Distance Matrix Research Articles

Enhanced group-level dorsolateral prefrontal cortex subregion parcellation through functional connectivity-based distance-constrained spectral clustering with application to autism spectrum disorder.

The dorsolateral prefrontal cortex (DLPFC) assumes a central role in cognitive and behavioral control, emerging as a crucial target region for interventions in autism spectrum disorder neuroregulation. Consequently, we endeavor to unravel the functional subregions within the DLPFC to shed light on the intricate functions of the brain. We introduce a distance-constrained spectral clustering (SC-DW) methodology that leverages functional connection to identify distinctive functional subregions within the DLPFC. Furthermore, we verify the relationship between the functional characteristics of these subregions and their clinical implications. Our methodology begins with principal component analysis to extract the salient features. Subsequently, we construct an adjacency matrix, which is constrained by the spatial properties of the brain, by linearly combining the distance matrix and a similarity matrix. The quality of spectral clustering is further optimized through multiple cluster evaluation coefficient. The results from SC-DW revealed four uniform and contiguous subregions within the bilateral DLPFC. Notably, we observe a substantial positive correlation between the functional characteristics of the third and fourth subregions in the left DLPFC with clinical manifestations. These findings underscore the unique insights offered by our proposed methodology in the realms of brain subregion delineation and therapeutic targeting.

Matrix Representation of Graph Theory in Hydrocarbons

Graph theory is one of the best-known research head subjects having many applications. Graph theory serves as a mathematical model to represent any system which has a binary relation. Chemical graph theory is branch of graph theory that is concerned with analysis of all consequences of connectivity in a chemical graph. An organic molecule can be represented by a graph, which can be converted to several matrices by using various graph characteristic, connecting of atoms through bonds lead to adjacency, incidence, cut-set, circuit and distance matrices. The object of this paper is to adopt graph theory matrix approach in hydrocarbons. Key Word: Matrix (Adjacency, cycle, cut-set, edge, incidence and path) and Undirected graph.

Structure-Activity Relationship Studies on VEGFR2 Tyrosine Kinase Inhibitors for Identification of Potential Natural Anticancer Compounds.

Over-expression of Vascular Endothelial Growth Factor Receptors (VEGFRs) leads to the hyperactivation of oncogenes. For inhibition of this hyperactivation, the USA Food Drug Administration (FDA) has approved many drugs that show adverse effects, such as hypertension, hypothyroidism, etc. There is a need to discover potent natural compounds that show minimal side effects. In the present study, we have taken structurally diverse known VEGFR2 inhibitors to develop a Quantitative Structure-Activity Relationship (QSAR) model and used this model to predict the inhibitory activity of natural compounds for VEGFR2. The QSAR model was developed through the forward stepwise Multiple Linear Regression (MLR) method. A developed QSAR model was used to predict the inhibitory activity of natural compounds. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) assessment and molecular docking studies were performed. The binding stability of the natural compounds with VEGFR2 was elucidated through Molecular Dynamics (MD) simulation. The developed QSAR model against VEGFR2 showed the regression coefficient of the training dataset (r2) as 0.81 and the external regression coefficient of the test dataset (r2 test) 0.71. Descriptors, viz., electro-topological state of potential hydrogen bonds (maxHBint2, nHBint6), atom types (minssNH), maximum topological distance matrix (SpMAD_Dt), and 2D autocorrelation (ATSC7v), have been identified. Using this model, 14 natural compounds have been selected that have shown inhibitory activity for VEGFR2, of which six natural compounds have been found to possess a strong binding affinity with VEGFR2. In MD simulation, four complexes have shown binding stability up to 50ns. The developed QSAR model has identified 5 conserved activity-inducing physiochemical properties, which have been found to be correlated with the anticancer activity of the nonidentical ligand molecules bound with the VEGFR2 kinase. Lavendustin_A, 3'-O-acetylhamaudol, and arctigenin have been obtained as possible lead natural compounds against the VEGFR2 kinase.

Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) enables precise and efficient phylogenetic estimation in viruses.

