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| 2. |
- Kim, Joon Tae, et al.
(författare)
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Dual antiplatelet Use for extended period taRgeted to AcuTe ischemic stroke with presumed atherosclerotic OrigiN (DURATION) trial : Rationale and design
- 2023
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Ingår i: International Journal of Stroke. - : SAGE Publications. - 1747-4930 .- 1747-4949. ; 18:8, s. 1015-1020
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Tidskriftsartikel (refereegranskat)abstract
- Rationale: The optimal duration of dual antiplatelet therapy (DAPT) with clopidogrel-aspirin for the large artery atherosclerotic (LAA) stroke subtype has been debated. Aims: To determine whether the 1-year risk of recurrent vascular events could be reduced by a longer duration of DAPT in patients with the LAA stroke subtype. Methods and study design: A total of 4806 participants will be recruited to detect a statistically significant relative risk reduction of 22% with 80% power and a two-sided alpha error of 0.05, including a 10% loss to follow-up. This is a registry-based, multicenter, prospective, randomized, open-label, blinded end point study designed to evaluate the efficacy and safety of a 12-month duration of DAPT compared with a 3-month duration of DAPT in the LAA stroke subtype. Patients will be randomized (1:1) to either DAPT for 12 months or DAPT for 3 months, followed by monotherapy (either aspirin or clopidogrel) for the remaining 9 months. Study outcomes: The primary efficacy outcome of the study is a composite of stroke (ischemic or hemorrhagic), myocardial infarction, and all-cause mortality for 1 year after the index stroke. The secondary efficacy outcomes are (1) stroke, (2) ischemic stroke or transient ischemic attack, (3) hemorrhagic stroke, and (4) all-cause mortality. The primary safety outcome is major bleeding. Discussion: This study will help stroke physicians determine the appropriate duration of dual therapy with clopidogrel-aspirin for patients with the LAA stroke subtype. Trial registration: URL: https://cris.nih.go.kr/cris. CRIS Registration Number: KCT0004407.
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| 3. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Berndt, Sonja I., et al.
(författare)
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Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:5, s. 501-U69
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Tidskriftsartikel (refereegranskat)abstract
- Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.
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| 5. |
- Lee, Sang Hyoung, et al.
(författare)
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Autonomous Framework for Segmenting Robot Trajectories of Manipulation Task
- 2015
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Ingår i: Autonomous Robots. - : Springer Science and Business Media LLC. - 0929-5593 .- 1573-7527. ; 38:2, s. 107-141
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Tidskriftsartikel (refereegranskat)abstract
- In manipulation tasks, motion trajectories are characterized by a set of key phases (i.e., motion primitives). It is therefore important to learn the motion primitives embedded in such tasks from a complete demonstration. In this paper, we propose a core framework that autonomously segments motion trajectories to support the learning of motion primitives. For this purpose, a set of segmentation points is estimated using a Gaussian Mixture Model (GMM) learned after investigating the dimensional subspaces reduced by Principal Component Analysis. The segmentation points can be acquired by two alternative approaches: (1) using a geometrical interpretation of the Gaussians obtained from the learned GMM, and (2) using the weights estimated along the time component of the learned GMM. The main contribution of this paper is the autonomous estimation of the segmentation points based on the GMM learned in a reduced dimensional space. The advantages of such an estimation are as follows: (1) segmentation points without any internal parameters to be manually predefined or pretuned (according to the types of given tasks and/or motion trajectories) can be estimated from a single training data, (2) segmentation points, in which non-linear motion trajectories can be better characterized than by using the original motion trajectories, can be estimated, and (3) natural motion trajectories can be retrieved by temporally rearranging motion segments. The capability of this autonomous segmentation framework is validated by four experiments. In the first experiment, motion segments are evaluated through a comparison with a human expert using a publicly available kitchen dataset. In the second experiment, motion segments are evaluated through a comparison with an existing approach using an open hand-writing database. In the third experiment, the segmentation performance is evaluated by retrieving motion trajectories from the reorganization of motion segments. In the fourth experiment, the segmentation performance is evaluated by clustering motion segments.
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| 6. |
- Lee, Sang Hyoung, et al.
(författare)
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Learning Basis Skills by Autonomous Segmentation of Humanoid Motion Trajectories
- 2012
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Ingår i: 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2012. - 2164-0572. ; , s. 112-119
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Konferensbidrag (refereegranskat)abstract
- Manipulation tasks are characterized by continuous motion trajectories containing a set of key phases. In this paper, we propose a probabilistic method to autonomously segment the motion trajectories for estimating the key phases embedded in such a task. The autonomous segmentation process relies on principal component analysis to adaptively project into one of the low-dimensional subspaces, in which a Gaussian mixture model is learned based on Bayesian information criterion and expectation-maximization algorithms. The basis skills are estimated by a set of Gaussians approximating quasi-linear key phases, and those times spent calculated from the segmentation points between two consecutive Gaussians representing the local changes of dynamics and directions of the trajectories. The basis skills are then used to build novel motion trajectories with possible motion alternatives and optional parts. After sequentially reorganizing the basis skills, a Gaussian mixture regression process is used to retrieve smooth motion trajectories. Two experiments are presented to demonstrate the capability of the autonomous segmentation approach.
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