Summary
The epithelial-to-mesenchymal transition (EMT) is an embryonic process that becomes latent in most normal adult tissues. Recently, we have shown that induction of EMT endows breast epithelial cells with stem cell traits. In this report, we have further characterized the EMT-derived cells and shown that these cells are similar to mesenchymal stem cells (MSCs) with the capacity to differentiate into multiple tissue lineages. For this purpose, we induced EMT by ectopic expression of Twist, Snail or TGF-β in immortalized human mammary epithelial cells (HMECs). We found that the EMT-derived cells and MSCs share many properties including the antigenic profile typical of MSCs, i.e. CD44+, CD24− and CD45−. Conversely, MSCs express EMT-associated genes, such as Twist, Snail and FOXC2. Interestingly, CD140b (PDGFR-β), a marker for naive MSCs, is exclusively expressed in EMT-derived cells and not in their epithelial counterparts. Moreover, functional analyses revealed that EMT-derived cells but not the control cells can differentiate into Alizarin Red S-positive mature osteoblasts, Oil Red O-positive adipocytes and Alcian Blue-positive chondrocytes similar to MSCs. We also observed that EMT-derived cells but not the control cells invade and migrate towards MDA-MB-231 breast cancer cells similar to MSCs. In vivo wound homing assays in nude mice revealed that the EMT-derived cells home to wound sites similar to MSCs. In conclusion, we have demonstrated that the EMT-derived cells are similar to MSCs in gene expression, multi-lineage differentiation, and ability to migrate towards tumor cells and wound sites.
To bridge the gap between the sequences and 3-dimensional (3D) structures of RNAs, some computational models have been proposed for predicting RNA 3D structures. However, the existed models seldom consider the conditions departing from the room/body temperature and high salt (1M NaCl), and thus generally hardly predict the thermodynamics and salt effect. In this study, we propose a coarse-grained model with implicit salt for RNAs to predict 3D structures, stability and salt effect. Combined with Monte Carlo simulated annealing algorithm and a coarse-grained force field, the model folds 46 tested RNAs (≤ 45 nt) including pseudoknots into their native-like structures from their sequences, with an overall mean RMSD of 3.5 Å and an overall minimu m RMSD of 1.9 Å from the experimental structures. For 30 RNA hairpins, the present model also gives 2 the reliable predictions for the stability and salt effect with the mean deviation ~ 1.0℃ of melting temperatures, as compared with the extensive experimental data. In addition, the model could provide the ensemble of possible 3D structures for a short RNA at a given temperature/salt condition.
Delineating faults from seismic images is a key step for seismic structural interpretation, reservoir characterization, and well placement. In conventional methods, faults are considered as seismic reflection discontinuities and are detected by calculating attributes that estimate reflection continuities or discontinuities. We consider fault detection as a binary image segmentation problem of labeling a 3D seismic image with ones on faults and zeros elsewhere. We have performed an efficient image-to-image fault segmentation using a supervised fully convolutional neural network. To train the network, we automatically create 200 3D synthetic seismic images and corresponding binary fault labeling images, which are shown to be sufficient to train a good fault segmentation network. Because a binary fault image is highly imbalanced between zeros (nonfault) and ones (fault), we use a class-balanced binary cross-entropy loss function to adjust the imbalance so that the network is not trained or converged to predict only zeros. After training with only the synthetic data sets, the network automatically learns to calculate rich and proper features that are important for fault detection. Multiple field examples indicate that the neural network (trained by only synthetic data sets) can predict faults from 3D seismic images much more accurately and efficiently than conventional methods. With a TITAN Xp GPU, the training processing takes approximately 2 h and predicting faults in a [Formula: see text] seismic volume takes only milliseconds.
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