Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

A chelator-free multifunctional [64Cu]CuS nanoparticle platform for simultaneous micro-PET/CT imaging and photothermal ablation therapy.

Abstract: We synthesized and evaluated a novel class of chelator-free [(64)Cu]CuS nanoparticles (NPs) suitable both for PET imaging and as photothermal coupling agents for photothermal ablation. These [(64)Cu]CuS NPs are simple to make, possess excellent stability, and allow robust noninvasive micro-PET imaging. Furthermore, the CuS NPs display strong absorption in the near-infrared (NIR) region (peak at 930 nm); passive targeting prefers the tumor site, and mediated ablation of U87 tumor cells occurs upon exposure to NIR light both in vitro and in vivo after either intratumoral or intravenous injection. The combination of small diameter (∼11 nm), strong NIR absorption, and integration of (64)Cu as a structural component makes these [(64)Cu]CuS NPs ideally suited for multifunctional molecular imaging and therapy.

Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition

Abstract: Action recognition with skeleton data has recently attracted much attention in computer vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local physical dependencies among joints, which may miss implicit joint correlations. To capture richer dependencies, we introduce an encoder-decoder structure, called A-link inference module, to capture action-specific latent dependencies, i.e. actional links, directly from actions. We also extend the existing skeleton graphs to represent higher-order dependencies, i.e. structural links. Combing the two types of links into a generalized skeleton graph, We further propose the actional-structural graph convolution network (AS-GCN), which stacks actional-structural graph convolution and temporal convolution as a basic building block, to learn both spatial and temporal features for action recognition. A future pose prediction head is added in parallel to the recognition head to help capture more detailed action patterns through self-supervision. We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics. The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods. As a side product, AS-GCN also shows promising results for future pose prediction.

Current Approaches of Photothermal Therapy in Treating Cancer Metastasis with Nanotherapeutics.

Abstract: Cancer metastasis accounts for the high mortality of many types of cancer. Owing to the unique advantages of high specificity and minimal invasiveness, photothermal therapy (PTT) has been evidenced with great potential in treating cancer metastasis. In this review, we outline the current approaches of PTT with respect to its application in treating metastatic cancer. PTT can be used alone, guided with multimodal imaging, or combined with the current available therapies for effective treatment of cancer metastasis. Numerous types of photothermal nanotherapeutics (PTN) have been developed with encouraging therapeutic efficacy on metastatic cancer in many preclinical animal experiments. We summarize the design and performance of various PTN in PTT alone and their combinational therapy. We also point out the lacking area and the most promising approaches in this challenging field. In conclusion, PTT or their combinational therapy can provide an essential promising therapeutic modality against cancer metastasis.

The SDSS-IV extended baryon oscillation spectroscopic survey: Overview and early data

Abstract: In a six-year program started in 2014 July, the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) will conduct novel cosmological observations using the BOSS spectrograph at Apache Point Observatory. These observations will be conducted simultaneously with the Time Domain Spectroscopic Survey (TDSS) designed for variability studies and the Spectroscopic Identification of eROSITA Sources (SPIDERS) program designed for studies of X-ray sources. In particular, eBOSS will measure with percent-level precision the distance-redshift relation with baryon acoustic oscillations (BAO) in the clustering of matter. eBOSS will use four different tracers of the underlying matter density field to vastly expand the volume covered by BOSS and map the large-scale-structures over the relatively unconstrained redshift range 0.6 0.6 sample of BOSS galaxies. With ~195,000 new emission line galaxy redshifts, we expect BAO measurements of d_A(z) to an accuracy of 3.1% and H(z) to 4.7% at an effective redshift of z = 0.87. A sample of more than 500,000 spectroscopically confirmed quasars will provide the first BAO distance measurements over the redshift range 0.9 2.1; these new data will enhance the precision of dA(z) and H(z) at z > 2.1 by a factor of 1.44 relative to BOSS. Furthermore, eBOSS will provide improved tests of General Relativity on cosmological scales through redshift-space distortion measurements, improved tests for non-Gaussianity in the primordial density field, and new constraints on the summed mass of all neutrino species. Here, we provide an overview of the cosmological goals, spectroscopic target sample, demonstration of spectral quality from early data, and projected cosmological constraints from eBOSS.