MAPPING SPATIAL ACCURACY OF FOREST TYPE CLASSIFICATION IN JAXA’s HIGH-RESOLUTION LAND USE AND LAND COVER MAP

Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures

Monoclonal Antibodies Recognizing the Non-Tandem Repeat Regions of the Human Mucin MUC4 in Pancreatic Cancer

The MUC4 mucin is a high molecular weight, membrane-bound, and highly glycosylated protein. It is a multi-domain protein that is putatively cleaved into a large mucin-like subunit (MUC4α) and a C-terminal growth-factor like subunit (MUC4β). MUC4 plays critical roles in physiological and pathological conditions and is aberrantly overexpressed in several cancers, including those of the pancreas, cervix, breast and lung. It is also a potential biomarker for the diagnosis, prognosis and progression of several malignancies. Further, MUC4 plays diverse functional roles in cancer initiation and progression as evident from its involvement in oncogenic transformation, proliferation, inhibition of apoptosis, motility and invasion, and resistance to chemotherapy in human cancer cells. We have previously generated a monoclonal antibody 8G7, which is directed against the TR region of MUC4, and has been extensively used to study the expression of MUC4 in several malignancies. Here, we describe the generation of anti-MUC4 antibodies directed against the non-TR regions of MUC4. Recombinant glutathione-S-transferase (GST)-fused MUC4α fragments, both upstream (MUC4α-N-Ter) and downstream (MUC4α-C-Ter) of the TR domain, were used as immunogens to immunize BALB/c mice. Following cell fusion, hybridomas were screened using the aforementioned recombinant proteins ad lysates from human pancreatic cell lines. Three anti MUC4α-N-Ter and one anti-MUC4α-C-Ter antibodies were characterized by several inmmunoassays including enzyme-linked immunosorbent assay (ELISA), immunoblotting, immunofluorescene, flow cytometry and immunoprecipitation using MUC4 expressing human pancreatic cancer cell lines. The antibodies also reacted with the MUC4 in human pancreatic tumor sections in immunohistochemical analysis. The new domain-specific anti-MUC4 antibodies will serve as important reagents to study the structure-function relationship of MUC4 domains and for the development of MUC4-based diagnostics and therapeutics

The Application of a Geographically Weighted Principal Component Analysis for Exploring Twenty-three Years of Goat Population Change across Mongolia

The dzud are extreme weather events in Mongolia of deep snow, severe cold, or other conditions that render forage unavailable or inaccessible, which in turn results in extensive livestock deaths. Mongolia is economically vulnerable to extreme events due to an increase in nonprofessional herders and the livestock population, brought about by a deregularized industry. Thus, it is hugely informative to try to understand the spatial and temporal trends of livestock population change. To this end, annual livestock census data are exploited and a geographically weighted principal component analysis (GWPCA) is applied to goat data recorded from 1990 to 2012 in 341 regions. This application of GWPCA to temporal data is novel and is able to account for both temporal and spatial patterns in goat population change. Furthermore, the GWPCA methodology is extended to simultaneously optimize the number of components to retain and the kernel bandwidth. In doing so, this study not only advances the GWPCA method but provides a useful insight into the spatiotemporal variations of the Mongolian goat population

Epigenetic regulation of mucin genes in human cancers

Mucins are high molecular weight glycoproteins that play important roles in diagnostic and prognostic prediction and in carcinogenesis and tumor invasion. Regulation of expression of mucin genes has been studied extensively, and signaling pathways, transcriptional regulators, and epigenetic modification in promoter regions have been described. Detection of the epigenetic status of cancer-related mucin genes is important for early diagnosis of cancer and for monitoring of tumor behavior and response to targeted therapy. Effects of micro-RNAs on mucin gene expression have also started to emerge. In this review, we discuss the current views on epigenetic mechanisms of regulation of mucin genes (MUC1, MUC2, MUC3A, MUC4, MUC5AC, MUC5B, MUC6, MUC16, and MUC17) and the possible clinical applications of this epigenetic information

Impact of MUC1 Mucin Downregulation in the Phenotypic Characteristics of MKN45 Gastric Carcinoma Cell Line

BACKGROUND: Gastric carcinoma is the second leading cause of cancer-associated death worldwide. The high mortality associated with this disease is in part due to limited knowledge about gastric carcinogenesis and a lack of available therapeutic and prevention strategies. MUC1 is a high molecular weight transmembrane mucin protein expressed at the apical surface of most glandular epithelial cells and a major component of the mucus layer above gastric mucosa. Overexpression of MUC1 is found in approximately 95% of human adenocarcinomas, where it is associated with oncogenic activity. The role of MUC1 in gastric cancer progression remains to be clarified. METHODOLOGY: We downregulated MUC1 expression in a gastric carcinoma cell line by RNA interference and studied the effects on cellular proliferation (MTT assay), apoptosis (TUNEL assay), migration (migration assay), invasion (invasion assay) and aggregation (aggregation assay). Global gene expression was evaluated by microarray analysis to identify alterations that are regulated by MUC1 expression. In vivo assays were also performed in mice, in order to study the tumorigenicity of cells with and without MUC1 downregulation in MKN45 gastric carcinoma cell line. RESULTS: Downregulation of MUC1 expression increased proliferation and apoptosis as compared to controls, whereas cell-cell aggregation was decreased. No significant differences were found in terms of migration and invasion between the downregulated clones and the controls. Expression of TCN1, KLK6, ADAM29, LGAL4, TSPAN8 and SHPS-1 was found to be significantly different between MUC1 downregulated clones and the control cells. In vivo assays have shown that mice injected with MUC1 downregulated cells develop smaller tumours when compared to mice injected with the control cells. CONCLUSIONS: These results indicate that MUC1 downregulation alters the phenotype and tumorigenicity of MKN45 gastric carcinoma cells and also the expression of several molecules that can be involved in tumorigenic events. Therefore, MUC1 should be further studied to better clarify its potential as a novel therapeutic target for gastric cancer

Mapping spatial accuracy of the forest type classification in JAXA’s high-resolution land use and land cover map

ISPRS Technical Commission III WG III/2, 10 Joint Workshop "Multidisciplinary Remote Sensing for Environmental Monitoring", 12-14 March, Kyoto, Japan.Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land usecover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures