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- Egecioglu, Emil, 1977, et al.
(författare)
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Growth hormone receptor deficiency in mice results in reduced systolic blood pressure and plasma renin, increased aortic eNOS expression, and altered cardiovascular structure and function
- 2007
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Ingår i: AMERICAN JOURNAL OF PHYSIOLOGY-ENDOCRINOLOGY AND METABOLISM. - : American Physiological Society. - 0193-1849 .- 1522-1555. ; 292:5, s. E1418-E1425
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Tidskriftsartikel (refereegranskat)abstract
- To study the role of the growth hormone receptor (GHR) in the development of cardiovascular structure and function, female GHR gene-disrupted or knockout (KO) and wild-type (WT) mice at age 18 wk were used. GHR KO mice had lower plasma renin levels (12 ± 2 vs. 20 ± 4 mGU/ml, P < 0.05) and increased aortic endothelial NO synthase (eNOS) expression (146%, P < 0.05) accompanied by a 25% reduction in systolic blood pressure (BP, 110 ± 4 vs. 147 ± 3 mmHg, P < 0.001) compared with WT mice. Aldosterone levels were unchanged, whereas the plasma potassium concentration was elevated by 14% ( P < 0.05) in GHR KO. Relative left ventricular weight was 14% lower in GHR KO mice ( P < 0.05), and cardiac dimensions as analyzed by echocardiography were similarly reduced. Myograph studies revealed a reduced maximum contractile response in the aorta to norepinephrine (NE) and K+ ( P < 0.05), and aorta media thickness was decreased in GHR KO ( P < 0.05). However, contractile force was normal in mesenteric arteries, whereas sensitivity to NE was increased ( P < 0.05). Maximal acetylcholine-mediated dilatation was similar in WT and GHR KO mice, whereas the aorta of GHR KO mice showed an increased sensitivity to acetylcholine ( P < 0.05). In conclusion, loss of GHR leads to low BP and decreased levels of renin in plasma as well as increase in aortic eNOS expression. Furthermore, GHR deficiency causes functional and morphological changes in both heart and vasculature that are beyond the observed alterations in body size. These data suggest an important role for an intact GH/IGF-I axis in the maintenance of a normal cardiovascular system.
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| 2. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Automatic Classification of Wood Defects using Support Vector Machines
- 2008
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 3642023444 ; 5337, s. 356-367
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issue of automatic wood defect classification. We propose a tree-structure support vector machine (SVM) to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by first partitioning the knot images into 3 distinct areas, followed by applying an order statistic filter to yield an average pseudo color feature in each area. Excellent results have been obtained for the proposed SVM classifier that is trained by 800 wood knot images. Performance evaluation has shown that the proposed SVM classifier has resulted in an average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Our future work includes more extensive tests on large data set and the extension of knot types.
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| 3. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Wood Defect Classification based on Image Analysis and Support Vector Machine
- 2010
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Ingår i: Wood Science and Technology. - : Springer Science and Business Media LLC. - 1432-5225 .- 0043-7719. ; 44:4, s. 693-704
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the issue of automatic wood defect classification. A tree-structure support vector machine (SVM) is proposed to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by partitioning the knot images into three distinct areas, followed by utilizing a novel order statistic filter to yield an average pseudo color feature in each area. Excellent results have been obtained for the proposed SVM classifier that is trained by 800 wood knot images. Performance evaluation has shown that the proposed SVM classifier resulted in an average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Future work will include more extensive tests on large data set and the extension of knot types.
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| 8. |
- Nilsson, Jonas, 1979, et al.
(författare)
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Pedestrian Detection using Augmented Training Data
- 2014
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9781479952083 ; , s. 4548-4553
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Konferensbidrag (refereegranskat)abstract
- Detecting pedestrians is a challenging and widely explored problem in computer vision. Many approaches rely on large quantities of manually labelled training data to learn apedestrian classifier. To reduce the need for collecting and manually labelling real image training data, this paper investigates the possibility to use augmented images to train a pedestrian classifier. Augmented images are generated by rendering virtual pedestrians onto real image backgrounds. Classifiers learned from real or augmented training data are evaluated on real image test data from the widely used Daimler Mono Pedestrian benchmark data set. Results show that augmented training data generated from a single 200 frame image sequence reach 70% average detection rate at one False Positives Per Image (FPPI), compared to 81% for a classifier trained by a large-scale real data set.Results also show that complementing real training data withaugmented data improves detection performance, compared tousing real training data only.
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