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- Bache, Iben, et al.
(author)
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An excess of chromosome 1 breakpoints in male infertility.
- 2004
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In: European Journal of Human Genetics. - : Springer Science and Business Media LLC. - 1476-5438 .- 1018-4813. ; 12:12, s. 993-1000
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Journal article (peer-reviewed)abstract
- In a search for potential infertility loci, which might be revealed by clustering of chromosomal breakpoints, we compiled 464 infertile males with a balanced rearrangement from Mendelian Cytogenetics Network database (MCNdb) and compared their karyotypes with those of a Danish nation-wide cohort. We excluded Robertsonian translocations, rearrangements involving sex chromosomes and common variants. We identified 10 autosomal bands, five of which were on chromosome 1, with a large excess of breakpoints in the infertility group. Some of these could potentially harbour a male-specific infertility locus. However, a general excess of breakpoints almost everywhere on chromosome 1 was observed among the infertile males: 26.5 versus 14.5% in the cohort. This excess was observed both for translocation and inversion carriers, especially pericentric inversions, both for published and unpublished cases, and was significantly associated with azoospermia. The largest number of breakpoints was reported in 1q21; FISH mapping of four of these breakpoints revealed that they did not involve the same region at the molecular level. We suggest that chromosome 1 harbours a critical domain whose integrity is essential for male fertility.
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2. |
- Hagen, Hans, et al.
(author)
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Variational modeling methods for Visualization
- 2004
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In: Visualization Handbook. - : Springer. - 9780123875822 ; , s. 381-392
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Book chapter (peer-reviewed)abstract
- Publisher Summary Variational modeling techniques are powerful tools for free-form modeling in CAD/CAM applications. Some of the basic principles are carrying over to scientific visualization. Others have to be modified and some totally new methods have been developed over the past couple of years. This chapter gives an extended survey of this area. Surfaces and solids designed in a computer graphics environment have many applications in modeling, animation, and visualization. The chapter concentrates on the visualization part. The chapter starts with the basics from differential geometry, which are essential for any variational method. Then, it surveys on variational surface modeling. The last step is the visualization part of geometric modeling. In this context, surface curves like geodesies and curvature lines play an important role. The corresponding differential equations are nonlinear, and in most cases numerical algorithms must be used. To be sure to visualize features at a high quality, algorithms with an inherent quality control are needed. The chapter presents the geometric algorithms, which satisfy this demand.
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3. |
- Hotz, Ingrid, et al.
(author)
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Isometric Embedding for a Discrete Metric
- 2004. - 1
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In: Geometric Modeling for Scientific Visualization. - Berlin, Heidelberg : Springer. - 9783540401162 - 9783642072635 ; , s. 19-36
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Book chapter (peer-reviewed)
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4. |
- Hotz, Ingrid, et al.
(author)
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Physically Based Methods for Tensor Field Visualization
- 2004
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Conference paper (peer-reviewed)abstract
- The physical interpretation of mathematical features of tensor fields is highly application-specific. Existing visualization methods for tensor fields only cover a fraction of the broad application areas. We present a visualization method tailored specifically to the class of tensor field exhibiting properties similar to stress and strain tensors, which are commonly encountered in geomechanics. Our technique is a global method that represents the physical meaning of these tensor fields with their central features: regions of compression or expansion. The method is based on two steps: first, we define a positive definite metric, with the same topological structure as the tensor field; second, we visualize the resulting metric. The eigenvector fields are represented using a texture-based approach resembling line integral convolution (LIC) methods. The eigenvalues of the metric are encoded in free parameters of the texture definition. Our method supports an intuitive distinction between positive and negative eigenvalues. We have applied our method to synthetic and some standard data sets, and "real" data from earth science and mechanical engineering application.
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