hierarchically structured
Dotaz
Zobrazit nápovědu
- MeSH
- deep learning * MeSH
- genetický výzkum MeSH
- genetika člověka * MeSH
- genomika * MeSH
- lidé MeSH
- Check Tag
- lidé MeSH
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
- klasifikace MeSH
- lehká atletika klasifikace MeSH
- lidé MeSH
- sporty klasifikace MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- srovnávací studie MeSH
Cíle. Cílem studie bylo odhadnout základní psychometrické charakteristiky české verze metody pro měření pěti obecných dimenzí osobnosti Big Five Inventory 2 (BFI-2) a jeho zkrácených verzí (BFI-2-S, BFI-2XS). Výzkumný soubor. BFI-2 byl předložen pro sebeposouzení 1733 respondentům (42,1 % mužů a 57,9 % žen) ve věku od 15 do 26 let s průměrným věkem 20,06 let (SD = 2,53). Hypotéza. Autoři předpokládali, že české verze inventáře budou mít srovnatelné psychometrické vlastnosti s originálními verzemi. Analýza dat. Pro odhad vnitřní konzistence škál BFI-2, BFI-2-S, BFI-2-XS a subškál BFI-2, BFI-2-S byl použit Cronbachův koeficient alfa doplněný o ordinální variantu McDonaldova koeficientu omega, test-retestová stabilita tří metod byla odhadnuta pomocí Pearsonova korelačního koeficientu. Struktura inventáře BFI-2 na úrovni položek a subškál byla odvozena z analýzy hlavních komponent s následnou rotací Varimax. Vnitřní struktura jednotlivých škál byla dále ověřována pomocí konfirmační faktorové analýzy (CFA). Schopnost škál BFI-2-S a BFI- 2-XS reprezentovat celkové skóry z nezkrácené verze BFI-2 byla zjišťována pomocí Pearsonova koeficientu korelace. Výsledky. Škály BFI-2 mají dobrou reliabilitu, která se pohybuje od 0,81 do 0,89. Reliabilita subškál je uspokojivá a pohybuje se od 0,56 do 0,83 (M = 0,74). Průměrná test-retestová stabilita BFI-2 po 6 měsících byla 0,86 pro škály a 0,80 pro subškály. Všechny položky BFI-2 dosahují faktorového náboje většího nebo rovného 0,30 na odpovídajícím faktoru. V české verzi BFI-2 se za použití CFA replikovala hierarchická struktura s 15 subškálami, stejně jako v původní verzi. Zkrácená verze BFI-2-S a BFI-2-XS rekonstruuje z 91 % a 77 % skóry škál BFI-2.
Objectives. The aim of the study was estimation of basic psychometric properties of the Czech adaptation of the Big Five Inventory 2 (BFI-2) measuring five basic personality dimensions and their short and extra short versions (BFI-2-S, BFI-2XS). Subject and settings. The BFI-2 was administered to 1,733 participants (42.1% men, 57.9% women) in age range from 15 to 26 years (M = 20.06, SD = 2.53). Hypothesis. Authors expected that the Czech adaptation of the BFI-2, BFI-2-S, BFI-2XS will retain comparable psychometric properties to the original versions. Statistical analysis. Internal consistency of BFI- 2, BFI-2-S, BFI-2XS domains and BFI-2, BFI- 2-S facets was estimated using Cronbach’s alpha coefficient and ordinal McDonald’s omega coefficient. Test-retest stability of the three methods was estimated using Pearson’s correlation coefficient. The structure of the BFI-2 at the level of items was explored using Principal Component Analysis with Varimax rotation; structures of domains were confirmed using Confirmatory Factor Analyses. The ability of the BFI-2-S and BFI-2-XS scales to represent BFI-2 scores was assessed using the Pearson correlation coefficient. Results. The BFI-2 domains showed good internal consistency, ranging from 0.81 to 0.89. Internal consistency of individual facets ranged from 0.56 to 0.83 (M = 0.74). Average BFI-2 test-retest reliability estimated over a 6 month period was r = 0.86 for domains and r = 0.80 for facets. All items of the BFI-2 showed factor loadings 0.30 or higher on intended factor. The BFI-2 hierarchical structure with 15 facets was confirmed using CFA. Short versions BFI-2-S and BFI-2-XS captured 91% and 77% of the domains of the full version of BFI-2 inventory. Study limitation. Convergent validity of the instrument and the self-other agreement was not evaluated.
OBJECTIVES: Artificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance. MATERIALS AND METHODS: Following a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis. RESULTS: From 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty. CONCLUSION: This review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice. CLINICAL RELEVANCE: This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.
- MeSH
- deep learning * MeSH
- lidé MeSH
- zubní lékařství * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled 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
- buněčná diferenciace MeSH
- inbrední kmeny myší MeSH
- kůže cytologie embryologie metabolismus MeSH
- myši MeSH
- rozvržení tělního plánu MeSH
- signální transdukce MeSH
- stanovení celkové genové exprese MeSH
- transformující růstový faktor beta metabolismus fyziologie MeSH
- vlasový folikul embryologie MeSH
- zvířata MeSH
- Check Tag
- mužské pohlaví MeSH
- myši MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Tight interactions exist between dopamine and acetylcholine signaling in the striatum. Dopaminergic neurons express muscarinic and nicotinic receptors, and cholinergic interneurons express dopamine receptors. All neurons in the striatum are pacemakers. An increase in dopamine release is activated by stopping acetylcholine release. The coordinated timing or synchrony of the direct and indirect pathways is critical for refined movements. Changes in neurotransmitter ratios are considered a prominent factor in Parkinson's disease. In general, drugs increase striatal dopamine release, and others can potentiate both dopamine and acetylcholine release. Both neurotransmitters and their receptors show diurnal variations. Recently, it was observed that reward function is modulated by the circadian system, and behavioral changes (hyperactivity and hypoactivity during the light and dark phases, respectively) are present in an animal model of Parkinson's disease. The striatum is one of the key structures responsible for increased locomotion in the active (dark) period in mice lacking M4 muscarinic receptors. Thus, we propose here a hierarchical model of the interaction between dopamine and acetylcholine signaling systems in the striatum. The basis of this model is their functional morphology. The next highest mode of interaction between these two neurotransmitter systems is their interaction at the neurotransmitter/receptor/signaling level. Furthermore, these interactions contribute to locomotor activity regulation and reward behavior, and the topmost level of interaction represents their biological rhythmicity.
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
