Vitamin D Binding Protein-Macrophage Activating Factor Directly Inhibits Proliferation, Migration, and uPAR Expression of Prostate Cancer Cells

Background: Vitamin D binding protein-macrophage activating factor (DBP-maf) is a potent inhibitor of tumor growth. Its activity, however, has been attributed to indirect mechanisms such as boosting the immune response by activating macrophages and inhibiting the blood vessel growth necessary for the growth of tumors. Methods and Findings: In this study we show for the first time that DBP-maf exhibits a direct and potent effect on prostate tumor cells in the absence of macrophages. DBP-maf demonstrated inhibitory activity in proliferation studies of both LNCaP and PC3 prostate cancer cell lines as well as metastatic clones of these cells. Flow cytometry studies with annexin V and propidium iodide showed that this inhibitory activity is not due to apoptosis or cell death. DBP-maf also had the ability to inhibit migration of prostate cancer cells in vitro. Finally, DBP-maf was shown to cause a reduction in urokinase plasminogen activator receptor (uPAR) expression in prostate tumor cells. There is evidence that activation of this receptor correlates with tumor metastasis. Conclusions: These studies show strong inhibitory activity of DBP-maf on prostate tumor cells independent of it

On-line measurement of soil properties without direct spectral response in near infrared spectral range

So far, the majority of reports on on-line measurement considered soil properties with direct spectral responses in near infrared spectroscopy (NIRS). This work reports on the results of on-line measurement of soil properties with indirect spectral responses, e.g. pH, cation exchange capacity (CEC), exchangeable calcium (Caex) and exchangeable magnesium (Mgex) in one field in Bedfordshire in the UK. The on-line sensor consisted of a subsoiler coupled with an AgroSpec mobile, fibre type, visible and near infrared (vis–NIR) spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a measurement range 305–2200 nm to acquire soil spectra in diffuse reflectance mode. General calibration models for the studied soil properties were developed with a partial least squares regression (PLSR) with one-leave-out cross validation, using spectra measured under non-mobile laboratory conditions of 160 soil samples collected from different fields in four farms in Europe, namely, Czech Republic, Denmark, Netherland and UK. A group of 25 samples independent from the calibration set was used as independent validation set. Higher accuracy was obtained for laboratory scanning as compared to on-line scanning of the 25 independent samples. The prediction accuracy for the laboratory and on-line measurements was classified as excellent/very good for pH (RPD = 2.69 and 2.14 and r2 = 0.86 and 0.78, respectively), and moderately good for CEC (RPD = 1.77 and 1.61 and r2 = 0.68 and 0.62, respectively) and Mgex (RPD = 1.72 and 1.49 and r2 = 0.66 and 0.67, respectively). For Caex, very good accuracy was calculated for laboratory method (RPD = 2.19 and r2 = 0.86), as compared to the poor accuracy reported for the on-line method (RPD = 1.30 and r2 = 0.61). The ability of collecting large number of data points per field area (about 12,800 point per 21 ha) and the simultaneous analysis of several soil properties without direct spectral response in the NIR range at relatively high operational speed and appreciable accuracy, encourage the recommendation of the on-line measurement system for site specific fertilisation

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

Predictive markers of efficacy for an angiopoietin-2 targeting therapeutic in xenograft models.

The clinical efficacy of anti-angiogenic therapies has been difficult to predict, and biomarkers that can predict responsiveness are sorely needed in this era of personalized medicine. CVX-060 is an angiopoietin-2 (Ang2) targeting therapeutic, consisting of two peptides that bind Ang2 with high affinity and specificity, covalently fused to a scaffold antibody. In order to optimize the use of this compound in the clinic the construction of a predictive model is described, based on the efficacy of CVX-060 in 13 cell line and 2 patient-derived xenograft models. Pretreatment size tumors from each of the models were profiled for the levels of 27 protein markers of angiogenesis, SNP haplotype in 5 angiogenesis genes, and somatic mutation status for 11 genes implicated in tumor growth and/or vascularization. CVX-060 efficacy was determined as tumor growth inhibition (TGI%) at termination of each study. A predictive statistical model was constructed based on the correlation of these efficacy data with the marker profiles, and the model was subsequently tested by prospective analysis in 11 additional models. The results reveal a range of CVX-060 efficacy in xenograft models of diverse tissue types (0-64% TGI, median = 27%) and define a subset of 3 proteins (Ang1, EGF, Emmprin), the levels of which may be predictive of TGI by Ang2 blockade. The direction of the associations is such that better efficacy correlates with high levels of target and low levels of compensatory/antagonizing molecules. This effort has revealed a set of candidate predictive markers for CVX-060 efficacy that will be further evaluated in ongoing clinical trials