Hierarchical Cluster Analysis Research Articles

Driving Factors of Forest Typological Diversity in the Moscow Region

The identification of the main factors influencing forest diversity, including both direct and indirect effects, as well as the compatibility of different-level approaches, is a key topic in community ecology and biogeography. The aim of the current study is to assess the contributions of natural and anthropogenic factors to forest diversity in the Moscow region (Russia). This study is based on a quantitative analysis of the linkage between forest diversity and biotopic local factors (LFs) at a lower spatial level, using geobotanical relevés, and external factors (EFs) at an upper spatial level, based on global environmental databases. The classification of 1040 field relevés (including forest-forming tree species, moisture conditions, and soil nutrients) resulted in the identification of eight forest types. A nonmetric multidimensional scaling algorithm, ANOVA post hoc test, hierarchical clustering, and multiple regression analysis were used in data processing. LFs are calculated based on complete species lists using Ellenberg ecological scales. According to a Duncan’s test, LFs provided significant differences between the eight forest types (p < 0.05). At the upper spatial level, the linkage between forest diversity and EFs was most pronounced for climatic factors, soil properties, and topography, including annual mean temperature, soil carbon, clay particle content, and DEM (elevation and slope). The contribution of anthropogenic factors was significantly smaller compared to the natural EFs in the study region.

Multivariate Statistical Approach and Assessment of Pollution of Water and Sediments in Karstic Springs of Transboundary Aquifer Žumberak–Samoborsko Gorje Mountain (Croatia/Slovenia)

This study highlights the geochemistry of water and sediments of the karstic springs of the transboundary aquifer (TBA) Žumberak–Samoborsko Gorje Mt. (NW Croatia). After calculating pollution indices, the analysis showed that the sediments are unpolluted. The geo-accumulation index (Igeo) showed only the elements Ba and Rb, indicating moderate levels of pollution, with the highest values in springs Vapnik and Bistrac. Statistical analysis confirmed their natural origin. The water of these springs is under possible anthropogenic influence as indicated by elevated concentrations of total nitrogen (TN) and total phosphorus (TP). According to a principal component analysis (PCA) for elements in sediments, PC1 described a combined lithogenic and oxidative–reductive influence, PC2 described a combined geological background including total organic carbon (TOC) content and oxidative–reductive influence, while TOC had the greatest influence on PC3. Depending on element composition, the factor scores related to PC1 and PC2 resulted in two different groups of sites, while the factor scores concerning PC1 and PC3 did not show separation in two groups. The hierarchical cluster analysis showed three clusters in relation to the content of the elements. The correlation coefficient between the sediment and related water samples showed that the springs placed in a low-permeability formation (dolomite) had a strong positive coefficient of correlation.

Operationalising critical thinking in postgraduate disciplinary writing: insights from corpus and cluster analyses of lecturer feedback

Research has revealed that critical thinking (CT) can predict undergraduate academic performance well. It is essential for nurturing students’ unique voice and creativity, especially in the era of Large Language Models (LLMs). LLMs can generate linguistically complex texts, potentially overshadowing students’ authorial voices—particularly those still developing language skills and domain-specific knowledge. Despite this, research on CT within subject education contexts remains scarce, leaving disciplinary lecturers’ expectations of CT in disciplinary writing largely unexplored. Our study analysed a 160,527-word corpus of lecturer feedback on 230 assignments from three education-related postgraduate programs to reveal their shared expectations of CT in disciplinary writing. We identified nine CT constructs, each illustrated with relevant feedback examples. Their intricate interrelationships were revealed through hierarchical cluster analyses. Drawing inspiration from Bloom’s Taxonomy, we established a CT taxonomy to guide effective and consistent assessment of CT. Further studies and practical applications of the CT taxonomy within local educational contexts are needed to develop students’ CT skills and empower them to retain their voice and creativity in an AI-empowered era.

