Means Clustering Research Articles

Time-series clustering of raw recordings of 24-hour ambulatory blood presssure for risk stratification in general population

Abstract Objective 24-hour ambulatory blood pressure measurements (ABPM) is the best out-of-office BP measurement used to refine cardiovascular (CV) risk stratification. Currently, only summary ABPM indexes are used in the clinical practice while the temporal characteristics of the raw BP recordings have not been explored. Therefore, in this study we employed an unsupervised machine learning (ML) algorithm suitable for multivariate time-series analysis to identify distinct BP and heart rate (HR) profiles associated with the incidence of adverse CV events. Design and Method In 1391 community-dwelling individuals (mean age, 46.9 years; 50.97% women), we acquired 24-hour ABPM and clinical data and collected CV events (n=399) on average 22 years later. We employed multivariate dynamic time warping and k-medoids algorithm on the raw recordings of systolic and diastolic BP and HR. To assess the clinical significance of the derived clusters, we compared the clinical characteristics and the incidence of adverse events. We validated the trained model using 24-hour ABPM obtained from external population cohort (n=1137). Results The silhouette score and Dunn index showed that the optimal number of clusters was 4. Across all clusters we observed significant difference in age and sex distribution. Cluster 4 had the worst CV risk factors profiling, with higher office BP and anti-hypertensive medication intake compared to the other clusters. We observed differences in the incidence of adverse events between clusters (Figure), with cluster 1 representing the lowest risk group and cluster 4 the highest. After adjusting for traditional CV risk factors, including office systolic BP, cluster 1 had the lowest risk (HR:0.92; 95%CI:0.84-0.99; P=0.036) and cluster 4 the highest (HR:1.13; 95%CI:1.03-1.24; P=0.006) as compared to the average population risk. The analysis of the external validation cohort revealed similar ABPM patterns and clinical characteristics. Conclusion Employing an unsupervised ML model on the raw ABPM recordings we identified clinically meaningful clusters associated with an increasing cumulative incidence of CV events. This ML analysis paves the way towards utilization of temporal BP and HR information and subsequently optimization of CV risk stratification.

Genetic Diversity and Association Analysis of Traits Related to Water-Use Efficiency and Nitrogen-Use Efficiency of Populus deltoides Based on SSR Markers

Populus deltoides is one of the primary tree species for bioenergy production in temperate regions. In arid/semi-arid northern China, the scarcity of water and nitrogen significantly limits the productivity of poplar plantations. The identification of relevant molecular markers can promote the breeding of resource-efficient varieties. In this study, 188 genotypes of P. deltoides from six provenances served as experimental material. Genetic differentiation analysis, analysis of molecular variance (AMOVA), principal coordinate analysis (PCoA), unweighted pair group method with arithmetic mean (UPGMA) clustering, and genetic structure analysis were performed using selected simple sequence repeat (SSR) markers. Based on these analyses, the association analysis of water-use efficiency (WUE) and nitrogen-use efficiency (NUE) were conducted using general linear model (GLM) and mixed linear model (MLM) approaches. The results showed that 15 pairs of SSR primers successfully amplified across all 188 individuals, with an average of 7.33 alleles (Na) observed per primer pair. The polymorphism information content (PIC) ranged from 0.060 to 0.897, with an average of 0.544, indicating high genetic diversity in the selected markers. The average inbreeding coefficient intra-population (Fis), inbreeding coefficient inter-population (Fit), and inter-population genetic fraction coefficient (Fst) values were 0.005, 0.135, and 0.132, respectively, indicating high heterozygosity, substantial inbreeding within populations, and moderate genetic differentiation, with an average gene flow (Nm) of 1.964, suggesting substantial gene flow between populations. Additionally, molecular variance was primarily within individuals (84.12%). Genetic structure analysis revealed four subgroups, with some degree of genetic admixture among the provenances. In the GLM model, 11 markers were significantly associated with five traits (p < 0.05), with an average contribution rate of 15.82%. Notably, SSR132 and SSR143 were significantly associated with multiple traits (p < 0.05). The MLM model identified two markers (SSR47 and SSR85) significantly associated with ground diameter (p < 0.05) and one marker (SSR80) significantly associated with NUE (p < 0.05). This study identifies loci associated with WUE and NUE, laying a foundation for future genetic improvement and marker-assisted breeding strategies in poplar.

