Broadening of transgenic adenocarcinoma of the mouse prostate (TRAMP) model to represent late stage androgen depletion independent cancer

BACKGROUND The transgenic adenocarcinoma of the mouse prostate (TRAMP) model closely mimics PC-progression as it occurs in humans. However, the timing of disease incidence and progression (especially late stage) makes it logistically difficult to conduct experiments synchronously and economically. The development and characterization of androgen depletion independent (ADI) TRAMP sublines are reported. METHODS Sublines were derived from androgen-sensitive TRAMP-C1 and TRAMP-C2 cell lines by androgen deprivation in vitro and in vivo. Epithelial origin (cytokeratin) and expression of late stage biomarkers (E-cadherin and KAI-1) were evaluated using immunohistochemistry. Androgen receptor (AR) status was assessed through quantitative real time PCR, Western blotting, and immunohistochemistry. Coexpression of AR and E-cadherin was also evaluated. Clonogenicity and invasive potential were measured by soft agar and matrigel invasion assays. Proliferation/survival of sublines in response to androgen was assessed by WST-1 assay. In vivo growth of subcutaneous tumors was assessed in castrated and sham-castrated C57BL/6 mice. RESULTS The sublines were epithelial and displayed ADI in vitro and in vivo. Compared to the parental lines, these showed (1) significantly faster growth rates in vitro and in vivo independent of androgen depletion, (2) greater tumorigenic, and invasive potential in vitro. All showed substantial downregulation in expression levels of tumor suppressor, E-cadherin, and metastatis suppressor, KAI-1. Interestingly, the percentage of cells expressing AR with downregulated E-cadherin was higher in ADI cells, suggesting a possible interaction between the two pathways. CONCLUSIONS The TRAMP model now encompasses ADI sublines potentially representing different phenotypes with increased tumorigenicity and invasiveness.

Validating a model of cooperative procurement in the construction industry

Project management in the construction industry involves coordination of many tasks and individuals, affected by complexity and uncertainty, which increases the need for efficient cooperation. Procurement is crucial since it sets the basis for cooperation between clients and contractors. This is true whether the project is local, regional or global in scope. Traditionally, procurement procedures are competitive, resulting in conflicts, adversarial relationships and less desirable project results. The purpose of this paper is to propose and empirically test an alternative procurement model based on cooperative procurement procedures that facilitates cooperation between clients and contractors in construction projects. The model is based on four multi-item constructs – incentive-based compensation, limited bidding options, partner selection and cooperation. Based on a sample of 87 client organisations, the model was empirically tested and exhibited strong support, including content, nomological, convergent and discriminant validity, as well as reliability. Our findings indicate that partner selection based on task related attributes mediates the relationship between two important pre-selection processes (incentive-based compensation and limited bid invitation) and preferred outcome of cooperation. The contribution of the paper is identifying valid and reliable measurement constructs and confirming a unique sequential order for achieving cooperation. Moreover, the findings are applicable for many types of construction projects because of the similarities in the construction industry worldwide.

A Bayesian model predicts the response of axons to molecular gradients

Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian “ideal observer” analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings.