpretation, a novel optimised scattering power decomposition model is proposed which comprises surface scattering, double‐bounce scattering, ±45° oriented dipole, and volume scattering components. Wherein, the oriented dipole component can be used to characterise the compounded scattering of the target which is composed of even‐bounce and odd‐bounce reflectors. Furthermore, pocket algorithm and support vector machine are adopted to solve linear non‐separable problems under complex experimental conditions in this study. Extensive experiments carried out on RADARSAT‐2 data and NASA/JPL AIRSAR data show that the oriented dipole component significantly contributes to the optimised four‐component decomposition method and can play a vital role in ship detection. Compared to other scattering power decompositions, the optimised decomposition method not only achieves better performance but is more in accordance with the real scattering mechanisms, which is more applicable to ship detection. [ABSTRACT FROM AUTHOR]

Objective: In this article, utilizing the European Social Survey (ESS), we empirically analyze the effect of individualism on working hours at the micro level. Method: We use the European Social Survey (ESS), which is a biennial survey conducted in European countries. The ESS provides us five different proxies to measure individualism. In our empirical analyses, we first use proxies separately as our measure of individualism in the regressions. Second, we create a composite indicator for individualism by using the principal component analysis (PCA). We use two different methods for polychoric PCA. Results: In four of our individualism proxies, we find that individualism is positively and significantly associated with working hours. Our PCA results also show that survey participants with higher individualistic attitudes tend to work longer hours. Conclusion: We find robust empirical evidence that people who place greater emphasis on individualistic values have a tendency to work longer. The results show that individuals who give more importance to money, expensive things, success, recognition of achievements, showing abilities and being admired, and freedom on his/her decisions are inclined to work longer hours. [ABSTRACT FROM AUTHOR]

The article in the Clinical & Translational Medicine journal focuses on identifying metabolic dysregulation and biomarkers for clear cell renal cell carcinoma (ccRCC). The study used targeted metabolomics to analyze metabolites in ccRCC patients, identifying 163 significantly altered metabolites in tumour tissues. The research also constructed a metabolite-based risk model to predict prognosis at different stages of ccRCC, offering potential diagnostic and therapeutic opportunities. The study provides valuable insights into novel biomarkers for diagnosing ccRCC and predicting prognosis, independent of clinicopathologic factors. [Extracted from the article]

Groundwater underpins water supply for most of the world's regions, yet its sustainable utilization has been markedly compromised by inappropriate exploitation and a multitude of pollution sources. Water quality evaluation has emerged as an essential strategy to guarantee the optimized utilization and vigilant conservation of water resources. In this study, principal component analysis (PCA), entropy weight method (EWM), coefficient of variation method (CVM), and Water Quality Index (WQI) were used to construct an integrated WQI groundwater quality assessment model that integrates PCA‐CVM‐EWM for dimensionality reduction and weight optimization. Taking a village in Shandong Province, China, as an example, PCA identified seven evaluation indicators. The CVM‐EWM were coupled to calculate comprehensive weights through the principle of minimum information entropy, followed by a comprehensive assessment of groundwater quality based on WQI values. The results indicated that Class III groundwater predominated in the study area, accounting for 74%, with localized pollution present. The hydrochemical type of the groundwater was primarily SO4·HCO3‐Ca, significantly influenced by human activities. The coefficients of variation for Fe, Mn, and NH4‐N all exceeded 1. Compared to other methods, the optimized WQI model demonstrated superior performance in the selection of evaluative indicators, weight distribution, and comprehensive water quality assessment, showing a distinct advantage for water quality data with numerous hydrochemical indicators and substantial coefficients of variation. The findings provided a scientific reference for diagnosing groundwater quality issues and formulating preventive and control measures. Practitioner Points: A comprehensive water quality index evaluation model was constructed.Optimized steps for selecting indicators and assigning weights for the water quality index model.Selection of evaluation indicators based on indicator correlation analysis.The variability of hydrochemical data is considered. [ABSTRACT FROM AUTHOR]

