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.

Targeting Brain-Adaptive Cancer Stem Cells Prohibits Brain Metastatic Colonization of Triple-Negative Breast Cancer.

Abstract: Triple-negative breast cancer (TNBC) exhibits more traits possessed by cancer stem cells (CSC) than other breast cancer subtypes and is more likely to develop brain metastases. TNBC patients usually have shorter survival time after diagnosis of brain metastasis, suggesting an innate ability of TNBC tumor cells in adapting to the brain. In this study, we establish novel animal models to investigate early tumor adaptation in brain metastases by introducing both patient-derived and cell line-derived CSC-enriched brain metastasis tumorsphere cells into mice. We discovered astrocyte-involved tumor activation of protocadherin 7 (PCDH7)-PLCβ-Ca2+-CaMKII/S100A4 signaling as a mediator of brain metastatic tumor outgrowth. We further identified and evaluated the efficacy of a known drug, the selective PLC inhibitor edelfosine, in suppressing the PCDH7 signaling pathway to prohibit brain metastases in the animal models. The results of this study reveal a novel signaling pathway for brain metastases in TNBC and indicate a promising strategy of metastatic breast cancer prevention and treatment by targeting organ-adaptive cancer stem cells.Significance: These findings identify a compound to block adaptive signaling between cancer stem cells and brain astrocytes. Cancer Res; 78(8); 2052-64. ©2018 AACR.

Gradient lithography of engineered proteins to fabricate 2D and 3D cell culture microenvironments

Abstract: Spatial patterning of proteins is a valuable technique for many biological applications and is the prevailing tool for defining microenvironments for cells in culture, a required procedure in developmental biology and tissue engineering research. However, it is still challenging to achieve protein patterns that closely mimic native microenvironments, such as gradient protein distributions with desirable mechanical properties. By combining projection dynamic mask lithography and protein engineering with non-canonical photosensitive amino acids, we demonstrate a simple, scalable strategy to fabricate any user-defined 2D or 3D stable gradient pattern with complex geometries from an artificial extracellular matrix (aECM) protein. We show that the elastic modulus and chemical nature of the gradient profile are biocompatible and allow useful applications in cell biological research.

Self-Assembled, Nanostructured, Tunable Metamaterials via Spinodal Decomposition.

Abstract: Self-assembly via nanoscale phase separation offers an elegant route to fabricate nanocomposites with physical properties unattainable in single-component systems. One important class of nanocomposites are optical metamaterials which exhibit exotic properties and lead to opportunities for agile control of light propagation. Such metamaterials are typically fabricated via expensive and hard-to-scale top-down processes requiring precise integration of dissimilar materials. In turn, there is a need for alternative, more efficient routes to fabricate large-scale metamaterials for practical applications with deep-subwavelength resolution. Here, we demonstrate a bottom-up approach to fabricate scalable nanostructured metamaterials via spinodal decomposition. To demonstrate the potential of such an approach, we leverage the innate spinodal decomposition of the VO2–TiO2 system, the metal-to-insulator transition in VO2, and thin-film epitaxy, to produce self-organized nanostructures with coherent interfaces and a ...

Deep Residual Learning for Image Recognition

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.

Very Deep Convolutional Networks for Large-Scale Image Recognition

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.

Deep Residual Learning for Image Recognition

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.

Going deeper with convolutions

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.