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 Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Abstract: Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.

Femtosecond Time-Resolved Transient Absorption Spectroscopy of CH3NH3PbI3 Perovskite Films: Evidence for Passivation Effect of PbI2

Abstract: CH3NH3PbI3 perovskite layered films deposited on substrates with and without a titania support structure have been prepared and studied using time-resolved femtosecond transient absorption (fs-TA) spectroscopy in the visible light range (450–800 nm). The electron injection dynamics from the photoexcited perovskite layers to the neighboring film structures could be directly monitored via the transient bleaching dynamics of the perovskite at ∼750 nm and thus systematically studied as a function of the layer-by-layer architecture. In addition, for the first time we could spectrally distinguish transient bleaching at ∼750 nm from laser-induced fluorescence that occurs red-shifted at ∼780 nm. We show that an additional bleach feature at ∼510 nm appears when PbI2 is present in the perovskite film. The amplitudes of the PbI2 and perovskite TA peaks were compared to estimate relative amounts of PbI2 in the samples. Kinetic analysis reveals that perovskite films with less PbI2 show faster relaxation rates than tho...

Janus Co/CoP Nanoparticles as Efficient Mott–Schottky Electrocatalysts for Overall Water Splitting in Wide pH Range

Abstract: A controllable vacuum-diffusion method for gradual phosphidation of carbon coated metallic Co nanoparticles into Co/CoP Janus nanoparticles is reported. Janus Co/CoP nanoparticles, as typical Mott–Schottky electrocatalysts, exhibit excellent hydrogen evolution reaction and oxygen evolution reaction performance in various electrolytes across wide pH range along with high durability. The Mott–Schottky Co/CoP catalyst can work as bifunctional electrode materials for overall water splitting in wide pH range and can achieve a current density of 10 mA cm−2 in neutral electrolyte at only 1.51 V.