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Clinical studies have shown that neuromodulation therapies, such as spinal cord stimulation (SCS) and transcutaneous electrical nerve stimulation (TENS), reduce symptoms of chronic neuropathic and visceral pain. The neural mechanisms underlying SCS and TENS therapy are poorly understood. The present study was designed to compare the effects of SCS and TENS on spinal neuronal responses to noxious stimuli applied to the heart and esophagus. Direct stimulation of an intercostal nerve (ICNS) was used to simulate the effects of TENS. Extracellular potentials of left thoracic (T3) spinal neurons were recorded in pentobarbital anesthetized, paralyzed and ventilated male rats. SCS (50 Hz, 0.2 ms, 3-5 min) at a clinical relevant intensity (90% of motor threshold) was applied on the C1-C2 or C8-T1 ipsilateral spinal segments. Intercostal nerve stimulation (ICNS) at T3 spinal level was performed using the same parameters as SCS. Intrapericardial injection of bradykinin (IB, 10 μg/ml, 0.2 ml, 1 min) was employed as the noxious cardiac stimulus. Noxious thoracic esophageal distension (ED, 0.4 ml, 20 s) was produced by water inflation of a latex balloon. C1-C2 SCS suppressed excitatory responses of 16/22 T3 spinal neurons to IB and 25/30 neurons to ED. C8-T1 SCS suppressed excitatory responses of 10/15 spinal neurons to IB and 17/23 neurons to ED. ICNS suppressed excitatory responses of 9/12 spinal neurons to IB and 17/22 neurons to ED. These data showed that SCS and ICNS modulated excitatory responses of T3 spinal neurons to noxious stimulation of the heart and esophagus.Perspective: Neuromodulation of noxious cardiac and esophageal inputs onto thoracic spinal neurons by spinal cord and intercostal nerves stimulation observed in the present study may help account for therapeutic effects on thoracic visceral pain by activating the spinal dorsal column or somatic afferents.

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Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations.

From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models’ accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper’s text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.