Sensitivity analyses of OH missing sinks over Tokyo metropolitan area in the summer of 2007

OH reactivity is one of key indicators which reflect impacts of photochemical reactions in the atmosphere. An observation campaign has been conducted in the summer of 2007 at the heart of Tokyo metropolitan area to measure OH reactivity. The total OH reactivity measured directly by the laser-induced pump and probe technique was higher than the sum of the OH reactivity calculated from concentrations and reaction rate coefficients of individual species measured in this campaign. And then, three-dimensional air quality simulation has been conducted to evaluate the simulation performance on the total OH reactivity including "missing sinks", which correspond to the difference between the measured and calculated total OH reactivity. The simulated OH reactivity is significantly underestimated because the OH reactivity of volatile organic compounds (VOCs) and missing sinks are underestimated. When scaling factors are applied to input emissions and boundary concentrations, a good agreement is observed between the simulated and measured concentrations of VOCs. However, the simulated OH reactivity of missing sinks is still underestimated. Therefore, impacts of unidentified missing sinks are investigated through sensitivity analyses. In the cases that unknown secondary products are assumed to account for unidentified missing sinks, they tend to suppress formation of secondary aerosol components and enhance formation of ozone. In the cases that unidentified primary emitted species are assumed to account for unidentified missing sinks, a variety of impacts may be observed, which could serve as precursors of secondary organic aerosols (SOA) and significantly increase SOA formation. Missing sinks are considered to play an important role in the atmosphere over Tokyo metropolitan area

Death of a tumor: targeting CCN in pancreatic cancer

The matricellular protein CCN2 (connective tissue growth factor, CTGF) has been previously implicated in tumorigenesis. In pancreatic cancer cells, CCN2 expression occurs downstream of ras/MEK/ERK. Direct evidence that CCN2 mediates tumor progression in pancreatic cancer has been lacking. An exciting recent report by Bennewith et al. (Cancer Res 69:775–784, 2009) has used shRNA knockdown of CCN2 to illustrate that CCN2 contributes to growth of pancreatic tumor cells, both in vitro and in vivo. This report briefly summarizes these findings

The role of CCN2 in cartilage and bone development

CCN2, a classical member of the CCN family of matricellular proteins, is a key molecule that conducts cartilage development in a harmonized manner through novel molecular actions. During vertebrate development, all cartilage is primarily formed by a process of mesenchymal condensation, while CCN2 is induced to promote this process. Afterwards, cartilage develops into several subtypes with different fates and missions, in which CCN2 plays its proper roles according to the corresponding microenvironments. The history of CCN2 in cartilage and bone began with its re-discovery in the growth cartilage in long bones, which determines the skeletal size through the process of endochondral ossification. CCN2 promotes physiological developmental processes not only in the growth cartilage but also in the other types of cartilages, i.e., Meckel’s cartilage representing temporary cartilage without autocalcification, articular cartilage representing hyaline cartilage with physical stiffness, and auricular cartilage representing elastic cartilage. Together with its significant role in intramembranous ossification, CCN2 is regarded as a conductor of skeletogenesis. During cartilage development, the CCN2 gene is dynamically regulated to yield stage-specific production of CCN2 proteins at both transcriptional and post-transcriptional levels. New functional aspects of known biomolecules have been uncovered during the course of investigating these regulatory systems in chondrocytes. Since CCN2 promotes integrated regeneration as well as generation (=development) of these tissues, its utility in regenerative therapy targeting chondrocytes and osteoblasts is indicated, as has already been supported by experimental evidence obtained in vivo

KIF2A silencing inhibits the proliferation and migration of breast cancer cells and correlates with unfavorable prognosis in breast cancer

Background; Kinesin family member 2a (KIF2A), a type of motor protein found in eukaryotic cells, is associated with development and progression of various human cancers. The role of KIF2A during breast cancer tumorigenesis and progression was studied. Methods; Immunohistochemical staining, real time RT-PCR and western blot were used to examine the expression of KIF2A in cancer tissues and adjacent normal tissues from breast cancer patients. Patients’ survival in relation to KIF2A expression was estimated using the Kaplan–Meier survival and multivariate analysis. Breast cancer cell line, MDA-MB-231 was used to study the proliferation, migration and invasion of cells following KIF2A-siRNA transfection. Results; The expression of KIF2A in cancer tissues was higher than that in normal adjacent tissues from the same patient (P < 0.05). KIF2A expression in cancer tissue with lymph node metastasis and HER2 positive cancer were higher than that in cancer tissue without (P < 0.05). A negative correlation was found between KIF2A expression levels in breast cancer and the survival time of breast cancer patients (P < 0.05). In addition, multivariate analysis indicated that KIF2A was an independent prognostic for outcome in breast cancer (OR: 16.55, 95% CI: 2.216-123.631, P = 0.006). The proliferation, migration and invasion of cancer cells in vitro were suppressed by KIF2A gene silencing (P < 0.05). Conclusions; KIF2A may play an important role in breast cancer progression and is potentially a novel predictive and prognostic marker for breast cancer

Characterizing the role of rice NRAMP5 in Manganese, Iron and Cadmium Transport

Metals like manganese (Mn) and iron (Fe) are essential for metabolism, while cadmium (Cd) is toxic for virtually all living organisms. Understanding the transport of these metals is important for breeding better crops. We have identified that OsNRAMP5 contributes to Mn, Fe and Cd transport in rice. OsNRAMP5 expression was restricted to roots epidermis, exodermis, and outer layers of the cortex as well as in tissues around the xylem. OsNRAMP5 localized to the plasma membrane, and complemented the growth of yeast strains defective in Mn, Fe, and Cd transport. OsNRAMP5 RNAi (OsNRAMP5i) plants accumulated less Mn in the roots, and less Mn and Fe in shoots, and xylem sap. The suppression of OsNRAMP5 promoted Cd translocation to shoots, highlighting the importance of this gene for Cd phytoremediation. These data reveal that OsNRAMP5 contributes to Mn, Cd, and Fe transport in rice and is important for plant growth and development

Classification across gene expression microarray studies

<p>Abstract</p> <p>Background</p> <p>The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.</p> <p>Results</p> <p>For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.</p> <p>Conclusions</p> <p>We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.</p