In Vivo Function and Evolution of the Eutherian-Specific Pluripotency Marker UTF1

Embryogenesis in placental mammals is sustained by exquisite interplay between the embryo proper and placenta. UTF1 is a developmentally regulated gene expressed in both cell lineages. Here, we analyzed the consequence of loss of the UTF1 gene during mouse development. We found that homozygous UTF1 mutant newborn mice were significantly smaller than wild-type or heterozygous mutant mice, suggesting that placental insufficiency caused by the loss of UTF1 expression in extra-embryonic ectodermal cells at least in part contributed to this phenotype. We also found that the effects of loss of UTF1 expression in embryonic stem cells on their pluripotency were very subtle. Genome structure and sequence comparisons revealed that the UTF1 gene exists only in placental mammals. Our analyses of a family of genes with homology to UTF1 revealed a possible mechanism by which placental mammals have evolved the UTF1 genes.This study was supported in part by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), and mostly by the Support Program for the Strategic Research Foundation at Private Universities, 2008–2012. This study was performed as a part of the Core Research for Evolutional Science and Technology (CREST) Agency. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

トリプル ネガティブ ニュウガン ニオケル プロテアソーム カンレン インシ PAG1 ニヨル シンキ ゾウショク キコウ ノ カイメイ

Triple negative breast cancer （TNBC） is considered to be one of the most aggressive subtypes of all breast cancers. To identify novel potential therapeutic targets and clarify pathophysiological features for TNBC, we conducted Meta-gene profiling analysis based on gene-expression profiling of TNBC cases purified by lasermicrobeam microdissection, and found that proteasome-associated genes （PAGs） were commonly upregulated in various pathways including cell cycle regulation in TNBC. Depletion of PAGs with RNAi caused the upregulation of p27 and p21 proteins in MDA-MB-231 and HCC1937 cells, respectively, resulting in growth inhibition. Interestingly, immunocytochmical staining revealed that PAG1 was observed in the nucleoli and/or cytoplasm （n-PAG1 and c-PAG1） in TNBC cell line and clinical specimens. Immunohistochemical staining of 100 TNBCs showed that high level of n-PAG1 was significantly associated with poor disease free and overall survival of TNBC patients. These results indicate that n-PAG1 plays a critical role in nucleus during cell cycle progression and might be a novel prognostic indicator or an attractive molecular target of TNBC

Furniture Recommendations Based on User Propensity and Furniture Style Compatibility

As digital information becomes more voluminous and e-commerce becomes more widespread, there is a growing demand for item recommendations that match the users’ sensibilities. However, learning users’ propensities is a difficult problem, especially in the field of furniture, which requires the consideration of many factors, such as color and shape. In addition, pieces of furniture should not be recommended only as stand-alone items, but must also be considered in terms of their affinity with other pieces, making the compatibility of styles among them an important factor. However, a consumer’s furniture style is an ambiguous concept that is difficult to define. To reduce this ambiguity, Siamese networks are often used to estimate style compatibility by adding various features that represent styles, but even when they make use of alternative features associated with images, they are difficult to represent accurately. This paper proposes a method for recommending multiple pieces of furniture by learning style compatibility properties with a high degree of accuracy, taking users’ preferences and styles’ compatibility into account. To this end, we engaged in two tasks: (1) extracting users’ preferences and (2) improving the accuracy of style suitability estimation. For (1), we applied matrix factorization to identify users whose sensitivities were close to those of the users who will receive recommendations. For (2), we used the Siamese network we have already proposed, which can learn from multiple furniture images simultaneously. Specifically, we propose a one-to-many input ratio to maintain high performance even when the input is ambiguous. Validation experiments were conducted for each task, and the results showed that the performance was improved; the actual recommendation results also showed a high performance