[(3aS,5aR,8aR,8bS)-2,2,7,7-Tetra­methyl­tetra­hydro-3aH-bis­[1,3]dioxolo[4,5-b:4′,5′-d]pyran-3a-yl]methyl (R)-N-(1-phenyl­eth­yl)sulfamate

In the title compound, C20H29NO8S, the two five-membered rings adopt envelope conformations (with an O atom at the flap in each case), while the six-membered pyran ring displays a twist-boat conformation. In the crystal, mol­ecules are linked by N—H⋯O hydrogen bonds into a supra­molecular chain running along the a axis

miR-15a and miR-16-1 inhibit the proliferation of leukemic cells by down-regulating WT1 protein level

<p>Abstract</p> <p>Background</p> <p>miR-15a and miR-16-1(miR-15a/16-1) have been implicated as tumor suppressors in chronic lymphocytic leukemia, multiple myeloma, and acute myeloid leukemic cells. However the mechanism of inhibiting the proliferation of leukemic cells is poorly understood.</p> <p>Methods</p> <p>K562 and HL-60 cells were transfected with pRS-15/16 or pRS-E, cell growth were measured by CCK-8 assay and direct cell count. Meanwhile WT1 protein and mRNA level were measured by Western blotting and quantitative real-time PCR.</p> <p>Results</p> <p>In this study we found that over-expression of miR-15a/16-1 significantly inhibited K562 and HL-60 cells proliferation. Enforced expression of miR-15a/16-1 in K562 and HL-60 cells significantly reduced the protein level of WT1 but not affected the mRNA level. However enforced expression of miR-15a/16-1 can not reduce the activity of a luciferase reporter carrying the 3'-untranslated region(3'UTR) of WT1. Silencing of WT1 by specific siRNA suppressed leukemic cells proliferation resembling that of miR-15a/16-1 over-expression. Anti-miR-15a/16-1 oligonucleotides (AMO) reversed the expression of WT1 in K562 and HL-60 cells. Finally, we found a significant inverse correlation between miR-15a or miR-16-1 expression and WT1 protein levels in primary acute myeloid leukemia (AML) blasts and normal controls.</p> <p>Conclusions</p> <p>These data suggest that miR-15a/16-1 may function as a tumor suppressor to regulate leukemic cell proliferation potentially by down-regulating the WT1 oncogene. However WT1 is not directly targeted by miR-15a/16-1 through miRNA-mRNA base pairing, therefore more study are required to understand the mechanism by which miR-15a/16-1 downregulate WT1.</p

The Nematic Energy Scale and the Missing Electron Pocket in FeSe

Superconductivity emerges in proximity to a nematic phase in most iron-based superconductors. It is therefore important to understand the impact of nematicity on the electronic structure. Orbital assignment and tracking across the nematic phase transition prove to be challenging due to the multiband nature of iron-based superconductors and twinning effects. Here, we report a detailed study of the electronic structure of fully detwinned FeSe across the nematic phase transition using angle-resolved photoemission spectroscopy. We clearly observe a nematicity-driven band reconstruction involving dxz, dyz, and dxy orbitals. The nematic energy scale between dxz and dyz bands reaches a maximum of 50 meV at the Brillouin zone corner. We are also able to track the dxz electron pocket across the nematic transition and explain its absence in the nematic state. Our comprehensive data of the electronic structure provide an accurate basis for theoretical models of the superconducting pairing in FeSe

I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections

Place recognition is an important technique for autonomous cars to achieve full autonomy since it can provide an initial guess to online localization algorithms. Although current methods based on images or point clouds have achieved satisfactory performance, localizing the images on a large-scale point cloud map remains a fairly unexplored problem. This cross-modal matching task is challenging due to the difficulty in extracting consistent descriptors from images and point clouds. In this paper, we propose the I2P-Rec method to solve the problem by transforming the cross-modal data into the same modality. Specifically, we leverage on the recent success of depth estimation networks to recover point clouds from images. We then project the point clouds into Bird's Eye View (BEV) images. Using the BEV image as an intermediate representation, we extract global features with a Convolutional Neural Network followed by a NetVLAD layer to perform matching. The experimental results evaluated on the KITTI dataset show that, with only a small set of training data, I2P-Rec achieves recall rates at Top-1\% over 80\% and 90\%, when localizing monocular and stereo images on point cloud maps, respectively. We further evaluate I2P-Rec on a 1 km trajectory dataset collected by an autonomous logistics car and show that I2P-Rec can generalize well to previously unseen environments.Comment: Accepted by IROS 202