Atmospheric-pressure chemical vapor deposition (CVD) is used to grow monolayer MoS 2 two-dimensional crystals at elevated temperatures on silicon substrates with a 300 nm oxide layer. Our CVD reaction is hydrogen free, with the sulfur precursor placed in a furnace separate from the MoO 3 precursor to individually control their heating profiles and provide greater flexibility in the growth recipe. We intentionally establish a sharp gradient of MoO 3 precursor concentration on the growth substrate to explore its sensitivity to the resultant MoS 2 domain growth within a relatively uniform temperature range. We find that the shape of MoS 2 domains is highly dependent upon the spatial location on the silicon substrate, with variation from triangular to hexagonal geometries. The shape change of domains is attributed to local changes in the Mo:S ratio of precursors (1:>2, 1:2, and 1:<2) and its influence on the kinetic growth dynamics of edges. These results improve our understanding of the factors that influence the growth of MoS 2 domains and their shape evolution.

The molecular pathogenesis of renal cell carcinoma (RCC) is poorly understood. Whole-genome and exome sequencing followed by innovative tumorgraft analyses (to accurately determine mutant allele ratios) identified several putative two-hit tumor suppressor genes including BAP1. BAP1, a nuclear deubiquitinase, is inactivated in 15% of clear-cell RCCs. BAP1 cofractionates with and binds to HCF-1 in tumorgrafts. Mutations disrupting the HCF-1 binding motif impair BAP1-mediated suppression of cell proliferation, but not H2AK119ub1 deubiquitination. BAP1 loss sensitizes RCC cells in vitro to genotoxic stress. Interestingly, BAP1 and PBRM1 mutations anticorrelate in tumors (P=3×10−5), and combined loss of BAP1 and PBRM1 in a few RCCs was associated with rhabdoid features (q=0.0007). BAP1 and PBRM1 regulate seemingly different gene expression programs, and BAP1 loss was associated with high tumor grade (q=0.0005). Our results establish the foundation for an integrated pathological and molecular genetic classification of RCC, paving the way for subtype-specific treatments exploiting genetic vulnerabilities.

Immunosuppression of tumor-infiltrating lymphocytes (TIL) is a common feature of advanced cancer, but its biological basis has remained obscure. We demonstrate here a molecular link between epithelial-to-mesenchymal transition (EMT) and CD8+ TIL immunosuppression, two key drivers of cancer progression. We show that microRNA-200 (miR-200), a cell-autonomous suppressor of EMT and metastasis, targets PD-L1. Moreover, ZEB1, an EMT activator and transcriptional repressor of miR-200, relieves miR-200 repression of PD-L1 on tumor cells, leading to CD8+ T cell immunosuppression and metastasis. These findings are supported by robust correlations between the EMT score, miR-200 levels and PD-L1 expression in multiple human lung cancer datasets. In addition to revealing a link between EMT and T cell dysfunction, these findings also show that ZEB1 promotes metastasis through a heretofore unappreciated cell non-autonomous mechanism, and suggest that subgroups of patients in whom malignant progression is driven by EMT activators may respond to treatment with PD-L1 antagonists.

This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and effective imaging.

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