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.

3D Printed High‐Strength Supramolecular Polymer Hydrogel‐Cushioned Radially and Circumferentially Oriented Meniscus Substitute

Abstract: Developing a meniscal replacement with reliable long‐term mechanical and functional support has faced a grand challenge due to difficulty in recapitulating the anisotropic microarchitecture and modulus. Herein, a high‐strength supramolecular polymer hydrogel‐cushioned biomimetic structured meniscus replacement is reported for the first time. The radially and circumferentially oriented poly(e‐caprolactone) (PCL) fiber framework is 3D printed to imitate collagen fibers in the native meniscus to provide circumferential tensile supports. Then, hydrogen bonding strengthened anti‐swelling poly(N‐acryloyl glycinamide) (PNAGA) hydrogel that replicates the function of proteoglycan in resisting axial compressive loads is infused into the 3D printed PCL framework, thus fulfilling a durable energy absorbing and cushion function, which far outperforms the performance of conventional polyacrylamide hydrogel. The PNAGA‐cushioned PCL construct can achieve Young's moduli of 20.15 ± 1.37 MPa in the circumferential direction and 10.43 ± 1.54 MPa in the radial direction, a compressive modulus of 1.11 ± 0.14 MPa as well as a tearing energy of 17.00 ± 2.07 kJ m−2. This 3D printed PCL‐PNAGA meniscus scaffold is implanted into rabbit knee joints for 12 weeks and in vivo outcome demonstrates the structural stability and efficient protection against wearing of the cartilage, meanwhile ameliorating the development of osteoarthritis.

Research on the Cross-Chain Model of Rice Supply Chain Supervision Based on Parallel Blockchain and Smart Contracts

Abstract: Rice is one of the three major staple foods in the world, and the quality and safety of rice are related to the development of human beings. The new crown epidemic, pesticide residues, insect pests, and heavy metal pollution have a certain security impact on the food supply chain. The rice supply chain is characterized by a long life cycle; complex roles in the main links; many types of hazards; and multidimensional, multisource, and heterogeneous information. To strengthen the rice supply chain’s supervision ability under the epidemic situation, a supervision cross-chain model suitable for the complicated data of the rice supply chain based on parallel blockchain theory and smart contract technology was built. Firstly, the data collected in the rice supply chain and different types of data stored in different parallel blockchains were analyzed. Secondly, based on data analysis, a collection/supervision cross-chain mechanism based on “hash lock + smart contract + relay chain”, a concurrency mechanism based on the K-means algorithm and a Bloom filter, and a consensus mechanism suitable for multichain consensus named the Supervision Practical Byzantine Fault Tolerance (SPBFT) were proposed. Furthermore, a cross-chain model of rice supply chain supervision was constructed. Finally, theoretical verification and simulation experiments were used to analyze the operation process, safety, cross-chain efficiency, and scalability of the model. The results showed that the application of parallel blockchains and smart contracts to supervision of rice supply chain information improved the convenience and security of information interaction between various links in the rice supply chain, the storage cost of supply chain data and the high latency of interaction was reduced, and the refined management of the rice supply chain data and personnel was realized. This research applied new information technology to the coordination and resource sharing of the food supply chain, and provides ideas for the digital transformation of the food industry.

Lactonization of 2-Alkynylbenzoates for the Assembly of Isochromenones Mediated by BF3·Et2O.

Abstract: A general and efficient lactonization method of readily available 2-alkynylbenzoates affording biologically important isochromenones has been realized via a solely BF3·Et2O-mediated 6-endo-dig cyclization process under mild conditions. An alternative mechanistic pathway in which BF3·Et2O activates the carbonyl of the ester moiety, rather than the alkyne triple bond, was postulated on the basis of control experiment results. Gram-scale reaction and further application for the assembly of more complex molecules demonstrated the practicability of the protocol.

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.