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

AbstractWithin High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Clusters of African countries based on the social contacts and associated socioeconomic indicators relevant to the spread of the epidemic

IntroductionIt is well known that social contact patterns differ from country to country. This variation coincides with significant socioeconomic heterogeneity that complicates the design of effective nonpharmaceutical interventions. This study examined how socioeconomic heterogeneity in selected African countries might be factored in to explain better patterns of social contact mix between countries.MethodsWe used a standardized contact matrix for 32 African countries, estimated in (Prem et al. in PLoS Comput Biol 17(7):e1009098, 2021). We scaled the matrices using an epidemic model from (Röst et al. in Viruses 12(7):708, 2020). We also analyzed aggregated data from the World Bank country website. The data includes 28 variables; social, economic, environmental, institutional, governance, health and well-being, education, gender inequality, and other development-related indicators that describe countries. Principal component analysis was used to visualize socioeconomic similarities between countries and identify the indicators for maximum variation. The (2D)2PCA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$(2D)^{2} PCA$\\end{document} approach was used to reduce the dimension of synthetic contact matrices for each country to avoid the dimensionality curse. Then, hierarchical aggregate clustering was used to identify groups of countries with similar social patterns, taking into account the country’s socioeconomic performance.ResultsOur model yielded four meaningful clusters, each with a few distinguishing features. Social contacts varied between groups, but were generally similar within each set. The socioeconomic performance of the country influenced the clusters.ConclusionsOur results suggest that integrating socioeconomic factors into social contacts can better explain infectious disease transmission dynamics and that similar interventions can be implemented in countries within the cluster

Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

As the basic guarantee for people’s production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school [Formula: see text] priori, [Formula: see text]-means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults.

New Approaches to Extremal Index Estimation

The extremal index is a parameter associated with the extreme value distributions of dependent stationary sequences. Under certain local dependence conditions, exceedances above a specified threshold tend to occur in isolated clusters. The reciprocal of the extremal index can be interpreted as the limiting size of these clusters. Accurately estimating the size of such clusters is crucial for analyzing real data and can significantly influence decision making processes that impact population well being. The paper presents a recent method for the estimation of the extremal index which starts by the estimation of the parameter itself and, only then, to use that estimate in the cluster mean size estimation. The procedure starts with the estimation of a specific proportion by the corresponding relative frequency. Thus, it is very simple, intuitive, it has good statistical properties, and it does not depend on the method used for the mean cluster estimation. The interpretation of the extremal index as a proportion is known, but it has not been used directly as an estimation method. In recent years, various authors have proposed different estimators for the extremal index. This paper applies some of the latest estimation methods for the extremal index to real data and analyses their performance using training and test samples. The results are compared with other well known estimators, for which R packages are available. The results show a better performance of the Proportion estimator, followed by the Gaps estimator, when compared to the other considered index estimators.

Identifying Clusters of Curatively-Treated Esophageal Cancer Patients Using Patient-Reported Outcome Measures at Three Months from Discharge: A Secondary Analysis from a Longitudinal Single Center Study.