Unlike lipid stability and oxidation studies in commonly used edible oils and margarines, margarines formulated with unconventional oils are not well characterized. This study investigated the effect of heat treatment (HT) on the stability and content of phytosterol in njangsa seed oil (NSO), bush mango oil (BMO), soybean oil (SBO), coconut oil (CCO), and margarines formulated from their blends: BN (BMO and NSO), BS (BMO and SBO), CN (CCO and NSO), CS (CCO and SBO), and commercial margarines (CM1 and CM2). Both oils and margarines were heat‐treated at 130, 170, and 210°C for 10, 15, 20, and 120 min (only oils). Changes in free fatty acid (FFA), peroxide value (PV), para‐anisidine value (AnV), and fatty acid (FA) composition and phytosterol content were determined after 20 min (margarines) and 120 min (oils) of HT and compared to their control/pre‐HT/unheated (UH) counterparts. The FA composition did not change significantly with HT. Polyunsaturated fatty acids (PUFA)‐rich oils such as NSO and SBO showed significantly higher increase in FFA content with HT compared with oils with higher saturated fatty acid content (BMO and CCO). Oils with higher proportions of linoleic acid, such as SBO (68.3%) and NSO (60.35%), had higher AnV at the end of the HT compared with oils with lower content, such as BMO (0.51%). Phytosterol content fluctuated with HT, and changes in content were generally more pronounced in β‐sitosterol than in stigmasterol and campesterol. Both principal component analysis (PCA) and partial least‐squares discriminant analysis (PLS‐DA) were carried out to observe possible clusters. The results suggest that changes in quality and content of oils and margarines during heating are dependent on more than their fatty acid composition. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) pose a global environmental risk, impacting human health. Enhancing phytoremediation with microbial‐plant interactions could help mitigate these pollutants. However, tree responses to PAHs are unclear, necessitating controlled studies before field experiments. This study examined how PAH‐degrading microbes affect black alder (Alnus glutinosa L.) seedlings grown hydroponically, hypothesizing that specific microbes improve growth and stress tolerance. Two half‐sib families (41–65–7 K, 13–99–1 K) were inoculated with Rhodotorula sphaerocarpa (R.s.), Pseudomonas putida (P.p.), and Sphingobium yanoikuyae (S.y.). Results showed family‐dependent and microbe‐specific effects, with family 41–65–7 K showing enhanced shoot growth (threefold increase by R.s.) and higher carotenoid levels. Antioxidant enzyme activities varied: R.s. elevated superoxide dismutase activity by 4.8‐fold in 13–99–1 K, while catalase activity increased but decreased in 41–65–7 K. Principal component analysis revealed distinct phytochemical clustering based on microbial treatment, highlighting genotype‐specific modulations. Each microorganism had unique plant growth‐promoting traits, with P.p. producing the most phytohormone and S.y. fixing nitrogen. These findings support targeted microbial inoculation for effective remediation of PAH‐contaminated environments. [ABSTRACT FROM AUTHOR]

Sauce glazed wares of Yaozhou kilns are famous for their high gloss and distinctive glaze color palette varying from yellowish‐brown to reddish‐brown. In this work, sauce glazes were successfully replicated using the traditional technology of Yaozhou kilns. Micro‐Raman spectroscopy, combined with optical microscopy, scanning electron microscopy, x‐ray fluorescence, and reflective spectroscopy, was systematically applied to analyze the chemical composition, nature and distribution of crystals, and the coloration of the glazes. The results show that the yellowish‐brown glaze is mainly original from dendritic ε‐Fe2O3 crystals, whereas the reddish‐brown color is mainly derived from dendritic hematite crystals. Such dendritic structure could account for slightly color variations of the glaze surface observed in different angles, which also reported in Japanese Bizen ceramics. The principal component analysis (PCA) was further applied to study the Raman spectra of these iron oxides. The PCA results effectively indicates the structural disorders of these crystals introduced by ion substitutions. These substitutions could not only stabilize the crystals but also darken the crystals color. Besides, high Mg in the raw materials was found to benefit the growth of magnesioferrite crystals. The relative low level of Fe2O3, high level of SiO2, and CaO may relate to the formation of ε‐Fe2O3 crystals. [ABSTRACT FROM AUTHOR]

Background: Photon‐counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. Purpose: This study proposes a lossy method to compress photon‐counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. Methods: The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon‐count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M < N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy‐bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel‐specific and pixel‐general. The pixel‐specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel‐general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20 cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon‐based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8‐bin data, after which material decomposition was performed. The mean, standard deviation, and contrast‐to‐noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8‐bin data and for the eigenbin data. Results: The pixel‐specific eigenbin method reduced photon‐counting CT data size by a factor of four with <5% change in mean values and a small noise penalty (mean change in noise of <12%, maximum change in noise of 20% for basis images). The pixel‐general eigenbin compression method reduced data size by a factor of 2.67 with <5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty ≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in <5% change in CNR in the VMIs. Conclusion: The eigenbin compression method reduced photon‐counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%–20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon‐counting CT data for applications that require rapid initial reconstructions. [ABSTRACT FROM AUTHOR]