Autori sa v príspevku zaoberajú problematikou klasifikácie skupín atletických disciplín ovplyvňuj-úcich športovú výkonnosť sedemboja žien. Na identifikáciu skupín boli využité ukazovatele najlepšíchsvetových výkonov sedemboja nad 6200 bodov podľa dostupných údajov z IAAF (N = 172). Z klasifi-kačných metód zhlukovania boli použité hierarchické modely ako Average linkage (Between & Within- group), Single Linkage - Nearest neigbor, Complete Linkage - Farthest neigbor, Centroid linkage,Median clustering, Ward ́s method.Všetkých sedem zhlukových metód sa zhodlo v dvoch skupinách zhlukov a v obsahu disciplínv 2. klastry [200 m, skok od diaľky, 800 m, 100 m prekážok, skok do výšky] [vrh guľou, hod oštepom].Test stability so štruktúrou zhlukov sedemboja na úrovni 2. klastra je 100 %. Najvyššiu stabilitu42,86 % javí vnútorná hierarchia disciplín [200 m, Skok do diaľky, 100 m prekážok, Skok do výšky,800 m] [Vrh guľou, Hod oštepom].Hierarchické modely umožnili identifikovať skupiny atletických disciplíny ovplyvňujúce športovývýkon v sedemboji žien. Poznanie štruktúry športového výkonu prispieva k zefektívneniu tréningovéhoprocesu a určeniu viacbojárskej typológie pretekárok svetovej výkonnosti.

Authors deals with the problematics of group classification of athletics disciplines, which influence thesports performance in the women's heptathlon. For the group identification, the indicators of the bestworld's performance in heptathlon above the 6200 points according to the data from IAAF (N = 172)were used. From the classification methods of clustering the hierarchical models as the Average linkage(Between & Within-group), Single Linkage - Nearest neighbor, Complete Linkage - Farthest neighbor,Centroid linkage, Median clustering, and Ward ́s method were used.All seven clustering methods agreed in two groups of clusters and in the content of disciplines in2 clusters [200 meters, Long jump, 800 meters, 100 meters hurdles, High jump] [Shot put, Javelinthrow]. The stability test with the cluster structure of heptathlon in the level of the second cluster is100 %. The highest stability, 42,86 %, shows the internal hierarchy of disciplines [200 meters, Longjump, 100 meters hurdles, High jump, 800 meters] [Shot put, Javelin throw].Hierarchical models allow identifying groups of athletics disciplines that influence the sports perfor-mance in women's heptathlon. Understanding the structure of sports performance contributes to thestreamlining the training process and determining the combined events typology of world performanceathletes.

• MeSH
• Classification MeSH
• Track and Field classification   MeSH
• Humans MeSH
• Sports classification   MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Comparative Study MeSH

• Publication type
• Abstracts MeSH

OBJECTIVE:  Blood monocyte subsets are emerging as biomarkers of cardiovascular inflammation. However, our understanding of human monocyte heterogeneity and their immunophenotypic features under healthy and inflammatory conditions is still evolving. RATIONALE:  In this study, we sought to investigate the immunophenome of circulating human monocyte subsets. METHODS:  Multiplexed, high-throughput flow cytometry screening arrays and computational data analysis were used to analyze the expression and hierarchical relationships of 242 specific surface markers on circulating classical (CD14++CD16-), intermediate (CD14++CD16+), and nonclassical (CD14+CD16++) monocytes in healthy adults. RESULTS:  Using generalized linear models and hierarchical cluster analysis, we selected and clustered epitopes that most reliably differentiate between monocyte subsets. We validated existing transcriptional profiling data and revealed potential new surface markers that uniquely define the classical (e.g., BLTR1, CD35, CD38, CD49e, CD89, CD96), intermediate (e.g., CD39, CD275, CD305, CDw328), and nonclassical (e.g., CD29, CD132) subsets. In addition, our analysis revealed phenotypic cell clusters, identified by dendritic markers CMRF-44 and CMRF-56, independent of the traditional monocyte classification. CONCLUSION:  These results reveal an advancement of the clinically applicable multiplexed screening arrays that may facilitate monocyte subset characterization and cytometry-based biomarker selection in various inflammatory disorders.

• MeSH
• Atherosclerosis diagnosis   immunology   MeSH
• Biodiversity MeSH
• Biomarkers metabolism   MeSH
• Phenotype MeSH
• Immunophenotyping methods   MeSH
• Blood Circulation MeSH
• Humans MeSH
• Lipopolysaccharide Receptors metabolism   MeSH
• Monocytes physiology   MeSH
• Flow Cytometry MeSH
• Receptors, IgG metabolism   MeSH
• High-Throughput Screening Assays MeSH
• Cell Separation MeSH
• Cluster Analysis MeSH
• Inflammation diagnosis   immunology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: The classification of sleep signals is a subjective and time consuming task. A large number of automatic classifiers have been published in the past decade but a sleep community has no strong confidence to use them in clinical practice and still remains using a standard manual scoring according standardized rules. NEW METHOD: We developed a semi-supervised data-driven approach for objective and efficient evaluation of polysomnographic (PSG) data. The proposed algorithm finds a representative set of signal segments that are subsequently scored by a sleep neurologist. The remaining part of the recording is then automatically classified using these templates. RESULTS: The method was evaluated on 36 PSG recordings (18 chronic insomniacs, 18 healthy controls). We show a faster and objective evaluation of PSG data compared to the manual scoring that is over-performing automated classifiers (accuracy increases ∼14%). The classification results are comparable on both datasets. COMPARISON WITH EXISTING METHOD(S): The methodology that we propose has not yet been published in the area of sleep PSG data processing. The performance of our method is comparable to various published automated approaches (a typical published classification accuracy is ∼75-95%). The method allows the evaluation of PSG recordings in more general terms and across different recording devices and standards. CONCLUSIONS: The proposed solution is not based on a single-purpose rules or heuristics and training model is not trained on other patient's sleep recordings. The method is applicable to wide range of similar tasks and various types of physiological signals.