This study aims to identify meaningful clusters based on Patient-Reported Outcome Measures (PROMs) in curatively-treated esophageal cancer patients at three months post-discharge. This secondary analysis of a longitudinal single-center study included 46 esophageal cancer patients who underwent curative surgery. Patients were selected based on their completion of PROMs surveys at three months post-discharge, were aged 18 years or older, and had undergone surgical resection (esophagectomy) with or without neoadjuvant chemotherapy and/or radiotherapy. The analysis utilized t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction and hierarchical clustering to analyze PROMs data collected three months post-discharge. Clustering was performed on physical, emotional, cognitive, and social functioning variables, symptom burden, and health literacy. Three distinct clusters were identified: Cluster 1 (n = 24) with higher functioning and moderate symptoms, Cluster 2 (n = 14) with moderate functioning, higher symptoms, and lower health literacy, and Cluster 3 (n = 8) with the highest functioning, lowest symptoms, and highest health literacy. Significant differences between squamous cell carcinoma and adenocarcinoma subtypes were observed across several PROMs domains, including critical health literacy, general health status/quality of life, nausea and vomiting, and insomnia. These clusters provide an exploratory framework for tailoring post-operative interventions to enhance patient recovery, which necessitates further confirmatory investigations, including outcomes such as complications and mortality, in the analysis. This study fills a research gap by demonstrating the utility of PROMs in identifying distinct recovery patterns in esophageal cancer patients post-surgery. The findings support the use of PROMs to guide personalized post-operative care, potentially improving patient outcomes and quality of life. Further research is needed to validate these findings in larger, diverse populations.

Quasi-two-dimensional dispersions of Brownian particles with competitive interactions: phase behavior and structural properties.

Competing short-range attractive (SA) and long range repulsive (LR) particle interactions can be used to describe three-dimensional charge-stabilized colloid or protein dispersions at low added salt concentrations, as well as membrane proteins with interaction contributions mediated by lipid molecules. Using Langevin dynamics (LD) simulations, we determine the generalized phase diagram, cluster shapes and size distributions of a generic quasi-two-dimensional (Q2D) dispersion of spherical SALR particles confined to in-plane motion inside a bulk fluid. The SA and LR interaction parts are modelled by a generalized Lennard-Jones potential and a screened Coulomb potential, respectively. The microstructures of the detected equilibrium and non-equilibrium Q2D phases are distinctly different from those observed in three-dimensional (3D) SALR systems, by exhibiting different levels of hexagonal ordering. We discuss a thermodynamic perturbation theory prediction for the metastable binodal line of a reference system of particles with SA interactions only, which in the explored Q2D-SALR phase diagram region separates cluster from non-clustered phases. The transition from the high-temperature (small SA) dispersed fluid (DF) phase to the lower-temperature equilibrium cluster (EC) fluid phase is characterised by a low-wavenumber peak height of the static structure factor (corresponding to a thermal correlation length of about twice the particle diameter) featuring a distinctly smaller value (≈1.4) than in 3D SALR systems. With decreasing temperature (increasing SA), the cluster morphology changes from disk-like shapes in the equilibrium cluster phase, to double-stranded anisotropic hexagonal cluster segments formed in a cluster-percolated (CP) gel-like phase. This transition can be quantified by a hexagonal order parameter distribution function. The mean cluster size and coordination number of particles in the CP phase are insensitive to changes in the attraction strength.

Gaussian Mixture Regression Model with Sparsity for Clustering of Territory Risk in Auto Insurance

Abstract Insurance rating territory design and accurate estimation of territory risk relativities are fundamental aspects of auto insurance rate regulation. It is crucial to develop methodologies that can facilitate the effective design of rating territories and their risk relativities estimate, as they directly impact the rate filing and the decision support of the rate change review process. This article proposes a Gaussian Mixture Regression model clustering approach for territory design. The proposed method incorporates a linear regression model, taking spatial location as model covariates, which helps estimate the cluster mean more accurately. Also, to further enhance the estimation of territory risk relativities, we impose sparsity through sparse matrix decomposition of the membership coefficient matrix obtained from the Gaussian Mixture Regression model. By transitioning from the current hard clustering method to a soft approach, our methodology could improve the evaluation of territory risk for rate-making purposes. Moreover, using non-negative sparse matrix approximation ensures that the estimation of risk relativities for basic rating units remains smooth, effectively eliminating data noise from the territory risk relativity estimate. Overall, our novel methodology aims to significantly enhance the accuracy and reliability of risk analysis in auto insurance. Furthermore, the proposed method exhibits potential for extension to various other domains that involve spatial clustering of data, thereby broadening its applicability and expanding its usefulness beyond auto insurance rate regulation.

