Institution
Shanghai Jiao Tong University
Education•Shanghai, Shanghai, China•
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.
Topics: Population, Cancer, Computer science, Microstructure, Medicine
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors summarized the latest research on one-dimensional and quasi-1D fillers based high-k polymer nanocomposites with the focus on the superiority of 1D or quasi-one-dimensional highk fillers in enhancing the dielectric properties and energy storage capability of polymer composites.
357 citations
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TL;DR: In this paper, the authors employ a difference-in-differences approach that compares employment rates across different ages for people with general and vocational education, finding strong and robust support for such a tradeoff, especially in countries emphasizing apprenticeship programs.
Abstract: Policy proposals promoting vocational education focus on the school-to-work transition. But with technological change, gains in youth employment may be offset by less adaptability and diminished employment later in life. To test for this tradeoff, we employ a difference-in-differences approach that compares employment rates across different ages for people with general and vocational education. Using microdata for 11 countries from IALS, we find strong and robust support for such a tradeoff, especially in countries emphasizing apprenticeship programs. German Microcensus data and Austrian administrative data confirm the results for within-occupational-group analysis and for exogenous variation from plant closures, respectively.
356 citations
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TL;DR: In this article, the effects of metal foams on heat transfer enhancement in phase change materials (PCMs) are investigated based on the two-equation non-equilibrium heat transfer model, in which the coupled heat conduction and natural convection are considered at phase transition and liquid zones.
356 citations
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TL;DR: In this paper, an open-label, phase 3 randomised controlled trial at 183 cancer centres in 23 countries worldwide was conducted to compare erlotinib (a reversible EGFR tyrosine kinase inhibitor) with afatinib (40 mg per day) until disease progression.
Abstract: Summary Background There is a major unmet need for effective treatments in patients with squamous cell carcinoma of the lung. LUX-Lung 8 compared afatinib (an irreversible ErbB family blocker) with erlotinib (a reversible EGFR tyrosine kinase inhibitor), as second-line treatment for patients with advanced squamous cell carcinoma of the lung. Methods We did this open-label, phase 3 randomised controlled trial at 183 cancer centres in 23 countries worldwide. We enrolled adults with stage IIIB or IV squamous cell carcinoma of the lung who had progressed after at least four cycles of platinum-based-chemotherapy. Participants were randomly assigned (1:1) to receive afatinib (40 mg per day) or erlotinib (150 mg per day) until disease progression. The randomisation was done centrally with an interactive voice or web-based response system and stratified by ethnic origin (eastern Asian vs non-eastern Asian). Clinicians and patients were not masked to treatment allocation. The primary endpoint was progression-free survival assessed by independent central review (intention-to-treat population). The key secondary endpoint was overall survival. This trial is registered with ClinicalTrials.gov, NCT01523587. Findings 795 eligible patients were randomly assigned (398 to afatinib, 397 to erlotinib). Median follow-up at the time of the primary analysis of progression-free survival was 6·7 months (IQR 3·1–10·2), at which point enrolment was not complete. Progression free-survival at the primary analysis was significantly longer with afatinib than with erlotinib (median 2·4 months [95% CI 1·9–2·9] vs 1·9 months [1·9–2·2]; hazard ratio [HR] 0·82 [95% CI 0·68–1·00], p=0·0427). At the time of the primary analysis of overall survival (median follow-up 18·4 months [IQR 13·8–22·4]), overall survival was significantly greater in the afatinib group than in the erloinib group (median 7·9 months [95% CI 7·2–8·7] vs 6·8 months [5·9–7·8]; HR 0·81 [95% CI 0·69–0·95], p=0·0077), as were progression-free survival (median 2·6 months [95% CI 2·0–2·9] vs 1·9 months [1·9–2·1]; HR 0·81 [95% CI 0·69–0·96], p=0·0103) and disease control (201 [51%] of 398 patients vs 157 [40%] of 397; p=0·0020). The proportion of patients with an objective response did not differ significantly between groups (22 [6%] vs 11 [3%]; p=0·0551). Tumour shrinkage occurred in 103 (26%) of 398 patients versus 90 (23%) of 397 patients. Adverse event profiles were similar in each group: 224 (57%) of 392 patients in the afatinib group versus 227 (57%) of 395 in the erlotinib group had grade 3 or higher adverse events. We recorded higher incidences of treatment-related grade 3 diarrhoea with afatinib (39 [10%] vs nine [2%]), of grade 3 stomatitis with afatinib (16 [4%] vs none), and of grade 3 rash or acne with erlotinib (23 [6%] vs 41 [10%]). Interpretation The significant improvements in progression-free survival and overall survival with afatinib compared with erlotinib, along with a manageable safety profile and the convenience of oral administration suggest that afatinib could be an additional option for the treatment of patients with squamous cell carcinoma of the lung. Funding Boehringer Ingelheim.
356 citations
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08 Oct 2016
TL;DR: This paper proposes a novel method Relay Backpropagation, which encourages the propagation of effective information through the network in training stage, and achieves the first place in ILSVRC 2015 Scene Classification Challenge.
Abstract: Learning deeper convolutional neural networks has become a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be attained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, which encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015 Scene Classification Challenge. Extensive experiments on two large scale challenging datasets demonstrate the effectiveness of our method is not restricted to a specific dataset or network architecture.
356 citations
Authors
Showing all 158621 results
Name | H-index | Papers | Citations |
---|---|---|---|
Meir J. Stampfer | 277 | 1414 | 283776 |
Richard A. Flavell | 231 | 1328 | 205119 |
Jie Zhang | 178 | 4857 | 221720 |
Yang Yang | 171 | 2644 | 153049 |
Lei Jiang | 170 | 2244 | 135205 |
Gang Chen | 167 | 3372 | 149819 |
Thomas S. Huang | 146 | 1299 | 101564 |
Barbara J. Sahakian | 145 | 612 | 69190 |
Jean-Laurent Casanova | 144 | 842 | 76173 |
Kuo-Chen Chou | 143 | 487 | 57711 |
Weihong Tan | 140 | 892 | 67151 |
Xin Wu | 139 | 1865 | 109083 |
David Y. Graham | 138 | 1047 | 80886 |
Bin Liu | 138 | 2181 | 87085 |
Jun Chen | 136 | 1856 | 77368 |