The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research. We describe an approach to predicting style of images, and perform a thorough evaluation of different image features for these tasks. We find that features learned in a multi-layer network generally perform besteven when trained with object class (not style) labels. Our large-scale learning methods results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We present two novel datasets: 80K Flickr photographs annotated with 20 curated style labels, and 85K paintings annotated with 25 style/genre labels. Our approach shows excellent classification performance on both datasets. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style.
Objective This study aimed to evaluate the proportion of patients with seizures and electroencephalography (EEG) abnormalities in autoimmune encephalitis (AE) and its most common subtypes. Methods This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) standards and was registered with the International Prospective Register of Systematic Reviews (PROSPERO). We searched Medline All, Embase, and PsychINFO in Ovid from inception to June 2019 for articles pertaining to AE and seizure. Included studies reported seizure and/or EEG data in cohorts of ≥10 AE patients. Patient demographics, antibody type, seizure incidence, and EEG findings were extracted. Review of studies and data extraction were performed in duplicate. In addition to descriptive analysis, quantitative synthesis stratified by autoantibody subtype was performed with logistic regression and chi‐square analyses. Results Our search yielded 3856 abstracts: 1616 were selected for full‐text review and 118 studies met eligibility criteria. Of 3722 antibody‐positive AE patients, 2601 (69.9%) had clinical seizures during the course of their illness. Of the 2025 patients with antibody‐positive AE and available EEG data, 1718 (84.8%) had some EEG abnormality (eg, epileptiform discharges, slowing, and so on). Anti‐ N‐methyl‐d‐aspartate (NMDA) receptor encephalitis (anti‐NMDARE) was the most commonly reported type of AE (1985/3722, 53.3%). Of the anti‐NMDARE patients with available seizure or EEG data, 71.8% (n = 1425/1985) had clinical seizures during their illness, and 89.7% (n = 1172/1306) had EEG abnormalities. For all AE patients and in the anti‐NMDARE subpopulation, seizures were more common in younger patients (p < .05). Significance This systematic review provides an estimate of the proportion of AE patients with seizures, confirming the magnitude of seizure burden in this population. Prospective studies are needed to understand population‐based prevalence of seizures, identify factors associated with seizures, and evaluate particular EEG findings as biomarkers of seizures and outcomes in AE.
Introduction. Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure product (rRPP) varies from one individual to the next, but its biochemical/cellular phenotype remains unknown. This study determined the degree to which an individual’s biochemical/cellular profile as characterized by a standard blood panel is predictive of rRPP, as well the importance of each blood biomarker in this prediction. Methods. We included data from 55,730 participants in this study with complete rRPP measurements and concurrently collected blood panel information from the Health Management Centre at the Affiliated Hospital of Hangzhou Normal University. We used the XGBoost machine learning algorithm to train a tree-based model and then assessed its accuracy on an independent portion of the dataset and then compared its performance against a standard linear regression technique. We further determined the predictive importance of each feature in the blood panel. Results. We found a fair positive correlation (Pearson r ) of 0.377 (95% CI: 0.375-0.378) between observed rRPP and rRPP predicted from blood biomarkers. By comparison, the performance for standard linear regression was 0.352 (95% CI: 0.351-0.354). The top three predictors in this model were glucose concentration, total protein concentration, and neutrophil count. Discussion/Conclusion. Blood biomarkers predict resting RPP when modeled in combination with one another; such models are valuable for studying the complex interrelations between resting cardiac workload and one’s biochemical/cellular phenotype.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.