Identification of false smut – resistant donors in Rice (Oryza sativa L.) and analysis of their morpho-molecular diversity for resistance breeding

False smut disease of rice caused by the pathogen Ustilaginoidea virens is a growing threat to the rice farmers as it affects both quality and quantity. Development of resistant variety becomes difficult, since very few resistant donors were available for false smut resistant breeding programme. Therefore, to identify potential donors for resistance breeding a total of 60 genotypes were screened at hotspot location (Gudalur) during kharif 2023 which led to identification of 12 highly resistant genotypes and the notable ones are Koolavalai, Periya chandikar, Kapikar selection and Earapalli. Genetic variability studies indicated the presence of additive gene action for all the agronomic and disease related traits. Principal component analysis revealed the first 5 principal components collectively contributing 78.79 % of the total variance with disease-related traits contributing significantly to divergence. Ten clusters were delineated using Mahalanobis D2 statistics with clusters IX and V showing higher inter cluster distance (3453.64). Forty-one polymorphic markers were used to analyse the genotypes and The Unweighted Pair Group method with Arithmetic Mean (UPGMA) clustering by Jaccard distance formed 6 clusters. The Bayesian clustering classified the entire population into 2 subpopulations. False smut linked marker RM336 and RM218 were found to be the most informative marker with high Polymorphism Information Content (0.71, 0.69) and Heterozygosity Index (0.76, 0.73). The resistant genotypes such as IG71, Thulasi vasanai sambha, Arupatham vellai, Kaltikar and Chinna aduku nel can be used in the future breeding programmes to develop the resistant cultivar and to identify the candidate genes governing resistance.

Examining graduate student perspectives on supervision and peer mentoring across four professional faculties

Purpose The purpose of the study was to examine graduate student perspectives on the common and unique roles peer mentors and supervisors play in supporting student success and wellbeing during their program. Design/methodology/approach A qualitative research design involving semistructured interviews with 62 thesis-based masters and doctoral students from four professional faculties, Education, Medicine, Nursing and Social Work, at a large public research-intensive university in Canada. Findings Findings transcend the four disciplines of study. Communities of support are described that involve both supervisors and peers in combination, clusters of meaning by supervisory paradigm are identified and original findings presented that expand upon the learning alliance framework by explicitly considering the role of peer mentors in graduate student success. Research limitations/implications While supervisors bear primary responsibility for fostering effective research-based relationships, this study’s findings strengthen the argument that mentoring and advising of graduate students is most effective when conducted within a collaborative community of support that involves learning alliances among faculty, peers, program staff and academic leaders across the institution. Practical implications A four-pronged approach to graduate education that emphasizes the collective responsibility of institutions, programs, supervisors and students in creating a supportive ecosystem for holistic graduate student academic success and wellbeing is recommended. Social implications Key argument that it is essential to embrace a collaborative and community of support mindset, where multiple stakeholders actively contribute to the wellbeing and academic development of graduate students throughout their programs. Originality/value A cross-disciplinary perspective is offered on the importance of both supervisors and peers in assisting thesis-based graduate students to successfully navigate academic, social and personal journeys through graduate school.

A Review of Convex Clustering From Multiple Perspectives: Models, Optimizations, Statistical Properties, Applications, and Connections.

Traditional partition-based clustering is very sensitive to the initialized centroids, which are easily stuck in the local minimum due to their nonconvex objectives. To this end, convex clustering is proposed by relaxing K -means clustering or hierarchical clustering. As an emerging and excellent clustering technology, convex clustering can solve the instability problems of partition-based clustering methods. Generally, convex clustering objective consists of the fidelity and the shrinkage terms. The fidelity term encourages the cluster centroids to estimate the observations and the shrinkage term shrinks the cluster centroids matrix so that their observations share the same cluster centroid in the same category. Regularized by the lpn -norm ( pn ∈ {1,2,+∞} ), the convex objective guarantees the global optimal solution of the cluster centroids. This survey conducts a comprehensive review of convex clustering. It starts with the convex clustering as well as its nonconvex variants and then concentrates on the optimization algorithms and the hyperparameters setting. In particular, the statistical properties, the applications, and the connections of convex clustering with other methods are reviewed and discussed thoroughly for a better understanding the convex clustering. Finally, we briefly summarize the development of convex clustering and present some potential directions for future research.

