The biological significance of a known normal and cancer stem cell marker CD133 remains elusive. We now demonstrate that the phosphorylation of tyrosine-828 residue in CD133 C-terminal cytoplasmic domain mediates direct interaction between CD133 and phosphoinositide 3-kinase (PI3K) 85 kDa regulatory subunit (p85), resulting in preferential activation of PI3K/protein kinase B (Akt) pathway in glioma stem cell (GSC) relative to matched nonstem cell. CD133 knockdown potently inhibits the activity of PI3K/Akt pathway with an accompanying reduction in the self-renewal and tumorigenicity of GSC. The inhibitory effects of CD133 knockdown could be completely rescued by expression of WT CD133, but not its p85-binding deficient Y828F mutant. Analysis of glioma samples reveals that CD133 Y828 phosphorylation level is correlated with histopathological grade and overlaps with Akt activation. Our results identify the CD133/PI3K/Akt signaling axis, exploring the fundamental role of CD133 in glioma stem cell behavior.

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Energy-efficient intelligent systems incorporating modern electronics, circuits, and power sources have rapidly become inseparable for a wide range of applications. Here, we propose a prototype of energy autonomous functional paper...

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model’s segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS subtrack. Our systems were built on deep Transformer and several back-translation methods. Iterative knowledge distillation and ensem-ble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {KK↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN↔DE} among all constrained submissions.

An unprecedented stereoselective synthesis of trisubstituted vinylboronates is reported to proceed by direct borylation of lithium ketone enolates under transition‐metal‐free conditions. The stereospecific C−O borylation of lithium enolates was triggered by a carbonyl‐induced 1,3‐metalate rearrangement via a C‐bound boron enolate. DFT calculations and control experiments revealed that the stereoselectivity is controlled by sterics. A variety of stereospecific trisubstituted vinylboronates, together with several tetrasubstituted vinylboronates, were conveniently synthesized with the newly developed methodology. Based on the transformation of stereospecific vinylboronate, a single isomer of Dienestrol was efficiently obtained.

Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. In this paper, we extend the search space of NAS. In particular, we present a general approach to learn both intra-cell and inter-cell architectures (call it ESS). For a better search result, we design a joint learning method to perform intra-cell and inter-cell NAS simultaneously. We implement our model in a differentiable architecture search system. For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and Wiki-Text data, with a new state-of-the-art on PTB. Moreover, the learned architectures show good transferability to other systems. E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity recognition (NER) tasks and CoNLL chunking task, indicating a promising line of research on large-scale prelearned architectures.

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