1,722,534 research outputs found
sj-pdf-1-ajs-10.1177_03635465221073991 – Supplemental material for Exosomes Derived From Human Urine–Derived Stem Cells Overexpressing miR-140-5p Alleviate Knee Osteoarthritis Through Downregulation of VEGFA in a Rat Model
No full textSupplemental material, sj-pdf-1-ajs-10.1177_03635465221073991 for Exosomes Derived From Human Urine–Derived Stem Cells Overexpressing miR-140-5p Alleviate Knee Osteoarthritis Through Downregulation of VEGFA in a Rat Model by Yuan Liu, Yi Zeng, Hai-Bo Si, Li Tang, Hui-Qi Xie and Bin Shen in The American Journal of Sports Medicine</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
sj-tif-4-tah-10.1177_20406207231201454 – Supplemental material for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab
No full textSupplemental material, sj-tif-4-tah-10.1177_20406207231201454 for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab by Hagop M. Kantarjian, Aaron C. Logan, Faraz Zaman, Nicola Gökbuget, Ralf C. Bargou, Yi Zeng, Gerhard Zugmaier and Franco Locatelli in Therapeutic Advances in Hematology</p
sj-docx-1-tah-10.1177_20406207231201454 – Supplemental material for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab
No full textSupplemental material, sj-docx-1-tah-10.1177_20406207231201454 for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab by Hagop M. Kantarjian, Aaron C. Logan, Faraz Zaman, Nicola Gökbuget, Ralf C. Bargou, Yi Zeng, Gerhard Zugmaier and Franco Locatelli in Therapeutic Advances in Hematology</p
sj-tif-5-tah-10.1177_20406207231201454 – Supplemental material for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab
No full textSupplemental material, sj-tif-5-tah-10.1177_20406207231201454 for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab by Hagop M. Kantarjian, Aaron C. Logan, Faraz Zaman, Nicola Gökbuget, Ralf C. Bargou, Yi Zeng, Gerhard Zugmaier and Franco Locatelli in Therapeutic Advances in Hematology</p
sj-tif-3-tah-10.1177_20406207231201454 – Supplemental material for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab
No full textSupplemental material, sj-tif-3-tah-10.1177_20406207231201454 for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab by Hagop M. Kantarjian, Aaron C. Logan, Faraz Zaman, Nicola Gökbuget, Ralf C. Bargou, Yi Zeng, Gerhard Zugmaier and Franco Locatelli in Therapeutic Advances in Hematology</p
sj-tif-2-tah-10.1177_20406207231201454 – Supplemental material for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab
No full textSupplemental material, sj-tif-2-tah-10.1177_20406207231201454 for Survival outcomes in patients with relapsed/refractory or MRD-positive B-cell acute lymphoblastic leukemia treated with blinatumomab by Hagop M. Kantarjian, Aaron C. Logan, Faraz Zaman, Nicola Gökbuget, Ralf C. Bargou, Yi Zeng, Gerhard Zugmaier and Franco Locatelli in Therapeutic Advances in Hematology</p
sj-doc-1-mde-10.1177_23821205241226818 - Supplemental material for The Effect of Uncertainty Training on the Improvement of Diagnostic Ability in Chinese Medical Students
No full textSupplemental material, sj-doc-1-mde-10.1177_23821205241226818 for The Effect of Uncertainty Training on the Improvement of Diagnostic Ability in Chinese Medical Students by Xiaomeng Xia, Yi Zeng, Pixiang Pi, Xianqing Wu, Xiaoling Fang, Jianlin Chen and Yan Zhong in Journal of Medical Education and Curricular Development</p
Clustering and labeling a web scale document collection using Wikipedia clusters
No full textClustering is an important technique in organising and categorising web scale documents. The main challenges faced in clustering the billions of documents available on the web are the processing power required and the sheer size of the datasets available. More importantly, it is nigh impossible to generate the labels for a general web document collection containing billions of documents and a vast taxonomy of topics. However, document clusters are most commonly evaluated by comparison to a ground truth set of labels for documents. This paper presents a clustering and labeling solution where the Wikipedia is clustered and hundreds of millions of web documents in ClueWeb12 are mapped on to those clusters. This solution is based on the assumption that the Wikipedia contains such a wide range of diverse topics that it represents a small scale web. We found that it was possible to perform the web scale document clustering and labeling process on one desktop computer under a couple of days for the Wikipedia clustering solution containing about 1000 clusters. It takes longer to execute a solution with finer granularity clusters such as 10,000 or 50,000. These results were evaluated using a set of external data
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