Sifting the debris: Patterns in the SNR population with unsupervised ML methods

Context. Supernova remnants (SNRs) carry vast amounts of mechanical and radiative energy that heavily influence the structural, dynamical, and chemical evolution of galaxies. To this day, more than 300 SNRs have been discovered in the Milky Way, exhibiting a wide variety of observational features. However, existing classification schemes are mainly based on their radio morphology. Aims. In this work, we introduce a novel unsupervised deep learning pipeline to analyse a representative subsample of the Galactic SNR population (~50% of the total) with the aim of finding a connection between their multi-wavelength features and their physical properties. Methods. The pipeline involves two stages: (1) a representation learning stage, consisting of a convolutional autoencoder that feeds on imagery from infrared and radio continuum surveys (WISE 22 μm, Hi-GAL 70 μm and SMGPS 30 cm) and produces a compact representation in a lower-dimensionality latent space; and (2) a clustering stage that seeks meaningful clusters in the latent space that can be linked to the physical properties of the SNRs and their surroundings. Results. Our results suggest that this approach, when combined with an intermediate uniform manifold approximation and projection (UMAP) reprojection of the autoencoded embeddings into a more clusterable manifold, enables us to find reliable clusters. Despite a large number of sources being classified as outliers, most clusters relate to the presence of distinctive features, such as the distribution of infrared emission, the presence of radio shells and pulsar wind nebulae, and the existence of dust filaments.

An Examination Of Traditional Craft As A Component Of Tsangaya System Of Education In North Central Zone Nigeria

An examination of traditional craft as component of tsangaya system of education in north central zone Nigeria. A survey research approach and a Multi-Stage Cluster Sampling procedure. Data was gathered from 297 respondents drawn from tsangaya schools from 3 selected states in north-central Nigeria, kwara, Nasarawa and Niger State. The collection of data was using a questionnaire that was structured and well-validated. Descriptive and inferential statistics were used for the data analysis. The outcome revealed the overall mean score of 3.93 for the ten skills needed as agreed by the respondents. The overall mean score of 3.71 of the five attitudinal items as agreed. The overall mean score of 4.05 of the four awareness statements listed as agreed. The cluster mean score of 3.46 shows serious challenges towards Traditional Craft Skills. The findings show that awareness has the highest Beta value (B=0.729, P<0.05). The findings supported the formulated hypothesis, therefore, we reject H0. Followed by attitude with (B=0.400, P<0.05) the findings also supported the hypothesis, therefore, we reject H0. The challenges had the lowest Beta value of (B=0.022, P<0.05) and also have a significant impact on the traditional craft skills. The findings equally supported the formulated hypothesis, therefore, we reject H0. In conclusion, attitude and awareness had a serious impact on entrepreneurship skills acquisition program. Hence, Adj.R2 of 98.6% as shown on Table 5 is an indication of another 1.4% unexplained factors that are not considered in this study. Therefore, its recommended that Governments at all levels (Federal, State and Local governments) should come up with more effective means of supporting the traditional craft in Tsangaya School’s system in north central Nigeria

Surface Roughness Prediction in Additive Manufacturing: Presenting the Power of Neural Networks Compared to Linear Regression