Understanding phylogenetic relationships among species is essential for many biological studies, which call for an accurate phylogenetic tree to understand major evolutionary transitions. The phylogenetic analyses present a major challenge in estimation accuracy and computational efficiency, especially recently facing a wave of severe emerging infectious disease outbreaks. Here, we introduced a novel, efficient framework called Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) for new sample placement for viruses. In this study, a brand-new recoding method called Frequency Vector Recoding was implemented to approximate the phylogenetic distance, and the Phylogenetic Simulated Annealing Search algorithm was developed to match the recoded distance matrix with the phylogenetic tree. Meanwhile, the indel (insertion/deletion) was heuristically introduced to foreign sequence recognition for the first time. Here, we compared the Bd-RPC with the recent placement software (PAGAN2, EPA-ng, TreeBeST) and evaluated it in Alphacoronavirus, Alphaherpesvirinae, and Betacoronavirus by using Split and Robinson-Foulds distances. The comparisons showed that Bd-RPC maintained the highest precision with great efficiency, demonstrating good performance in new sample placement on all three virus genera. Finally, a user-friendly website (http://www.bd-rpc.xyz) is available for users to classify new samples instantly and facilitate exploration of the phylogenetic research in viruses, and the Bd-RPC is available on GitHub (http://github.com/Bin-Ma/bd-rpc).

Euclidean and circum-Euclidean distance matrices: characterizations and interlacing property

Motivated by the inverse formula of the distance matrix of a tree and the Moore–Penrose inverse of a circum-Euclidean distance matrix (CEDM), in this paper, we study a general real square matrix M whose Moore–Penrose inverse can be expressed as the sum of a Laplacian-like matrix L and a rank one matrix. In particular, for a symmetric hollow matrix M, under an assumption, we show that M is a Euclidean distance matrix if and only if L is positive semidefinite. Based on this, we obtain a new characterization for CEDMs involving their Moore–Penrose inverses. As an application, we show that the distance matrices of block graphs and odd-cycle-clique graphs are CEDMs. Finally, we establish an interlacing property between the eigenvalues of a Euclidean distance matrix M (including the singular case) and its associated Laplacian-like matrix L, which generalizes the interlacing property proved for the distance matrices of trees.

Achieving Privacy Preservation Constraints based on K-Anonymity in conjunction with Adjacency Matrix and Weighted Graphs

A well-known privacy preservation model is k-anonymity. It is simple and widely applied in several real-life systems. To achieve k-anonymity constraints in datasets, all explicit identifiers of users are removed. Furthermore, the unique quasi-identifiers of users are distorted by their less specific values to be at least k indistinguishable tuples. For this reason, after datasets are satisfied by k-anonymity constraints, they can guarantee that all possible query conditions to them always have at least k tuples that are satisfied. Aside from achieving privacy preservation constraints, the data utility and the complexity of data transformation are serious issues that must also be considered when datasets are released. Therefore, both privacy preservation models are proposed in this work. They are based on k-anonymity constraints in conjunction with the weighted graph of correlated distortion tuples and the adjacency matrix of tuple distances. The proposed models aim to preserve data privacy in datasets. Moreover, the data utility and data transform complexities are also considered in the privacy preservation constraint of the proposed models. Furthermore, we show that the proposed data transformation technique is more efficient and effective by using extensive experiments.

The Maternal Diet Index and Offspring Microbiota at 1 Month of Life: Insights from the Mediterranean Birth Cohort MAMI.

