Bone morphogenetic proteins (BMPs) belong to the TGF-β super family, and are essential for the regulation of foetal development, tissue differentiation and homeostasis and a multitude of cellular functions. Naturally, this has led to the exploration of aberrance in this highly regulated system as a key factor in tumourigenesis. Originally identified for their role in osteogenesis and bone turnover, attention has been turned to the potential role of BMPs in tumour metastases to, and progression within, the bone niche. This is particularly pertinent to breast cancer, which commonly metastasises to bone, and in which studies have revealed aberrations of both BMP expression and signalling, which correlate clinically with breast cancer progression. Ultimately a BMP profile could provide new prognostic disease markers. As the evidence suggests a role for BMPs in regulating breast tumour cellular function, in particular interactions with tumour stroma and the bone metastatic microenvironment, there may be novel therapeutic potential in targeting BMP signalling in breast cancer. This review provides an update on the current knowledge of BMP abnormalities and their implication in the development and progression of breast cancer, particularly in the disease-specific bone metastasis.

Epithelial protein lost in neoplasm-α (EPLIN-α) is a cytoskeletal protein whose expression is often lost or is aberrant in cancerous cells and tissues and whose loss is believed to be involved in aggressive phenotypes. This study examined this molecule in human epithelial ovarian tissues and investigated the cellular impact of EPLIN-α on ovarian cancer cells (EOC), SKOV3 and COV504. The expression of EPLIN-α in human ovarian tissues and EOC was assessed at both the mRNA and protein levels using reverse transcription-PCR (RT-PCR) and immunohistochemistry, respectively. In vitro assays for cellular matrix adhesion and migration (confirmed by an electrical cell substrate impedance sensing (ECIS) based method), invasion and cell growth were employed in order to assess the biological influence of EPLIN-α expression on EOC cells. Immunohistochemical analysis of ovarian cancer samples demonstrated that only a small number expressed EPLIN-α protein. Downregulation of EPLIN-α protein in EOC cell lines increased the growth, invasion, adhesion and migration in vitro. This EPLIN-α downregulation may have a prognostic value. From these data, we conclude that downregulation of EPLIN-α may be associated with poorer patient prognosis, and that this molecule appears to play a tumour suppressor role by inhibition of EOC growth and migration.

A series of mesoporous Nb and Nb‐W oxides were employed as highly active solid acid catalysts for the conversion of glucose to 5‐hydroxymethylfurfural (HMF). The results of solid state 31P MAS NMR spectroscopy with adsorbed trimethylphosphine as probe molecule show that the addition of W in niobium oxide increases the number of Brønsted acid sites and decreases the number of Lewis acid sites. The catalytic performance for Nb‐W oxides varied with the ratio of Brønsted to Lewis acid sites and high glucose conversion was observed over Nb5W5 and Nb7W3 oxides with high ratios of Brønsted to Lewis acid sites. All Nb‐W oxides show a relatively high selectivity of HMF, whereas no HMF forms over sulfuric acid due to its pure Brønsted acidity. The results indicate fast isomerization of glucose to fructose over Lewis acid sites followed by dehydration of fructose to HMF over Brønsted acid sites. Moreover, comparing to the reaction occurred in aqueous media, the 2‐butanol/H2O system enhances the HMF selectivity and stabilizes the activity of the catalysts which gives the highest HMF selectivity of 52% over Nb7W3 oxide. The 2‐butanol/H2O catalytic system can also be employed in conversion of sucrose, achieving HMF selectivity of 46% over Nb5W5 oxide.

Zooplankton play a critical role in aquatic ecosystems and are commonly used as bioindicators to assess anthropogenic and climate impacts. Nevertheless, traditional microscopebased identification of zooplankton is inefficient. To overcome the low efficiency, computer-based methods have been developed. Yet, the performance of automated classification remains unsatisfactory because of the low accuracy of recognition. Here we propose a novel framework for automated plankton classification based on a naïve Bayesian classifier (NBC). We take advantage of the posterior probability of NBC to facilitate category aggregation and to single out objects of low predictive confidence for manual re-classifying in order to achieve a high level of final accuracy. This method was applied to East China Sea zooplankton samples with 154 289 objects, and the Bayesian automated zooplankton classification model showed a reasonable overall accuracy of 0.69 in unbalanced and 0.68 in balanced training for 25 planktonic and 1 aggregated non-planktonic categories. More importantly, after manually checking 17 to 38% of the objects of low confidence (depending on how one defines 'low confidence'), the final accuracy increased to 0.85−0.95 in the unbalanced training case, and after checking 18 to 42% of the low-confidence objects in the balanced training case, the final accuracy increased to 0.84−0.95. Our semi-automated approach is significantly more accurate than automated classifiers in recognizing rare categories, thereby facilitating ecological applications by improving the estimates of taxa richness and diversity. Our approach can make up for the deficiencies in current automated zooplankton classifiers and facilitates an efficient semi-automated zooplankton classification, which may have a broad application in environmental monitoring and ecological research. KEY WORDS: Automated classification · Naïve Bayesian classifier · Predictive confidence · Rapid category aggregation · Zooplankton community · ZooScan Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 441: [185][186][187][188][189][190][191][192][193][194][195][196] 2011 the traditional approach is labor-intensive and timeconsuming, limiting our ability to analyze zooplankton samples and understand processes controlling aquatic ecosystem dynamics (Suthers & Rissik 2008, Gorsky et al. 2010. Therefore, an important issue in aquatic ecology concerns how to improve the efficiency in plankton analysis and make the data more comparable at different spatial and temporal scales (Wiebe & Benfield 2003, Perry et al. 2004.Automated or semi-automated computer-aided systems can improve the efficiency in sample analyses and may even deliver more accurate and consistent results than human taxonomists in some cases (Culverhouse et al. 2003, Benfield et al. 2007, MacLeod et al. 2010. Recently, a large amount of effort has been invested in developing automated plankton identification systems (Ortner et al. 1979, Balfoort et al. 1992, Boddy e...