This paper investigates surface roughness prediction in additive manufacturing through a comprehensive comparative analysis of linear regression (LR) and neural network (NN) models. Employing [Formula: see text]-means clustering, we identify four distinct clusters within the experimental data, each closely associated with specific 3D printing parameters. Within each cluster, we explore the optimal combination of factors that contribute to surface roughness and power efficiency. The main objective focuses on predicting a target variable, with an emphasis on evaluating model performance via key metrics such as [Formula: see text]-squared ([Formula: see text]2), adjusted [Formula: see text]-squared, predicted [Formula: see text]-squared, mean squared error (MSE), and correlation coefficient ([Formula: see text]). Our study’s results illuminate the robust predictive capabilities of both LR and NN models. However, it becomes evident that the Neural Network model outperforms Linear Regression. It exhibits excellent performance metrics, characterized by higher [Formula: see text]2and correlation values, reduced MSE, and greater resilience to outliers. This pronounced disparity underscores the Neural Network model’s exceptional suitability for tasks requiring precise predictions and the identification of nonlinear patterns, particularly in the field of surface roughness prediction in additive manufacturing. These findings emphasize the key role of advanced machine learning techniques, illustrated by neural networks, in achieving precision within similar domains.

The Patterns and Meanings of Tomb Clusters in the Bronze Age

The Patterns and Meanings of Tomb Clusters in the Bronze Age

Characteristics of Clustering and Switching in Verbal Fluency in Mild Cognitive Impairment and Dementia of Alzheimer’s Type

Objectives: The purpose of this study was to compare the performance on a verbal fluency task, clustering and switching characteristics, and proportion by error type between healthy elderly, Mild Cognitive Impairment (MCI) and Dementia of Alzheimer’s Type (DAT) group. Methods: Thirty healthy elderly, 30 MCI and 29 DAT individuals were asked to produce words in the category of ‘supermarket items’ for semantic fluency and ‘ㄱ’, ‘ㅇ’, and ‘ㅅ’ Korean letter categories for phonemic fluency with one minute allotted per category. Results: First, the number of correct responses in the verbal fluency task was significantly different between groups, with post hoc analyses showing significant differences between the healthy elderly and DAT groups and between the MCI and DAT groups. Second, the mean cluster size was not significant between groups in the semantic fluency task. However, it was significant between the healthy elderly and DAT groups, and between MCI and DAT groups in the phonemic fluency task. Third, the number of switches was significant between the healthy elderly and DAT elderly. Finally, the three groups had the highest proportion of perseverative errors in the semantic fluency task and the highest proportion of other errors in the phonemic fluency task. Conclusion: This study has clinical significance in that it examined the number of correct responses and characteristics of clustering and switching on verbal fluency tasks in the healthy elderly, MCI and DAT groups.

How to optimize the CAR-T Cell therapy process? A group concept mapping analysis of preconditions for a frictionless process from a German multistakeholder perspective.

Treatment with chimeric antigen receptor T (CAR-T) cells involves a large number of interdisciplinary stakeholders and is associated with complex processes ranging from patient-specific production to follow-up care. Due to the complexity, maximum process optimization is required in order to avoid efficiency losses. This study aimed at systematically determining the preconditions for a frictionless flow of the CAR-T process by surveying the stakeholders involved. A Group Concept Mapping (GCM) analysis, a mixed-methods participatory research, was conducted. CAR-T experts from different professional backgrounds went through three steps: 1) Brainstorming relevant aspects (statements) for a frictionless process, 2) Sorting the collected statements based on their similarity, and 3) Rating the importance and feasibility of each statement. A cluster map reflecting the overarching topics was derived, and mean ratings per statement and cluster were calculated. Overall, 20 CAR-T experts participated. A total of 80 statements were collected, resulting in a map of the following 10 clusters (mean importance/feasibility): Information for patients and physicians (4.16/3.77), Supportive network (4.03/3.53), Eligibility of patients (4.41/3.63), Evidence, transparency and communication (4.01/3.33), Paperwork (4.1/2.52), Interface with pharmaceutical manufacturer (4.03/2.85), Reimbursement (4.29/2.31), Quality Management (4.17/3.18), Infrastructure of CAR-T clinics (4.1/2.93), and Patient-oriented processes (4.46/3.32). The 80 statements underlined the complex and manifold nature of the CAR-T treatment process. Our results reflect the first step in overcoming hurdles: identifying potential hurdles and required preconditions. Decision-makers and stakeholders can use the results to derive strategies and measures to further promote a frictionless process.

