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Author

Xiang Zhang

Bio: Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.


Papers
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Journal ArticleDOI
TL;DR: In this article, the influence of three deposition strategies on the fatigue crack growth behavior of wire-plus-arc-additive manufactured (WAAM) Ti-6Al-4V has been investigated in the as-built condition.
Abstract: The influence of three deposition strategies on the fatigue crack growth behaviour of Wire + Arc Additive Manufactured (WAAM) Ti–6Al–4V has been investigated in the as-built condition. Test samples were prepared using single pass, parallel pass, and oscillation deposition strategies and tested with cracks propagating parallel and normal to the plane of deposition. Due to the higher local heat input, the oscillation build exhibited a significantly coarser columnar β grain structure as well as a coarser transformation microstructure, compared to the single pass and parallel pass builds, which were very similar. Among the three build methods, the lowest crack growth rates were found with the oscillation build. The crack growth data was found to broadly fall between that of a recrystallized α (mill-annealed) and β annealed wrought material, with the oscillation strategy build behaving more similarly to a β annealed microstructure. The fatigue crack growth rate was lower when cracks were propagated perpendicular to the build layers. For each build strategy, a greater microstructural influence on crack growth rate was found at lower levels of stress intensity factor range (

29 citations

Posted Content
TL;DR: Extensive experiments on a number of real-world datasets show that the proposed novel recommender technique, Em Metric Factorization, outperforms existing state-of-the-art by a large margin on both rating prediction and item ranking tasks.
Abstract: In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot product adopted in matrix factorization based recommender models does not satisfy the inequality property, which may limit their expressiveness and lead to sub-optimal solutions. To overcome this problem, we propose a novel recommender technique dubbed as {\em Metric Factorization}. We assume that users and items can be placed in a low dimensional space and their explicit closeness can be measured using Euclidean distance which satisfies the inequality property. To demonstrate its effectiveness, we further designed two variants of metric factorization with one for rating estimation and the other for personalized item ranking. Extensive experiments on a number of real-world datasets show that our approach outperforms existing state-of-the-art by a large margin on both rating prediction and item ranking tasks.

29 citations

Journal ArticleDOI
TL;DR: Chicken ovalbumin upstream promoter-transcription factor II was found to be a biomarker associated with patient survival and colorectal cancer metastasis and regulated cell migration and metastasis in conjunction with Snail1.
Abstract: Chicken ovalbumin upstream promoter-transcription factor II (COUP-TFII, also known as NR2F2) promotes metastasis by functioning in the tumour microenvironment; however, the role of COUP-TFII in colorectal cancer remains unknown. Human colon adenocarcinoma tissues were collected to test COUP-TFII expression. Wound-healing and cell invasion assay were used to evaluate migration and invasion of cells. Chicken ovalbumin upstream promoter-transcription factor II and related protein expression was assessed by immunostaining, immunoblotting and real-time PCR assay. Tamoxifen-inducible COUP-TFII knockout mice were employed to test COUP-TFII functions on colon cancer metastasis in vivo. Elevated expression of COUP-TFII in colorectal adenocarcinoma tissue correlated with overexpression of the Snail1 transcription factor. High COUP-TFII expression correlated with metastasis and shorter patient survival. Chicken ovalbumin upstream promoter-transcription factor II regulated the migration and invasion of cancer cells. With Snail1, COUP-TFII inhibited expression of adherence molecules such as ZO-1, E-cadherin and β-catenin in colorectal cancer cells. Overexpression of COUP-TFII was required for cancer cells to metastasise in vivo. Chicken ovalbumin upstream promoter-transcription factor II regulated the transcription and expression of Snail1 by directly targeting the Snail1 promoter and regulated associated genes. Chicken ovalbumin upstream promoter-transcription factor II was crucial for colorectal cancer metastasis and regulated cell migration and metastasis in conjunction with Snail1. Chicken ovalbumin upstream promoter-transcription factor II was found to be a biomarker associated with patient survival and colorectal cancer metastasis.

29 citations

Journal ArticleDOI
TL;DR: It is shown that transcriptional repression of mitochondrial deacetylase sirtuin 3 (SIRT3) by androgen receptor (AR) and its coregulator steroid receptor coactivator-2 (SRC-2) enhances mitochondrial aconitase (ACO2) activity to favor aggressive prostate cancer.
Abstract: Metabolic dysregulation is a known hallmark of cancer progression, yet the oncogenic signals that promote metabolic adaptations to drive metastatic cancer remain unclear. Here we show that transcriptional repression of mitochondrial deacetylase sirtuin 3 (SIRT3) by androgen receptor (AR) and its coregulator steroid receptor coactivator (SRC-2) enhances mitochondrial aconitase (ACO2) activity to favor aggressive prostate cancer. ACO2 promoted mitochondrial citrate synthesis to facilitate de novo lipogenesis, and genetic ablation of ACO2 reduced total lipid content and severely repressed in vivo prostate cancer progression. A single acetylation mark lysine258 on ACO2 functioned as a regulatory motif, and the acetylation-deficient Lys258Arg-mutant was enzymatically inactive and failed to rescue growth of ACO2-deficient cells. Acetylation of ACO2 was reversibly regulated by SIRT3, which was predominantly repressed in many tumors including prostate cancer. Mechanistically, SRC-2 bound AR formed a repressive complex by recruiting histone deacetylase 2 (HDAC2) to the SIRT3 promoter, and depletion of SRC-2 enhanced SIRT3 expression and simultaneously reduced acetylated-ACO2. In human prostate tumors, ACO2 activity was significantly elevated and increased expression of SRC-2 with concomitant reduction of SIRT3 was found to be a genetic hallmark enriched in prostate cancer metastatic lesions. In a mouse model of spontaneous bone metastasis, suppression of SRC-2 reactivated SIRT3 expression and was sufficient to abolish prostate cancer colonization in the bone microenvironment, implying this nuclear-mitochondrial regulatory axis is a determining factor for metastatic competence.

29 citations

Journal ArticleDOI
TL;DR: In this article, the effect of flange reinforcement on the buckling and post-buckling behavior of a carbon/epoxy composite C-section structure was investigated in finite element analysis by using MSC Nastran.
Abstract: This paper presents an investigation into the effect of cutout and flange reinforcement on the buckling and post-buckling behaviour of a carbon/epoxy composite C-section structure. The C-section having a cutout in the web is clamped at one end and subjected to a shear load at the other free end. Three different stiffener reinforcements were investigated in finite element analysis by using MSC Nastran. Buckling load was predicted by using both linear and nonlinear FE analysis. Experiments were carried out to validate the numerical model and results. Subsequently post-buckling analysis was carried out by predicting the load–deflection response of the C-section beam in nonlinear analysis. Tsai-Wu failure criterion was used to detect the first-play-failure load. The effect of circular and diamond cutout shape and effective flange reinforcements were investigated. The results show that the cutout and reinforcement have little effect on the buckling stability. However an L-shape stiffener to reinforce the C-section flange can improve the critical failure load by 20.9%.

29 citations


Cited by
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Proceedings ArticleDOI
27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

123,388 citations

Proceedings Article
04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

55,235 citations

Journal ArticleDOI
04 Mar 2011-Cell
TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.

51,099 citations

Posted Content
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

44,703 citations

Proceedings ArticleDOI
07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

40,257 citations