Improved platelet separation performance from whole blood using an acoustic fluidics system

AbstractThis study investigated the effectiveness of acoustic separation for platelet analysis in patients with non–small‐cell lung cancer (NSCLC), comparing it with traditional centrifugation methods. In total, 10 patients with NSCLC and 10 healthy volunteers provided peripheral blood samples, which were processed using either acoustic separation or centrifugation to isolate platelets. The study included whole transcriptome analysis of platelets, peripheral blood mononuclear cells, and tumor tissue samples, employing hierarchical clustering and Gene Ontology analysis to explore gene expression differences. Acoustic separation proved more efficient than centrifugation in terms of platelet yield, recovery rate, and RNA yield. Gene expression profiles of platelets from patients with NSCLC showed distinct patterns compared with healthy volunteers, indicating tumor‐influenced alterations. Gene Ontology analysis revealed enrichment in pathways associated with platelet activation and the tumor microenvironment. This finding indicates the potential of acoustic isolation in platelet separation and its relevance in understanding the unique gene expression profile of platelets in patients with NSCLC. The findings of this study suggested that platelets from cancer patients separated by acoustic techniques exhibited tumor‐specific alterations and provided new insights into the diagnosis of cancer in platelet analysis systems in clinical practice.

Reliability of Serum-Derived Connectome Indicators in Identifying Cirrhosis.

Patients with cirrhosis face a heightened risk of complications, underscoring the importance of identification. We have developed a Connectome strategy that combines metabolites with peptide spectral matching (PSM) in proteomics to integrate metabolomics and proteomics, identifying specific metabolites bound to blood proteins in cirrhosis using open search proteomics methods. Analysis methods including Partial Least Squares Discriminant Analysis (PLS-DA), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering were used to distinguish significant differences among the Cirrhosis group, Chronic Hepatitis B (CHB) group, and Healthy group. In this study, we identified 81 cirrhosis-associated connectomes and established an effective model distinctly distinguishing cirrhosis from chronic hepatitis B and healthy samples, confirmed by PLS-DA, hierarchical clustering analysis, and UMAP analysis, and further validated using six new cirrhosis samples. We established a Unified Indicator for Identifying cirrhosis, including tyrosine, Unnamed_189.2, thiazolidine, etc., which not only enables accurate identification of cirrhosis groups but was also further validated using six new cirrhosis samples and extensively supported by other cirrhosis research data (PXD035024). Our study reveals that characteristic cirrhosis connectomes can reliably distinguish cirrhosis from CHB and healthy groups. The established unified cirrhotic indicator facilitates the identification of cirrhosis cases in both this study and additional research data.

Unraveling Shengmuxiang in Jiang-flavor base baijiu using a combination of metabolomics and sensomics strategy

Shengmuxiang (SMX), an important aroma in Jiang-flavor base baijiu, significantly influences the quality of the product. This study employed untargeted metabolomics combined with sensomics to explore the key compounds responsible for SMX. Results indicated that SMX samples had higher intensities of green and woody-like odors compare to control samples. A total of 87 aroma compounds were identified by headspace solid phase microextraction combined with gas chromatography–mass spectrometry technology. Based on the variable projection importance, PCA and OPLS-DA were employed to identify 22 potential marker compounds. Quantitative results combined with hierarchical cluster and OAV analysis revealed that 9 aroma compounds (OAV > 1) had high concentrations in SMX samples. Aroma recombination and omission experiments further indicated that acetaldehyde and acetal were the key compounds responsible for the characteristic aroma of SMX in Jiang-flavor base baijiu. These findings provide valuable insights into the distinct aroma profile of SMX and offer a basis for quality control of Jiang-flavor base baijiu.

Risk of developing Type 2 Diabetes Mellitus in a vulnerable community in northern Argentina

Introduction: Prevention of diabetes requires sustained lifestyle changes as well as identification of groups at higher risk. Objective: The aim of this research was to estimate the risk of diabetes in vulnerable subjects from a primary care center in northern Argentina, with no known glucose abnormalities, using the FINDRISC questionnaire, investigate the relationship between survey variables and the final score, and explore its association with metabolic risk factors and body composition. Methodology: This cross-sectional design included 498 patients without type 2 diabetes or known glycemic abnormalities. All subjects underwent a complete medical history and completed the FINDRISC questionnaire. Results: The predominant age group was 18-45 years old. Around 64% were physically active and 44% reported daily consumption of fruit and vegetables. Most of them had a BMI higher than 25 kg/m2. Regarding the risk of developing type 2 diabetes in the next 10 years, 24.3% were at low risk and the remaining fraction was distributed in slightly elevated, moderate, high and very high risk. All variables influenced the individuals' variance (p < 0.05). The hierarchical clustering and principal component analysis (PCA) revealed that elevated FINDRISC score was strongly associated with: age > 65 years, fasting blood glucose, BMI ? 30 kg/m2 and antihypertensives use. CONCLUTION: We found a high percentage of obesity and overweight, as well as a high risk of cardiometabolic disease and of developing T2D. In addition, in the population studied, the variables that compose the FINDRISC did not influence the highest score in the same way.

