Cognitive Profiling Related to Cerebral Amyloid Beta Burden Using Machine Learning Approaches

Background: Cerebral amyloid beta (Aβ) is a hallmark of Alzheimer’s disease (AD). Aβ can be detected in vivo with amyloid imaging or cerebrospinal fluid assessments. However, these technologies can be both expensive and invasive, and their accessibility is limited in many clinical settings. Hence the current study aims to identify multivariate cost-efficient markers for Aβ positivity among non-demented individuals using machine learning (ML) approaches.Methods: The relationship between cost-efficient candidate markers and Aβ status was examined by analyzing 762 participants from the Alzheimer’s Disease Neuroimaging Initiative-2 cohort at baseline visit (286 cognitively normal, 332 with mild cognitive impairment, and 144 with AD; mean age 73.2 years, range 55–90). Demographic variables (age, gender, education, and APOE status) and neuropsychological test scores were used as predictors in an ML algorithm. Cerebral Aβ burden and Aβ positivity were measured using 18F-florbetapir positron emission tomography images. The adaptive least absolute shrinkage and selection operator (LASSO) ML algorithm was implemented to identify cognitive performance and demographic variables and distinguish individuals from the population at high risk for cerebral Aβ burden. For generalizability, results were further checked by randomly dividing the data into training sets and test sets and checking predictive performances by 10-fold cross-validation.Results: Out of neuropsychological predictors, visuospatial ability and episodic memory test results were consistently significant predictors for Aβ positivity across subgroups with demographic variables and other cognitive measures considered. The adaptive LASSO model using out-of-sample classification could distinguish abnormal levels of Aβ. The area under the curve of the receiver operating characteristic curve was 0.754 in the mild change group, 0.803 in the moderate change group, and 0.864 in the severe change group, respectively.Conclusion: Our results showed that the cost-efficient neuropsychological model with demographics could predict Aβ positivity, suggesting a potential surrogate method for detecting Aβ deposition non-invasively with clinical utility. More specifically, it could be a very brief screening tool in various settings to recruit participants with potential biomarker evidence of AD brain pathology. These identified individuals would be valuable participants in secondary prevention trials aimed at detecting an anti-amyloid drug effect in the non-demented population

The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies

There is a growing interest on how we represent and share tagging data for the purpose of collaborative tagging systems. Conventional tags, however, are not naturally suited for collaborative processes. Being free-text keywords, they are exposed to linguistic variations like case (upper vs lower), grammatical number (singular vs. plural) as well as human typing errors. Additionally, tags depend on the personal views of the world by individual users, and are not normalized for synonymy, morphology or any other mapping. The bottom line of the problem is that tags have no semantics whatsoever. Moreover, even if a user gives some semantics to a tag while using or viewing it, this meaning is not automatically shared with computers since it’s not defined in a machine-readable way. With tagging systems increasing in popularity each day, the evolution of this technology is hindered by this problem. In this paper we discuss approaches to represent tagging activities at a semantic level. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria

Imaging in Acute Anterior Circulation Ischemic Stroke: Current and Future

Clinical trials on acute ischemic stroke have demonstrated the clinical effectiveness of revascularization treatments within an appropriate time window after stroke onset: intravenous thrombolysis (NINDS and ECASS-III) through the administration of tissue plasminogen activator within a 4.5-hour time window, endovascular thrombectomy (ESCAPE, REVASCAT, SWIFT-PRIME, MR CLEAN, EXTEND-IA) within a 6-hour time window, and extending the treatment time window up to 24 hours for endovascular thrombectomy (DAWN and DEFUSE 3). However, a substantial number of patients in these trials were ineligible for revascularization treatment, and treatments of some patients were considerably futile or sometimes dangerous in the clinical trials. Guidelines for the early management of patients with acute ischemic stroke have evolved to accept revascularization treatment as standard and include eligibility criteria for the treatment. Imaging has been crucial in selecting eligible patients for revascularization treatment in guidelines and clinical trials. Stroke specialists should know imaging criteria for revascularization treatment. Stroke imaging studies have demonstrated imaging roles in acute ischemic stroke management as follows: 1) exclusion of hemorrhage and stroke mimic disease, 2) assessment of salvageable brain, 3) localization of the site of vascular occlusion and thrombus, 4) estimation of collateral circulation, and 5) prediction of acute ischemic stroke expecting hemorrhagic transformation. Here, we review imaging methods and criteria to select eligible patients for revascularization treatment in acute anterior circulation stroke, focus on 2019 guidelines from the American Heart Association/American Stroke Association, and discuss the future direction of imaging-based patient selection to improve treatment effects

Active Relation Discovery: Towards General and Label-aware Open Relation Extraction

Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Prognosis Prediction for Class III Malocclusion Treatment by Feature Wrapping Method

