Study on the Rheological Properties and Constitutive Model of Shenzhen Mucky Soft Soil

In order to obtain the basic parameters of numerical analysis about the time-space effect of the deformation occurring in Shenzhen deep soft-soil foundation pit, a series of triaxial consolidated-undrained shear rheology tests on the peripheral mucky soft soil of a deep foundation pit support were performed under different confining pressures. The relations between the axial strain of the soil and time, as well as between the pore-water pressure of the soil and time, were achieved, meanwhile on the basis of analyzing the rheological properties of the soil, the relevant rheological models were built. Analysis results were proved that the rheology of Shenzhen mucky soft soil was generally viscous, elastic, and plastic, and had a low yield stress between 90 and 150 kPa. The increase in pore-water pressure made the rheological time effect of the mucky soft soil more remarkable. Thus, the drainage performance in practical engineering should be improved to its maximum possibility extent to decrease the soft-soil rheological deformation. Lastly, a six-component extended Burgers model was employed to fit the test results and the parameters of the model were determined. Findings showed that the extended Burgers model could satisfactorily simulate the various rheological stages of the mucky soft soil. The constitutive model and the determination of its parameters can be served as a foundation for the time-space effect analysis on the deformation of deep soft-soil foundation pits

Zebrafish foxo3b Negatively Regulates Canonical Wnt Signaling to Affect Early Embryogenesis

FOXO genes are involved in many aspects of development and vascular homeostasis by regulating cell apoptosis, proliferation, and the control of oxidative stress. In addition, FOXO genes have been showed to inhibit Wnt/β-catenin signaling by competing with T cell factor to bind to β-catenin. However, how important of this inhibition in vivo, particularly in embryogenesis is still unknown. To demonstrate the roles of FOXO genes in embryogenesis will help us to further understand their relevant physiological functions. Zebrafish foxo3b gene, an orthologue of mammalian FOXO3, was expressed maternally and distributed ubiquitously during early embryogenesis and later restricted to brain. After morpholino-mediated knockdown of foxo3b, the zebrafish embryos exhibited defects in axis and neuroectoderm formation, suggesting its critical role in early embryogenesis. The embryo-developmental marker gene staining at different stages, phenotype analysis and rescue assays revealed that foxo3b acted its role through negatively regulating both maternal and zygotic Wnt/β-catenin signaling. Moreover, we found that foxo3b could interact with zebrafish β-catenin1 and β-catenin2 to suppress their transactivation in vitro and in vivo, further confirming its role relevant to the inhibition of Wnt/β-catenin signaling. Taken together, we revealed that foxo3b played a very important role in embryogenesis and negatively regulated maternal and zygotic Wnt/β-catenin signaling by directly interacting with both β-catenin1 and β-catenin2. Our studies provide an in vivo model for illustrating function of FOXO transcription factors in embryogenesis

A Sodium laser guide star coupling efficiency measurement method

Large telescope's adaptive optics (AO) system requires one or several bright artificial laser guide stars to improve its sky coverage. The recent advent of high power sodium laser is perfect for such application. However, besides the output power, other parameters of the laser also have significant impact on the brightness of the generated sodium laser guide star mostly in non-linear relationships. When tuning and optimizing these parameters it is necessary to tune based on a laser guide star generation performance metric. Although return photon flux is widely used, variability of atmosphere and sodium layer make it difficult to compare from site to site even within short time period for the same site. A new metric, coupling efficiency is adopted in our field tests. In this paper, we will introduce our method for measuring the coupling efficiency of a 20W class pulse sodium laser for AO application during field tests that were conducted during 2013-2015

MODEL : motif-based deep feature learning for link prediction

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field

Upregulation of microRNA-451 increases cisplatin sensitivity of non-small cell lung cancer cell line (A549)

<p>Abstract</p> <p>Background</p> <p>Recently, miR-451 as a tumor suppressor has been reported in other studies. However, whether miR-451 can affect the sensitivity of non-small cell lung cancer (NSCLC) cells to cisplatin (DDP) remains unclear. The aim of this study is to evaluate the roles of miR-451 in the sensitivity of NSCLC cells to DDP.</p> <p>Methods</p> <p>Quantitative RT-PCR assay was performed to detect the expression of miR-451 in 10 pairs of NSCLC and noncancerous tissue samples. pcDNA-GW/EmGFP-miR-451 was stably transfected into NSCLC cell line (A549). Then, the effects of miR-451 upregulation on growth, colony formation and apoptosis of A549 cells were investigated. Finally, the effects of miR-451 upregulation on in vitro and in vivo sensitivity of A549 cells of DDP were also determined.</p> <p>Results</p> <p>The level of miR-451 expression in NSCLC tissues was significantly higher than that in corresponding noncancerous tissues. Ectopic overexpression of miR-451 could significantly inhibit growth and induce apoptosis of A549 cells. Moreover, ectopic overexpression of miR-451 could sensitize A549 cells to DDP possibly by increasing DDP-induced apoptosis which might be associated with the inactivation of Akt signaling pathway.</p> <p>Conclusions</p> <p>This study demonstrated for the first time that combination of DDP application with miR-451 upregulation might be a potential strategy for the treatment of human NSCLC.</p

Conditional Positional Encodings for Vision Transformers

We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings, which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during training. Besides, CPE can keep the desired translation-invariance in the image classification task, resulting in improved classification accuracy. CPE can be effortlessly implemented with a simple Position Encoding Generator (PEG), and it can be seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings. Benefit from the conditional positional encoding scheme, we obtain state-of-the-art results on the ImageNet classification task compared with vision Transformers to date. Our code will be made available at https://github.com/Meituan-AutoML/CPVT .Comment: A general purpose conditional position encoding for vision transformer