Ethics and Embryo/Foetus Research

Research with embryos and foetuses raise moral problems. In this article the origins of these moral intuitions are analyzed and discussed at four levels: moral problems regarding the treatment of the foetus and the mother animal; ethical problems experienced by the moral actor (researcher); ethical problems related to a justification based upon a balance between means and goals; and ethical problems raised by society.     

Buying or making, what's it to be? The choice between acquiring or generating genetically modified animals or embryos in the light of the three Rs

This paper discusses whether acquiring genetically modified animals or embryos from an existing strain, as opposed to generating a new founder, is to be preferred, with respect to animal use and animal distress. To address this question, the results of an inventory performed in 17 Dutch research institutes are presented. It is argued that the acquisition of embryos is to be preferred to the acquisition or generation of animals. Furthermore, it is argued that patenting may lead to the generation of already existing strains.     

Microbial Transport, Survival, and Succession in a Sequence of Buried Sediments

Abstract Two chronosequences of unsaturated, buried loess sediments, ranging in age from <10,000 years to >1 million years, were investigated to reconstruct patterns of microbial ecological succession that have occurred since sediment burial. The relative importance of microbial transport and survival to succession was inferred from sediment ages, porewater ages, patterns of abundance (measured by direct counts, counts of culturable cells, and total phospholipid fatty acids), activities (measured by radiotracer and enzyme assays), and community composition (measured by phospholipid fatty acid patterns and Biolog substrate usage). Core samples were collected at two sites 40 km apart in the Palouse region of eastern Washington State, near the towns of Washtucna and Winona. The Washtucna site was flooded multiple times during the Pleistocene by glacial outburst floods; the Winona site elevation is above flood stage. Sediments at the Washtucna site were collected from near surface to 14.9 m depth, where the sediment age was approximately 250 ka and the porewater age was 3700 years; sample intervals at the Winona site ranged from near surface to 38 m (sediment age: approximately 1 Ma; porewater age: 1200 years). Microbial abundance and activities declined with depth at both sites; however, even the deepest, oldest sediments showed evidence of viable microorganisms. Same-age sediments had equal quantities of microorganisms, but different community types. Differences in community makeup between the two sites can be attributed to differences in groundwater recharge and paleoflooding. Estimates of the microbial community age can be constrained by porewater and sediment ages. In the shallower sediments (<9 m at Washtucna, <12 m at Winona), the microbial communities are likely similar in age to the groundwater; thus, microbial succession has been influenced by recent transport of microorganisms from the surface. In the deeper sediments, the populations may be considerably older than the porewater ages, since microbial transport is severely restricted in unsaturated sediments. This is particularly true at the Winona site, which was never flooded.     

3D cell nuclei segmentation based on gradient flow tracking

Background  

Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.     

Context based mixture model for cell phase identification in automated fluorescence microscopy

Background  

Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task.