Lactiplantibacillus plantarum Interstrain Variability in the Production of Bioactive Phenolic Metabolites from Flavan-3-ols.

Flavan-3-ols intake is associated with numerous health benefits, but these are influenced by their conversion into smaller phenolic metabolites by the gut microbiota. Thus, the identification of bacteria that metabolize flavan-3-ols could lead to targeted interventions to enhance their benefits. To this end, we screened 47 Lactiplantibacillus plantarum strains for their ability to metabolize (+)-catechin, a flavan-3-ol. Then, we assessed these strains for their capacity to convert various flavan-3-ol structures. Out of the 47 isolates, 12 released 3-(3',4'-dihydroxyphenyl)-1-(2,4,6-trihydroxyphenyl)-propan-2-ol (a form of diphenylpropan-2-ol) from (+)-catechin. All strains metabolized (+)-catechin, (-)-epicatechin, (-)-epigallocatechin, but only a subset transformed (-)-gallocatechin. Among these simple flavan-3-ol structures, (-)-epicatechin was metabolized the most. A hierarchical cluster analysis identified two groups of flavan-3-ol-metabolizing strains categorized as having "high" and "low" production of diphenylpropan-2-ols. Notably, the strains that produced higher levels of diphenylpropan-2-ol from (+)-gallocatechin and (+)-catechin also performed better with a camu-camu extract, which was studied as a complex source of flavan-3-ols and predominantly contained these two flavan-3-ols. These results demonstrate the interstrain variability in L. plantarum metabolism, which may be useful for developing tailored formulations to enhance the production of flavan-3-ols bioactive metabolites.

Comparative Analysis of Phytohormone Biosynthesis Genes Responses to Long-Term High Light in Tolerant and Sensitive Wheat Cultivars.

Phytohormones are vital for developmental processes, from organ initiation to senescence, and are key regulators of growth, development, and photosynthesis. In natural environments, plants often experience high light (HL) intensities coupled with elevated temperatures, which pose significant threats to agricultural production. However, the response of phytohormone-related genes to long-term HL exposure remains unclear. Here, we examined the expression levels of genes involved in the biosynthesis of ten phytohormones, including gibberellins, cytokinins, salicylic acid, jasmonic acid, abscisic acid, brassinosteroids, indole-3-acetic acid, strigolactones, nitric oxide, and ethylene, in two winter wheat cultivars, Xiaoyan 54 (XY54, HL tolerant) and Jing 411 (J411, HL sensitive), when transferred from low light to HL for 2-8 days. Under HL, most genes were markedly inhibited, while a few, such as TaGA2ox, TaAAO3, TaLOG1, and TaPAL2, were induced in both varieties. Interestingly, TaGA2ox2 and TaAAO3 expression positively correlated with sugar content but negatively with chlorophyll content and TaAGP expression. In addition, we observed that both varieties experienced a sharp decline in chlorophyll content and photosynthesis performance after prolonged HL exposure, with J411 showing significantly more sensitivity than XY54. Hierarchical clustering analysis classified the phytohormone genes into the following three groups: Group 1 included six genes highly expressed in J411; Group 2 contained 25 genes drastically suppressed by HL in both varieties; and Group 3 contained three genes highly expressed in XY54. Notably, abscisic acid (ABA), and jasmonic acid (JA) biosynthesis genes and their content were significantly higher, while gibberellins (GA) content was lower in XY54 than J411. Together, these results suggest that the differential expression and content of GA, ABA, and JA play crucial roles in the contrasting responses of tolerant and sensitive wheat cultivars to leaf senescence induced by long-term HL. This study enhances our understanding of the mechanisms underlying HL tolerance in wheat and can guide the development of more resilient wheat varieties.

