The low spatial resolution of light-field image poses significant difficulties in exploiting its advantage. To mitigate the dependency of accurate depth or disparity information as priors for light-field image super-resolution, we propose an implicitly multi-scale fusion scheme to accumulate contextual information from multiple scales for super-resolution reconstruction. The implicitly multi-scale fusion scheme is then incorporated into bidirectional recurrent convolutional neural network, which aims to iteratively model spatial relations between horizontally or vertically adjacent sub-aperture images of light-field data. Within the network, the recurrent convolutions are modified to be more effective and flexible in modeling the spatial correlations between neighboring views. A horizontal sub-network and a vertical sub-network of the same network structure are ensembled for final outputs via stacked generalization. Experimental results on synthetic and real-world data sets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in peak signal-to-noise ratio and gray-scale structural similarity indexes, which also achieves superior quality for human visual systems. Furthermore, the proposed method can enhance the performance of light field applications such as depth estimation.

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Purpose: Although metastasis is the primary determinant of poor survival of patients with osteogenic sarcoma, some patients live much longer than others, indicating metastatic heterogeneity underlying survival outcome. The purpose of the investigation was to identify genes underlying survival outcome of patients with osteogenic sarcoma metastasis. Experimental Design: We have used microarray to first compare mRNA expression between normal bone and osteogenic sarcoma specimens, identified genes overexpressed in osteogenic sarcoma, and compared expression of the selected gene between a poorly metastatic (SAOS) and two highly metastatic cell lines (LM8 and 143B). Finally, expression of the selected gene was assessed by immunostaining of osteogenic sarcoma samples with known survival outcome.Results: Microarray analysis revealed 5.3-fold more expression of WT1 mRNA in osteogenic sarcoma compared with normal bone and >2-fold overexpression in 143B and LM8 cells compared with SAOS. Furthermore,WT1mRNA was absent in normal bone (10 of 10) by reverse transcription-PCR but present in osteogenic sarcoma^derived cell lines (5 of 8). One hundred percent (42 of 42) of low-grade osteogenic sarcoma specimens expressed noWT1as determined by immunostaining; however, 24% (12 of 49) of the high-grade specimens showed intense staining. Mean survival of patients with high-grade metastatic osteogenic sarcoma but lowWT1 staining (27 of 37) was 96.5 F 129.3 months, whereas mean survival of patients with high-grade metastatic osteogenic sarcoma having intense staining (10 of 37) was 18.3 F 12.3 months (P > 0.0143). All splice variants of WT1 mRNA, including a hitherto unknown variant (lacking exons 4 and 5), were found to be expressed in osteogenic sarcoma. Conclusion: WT1 seems to be associated with very poor survival of patients with osteogenic sarcoma metastasis.

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