Energy dependent partial wave anlyses for the reaction rc-+p ':"'J+n are carried out with respect to S, P, D and F waves in the energy range 561-1300 MeV. Two different types of the solutions are obtained. In the first solution, the "'J-production peak is dominated by the S 11 resonance with mass M = 1570 MeV, total width TT = 140 MeV, branching ratios (S 11 res. --->rcN) I (S 11 res.->all) =40% and (S 11 res.-----'7"1JN)/(S 11 res.--'lall) =50%. In the second solution, it is dominated by the P 11 resonance with M=1580 MeV, TT=130 MeV, (P 11 res.-'TrcN)/(P 11 res.-----'7all) =35% and (P 11 res.-?"'JN)j(P 11 res.-'Tall) =305-~-This P 11 resonance is a new one which is different from Roper's resonance. In this analysis the second solution_ is better fitting experimental data than the first solution.P 11 and D 13 • When the peak is dominated by a resonance (called the Resonant Case) , we can consider the following three cases ;

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These data suggest that intracolonic administration of R-ODN may be effective to treat DSS-induced colitis.

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Background: The Imaise Branch of Ichinomiya City Hospital proactively

We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is based on embedding the images in a feature-space using a convolutional neural network trained with a cosine softmax loss. Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity. Our proposed re-ranking approach improves the results in two steps: in the sort-step, k-nearest neighbor search with soft-voting to sort the retrieved results based on their label similarity to the query images, and in the insert-step, we add additional samples from the dataset that were not retrieved by image-similarity. This approach allows overcoming the low visual diversity in retrieved images. In-depth experimental results show that the proposed approach significantly outperforms existing approaches on the challenging Google Landmarks Datasets. Using our methods, we achieved 1st place in the Google Landmark Retrieval 2019 challenge and 3rd place in the Google Landmark Recognition 2019 challenge on Kaggle. Our code is publicly available here: https://github.com/lyakaap/ Landmark2019-1st-and-3rd-Place-Solution

The proliferating cell nuclear antigen (PCNA) is a nuclear protein that leads DNA synthesis by the DNA polymerase delta. As the PCNA gene is strongly expressed in invasive gastric cancer cells with high proliferative activity, PCNA is suspected of playing an important role in the proliferation and advancement of gastric cancer. Thus, the effects of antisense oligonucleotides specific for PCNA mRNA were examined in seven gastric cancer cell lines. It was found that treatment with antisense oligonucleotides at concentrations of 10-40 microM dose-dependently inhibited the growth of all cell lines; however, random sequence oligonucleotides did not modify the proliferation of any type of cells. These results indicate that PCNA is essential for cell proliferation in gastric cancer cells, and that the growth inhibitory effect results from the inhibition of PCNA gene expression. Therefore, PCNA-specific antisense oligonucleotides may be effective in the treatment of gastric cancer.

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