11,888 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
sj-pdf-1-ang-10.1177_00033197221087778 – Supplemental Material for High-Risk Culprit Plaque Predicts Cardiovascular Outcomes Independently of Plaque Rupture in ST-Segment Elevation Myocardial Infarction: Insight From Optical Coherence Tomography
No full textSupplemental Material, sj-pdf-1-ang-10.1177_00033197221087778 for High-Risk Culprit Plaque Predicts Cardiovascular Outcomes Independently of Plaque Rupture in ST-Segment Elevation Myocardial Infarction: Insight From Optical Coherence Tomography by Ying Wang, Xiaoxiao Zhao, Peng Zhou, Chen Liu, Zhiyong Liao, Xiaoqing Wang, Shaodi Yan, Zhaoxue Sheng, Jiannan Li, Jinying Zhou, Runzhen Chen, Yi Chen, Li Song, Hanjun Zhao and Hongbing Yan in Angiology</p
Raw data of Zhao et al., 2022, Geoderma
No full textRaw data associated with Zhao et al., 2022, Geoderma. Any use of the data set should be approved by the corresponding author Kai Yue at "[email protected]".</p
Chao Yuen Ren (1892–1982)
No full textY. R. Chao is easily the most famous linguist to have come out of China. Born before the end of the last dynasty in China, he received a traditional Confucian education, but was also one of the first Chinese people to be sent to the West for training in modern Western science (under the Boxer Indemnity Fund). The remarkable breadth and scope of his studies included physics, mathematics, linguistics, musical and literary composition, and translation, and he was a pioneer in many of these fields
Development and Validation of a Prediction Rule for Major Adverse Cardiac and Cerebrovascular Events in High-Risk Myocardial Infarction Patients After Primary Percutaneous Coronary Intervention
No full textXiaoxiao Zhao,1 Chen Liu,1 Peng Zhou,1 Zhaoxue Sheng,1 Jiannan Li,1 Jinying Zhou,1 Runzhen Chen,1 Ying Wang,1 Yi Chen,1 Li Song,1 Hanjun Zhao,1 Hongbing Yan2 1Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College & Chinese Academy of Medical Sciences, BeiJing, People’s Republic of China; 2Fuwai Hospital, Chinese Academy of Medical Sciences, Shenzhen, People’s Republic of ChinaCorrespondence: Hongbing Yan, Fuwai Hospital, Chinese Academy of Medical Sciences, 12 Langshan Road, Shenzhen, 518000, People’s Republic of China, Tel +86-13701339287, Email [email protected] Hanjun Zhao, Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences, No. 167, Beijing, 100037, People’s Republic of China, Tel +86-15210020808, Email [email protected] and Aims: We aimed to develop a clinical prediction tool to improve the prognosis of major adverse cardiac and cerebrovascular events (MACCE) among high-risk myocardial infarction (MI) patients undergoing primary percutaneous coronary intervention (PCI).Methods: The present study was a prospective and observational study. A total of 4151 consecutive MI patients who underwent primary PCI at Fuwai Hospital in Beijing, China (January 2010 and June 2017) were enrolled. Forty-eight patients without follow-up data were excluded from the study. The pre-specified criteria (Supplementary Information 1) were chosen to enroll MI patients at high risk for MACCE complications after PCI.Results: The full model included seven variables, with a risk score of 160 points. Derivation and validation cohort models predicting MACCE had C-statistics of 0.695 and 0.673. The area under the curve (AUC) of the survival receiver operating characteristic curve (ROC) for predicting MACCE was 0.991 and 0.883 in the derivation and validation cohorts, respectively.Conclusion: The predicted model was internally validated and calibrated in large cohorts of patients with high-risk MI receiving primary PCI to predict MACCE and showed modest accuracy in the derivation and validation cohorts.Keywords: primary percutaneous coronary intervention, high-risk, follow-up, risk prediction scor
Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation
No full textCitation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods
Modeling nitrogen nutrient loss and ammonia emissions from animal farms
No full textAuthor institution (Zhao): Department of Food, Agricultural, and Biological Engineering, The Ohio State Universit
Author Correction: Antiviral fibrils of self-assembled peptides with tunable compositions
No full textCorrection to: Nature Communicationshttps://doi.org/10.1038/s41467-024-45193-3, published online 07 February 2024 This Article contains an error in the spelling of the author Boyang Zhao, which was incorrectly given as Boyan Zhao. The error has been corrected in the PDF and HTML versions of the Article
Creating a versatile toolkit for transgene expression using BACS and for dissecting large-scale chromatin organization
No full textIn eukaryotes, the genetic material, DNA, is highly compacted with histone proteins to form chromatin in interphase nuclei. Both the higher levels of chromatin folding and the spatial organization of the chromatin, referred to as large-scale chromatin organization, have been shown to correlate with transcriptional activity. One example suggesting transcriptional regulation by large-scale chromatin organization is position effects and position effect variegations observed in transgene expressions, which, as well as other epigenetic silencing mechanisms, have been obstacles to achieving predictable and stable transgene expression. Molecular dissections of the determinants regulating large-scale chromatin organization would help to elucidate the real relationship between large-scale chromatin organization and transcriptional regulation, yet are difficult due to the complexity of the mammalian genome. Bacterial artificial chromosomes (BACs), containing 100-300 kb mammalian genomic regions have been shown to recapitulate the expression level and nuclear localization of the corresponding genomic regions, and to protect embedded reporter mini-genes from epigenetic silencing.
Here in Chapter 2 we show that BACs could provide a versatile platform for achieving reproducible, stable simultaneous expression of multiple transgenes maintained either as episomes or stably integrated copies. Moreover, in Chapter 3 we show that BACs could be used as a powerful model system for dissecting mechanisms regulating large-scale chromatin organization, by demonstrating distinctive large-scale chromatin conformations formed by BAC transgene arrays and results indicating separation of large-scale chromatin compaction, nuclear localization and transcriptional activities.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01The student, Binhui Zhao, accepted the attached license on 2019-07-08 at 10:18.The student, Binhui Zhao, submitted this Dissertation for approval on 2019-07-08 at 10:21.This Dissertation was approved for publication on 2019-07-09 at 11:09.DSpace SAF Submission Ingestion Package generated from Vireo submission #14187 on 2019-11-26 at 14:01:40Made available in DSpace on 2019-11-26T20:59:34Z (GMT). No. of bitstreams: 6
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Previous issue date: 2019-07-09Embargo set by: Seth Robbins for item 113056
Lift date: 2021-11-26T20:59:54Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 113056 on 2021-11-27T10:15:09Z
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