An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
While global climate risk is increasing substantially with greenhouse gas emission, energy transition is a key approach to alleviate this concern. This paper constructs the difference in difference model (DID) and the difference-in-differences based propensity score matching model (PSM-DID) to estimate the effects of China’s Environmental Protection Tax Law on energy transition and its transmission path. The empirical results show that (1) China’s Environmental Protection Tax Law can promote energy transition through electrification, and this conclusion is still valid after a series of robustness tests. (2) China’s Environmental Protection Tax Law can facilitate energy transition by improving both the quantity and the quality of industrial structure upgrading. The findings of this paper not only enrich the literature on the energy transition and environmental tax designs in developing countries but also provide an empirical reference for the government to promote energy transition orderly by implementing environmental tax.
Blockchain ushers in a new era for the global financial system with the advent of digital currency (cryptocurrency), and its impact can be felt in many related industries. Because of its possible applications, cryptocurrency draws significant attention from researchers. Although there are a number of risks (e.g., speculation, 51% attack) related to cryptocurrency, billions of dollars are invested in them, because of transparency, traceability, low transaction cost, and highly profitable potential. In December 2017, the most famous cryptocurrency, Bitcoin, has reached almost $20,000.00 per coin. Such short-term, high gain potential attracts many new small investors. However, speculative movements raise many questions related to safety and privacy, just to name a few. In order to understand public opinion about cryptocurrency and to protect small investors financial interests, sentiment analysis can be done by using social media activities of individuals who are interested or investing in cryptocurrencies. One of the most important steps in the analysis is to understand the profiles of the users. Therefore, in this paper, we determine education levels of investors or users who are interested in eight cryptocurrencies by using seven readability techniques on Reddit comments as a part of profiling. Results show that the education levels of users are approximately 60% in middle school, 30% in high school, and 10% in other levels according to the average of the seven readability technique results. The results and analysis, which are provided in this paper, help new investors and developers to obtain profile information about the users who are interested or investing in cryptocurrency.
Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple (>2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.
【Objective】Phospholipase C(PLC) was investigated in response to ParA1,a protein elicitor from Phytophthora parasitica,and could trigger cell death and defense responses in tobacco suspension cells.【Method】Using a pharmacological approach,tobacco suspension cells pretreated with PLC inhibitors(neomycin and U73122) in ParA1-induced cells were used to compare cell death and a series of defense responses.【Result】Tobacco suspension cells treated with ParA1 resulted in defense responses,such as hypersensitive cell death,an oxidative burst,alkalization of the extracellular medium,expression of 5 defense-related genes and accumulation of scopoletin,defense-related genes hin1,hsr203J,PR-1a,PR-1b and PAL were expressed in ParA1-induced cells.Cell death induced by ParA1 was entirely inhibited by the addition of U73122,a PLC specific inhibitor. Oxidative burst,alkalization of the extracellular medium,expression of 5 defense-related genes and scopoletin accumulation was significantly suppressed by U73122,respectively.【Conclusion】PLC is required for ParA1-mediated cell death and defense response in tobacco suspension cells which may involve in signal transduction.
To assess the correlation between the incidence of non-erosive reflux disease (NERD) and mental and psychological factors, deepen the understanding of the pathogenesis of NERD and explore effective treatments.NERD patients with mood disorders who met the inclusion criteria were randomly divided into a drug treatment group, a psychotherapy group, and a psychotherapy combined with drug treatment group. Before and after treatment, the patients were retrospectively analyzed using the gastroesophageal reflux disease Questionnaire, Hamilton Depression Scale, Hamilton Anxiety Scale, and SF-36 Quality of Life Scale.All three treatments were found to relieve patients' symptoms and improve their quality of life to some extent. The psychotherapy combined with drug treatment group showed the best overall curative effect. The Hamilton Depression and Anxiety Scale scores were significantly lower in the psychotherapy-alone group and psychotherapy combined with drug treatment group than in the drug treatment alone group at 4, 8, and 12 weeks (P < 0.05).Medication, psychotherapy, and psychotherapy combined with medication can relieve clinical symptoms and improve quality of life to varying degrees in patients with NERD.
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