Development of a biomechanical energy harvester

© 2009 Li et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Movement and habitat use of the snapping turtle in an urban landscape

In order to effectively manage urban habitats, it is important to incorporate the spatial ecology and habitat use of the species utilizing them. Our previous studies have shown that the distribution of upland habitats surrounding a highly urbanized wetland habitat, the Central Canal (Indianapolis, IN, USA) influences the distribution of map turtles (Graptemys geographica) and red-eared sliders (Trachemys scripta) during both the active season and hibernation. In this study we detail the movements and habitat use of another prominent member of the Central Canal turtle assemblage, the common snapping turtle, Chelydra serpentina. We find the same major upland habitat associations for C. serpentina as for G. geographica and T. scripta, despite major differences in their activity (e.g., C. serpentina do not regularly engage in aerial basking). These results reinforce the importance of recognizing the connection between aquatic and surrounding terrestrial habitats, especially in urban ecosystems

Cell Hierarchy and Lineage Commitment in the Bovine Mammary Gland

The bovine mammary gland is a favorable organ for studying mammary cell hierarchy due to its robust milk-production capabilities that reflect the adaptation of its cell populations to extensive expansion and differentiation. It also shares basic characteristics with the human breast, and identification of its cell composition may broaden our understanding of the diversity in cell hierarchy among mammals. Here, Lin− epithelial cells were sorted according to expression of CD24 and CD49f into four populations: CD24medCD49fpos (putative stem cells, puStm), CD24negCD49fpos (Basal), CD24highCD49fneg (putative progenitors, puPgt) and CD24medCD49fneg (luminal, Lum). These populations maintained differential gene expression of lineage markers and markers of stem cells and luminal progenitors. Of note was the high expression of Stat5a in the puPgt cells, and of Notch1, Delta1, Jagged1 and Hey1 in the puStm and Basal populations. Cultured puStm and Basal cells formed lineage-restricted basal or luminal clones and after re-sorting, colonies that preserved a duct-like alignment of epithelial layers. In contrast, puPgt and Lum cells generated only luminal clones and unorganized colonies. Under non-adherent culture conditions, the puPgt and puStm populations generated significantly more floating colonies. The increase in cell number during culture provides a measure of propagation potential, which was highest for the puStm cells. Taken together, these analyses position puStm cells at the top of the cell hierarchy and denote the presence of both bi-potent and luminally restricted progenitors. In addition, a population of differentiated luminal cells was marked. Finally, combining ALDH activity with cell-surface marker analyses defined a small subpopulation that is potentially stem cell- enriched

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Patient-derived xenograft (PDX) models in basic and translational breast cancer research

Patient-derived xenograft (PDX) models of a growing spectrum of cancers are rapidly supplanting long-established traditional cell lines as preferred models for conducting basic and translational preclinical research. In breast cancer, to complement the now curated collection of approximately 45 long-established human breast cancer cell lines, a newly formed consortium of academic laboratories, currently from Europe, Australia, and North America, herein summarizes data on over 500 stably transplantable PDX models representing all three clinical subtypes of breast cancer (ER+, HER2+, and "Triple-negative" (TNBC)). Many of these models are well-characterized with respect to genomic, transcriptomic, and proteomic features, metastatic behavior, and treatment response to a variety of standard-of-care and experimental therapeutics. These stably transplantable PDX lines are generally available for dissemination to laboratories conducting translational research, and contact information for each collection is provided. This review summarizes current experiences related to PDX generation across participating groups, efforts to develop data standards for annotation and dissemination of patient clinical information that does not compromise patient privacy, efforts to develop complementary data standards for annotation of PDX characteristics and biology, and progress toward "credentialing" of PDX models as surrogates to represent individual patients for use in preclinical and co-clinical translational research. In addition, this review highlights important unresolved questions, as well as current limitations, that have hampered more efficient generation of PDX lines and more rapid adoption of PDX use in translational breast cancer research