Long COVID is not the same for everyone: a hierarchical cluster analysis of Long COVID symptoms 9 and 12 months after SARS-CoV-2 test

BackgroundIdentifying symptom clusters in Long COVID is necessary for developing effective therapies for this diverse condition and improving the quality of life of those affected by this heterogeneous condition. In this study, we aimed to identify and compare symptom clusters at 9 and 12 months after a SARS-CoV-2 positive test and describe each cluster regarding factors at infection.MethodsThis is a cross-sectional study with individuals randomly selected from the Portuguese National System of Epidemiological Surveillance (SINAVE) database. Individuals who had a positive SARS-CoV-2 test in August 2022 were contacted to participate in a telephonic interview approximately 9 and 12 months after the test. A hierarchical clustering analysis was performed, using Euclidean distance and Ward’s linkage. Clustering was performed in the 35 symptoms reported 9 and 12 months after the SARS-CoV-2 positive test and characterised considering age, sex, pre-existing health conditions and symptoms at time of SARS-CoV-2 infection.Results552 individuals were included at 9 months and 458 at 12 months. The median age was 52 years (IQR: 40–64 years) and 59% were female. Hypertension and high cholesterol were the most frequently reported pre-existing health conditions. Memory loss, fatigue or weakness and joint pain were the most frequent symptoms reported 9 and 12 months after the positive test. Four clusters were identified at both times: no or minor symptoms; multi-symptoms; joint pain; and neurocognitive-related symptoms. Clusters remained similar in both times, but, within the neurocognitive cluster, memory loss and concentration issues increased in frequency at 12 months. Multi-symptoms cluster had older people, more females and more pre-existing health conditions at 9 months. However, at 12 months, older people and those with more pre-existing health conditions were in joint pain cluster.ConclusionsOur results suggest that Long COVID is not the same for everyone. In our study, clusters remained similar at 9 and 12 months, except for a slight variation in the frequency of symptoms that composed each cluster. Understanding Long COVID clusters might help identify treatments for this condition. However, further validation of the observed clusters and analysis of its risk factors is needed.

Deciphering metabolomics and lipidomics landscape in zebrafish hypertrophic cardiomyopathy model

To elucidate the lipidomic and metabolomic alterations associated with hypertrophic cardiomyopathy (HCM) pathogenesis, we utilized cmybpc3-/- zebrafish model. Fatty acid profiling revealed variability of 10 fatty acids profiles, with heterozygous (HT) and homozygous (HM) groups exhibiting distinct patterns. Hierarchical cluster analysis and multivariate analyses demonstrated a clear separation of HM from HT and control (CO) groups related to cardiac remodeling. Lipidomic profiling identified 257 annotated lipids, with two significantly dysregulated between CO and HT, and 59 between HM and CO. Acylcarnitines and phosphatidylcholines were identified as key contributors to group differentiation, suggesting a shift in energy source. Untargeted metabolomics revealed 110 and 53 significantly dysregulated metabolites. Pathway enrichment analysis highlighted perturbations in multiple metabolic pathways in the HM group, including nicotinate, nicotinamide, purine, glyoxylate, dicarboxylate, glycerophospholipid, pyrimidine, and amino acid metabolism. Our study provides comprehensive insights into the lipidomic and metabolomic unique signatures associated with cmybpc3-/- induced HCM in zebrafish. The identified biomarkers and dysregulated pathways shed light on the metabolic perturbations underlying HCM pathology, offering potential targets for further investigation and potential new therapeutic interventions.

Differentiating Pond-Intensive, Paddy-Ecologically, and Free-Range Cultured Crayfish (Procambarus clarkii) Using Stable Isotope and Multi-Element Analysis Coupled with Chemometrics.

The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ13C, δ15N, δ2H, δ18O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ13C, δ15N, δ2H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication.

Quantifying Western Discourses about Technology

In this article, we explore the social impact of technology in contemporary societies using a dataset of multilingual newspapers in English, French, Spanish, Italian, and German. Our observational time covers twenty years (1999-2018). We filter documents using four technological key terms: nuclear, oil, internet, and automation, that we consider as proxies reflecting Smil 2006 taxonomy of technology. Our methodology is formed by a five-step pipeline: Topic modelling (Pachinko Allocation), word embeddings, Ward’s hierarchical cluster analysis, network analysis, and sentiment analysis. We analyze information stability (which we define as low levels of semantic diversity in our data outcomes over time at the individual key term level) and information homogeneity (which we understand as low levels of semantic diversity in our data outcomes across our selection of key terms). We seek to observe to what extent our selection of key terms permeates into press discourses similarly or differently, and whether those discourses fall into semantic categories that could be considered as essential elements of the fabric of contemporary societies (such as finance, education, or politics). Results show, firstly, a consistent overlap of content across newspapers’ semantic fields that could be considered as pillars of society. Secondly, we notice a progressive simplification of information historically, reflecting less polarizing views across countries and, therefore, demonstrating an increasing agreement on technologically related discourses. We interpret these results as an indicator of the rising intrusion of technology in Western, industrialized countries.