Diversity of ethnomycological knowledge and mushroom foraging culture in a small nation: case of Lithuania

BackgroundEven in mycophilic nations, mushroom foraging and use traditions may vary from region to region, making it part of cultural diversity and a source of traditional knowledge even in modern and changing societies. The main objective of our study was to investigate mushroom foraging and use traditions in major ethno-regions of Lithuania, to record and analyse whether and how biogeographical and ethno-cultural features influence these traditions, what is their biocultural significance, and whether they persist in the face of societal and environmental change.MethodsThe study conducted a survey among 106 respondents from eleven administrative units of Lithuania, representing four ethno-regions (Žemaitija, Suvalkija, Dzūkija and Aukštaitija, the latter divided into North and East). The age of the respondents ranged from 32 to 97 years (mean 70 years, median 68 years). Each respondent was asked six questions about mushroom species and mushroom foraging. The interviews were accompanied by 50 photographs of different mushroom species. The similarity between mushroom foragers' preferences in ethno-regions and the sets of mushroom species used for food and medicine was tested using cluster analysis. Clustering was carried out using Sørensen distances and the method of cluster mean linkage method.ResultsThe number of mushroom species recognised per respondent was quite similar, with the lowest number of mushroom species recognised in Žemaitija and the highest in Eastern Aukštaitija. Species of no economic or other importance were not well recognised in any of the regions. The number of names applied to mushroom species varied from region to region and did not coincide with the popularity of mushrooms among mushroom pickers. The number of mushroom species used for food was also similar between regions, except for Dzūkija, which had the lowest number of species collected. Nine mushroom species were identified by respondents as medicinal mushrooms, the most popular being Amanita muscaria and Inonotus obliquus. When analysing the similarity of mushroom species collected for food and medicine, it was found that ethno-regions clustered into three distinct groups. Two groups depended on the prevailing forest types, while Žemaitija formed a separate cluster defined only by local traditions.ConclusionsThe work represents the largest study of ethnomycological tradition in Lithuania, covering major ethno-regions. We have found that ethnomycological knowledge and tradition are not influenced by any long-term historical events, ethnicity or religion, but rather by the prevailing forest types and regional ethno-culture. Knowledge of edible mushrooms is considered to be inherited from the older generation and is conservative regarding the changes in the set of species consumed and mushroom-related gastronomy. This is also reflected in the safety of mushroom consumption, as mushroom poisoning was very rare among the respondents. Economically insignificant mushrooms are not distinguished and overlooked by traditional mushroom pickers who are not a good source of information on rare or otherwise noteworthy species. However, from a biocultural point of view, mushrooms and mushroom-related traditions remain important for linguistic diversity, traditional knowledge systems and their transmission.

Modular stochastic configuration network with attention mechanism for soft measurement of water quality parameters in wastewater treatment processes

Due to the complex and multi-distributed nature of wastewater treatment process data, stochastic configuration networks (SCNs) face difficulties such as large network structure and complex construction process when dealing with data modeling tasks. To address these challenges, this paper proposes a modular stochastic configuration network with attention mechanism, which integrates the advantages of modular partitioning, attention mechanism, and SCN. The model employs a fuzzy mean clustering strategy to simplify and decompose the complex multi-case and multi-distribution nonlinear modeling task, thereby reducing the modeling complexity of a single model. Furthermore, the model utilizes a stochastic configuration algorithm to learn the subtasks and construct corresponding sub-networks. The output weights are then assigned to each sub-network based on the attention mechanism to obtain the model’s final output. The validity of the proposed method is validated through two benchmark experiments. Then the model is applied to measure effluent ammonia nitrogen. The results indicate that compared with the classical randomized and modularized network models, the proposed model exhibits certain advantages in terms of learning accuracy and generalization performance.