Background: Maternal diet during pregnancy may play a role in infant health outcomes via the maternal microbiota. We assessed the association of the maternal diet index for the Mediterranean area (MDI-med) with infant gut microbiota at 1 month of life. Methods: The MAMI study is a longitudinal birth cohort in the Mediterranean area. In this work, a cross-sectional study, including 120 mother-infant dyads with available maternal diet and infant microbiota at 1-month-old data, was undertaken. The MDI developed in the US (MDI-US) was adapted for the MAMI cohort (MDI-med). Stratification based on extreme values resulted (22 in the "lower" MDI-med group and 23 in the "upper" group from the mean). Relative microbial abundances and alpha (microbial richness and diversity indexes) and beta diversity (Bray-Curtis distance matrix) were compared between the groups. Results: Higher maternal daily vegetable intake and lower red meat intake were the characteristics of the "upper" MDI-med group. Significantly lower microbial diversity (Shannon and InvSimpson index (p = 0.01)), but no changes in richness (Chao1 index) nor in beta-diversity, using Bray-Curtis distance, were observed in the "upper" group, compared to the "lower" MDI-med group. A higher relative abundance of the Bifidobacterium genus (Actinomycetota phylum) was associated with maternal daily vegetable and yogurt intake. Conclusion: Reduced infant microbial diversity at 1 month of age was associated with "upper" MDI-med scores. Higher maternal intakes of vegetables and yogurt were associated with higher relative abundances of the Bifidobacterium genus in the infant gut. Further studies are needed to understand the link between pregnancy diet, infant microbiota, and health outcomes.

An Approach for Detecting Local Outliers in Grid Queries

The density local outlier factor algorithm (LOF) needs to calculate the distance matrix for k-nearest neighbor search. The algorithm has high time complexity and is not suitable for the detection of large-scale data sets. A local outlier detection algorithm is proposed based on grid query (LOGD). In the algorithm, the k other data points closest to the data point in the target grid must be in the target grid or in the nearest neighboring grid of the target grid, it is used to improve the neighborhood query operation of the LOF algorithm, the calculation amount of the LOF algorithm is reduced in the neighborhood query. Experimental results show that the proposed LODG algorithm can effectively reduce the time of outlier detection under the condition, the detection accuracy of the original LOF algorithm is basically the same.

Genetic structure and ecological niche space of lentil's closest wild relative, Lens orientalis (Boiss.) Schmalh.

Crops arose from wild ancestors and to understand their domestication it is essential to compare the cultivated species with their crop wild relatives. These represent an important source of further crop improvement, in particular in relation to climate change. Although there are about 58,000 Lens accessions held in genebanks, only 1% are wild. We examined the geographic distribution and genetic diversity of the lentil's immediate progenitor L. orientalis. We used Genotyping by Sequencing (GBS) to identify and characterize differentiation among accessions held at germplasm collections. We then determined whether genetically distinct clusters of accessions had been collected from climatically distinct locations. Of the 195 genotyped accessions, 124 were genuine L. orientalis with four identified genetic groups. Although an environmental distance matrix was significantly correlated with geographic distance in a Mantel test, the four identified genetic clusters were not found to occupy significantly different environmental space. Maxent modelling gave a distinct predicted distribution pattern centred in the Fertile Crescent, with intermediate probabilities of occurrence in parts of Turkey, Greece, Cyprus, Morocco, and the south of the Iberian Peninsula with NW Africa. Future projections did not show any dramatic alterations in the distribution according to the climate change scenarios tested. We have found considerable diversity in L. orientalis, some of which track climatic variability. The results of the study showed the genetic diversity of wild lentil and indicate the importance of ongoing collections and in situ conservation for our future capacity to harness the genetic variation of the lentil progenitor.

Communicability cosine distance: similarity and symmetry in graphs/networks

A distance based on the exponential kernel of the adjacency matrix of a graph and representing how well two vertices connect to each other in a graph is defined and studied. This communicability cosine distance (CCD) is a Euclidean spherical distance accounting for the cosine of the angles spanned by the position vectors of the graph vertices in this space. The Euclidean distance matrix (EDM) of CCD is used to quantify the similarity between vertices in graphs and networks as well as to define a local vertex invariant—a closeness centrality measure, which discriminate very well vertices in small graphs. It allows to distinguish all nonidentical vertices, also characterizing all identity (asymmetric) graphs–those having only the identity automorphism–among all connected graphs of up to 9 vertices. It also characterizes several other classes of identity graphs. We also study real-world networks in term of both the discriminating power of the new centrality on their vertices as well as in ranking their vertices. We analyze some dictionary networks as well as the network of copurshasing of political books, remarking some of the main advantages of the new approaches studied here.

Context is key: A 34‐country analysis investigating how similar HRM systems emerge from similar contexts

AbstractUsing an institutional lens, we investigate the isomorphic effects of both external and internal contexts on human resource management (HRM) systems. Our analysis uses data from 4768 organizations across 34 countries to focus on the similarities in HRM systems. By employing distance matrices, a commonly adopted method in geographic science, we find that both external and internal contexts affect (dis)similarities in HRM systems. Organizations in similar environments exhibit more similar HRM systems. Furthermore, we find that the devolvement of HRM activities from HRM to line management reduces the similarity of HRM systems across organizations. By contrast, a strong strategic position of HRM does not yield a comparable effect. Our study's main contributions include elucidating the multifaceted relationship between context and HRM, highlighting the HRM department's role in this relationship, clarifying the context–HRM connection via the concept of isomorphic pressures, and illustrating the use of distance matrices as tool with great explanatory power for the analysis of similarities among HRM phenomena.

Unveiling Transitions in Disease States: Study of Depressive and Anxiety Symptom Networks over Time

Background. Major depressive disorder (MDD) and anxiety disorders (AD) have high degrees of comorbidity and show great overlap in symptoms. The analysis of covarying depressive‐ and anxiety symptoms in longitudinal, sparse data panels has received limited attention. Dynamic time warping (DTW) analysis may help to provide new insights into symptom network properties based on diagnostic‐ and disease‐state stability criteria. Materials and Methods. In the Netherlands Study of Depression and Anxiety depressive‐, anxiety‐, and worry symptoms were assessed four or five times over the course of 9 years using self‐report questionnaires. The sample included 1,649 participants at baseline, comprising controls (n = 360), AD patients (n = 158), MDD patients (n = 265), and comorbid AD–MDD patients (n = 866). With DTW, 1,649 distance matrices were calculated, which yielded symptom networks and enabling comparison of network densities among subgroups. Results. The mean age of the sample was 41.5 years (standard deviations, 13.2), of whom 66.4% were female. The largest distance was between worry symptoms and physiological arousal symptoms, implicating the most dissimilar dynamics over time. The network density in the groups, from lowest to highest, followed the order: controls, AD, MDD, and comorbid AD–MDD. The comorbid group showed strongly connected mood and cognitive symptoms, which contrasted with the more strongly connected somatic and arousal symptoms in the AD and MDD groups. Groups that showed more transitions in disease states over follow‐up, regardless of the diagnoses, had the highest network density compared to more stable states of health or disease (beta for quadratic term = −0.095; P < 0.001). Conclusions. Symptom networks over time can be visualized by applying DTW methods on sparse panel data. Network density was highest in patients with comorbid anxiety and depressive disorders and those with more instability in disease states, suggesting that a stronger internal connectivity may facilitate “critical transitions” within the complex systems framework.

Intelligent path planning for cognitive mobile robot based on Dhouib-Matrix-SPP method

The Mobile Robot Path Problem looks to find the optimal shortest path from the starting point to the target point with collision-free for a mobile robot. This is a popular issue in robotics and in this paper the environment is considered as static and represented as a bidirectional grid map. Besides, the novel optimal method Dhouib-Matrix-SPP (DM-SPP) is applied to create the optimal shortest path for a mobile robot in a static environment. DM-SPP is a greedy method based on a column row navigation in the distance matrix and characterized by its rapidity to solve sparse graphs. The comparative analysis is conducted by applying DM-SPP on thirteen test cases and comparing its results to the results given by four metaheuristics the Max-Min Ant System, the Ant System with punitive measures, the A* and the Improved Hybrid A*. The outcomes acquired from different scenarios indicate that the proposed DM-SPP method can rapidly outperform the four predefined artificial intelligence methods.

Discovering different acupoint combinations of manual or electro-acupuncture to treat chemotherapy-induced nausea and vomiting based on the complex networks analysis.