A hierarchical clustering analysis of classroom emotional profiles of Grade-4-to-5 EFL learners: Classroom emotions, motivation, family backgrounds, and proficiency development

Although research into emotions has flourished in second language (L2) / foreign language (FL) learning in recent decades, few attempts have been made to examine learners as clusters based on their emotional profiles. The current study set out to first investigate the nature of 98 Grade-4-to-5 English as a foreign language (EFL) learners’ emotional profiles, as represented by classroom anxiety, boredom, and enjoyment. The hierarchical clustering analysis revealed three distinct profiles: Negative Emotion (NE; 21.43%), High Enjoyment (HE; 46.93%), and high in both Enjoyment and Anxiety (EA; 31.63%). We then analysed FL motivation and family background (household income and parental education) between the three groups. We also compared the FL proficiency levels (listening, reading, speaking, and writing) between the three groups from three perspectives: baseline scores (T1), scores one year later (T2), and growth (from T1 to T2). Multiple significant differences were found. The most common pattern was HE demonstrated higher scores than NE (e.g. in various aspects of language proficiency). EA also had higher scores than NE in some areas. HE and EA were largely similar, the only significant difference being HE having higher listening and speaking motivation than EA did. Interestingly, no significant differences were found in family background between the three groups.

Pollution source characterization and evaluation of groundwater quality utilizing an integrated approach of Water Quality Index, GIS and multivariate statistical analysis

ABSTRACT The Mid-Gangetic Plain, a vital farmland in India, faces increasing groundwater quality deterioration due to anthropogenic activities. This study aimed to assess groundwater quality and contamination sources in the region utilizing statistical methods. A total of 78 groundwater samples were collected and analyzed using standard methods. The hydrochemistry analysis of samples revealed that several parameters such as Ca2+, Mg2+, HCO3−, NO3−, F− and PO43− surpassed the limits prescribed by the Bureau of Indian Standards (BIS). The principal component analysis yielded three significant factors, explaining 68.96% variation, highlighting geogenic and anthropogenic influences on groundwater chemistry. Hierarchical cluster analysis categorized groundwater into three clusters based on the parameters with similar trends of variation. Furthermore, Discriminant analysis identified four significant variables (Mg2+, F−, Cl− and NO3−) responsible for creating the distinction among the identified clusters. Hydrogeochemical categorization and multivariate statistical analyses indicated that rock–water interaction, weathering, leaching and anthropogenic activities collectively influenced groundwater quality throughout the studied region. The Water Quality Index reveals that 59% of samples have good water quality, while 41% exhibit poor quality predominantly concentrated in the south-western, south-eastern and central regions. This study demonstrates the efficacy of statistical techniques to interpret complex datasets and grasp water quality dynamics, enhancing groundwater management.

Systematization of sunflower genotypes based on seed phenotypic characteristics using neural networks

In recent years, various datasets related to the phenotyping of sunflower genotypes have become increasingly accessible. However, one of the key challenges remains the efficient and accurate prediction of phenotypes based on genotypes in the context of climate change. Analyzing phenotypes at different levels of organization and detecting connections between phenotypes and genotypes require the integration and processing of large, diverse, and often noisy datasets. Machine learning offers a broad arsenal of methods and approaches for identifying predictive patterns in such data. Therefore, the research aimed to develop a methodology for the systematization of sunflower genotypes based on seed phenotypic characteristics using the data vector quantization method and neural networks. The study revealed the phenotypic characteristics of sunflower seeds from various genotypes selected by the Institute of Oilseed Crops of NAAS, grown in the southern Steppe of Ukraine, including seed length, width, thickness, seed mass, kernel mass, and seed coat cracking force. For this purpose, appropriate laboratory equipment was developed, including two modules for determining the morphological and rheological properties of seeds. The developed methodology for the systematization of sunflower genotypes based on seed phenotypic characteristics includes the following steps: measuring the characteristics of sunflower seeds from various samples (parental components); studying the mutual correlation of characteristics; conducting hierarchical cluster analysis of the data using the Ward's method; determining the optimal number of groups; performing k-means clustering using the vector quantization method; determining the correspondence of ranges of characteristics to the group; training a neural network to group the data by samples and created groups; verifying the adequacy of the neural network on test data. The developed methodology was tested, and the MLP 30-15-3 neural network for grouping data by samples and created groups of sunflower seeds was developed in the Statistica software package. The network's training efficiency was 99.4%, and such of testing and validation was 95.6% and 96.7%, respectively.