• MeSH
• Algorithms MeSH
• Adult MeSH
• Electroencephalography * MeSH
• Middle Aged MeSH
• Humans MeSH
• Brain physiology   MeSH
• Brain Waves MeSH
• Signal Processing, Computer-Assisted MeSH
• Polysomnography methods   MeSH
• Sleep Initiation and Maintenance Disorders physiopathology   MeSH
• Cluster Analysis MeSH
• Sleep physiology   MeSH
• Machine Learning MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• Publication type
• Meeting Abstract MeSH

Two theories address the origin of repeating patterns, such as hair follicles, limb digits, and intestinal villi, during development. The Turing reaction-diffusion system posits that interacting diffusible signals produced by static cells first define a prepattern that then induces cell rearrangements to produce an anatomical structure. The second theory, that of mesenchymal self-organisation, proposes that mobile cells can form periodic patterns of cell aggregates directly, without reference to any prepattern. Early hair follicle development is characterised by the rapid appearance of periodic arrangements of altered gene expression in the epidermis and prominent clustering of the adjacent dermal mesenchymal cells. We assess the contributions and interplay between reaction-diffusion and mesenchymal self-organisation processes in hair follicle patterning, identifying a network of fibroblast growth factor (FGF), wingless-related integration site (WNT), and bone morphogenetic protein (BMP) signalling interactions capable of spontaneously producing a periodic pattern. Using time-lapse imaging, we find that mesenchymal cell condensation at hair follicles is locally directed by an epidermal prepattern. However, imposing this prepattern's condition of high FGF and low BMP activity across the entire skin reveals a latent dermal capacity to undergo spatially patterned self-organisation in the absence of epithelial direction. This mesenchymal self-organisation relies on restricted transforming growth factor (TGF) β signalling, which serves to drive chemotactic mesenchymal patterning when reaction-diffusion patterning is suppressed, but, in normal conditions, facilitates cell movement to locally prepatterned sources of FGF. This work illustrates a hierarchy of periodic patterning modes operating in organogenesis.

• MeSH
• Cell Differentiation MeSH
• Mice, Inbred Strains MeSH
• Skin cytology   embryology   metabolism   MeSH
• Mice MeSH
• Body Patterning MeSH
• Signal Transduction MeSH
• Gene Expression Profiling MeSH
• Transforming Growth Factor beta metabolism   physiology   MeSH
• Hair Follicle embryology   MeSH
• Animals MeSH
• Check Tag
• Male MeSH
• Mice MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

There is an emerging body of evidence that patients with chronic myeloid leukaemia (CML) may carry not only breakpoint cluster region-Abelson murine leukaemia viral oncogene homologue 1 (BCR-ABL1) kinase domain mutations (BCR-ABL1 KD mutations), but also mutations in other genes. Their occurrence is highest during progression or at failure, but their impact at diagnosis is unclear. In the present study, we prospectively screened for mutations in 18 myeloid neoplasm-associated genes and BCR-ABL1 KD in the following populations: bulk leucocytes, CD34+ CD38+ progenitors and CD34+ CD38- stem cells, at diagnosis and early follow-up. In our cohort of chronic phase CML patients, nine of 49 patients harboured somatic mutations in the following genes: six ASXL1 mutations, one SETBP1, one TP53, one JAK2, but no BCR-ABL1 KD mutations. In seven of the nine patients, mutations were detected in multiple hierarchical populations including bulk leucocytes at diagnosis. The mutation dynamics reflected the BCR-ABL1 transcript decline induced by treatment in eight of the nine cases, suggesting that mutations were acquired in the Philadelphia chromosome (Ph)-positive clone. In one patient, the JAK2 V617F mutation correlated with a concomitant Ph-negative myeloproliferative neoplasm and persisted despite a 5-log reduction of the BCR-ABL1 transcript. Only two of the nine patients with mutations failed first-line therapy. No correlation was found between the mutation status and survival or response outcomes.

• MeSH
• Fusion Proteins, bcr-abl genetics   MeSH
• Leukemia, Myelogenous, Chronic, BCR-ABL Positive diagnosis   genetics   therapy   MeSH
• Humans MeSH
• Mutation MeSH
• Follow-Up Studies MeSH
• Prognosis MeSH
• Prospective Studies MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND AND OBJECTIVES: Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS: We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS: We included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters. DISCUSSION: Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features.

• MeSH
• Idiopathic Hypersomnia * diagnosis   MeSH
• Cataplexy * diagnosis   MeSH
• Humans MeSH
• Adolescent MeSH
• Narcolepsy * diagnosis   drug therapy   MeSH
• Disorders of Excessive Somnolence * diagnosis   epidemiology   MeSH
• Cluster Analysis MeSH
• Check Tag
• Humans MeSH
• Adolescent MeSH
• Publication type
• Journal Article MeSH
• Observational Study MeSH

Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

• Publication type
• Journal Article MeSH

• Publication type
• Meeting Abstract MeSH