Objective: To use the feature wrapping (FW) method to identify which cephalometric markers show the highest classification accuracy in prognosis prediction for Class III malocclusion and to compare the prediction accuracy between the FW method and conventional statistical methods such as discriminant analysis (DA). Materials and Methods: The sample set consisted of 38 patients (15 boys and 23 girls, mean age 8.53 ± 1.36 years) who were diagnosed with Class III malocclusion and received both first-phase (orthopedic) and second-phase (fixed orthodontic) treatments. Lateral cephalograms were taken before (T0) and after first-phase treatment (T1) and after second-phase treatment and retention (T2). Based on the measurements taken at the T2 stage, the patients were allocated into good (n = 20) or poor (n = 18) prognosis groups. Forty-six cephalometric variables on T0 lateral cephalograms were analyzed by the FW method to identify key determinants for discriminating between the two groups. Sequential forward search (SFS) algorism and support vector machine (SVM) were used in conjunction with the FW method to improve classification accuracy. To compare the prediction accuracy of the FW method with conventional statistical methods, DA was performed for the same data set. Results: AB to mandibular plane angle (°) and A to N-perpendicular (mm) were selected as the most accurate cephalometric predictors by both the FW and DA methods. However, classification accuracy was higher with the FW method (97.2%) compared with DA (92.1%), because the FW method with SFS and SVM has a more precise classification algorithm. Conclusions: The FW method, which uses a learning algorithm, might be an effective alternative to DA for prognosis prediction

Long-term outcome of vertebral artery origin stenosis in patients with acute ischemic stroke

BACKGROUND: Vertebral artery origin (VAO) stenosis is occasionally observed in patients who have acute ischemic stroke. We investigated the long-term outcomes and clinical significance of VAO stenosis in patients with acute ischemic stroke. METHODS: We performed a prospective observational study using a single stroke center registry to investigate the risk of recurrent stroke and vascular outcomes in patients with acute ischemic stroke and VAO stenosis. To relate the clinical significance of VAO stenosis to the vascular territory of the index stroke, patients were classified into an asymptomatic VAO stenosis group and a symptomatic VAO stenosis group. RESULTS: Of the 774 patients who had acute ischemic stroke, 149 (19.3%) of them had more than 50% stenosis of the VAO. During 309 patient-years of follow-up (mean, 2.3 years), there were 7 ischemic strokes, 6 hemorrhagic strokes, and 2 unknown strokes. The annual event rates were 0.97% for posterior circulation ischemic stroke, 4.86% for all stroke, and 6.80% for the composite cardiovascular outcome. The annual event rate for ischemic stroke in the posterior circulation was significantly higher in patients who had symptomatic VAO stenosis than in patients who had asymptomatic stenosis (1.88% vs. 0%, p = 0.046). In a multivariate analysis, the hazard ratio, per one point increase of the Essen Stroke Risk Score (ESRS) for the composite cardiovascular outcome, was 1.46 (95% CI, 1.02-2.08, p = 0.036). CONCLUSIONS: Long-term outcomes of more than 50% stenosis of the VAO in patients with acute ischemic stroke were generally favorable. Additionally, ESRS was a predictor for the composite cardiovascular outcome. Asymptomatic VAO stenosis may not be a specific risk factor for recurrent ischemic stroke in the posterior circulation. However, VAO stenosis may require more clinical attention as a potential source of recurrent stroke when VAO stenosis is observed in patients who have concurrent ischemic stroke in the posterior circulation

An architecture for privacy-enabled user profile portability on the web of data

Providing relevant recommendations requires access to user profile data. Current social networking ecosystems allow third party services to request user authorisation for accessing profile data, thus enabling cross-domain recommendation. However these ecosystems create user lock-in and social networking data silos, as the profile data is neither portable nor interoperable. We argue that innovations in reconciling heterogeneous data sources must be also be matched by innovations in architecture design and recommender methodology. We present and qualitatively evaluate an architecture for privacy-enabled user profile portability, which is based on technologies from the emerging Web of Data (FOAF, WebIDs and the Web Access Control vocabulary). The proposed architecture enables the creation of a universal "private by default" ecosystem with interoperability of user profile data. The privacy of the user is protected by allowing multiple data providers to host their part of the user profile. This provides an incentive for more users to make profile data from different domains available for recommendations.The work presented in this paper has been funded in part by Science Foundation Ireland under Grant No.SFI/08/CE/I1380 (Lion-2) and by Korean Small and Medium Business Administration under Grant No. 0420-2009-0061

Current advances of epigenetics in periodontology from ENCODE project: a review and future perspectives

Background The Encyclopedia of DNA Elements (ENCODE) project has advanced our knowledge of the functional elements in the genome and epigenome. The aim of this article was to provide the comprehension about current research trends from ENCODE project and establish the link between epigenetics and periodontal diseases based on epigenome studies and seek the future direction. Main body Global epigenome research projects have emphasized the importance of epigenetic research for understanding human health and disease, and current international consortia show an improved interest in the importance of oral health with systemic health. The epigenetic studies in dental field have been mainly conducted in periodontology and have focused on DNA methylation analysis. Advances in sequencing technology have broadened the target for epigenetic studies from specific genes to genome-wide analyses. Conclusions In line with global research trends, further extended and advanced epigenetic studies would provide crucial information for the realization of comprehensive dental medicine and expand the scope of ongoing large-scale research projects.This research was supported by Grants from MSIP/IITP (2017-0-00398) and Basic Science Research Program (2016R1A1A3A04004838/2020R1 C1C1005830) through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning