The purpose of this study was to develop a computer-assisted inference model for selecting appropriate types of headgear appliance for orthodontic patients and to investigate its clinical versatility as a decision-making aid for inexperienced clinicians. Fuzzy rule bases were created for degrees of overjet, overbite, and mandibular plane angle variables, respectively, according to subjective criteria based on the clinical experience and knowledge of the authors. The rules were then transformed into membership functions and the geometric mean aggregation was performed to develop the inference model. The resultant fuzzy logic was then tested on 85 cases in which the patients had been diagnosed as requiring headgear appliances. Eight experienced orthodontists judged each of the cases, and decided if they 'agreed', 'accepted', or 'disagreed' with the recommendations of the computer system. Intra-examiner agreements were investigated using repeated judgements of a set of 30 orthodontic cases and the kappa statistic. All of the examiners exceeded a kappa score of 0.7, allowing them to participate in the test run of the validity of the proposed inference model. The examiners' agreement with the system's recommendations was evaluated statistically. The average satisfaction rate of the examiners was 95.6 per cent and, for 83 out of the 85 cases, 97.6 per cent. The majority of the examiners (i.e. six or more out of the eight) were satisfied with the recommendations of the system. Thus, the usefulness of the proposed inference logic was confirmed.
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Magn Reson Med
January 2025
Department of Radiology, University of Missouri, Columbia, Missouri, USA.
Purpose: The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low-resolution images.
Methods: We introduced TagGen, a diffusion-based conditional generative model that uses low-resolution MR tagging images as guidance to generate corresponding high-resolution tagging images. The model was developed on 50 patients with long-axis-view, high-resolution tagging acquisitions.
Nat Commun
January 2025
Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
The rate at which transcription factors (TFs) bind their cognate sites has long been assumed to be limited by diffusion, and thus independent of binding site sequence. Here, we systematically test this assumption using cell-to-cell variability in gene expression as a window into the in vivo association and dissociation kinetics of the model transcription factor LacI. Using a stochastic model of the relationship between gene expression variability and binding kinetics, we performed single-cell gene expression measurements to infer association and dissociation rates for a set of 35 different LacI binding sites.
View Article and Find Full Text PDFJ Neurosci
January 2025
Department of Ophthalmology, Harvard Medical School, Boston, MA, United States.
We employed high-resolution fMRI to distinguish the impacts of anisometropia and strabismus amblyopia on the evoked ocular dominance (OD) response. Sixteen amblyopic participants (8 females) plus 8 individuals with normal vision (1 female), participated in this study for whom, we measured the difference between the response to stimulation of the two eyes, across areas V1-V4.In controls, the evoked OD response formed the expected striped pattern within V1.
View Article and Find Full Text PDFBrain Res Bull
January 2025
Department of Anatomy, Physiology & Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, 36849, USA.
Mice are the dominant model system to study human health and disease. Yet, there is a pressing need to use diverse model systems to address long-standing issues in biomedical sciences. Mice do not spontaneously recapitulate many of the diseases we seek to study.
View Article and Find Full Text PDFAm J Hum Genet
January 2025
Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Electronic address:
Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations.
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