Explanation of morphological and biochemical diversity of autochthonous grapes grown in Türkiye (Kelkit Basin) using multivariate analysis

Grapes are widely grown around the world thanks to their different uses and nutritional importance. The demand for grapes is changing day by day in line with consumer preferences. This situation reveals the importance of identifying and protecting autochthonous grape varieties. This study was carried out to evaluate the morphological and biochemical characteristics of a previously unexplored autochthonous grape (Vitis vinifera) population using multivariate analyses. Morphological and biochemical characteristics were evaluated using principal component analysis (PCA), correlation analysis and hierarchical clustering analysis based on Ward's method. In the study, bunch weight varied between 71.67 g and 554.17 g, berry weight varied between 1.54 g and 10.98 g, and the number of seeds in berries varied between 0.00 and 3.50. Among the biochemical properties, total antioxidant content varied between 10.12% and 91.75%, total phenolic content varied between 123.77 mg 100 g-1 and 664.58 mg 100 g-1, total flavonoid content varied between 16.48 mg 100 g-1 and 270.92 mg 100 g-1 and total anthocyanin content varied between 3.35 mg 100 g-1 and 74.42 mg 100 g-1. The coefficient of variation (CV) among the characteristics examined ranged from 5.16% to 102.58%. As a result of PCA, the first two components explained 43.43% of the variation. The autochthonous grapes examined were divided into two main groups with different sub-clusters as a result of hierarchical clustering analysis. As a result of multivariate analysis, was detected significant variation among autochthonous grapes. The variations obtained show that the germplasm examined will be a valuable genetic resource for future grape breeding.

Pitaya (Hylocereus polyrhizus) extract rich in betanin encapsulated in electrospun sweet potato starch nanofibers.

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. 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. 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.

Variations in Clustering of Multielectrode Local Field Potentials in the Motor Cortex of Macaque Monkeys during a Reach-and-Grasp Task.

There is experimental evidence of varying correlation among the elements of the neuromuscular system over the course of the reach-and-grasp task. The aim of this study was to investigate if modifications in correlations and clustering can be detected in the local field potential (LFP) recordings of the motor cortex during the task. To this end, we analyzed the LFP recordings from a previously published study on monkeys that performed a reach-and-grasp task for targets with a vertical or horizontal orientation. LFP signals were recorded from the motor and premotor cortex of macaque monkeys as they performed the task. We found very robust changes in the correlations of the multielectrode LFP recordings that corresponded to task epochs. Mean LFP correlation increased significantly during reach and then decreased during grasp. This pattern was very robust for both left and right arm reaches irrespective of target orientation. A hierarchical cluster analysis also demonstrated similar changes. In focusing on correlations, our study has contributed new insights to the understanding of LFP signals and their relationship to movement. A sliding window computation of the number of clusters was performed to probe the capacities of the LFP clusters for detecting upcoming task events. For a very high percentage of trials (97.89%), there was a downturn in cluster number following the Pellet Drop (GO signal) that reached a minimum preceding the Start of grasp, hence indicating that cluster analyses of LFPs could contribute to signaling an increased probability of the Start of grasp.

Comprehensive physicochemical indicators analysis and quality evaluation model construction for the post-harvest ripening rapeseeds

Rapeseed (Brassica napus L.) is the second largest globally cultivated oil crop, but the effects of post-harvested ripening on rapeseed quality is unclear and unpredictable. This study reveals the relationship between post-harvest ripening periods (PHR) and physicochemical quality of different rapeseed cultivars using comprehensive physicochemical indicators analysis. The results indicate that PHR led to a gradual decrease in chlorophyll, carotenoid and moisture content but continually increased oil and total phenol content (TPC). Besides, 295 lipid molecules from 13 lipid subclasses were identified, revealing that the relative content of triacylglycerol (TG) was progressively increased while diacylglycerol (DG) demonstrated a consistent decline throughout the PHR. Correlation analysis, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were employed to construct and verify the comprehensive quality evaluation model for rapeseeds in PHR. This paper develops a comprehensive quality evaluation model for post-harvest ripening rapeseeds and advances the development of agricultural products.