12,024 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
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
Key Magnetized Exosomes for Effective Targeted Delivery of Doxorubicin Against Breast Cancer Cell Types in Mice Model [Letter]
No full textHanzhe Zhao,1 Mingzheng Hu2 1China Three Gorges University, College of Basic Medical Sciences, Yichang, Hubei, People’s Republic of China; 2Department of Hepatobiliary Surgery, Yichang Central People’s Hospital, Yichang, Hubei, People’s Republic of ChinaCorrespondence: Mingzheng Hu, Yichang Central People’s Hospital, No. 183 Yiling Avenue, Wujia District, Yichang, Hubei, People’s Republic of China, Email [email protected]
Genetic background analysis and breed evaluation of Yiling yellow cattle
No full textAbstract: Traditionally, Chinese indigenous cattle is geographically widespread. The present study analyzed based on genome-wide variants to evaluate the genetic background among 157 individuals from four representative indigenous cattle breeds of Hubei Province of China: Yiling yellow cattle (YL), Bashan cattle (BS), Wuling cattle (WL), Zaobei cattle (ZB), and 21 individuals of Qinchuan cattle (QC) from the nearby Shanxi Province of China. Linkage disequilibrium (LD) analysis showed the LD of YL was the lowest (r2=0.32) when the distance between markers was approximately 2 kb. Principle component analysis (PCA), and neighbor-joining (NJ)-tree revealed a separation of Yiling yellow cattle from other geographic nearby local cattle breeds. In PCA plot, the YL and QC groups were segregated as expected; moreover, YL individuals clustered together more obviously. In the NJ-tree, the YL group formed an independent branch and BS, WL, ZB groups were mixed. We then used the FST statistic approach to reveal long-term selection sweep of YL and other 4 cattle breeds. According to the selective sweep, we identified the unique pathways of YL, associated with production traits. Based on the results, it can be proposed that YL has its unique genetic characteristics of excellence resource, and it is an indispensable cattle breed in China
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
One‐ and Two‐Electron Transfer Oxidation of 1,4‐Disilabenzene with Formation of Stable Radical Cations and Dications
No full textElectron‐transferable oxidants such as B(C(6)F(5))(3)/nBuLi, B(C(6)F(5))(3)/LiB(C(6)F(5))(4), B(C(6)F(5))(3)/LiHBEt(3), Al(C(6)F(5))(3)/(o‐RC(6)H(4))AlH(2) (R=N(CMe(2)CH(2))(2)CH(2)), B(C(6)F(5))(3)/AlEt(3), Al(C(6)F(5))(3), Al(C(6)F(5))(3)/nBuLi, Al(C(6)F(5))(3)/AlMe(3), (CuC(6)F(5))(4), and Ag(2)SO(4), respectively were employed for reactions with (L)(2)Si(2)C(4)(SiMe(3))(2)(C(2)SiMe(3))(2) (L=PhC(NtBu)(2), 1). The stable radical cation [1](+.) was formed and paired with the anions [nBuB(C(6)F(5))(3)](−) (in 2), [B(C(6)F(5))(4)](−) (in 3), [HB(C(6)F(5))(3)](−) (in 4), [EtB(C(6)F(5))(3)](−) (in 5), {[(C(6)F(5))(3)Al](2)(μ‐F)](−) (in 6), [nBuAl(C(6)F(5))(3)](−) (in 7), and [Cu(C(6)F(5))(2)](−) (in 8), respectively. The stable dication [1](2+) was also generated with the anions [EtB(C(6)F(5))(3)](−) (9) and [MeAl(C(6)F(5))(3)](−) (10), respectively. In addition, the neutral compound [(L)(2)Si(2)C(4)(SiMe(3))(2)(C(2)SiMe(3))(2)][μ‐O(2)S(O)(2)] (11) was obtained. Compounds 2–11 are characterized by UV‐vis absorption spectroscopy, X‐ray crystallography, and elemental analysis. Compounds 2–8 are analyzed by EPR spectroscopy and compounds 9–11 by NMR spectroscopy. The structure features are discussed on the central Si(2)C(4)‐rings of 1, [1](+.), [1](2+), and 11, respectively
Geometrically Compelled Silicon(II)/Silicon(IV) Donor‐Acceptor Interaction Enables the Enamination of Nitriles
No full textDiscovering new bonding scenarios and subsequently exploring the reactivity contribute substantially to advance the main group element chemistry. Herein, we report on the isolation and characterization of an intriguing class of the hydrido‐benzosiloles 2–4. These compounds exhibit a side arm of the amidinatosilylenyl group, featuring unidirectional silicon(II)/silicon(IV) donor‐acceptor interaction on account of the geometric constraint. Furthermore, the reactions involving 2–4 with nitriles yield the tricyclic compounds that edge‐fused of the Si‐heteroimidazolidine‐CN2Si2, silole‐C4Si, and phenyl‐C6‐rings (5–13). These compounds are manifesting a unique reaction that the silicon(II)/silicon(IV) interaction enables the enamination of the α‐H‐bearing nitriles. The reaction mechanism involved in H‐shift under oxidative addition at silylene followed by hydrosilylation of a ketenimine intermediate was revealed by density function theory (DFT) calculations
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
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