The aim of this research was to find the acupoint combinations of manual and electro-acupuncture to treat chemotherapy-induced nausea and vomiting via the complex networks analysis. We conducted searches using PubMed, ScienceDirect, MEDLINE, Ovid, spring, Wiley, EMBASE, the Chinese biomedicine database, VIP information network, and China National Knowledge Infrastructure from the establishment of the databases to the August, 2023. Information about titles, journals, interventions, and main acupoints was extracted using the self-established "acupoint for prevention CINV data base" powered by EpiData. According to the level of literature evidence and sample size, the clinical trials and weights of the outcome indicators including nausea/vomiting efficiency were combined. After identifying articles, literature processing and complex network analysis were conducted. The degree distribution of each node, the probability distribution of node degree, the node clustering coefficient, and the distance matrix are calculated by software. Of the 4001 screened publications, 489 were eligible after careful selection. Our result showed the acupoints ST36 and PC6 were the most common combination acupoints in both electro and manual acupuncture. In terms of efficiency, ST36, PC6, and CV12 are significantly effective acupoints for manual acupuncture, and the PC6 and ST36 are effective acupoint for electro-acupuncture. We found that the near-far collocation method has been commonly used for different types of acupuncture treatment in CINV. Zhongwan, Shangwan, and Liangmen have been mainly used as local acupoints, while Neiguan, Hegu, Quchi, Zusanli, Gongsun, TaiChong, and Neiguan have been mainly used as distal acupoints. From the effect analysis, acupuncture treatment of nausea manual acupuncture effect is better; acupuncture treatment of vomiting or electro-acupuncture effect is better.

Wandering through quantum-mechanochemistry: from concepts to reactivity and switches.

Mechanochemistry has experienced a renaissance in recent years witnessing, at the molecular level, a remarkable interplay between theory and experiment. Molecular mechanochemistry has welcomed a broad spectrum of quantum-chemical methods to evaluate the influence of an external mechanical force on molecular properties. In this contribution, an overview is given on recent work on quantum mechanochemistry in the Brussels Quantum Chemistry group (ALGC). The effect of an external force was scrutinized both in fundamental topics, like reactivity descriptors in Conceptual DFT, and in applied topics, such as designing molecular force probes and tuning the stereoselectivity of certain types of reactions. In the conceptual part, a brief overview of the techniques introducing mechanical forces into a quantum-mechanical description of a molecule is followed by an introduction to conceptual DFT. The evolution of the electronic chemical potential (or electronegativity), chemical hardness and electrophilicity are investigated when a chemical bond in a series of diatomics is put under mechanical stress. Its counterpart, the influence of mechanical stress on bond angles, is analyzed by varying the strain present in alkyne triple bonds by applying a bending force, taking the strain promoted alkyne-azide coupling cycloaddition as an example. The increase of reactivity of the alkyne upon bending is probed by Fukui functions and the local softness. In the applied part, a new molecular force probe is presented based on an intramolecular 6π-electrocyclization in constrained polyenes operating under thermal conditions. A cyclic process is conceived where ring opening and closure are triggered by applying or removing an external pulling force. The efficiency of mechanical activation strongly depends on the magnitude of the applied force and the distance between the pulling points. The idea of pulling point distances as a tool to identify new mechanochemical processes is then tested in [28]hexaphyrins with an intricate equilibrium between Möbius aromatic and Hückel antiaromatic topologies. A mechanical force is shown to trigger the interconversion between the two topologies, using the distance matrix as a guide to select appropriate pulling points. In a final application, the Felkin-Anh model for the addition of nucleophiles to chiral carbonyls under the presence of an external mechanical force is scrutinized. By applying a force for restricting the conformational freedom of the chiral ketone, otherwise inaccessible reaction pathways are promoted on the force-modified potential energy surfaces resulting in a diastereoselectivity different from the force-free reaction.

ITaxoTools 1.0: Improved DNA Barcode Exploration with TaxI2.

The overall availability of user-friendly software tools tailored to the analysis of DNA barcodes is limited. Several obvious functions such as detecting and visualizing the DNA barcode gap, the calculation of matrices of pairwise distances at the level of species, or the filtering and decontaminating of sets of sequences based on comparisons with reference databases can typically be carried out only by complex procedures that involve various programs and/or a substantial manual work of formatting. The iTaxoTools project aims at contributing user-friendly software solutions to improve the speed and quality of the workflow of alpha-taxonomy. In this chapter, we provide detailed protocols for the use of a substantially improved version of the tool TaxI2 for distance-based exploration of DNA barcodes. The program calculates genetic distances from prealigned data sets, or based on pairwise alignments, or with an alignment-free approach. Sequence and metadata input can be formatted as tab-delimited files and TaxI2 then computes tables, matrices and graphs of distances, and distance summary statistics within and between species and genera. TaxI2 also includes modes to compare a set of sequences against one or two reference data sets and output lists of best matches or filter data according to thresholds or reciprocal matches. Here, detailed step-by-step protocols are provided for the use of TaxI2, as well as for the interpretation of the program's output.

