3-(4-Hy­droxy-3-meth­oxy­phen­yl)acrylic acid–2,3,5,6-tetra­methyl­pyrazine (2/1)

The asymmetric unit of the title compound, C8H12N2·2C10H10O4, contains a tetra­methyl­pyrazine mol­ecule, situated about an inversion center, and two substituted acrylic acid derivatives. The dihedral angle between the phenyl and pyrazine rings is 69.45 (9)°. In the crystal, inter­molecular O—H⋯O, O—H⋯N hydrogen bonds and weak C—H⋯O inter­actions lead to the formation of a supra­molecular network. The acrylic acid side chain is positionally disordered [occupancy ratio 0.852 (7):0.148 (7)]

Single-site catalyst promoters accelerate metal- catalyzed nitroarene hydrogenation

Atomically dispersed supported metal catalysts are drawing wide attention because of the opportunities they offer for new catalytic properties combined with efficient use of the metals. We extend this class of materials to catalysts that incorporate atomically dispersed metal atoms as promoters. The catalysts are used for the challenging nitroarene hydro- genation and found to have both high activity and selectivity. The promoters are single-site Sn on TiO2 supports that incorporate metal nanoparticle catalysts. Represented as M/Sn- TiO2 (M = Au, Ru, Pt, Ni), these catalysts decidedly outperform the unpromoted supported metals, even for hydrogenation of nitroarenes substituted with various reducible groups. The high activity and selectivity of these catalysts result from the creation of oxygen vacancies on the TiO2 surface by single-site Sn, which leads to efficient, selective activation of the nitro group coupled with a reaction involving hydrogen atoms activated on metal nanoparticles

Genome-Wide Analysis of Lung Adenocarcinoma Identifies Novel Prognostic Factors and a Prognostic Score

Background and ObjectiveLung adenocarcinoma (LUAD) is the most common histological type of all lung cancers and is associated with genetic and epigenetic aberrations. The tumor, node, and metastasis (TNM) stage is the most authoritative indicator of the clinical outcome in LUAD patients in current clinical practice. In this study, we attempted to identify novel genetic and epigenetic modifications and integrate them as a predictor of the prognosis for LUAD, to supplement the TNM stage with additional information.MethodsA dataset of 445 patients with LUAD was obtained from The Cancer Genome Atlas database. Both genetic and epigenetic aberrations were screened for their prognostic impact on overall survival (OS). A prognostic score (PS) integrating all the candidate prognostic factors was then developed and its prognostic value validated.ResultsA total of two micro-RNAs, two mRNAs and two DNA methylation sites were identified as prognostic factors associated with OS. The low- and high-risk patient groups, divided by their PS level, showed significantly different OS (p < 0.001) and recurrence-free survival (RFS; p = 0.005). Patients in the early stages (stages I/II) and advanced stages (stages III/IV) of LUAD could be further subdivided by PS into four subgroups. PS remained efficient in stratifying patients into different OS (p < 0.001) and RFS (p = 0.005) when the low- and high-risk subgroups were in the early stages of the disease. However, there was only a significant difference in OS (p = 0.04) but not RFS (p = 0.2), between the low-risk and high-risk subgroups when both were in advanced stages.ConclusionPS, in combination with the TNM stage, provides additional precision in stratifying patients with significantly different OS and RFS prognoses. Further studies are warranted to assess the efficiency of PS and to explain the effects of the genetic and epigenetic aberrations observed in LUAD

Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec

Abstract Background Understanding semantic relatedness and similarity between biomedical terms has a great impact on a variety of applications such as biomedical information retrieval, information extraction, and recommender systems. The objective of this study is to examine word2vec’s ability in deriving semantic relatedness and similarity between biomedical terms from large publication data. Specifically, we focus on the effects of recency, size, and section of biomedical publication data on the performance of word2vec. Methods We download abstracts of 18,777,129 articles from PubMed and 766,326 full-text articles from PubMed Central (PMC). The datasets are preprocessed and grouped into subsets by recency, size, and section. Word2vec models are trained on these subtests. Cosine similarities between biomedical terms obtained from the word2vec models are compared against reference standards. Performance of models trained on different subsets are compared to examine recency, size, and section effects. Results Models trained on recent datasets did not boost the performance. Models trained on larger datasets identified more pairs of biomedical terms than models trained on smaller datasets in relatedness task (from 368 at the 10% level to 494 at the 100% level) and similarity task (from 374 at the 10% level to 491 at the 100% level). The model trained on abstracts produced results that have higher correlations with the reference standards than the one trained on article bodies (i.e., 0.65 vs. 0.62 in the similarity task and 0.66 vs. 0.59 in the relatedness task). However, the latter identified more pairs of biomedical terms than the former (i.e., 344 vs. 498 in the similarity task and 339 vs. 503 in the relatedness task). Conclusions Increasing the size of dataset does not always enhance the performance. Increasing the size of datasets can result in the identification of more relations of biomedical terms even though it does not guarantee better precision. As summaries of research articles, compared with article bodies, abstracts excel in accuracy but lose in coverage of identifiable relations

Q (2010) Mapping library and information science in China: A coauthorship network analysis

Abstract This paper aims to identify the collaboration pattern and network structure of the coauthorship network of library and information science (LIS) in China. Using data from 18 core source LIS journals in China covering 6 years, we construct the LIS coauthorship network. We analyze the network from both macro and micro perspectives and identify some key features of this network: this network is a small-world network, and follows the scale-free character. In the micro-level, we calculate each author's centrality values and compare them with citation counts. We find that centrality rankings are highly correlated with citation rankings. We also discuss the limitation of current centrality measures for coauthorship network analysis

Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach - Fig 4

<p>PageRank-based (a) and Betweenness centrality-based (b) drug networks.</p