МЕТОДИКА ИНТЕГРАЦИИ ПРОИЗВОДСТВЕННО-ТЕХНИЧЕСКИХ БАЗ ОБСЛУЖИВАНИЯ И РЕМОНТА ВЕДОМСТВЕННЫХ АВТОМОБИЛЕЙ

The relevance of this study lies in the fact that motor transport services of federal ministries and departments of the Russian Federation, agencies subordinate to them, law enforcement and supervisory authorities are carried out by departmental road transport. The operation of departmental road transport in modern conditions is based on documents developed for public road transport. In some cases, departmental acts on the organization of the work of vehicles and personnel in relation to the functions and tasks of the relevant department are not systematically used. The operation of departmental road transport is characterized by excessive dispersion of regional production and technical bases for the maintenance, maintenance and repair of a specialized fleet, as a rule, assigned to individual institutions. As a result, more material and technical resources, rolling stock and personnel are involved in the transportation process of departmental road transport than at public road transport. Increasing the efficiency of departmental road transport consists in the consolidation and centralization of regional production and technical bases, as well as in rationing the resource provision of the transport process. The subject of the study is the regularities of the functioning of the production and technical bases units of the departmental structure. The purpose of this study is to increase the efficiency of departmental road transport operation due to the territorial integration of production and technical bases. To achieve this goal, it is necessary to solve a number of tasks, the specifics of which are determined by the features of a specialized departmental road transport on the example of the divisions of the regional Department of the Federal Penitentiary Service for the Rostov region, concluded: a) in the collection, grouping and processing of statistical information from divisions by type, model, number and costs of operation and performance of transportation in the field of transport by specialized rolling stock; b) determination by geographical coordinates of the location of divisions in the region; c) the compilation of an adjacent matrix of distances between divisions. The methodology of the study includes a methodology for the territorial integration of production and technical bases of regional units of departmental road transport based on the «center of gravity» method, taking into account the costs of operating a specialized fleet and determining the head production and technical bases. The obtained research results can be used for other departments, divisions, organizations and services of departmental structures in which a specialized fleet is involved.

Bacterial diversity of cantaloupes and soil from Arizona and California commercial fields at the point of harvest.

Across the United States, melons are a high demand crop reaching a net production of 2.7 million tons in 2020 with an economic value of $915 million dollars. The goal of this study was to characterize the bacterial diversity of cantaloupe rinds and soil from commercial melon fields at the point of harvest from two major production regions, Arizona, and California. Cantaloupes and composite soil samples were collected from three different commercial production fields, including Imperial Valley, CA, Central Valley, CA, and Yuma Valley, AZ, at the point of harvest over a three-month period, and 16S rRNA gene amplicon sequencing was used to assess bacterial diversity and community structure. The Shannon Diversity Index showed higher diversity among soil compared to the cantaloupe rind regardless of the sampling location. Regional diversity of soil differed significantly, whereas there was no difference in diversity on cantaloupe surfaces. Bray-Curtis Principal Coordinate Analysis (PCoA) dissimilarity distance matrix found the samples clustered by soil and melon individually, and then clustered tighter by region for the soil samples compared to the cantaloupe samples. Taxonomic analysis found total families among the regions to be 52 for the soil samples and 12 among cantaloupes from all three locations, but composition and abundance did vary between the three locations. Core microbiome analysis identified two taxa shared among soil and cantaloupe which were Bacillaceae and Micrococcaceae. This study lays the foundation for characterizing the cantaloupe microbiome at the point of harvest that provides the cantaloupe industry with those bacterial families that are potentially present entering post-harvest processing, which could assist in improving cantaloupe safety, shelf-life, cantaloupe quality and other critical aspects of cantaloupe post-harvest practices.

Model development for the application of sports data visualization techniques in sports science research

Abstract With the continuous development of visualization technology and machine learning algorithms, sports data visualization is more and more widely used in sports training and management. In this paper, the visualization interface based on MFC and the drawing virtual environment of OpenGL is used to display the human motion data visualization function. Meanwhile, an algorithm based on a normalized Euclidean distance matrix is proposed in the process of mapping the sports data to virtual character models with different morphologies in order to maintain the specific relationship between body parts and between the body and the environment space. Applying motion data visualization to the individual performance of sportspersons, the similarity matching algorithm for motion data streams is investigated, and a DTW algorithm based on early stopping with lower complexity and less computation is proposed. The application of sports data visualization technology involves analyzing human movement patterns and proposing a gait prediction method that improves prediction accuracy by 84.8% compared to the prediction method proposed by Alexander. In conducting a comparative study of athletes’ movement flow, the scoring results of this paper’s sports data visualization technology have a P-value of 0.864 compared with the scoring results of the coach. Cohen’s d-value of the magnitude of the difference is 0.087, which is not a significant difference and is able to satisfy the requirements of sports training methods.

Weekend physical activity profiles and their relationship with quality of life: The SOPHYA cohort of Swiss children and adolescents.

Quality of life (QoL) is an important health indicator among children and adolescents. Evidence on the effect of physical activity (PA)-related behaviors on QoL among youth remains inconsistent. Conventional accelerometer-derived PA metrics and guidelines with a focus on whole weeks may not adequately characterize QoL relevant PA behavior. This study aims to a) identify clusters of accelerometer-derived PA profiles during weekend days among children and adolescents living in Switzerland, b) assess their cross-sectional and predictive association with overall QoL and its dimensions, and c) investigate whether the associations of QoL with the newly identified clusters persist upon adjustment for the commonly used PA metrics moderate-to-vigorous physical activity (MVPA) and time spent in sedentary behavior (SB). The population-based Swiss children's Objectively measured PHYsical Activity (SOPHYA) cohort among children and adolescents aged 6 to 16 years was initiated at baseline in 2013. PA and QoL information was obtained twice over a five-year follow-up period. The primary endpoint is the overall QoL score and its six dimension scores obtained by KINDL® questionnaire. The primary predictor is the cluster membership of accelerometer-derived weekend PA profile. Clusters were obtained by applying the k-medoid algorithm to the distance matrix of profiles obtained by pairwise alignments of PA time series using the Dynamic Time Warping (DTW) algorithm. Secondary predictors are accelerometer-derived conventional PA metrics MVPA and SB from two combined weekend days. Linear regression models were applied to assess a) the cross-sectional association between PA cluster membership and QoL at baseline and b) the predictive association between PA cluster membership at baseline and QoL at follow-up, adjusting for baseline QoL. The study sample for deriving PA profile clusters consisted of 51.4% girls and had an average age of 10.9 [SD 2.5] years). The elbow and silhouette methods indicated that weekend PA profiles are best classified in two or four clusters. The most differentiating characteristic for the two-clusters classification ("lower activity" and "high activity"), and the four-clusters classification ("inactive", "low activity", "medium activity", and "high activity"), respectively was the participant's mean counts per 15-seconds epoch. Participants assigned to high activity clusters were younger and more often male. Neither the clustered PA profiles nor MVPA or SB were cross-sectionally or predictively associated with overall QoL. The only association of a conventional PA metrics with QoL while adjusting for cluster membership was observed between MVPA during the weekend days and social well-being with a mean score difference of 2.4 (95%CI: 0.3 to 4.5; p = 0.025). The absence of strong associations of PA metrics for the weekend with QoL, except for the positive association between MVPA during the weekend days and social well-being, is in line with results from two randomized studies not showing efficacy of PA interventions on youth QoL. But because PA decreases with age, its promotion and relevance to QoL remain important research topics. Larger longitudinal study samples with more than two follow-up time points of children and adolescents are needed to derive new novel accelerometer-derived PA profiles and to associate them with QoL dimensions.