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Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either “potentially COVID-19 infected” or “no evident signs of infection”. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task.

Abstract In the late seventeenth century, the head of the Salihiyya Sufi lodge in the far south of Morocco, Abu al-ʿAbbas Sidi Ahmad al-Salihi al-Dar‘i (d. 1144/1731), wrote a poem of over a thousand lines on medicine, a long composition that went on to enjoy great popularity. The Worthy Gift of Medicine (al-Hadiya al-maqbula fi l-tibb) drew on a wide range of sources, including the Arab-Galenic tradition and Prophetic medicine, and in the fashion of the time, al-Salihi wrote a long commentary to fully explain it. Al-Salihi’s medical writings thus provide a productive entry point into the nature of medical writing and practice in early modern Morocco, as well as the historiographical narratives that have structured the ways in which they have been studied.

يسلط هذا البحث الضوء على مخطوطات السيرة النبوية والجهود المبذولة للاعتناء بها، سواء من خلال المؤسسات أم العلماء والأفراد، وبيان قيمتها ومكانتها العلمية التاريخية وأثرها في الدراسات في هذا المجال. إن أهمية موضوع البحث تنبع من كونه يرتكز على بيان أهمية نخبة من مخطوطات السيرة النبوية الموجودة في مكتبات عدة في أرجاء المعمورة، وماهي السبل والطرق التي اتبعت للعناية بها، والواقع والمأمول لها، مع تبيان وتفصيل لنماذج لاتزال مخطوطة من كتب السيرة النبوية وأخرى مطبوعة، ومدى التنوع الموجود في مخطوطات السيرة النبوية، والأقسام والتفريعات في أوصاف كتب السيرة النبوية. ويتبين من خلال البحث أن مخطوطات السيرة النبوية شأنها شأن الكثير من مصادر تراثنا تعاني من عدم توحيد الجهود في عملية الاعتناء بهذه المخطوطات خصوصاً في مجال الدراسة والتحقيق حيث لايزال النقص يعتريها وبشكل كبير. كما نلمس تمكن الكثير من المستشرقين من إنجاز أعمال عدة أسهمت في الاعتناء بالمخطوطات وتحقيقها وإظهارها للنور بشكل قد يفوق أحياناً ما قدمه الباحثون المسلمون، مما يستوجب حث الخطى في هذا المجال للوصول إلى نتائج مرضية في مجال السيرة النبوية. ويبين البحث بأن مخطوطات السيرة النبوية لم تكن على شكل واحد بل تعددت فمنها المستقلة والمفردة والمدمجة وغيرها وكل ذلك تابع لأسلوب الكتابة المتبع في التأليف والدوافع الكامنة وراء تأليف كتب السيرة النبوية والمرحلة التارخية التي كتبت فيها.

AbstractAimTo identify the top 100 most‐cited case reports and case series published in Endodontic journals and to analyse their bibliometric characteristics.MethodologyThe Clarivate Analytics’ Web of Science (WoS), Scopus and PubMed databases were used to identify the top 100 most‐cited case reports and case series in Endodontic journals. Complete bibliographic records of the selected case reports and case series were exported in plain text or BibTeX format and imported into the R environment for statistical computing and graphics. The following parameters were then analysed: names and affiliations of the authors, title, year of publication, journal of publication, first author, corresponding author, literature cited within reports, language, citation counts, impact factor of the journal, keywords, Keywords Plus and research topic.ResultsIn total, 88 case reports and 12 case series published in English between 1977 and 2016 were identified as the most‐cited reports in the field of Endodontics. The terms “case report(s)” or “case series” were not included in the title of 57 articles. The number of authors per report ranged from one to seven, with the average number of co‐authors per report being 3.14. The most‐cited author was M Trope (University of Pennsylvania, USA). The University of Washington and Private Practice, Cetraro, Italy, were the most productive institutions. The country whose case reports received the largest total number of citations was the United States. The largest number of the most‐cited reports appeared in 2002, 2004 and 2007 (n = 7, respectively). According to the WoS database, the total number of citations ranged from 42 to 453, with the average number of citations per report being 79.97. The majority of the top 100 most‐cited articles were published in the Journal of Endodontics and the International Endodontic Journal. The most frequently used author keywords were revascularization and mineral trioxide aggregate. The majority of the case reports and case series dealt with topics related to pulp regeneration.ConclusionThis bibliometric study provides a comprehensive overview on the progress, trends and current directions in clinical practice within the field of Endodontics.

Abstract Background/introduction Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. Methods This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis. Results and conclusions Experimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).

Abstract This article aims to restore the pairing of Claudio and Hero to prominence. The positioning of Hero by Claudio and the play’s other powerful men is central to the plotline, especially in terms of the “nothing” of Hero’s supposed sexual incontinence, as well as being dramatically pivotal to the play’s meanings and structure. The fact that the scene is absent from the play underscores the crucial symbolic importance of the role of Hero to the patriarchal system, drawing attention to the ways in which her function needs to be noted and understood. The analysis undertaken here therefore redresses the balance, since the pairing of Beatrice and Benedick seems so much more alive to modern sensibilities. This article argues that the reason for this lies in their seeming attractiveness as characters who are more easily recuperated to a historically later form of patriarchy from Shakespeare’s period, one that resonates powerfully with the rise of individualism to elevate them over Hero and Claudio.

Background: Pre- and intra-operative language mapping in neurosurgery patients frequently involves an object naming task. The choice of the optimal object naming paradigm remains challenging due to lack of normative data and standardization in mapping practices. The aim of this study was to identify object naming paradigms that robustly and consistently activate classical language regions and could therefore be used to improve the sensitivity of language mapping in brain tumor and epilepsy patients.Methods: Functional magnetic resonance imaging (fMRI) data from two independent groups of healthy controls (total = 79) were used to generate threshold-weighted voxel-based consistency maps. This novel approach allowed us to compare inter-subject consistency of activation for naming single objects in the visual and auditory modality and naming two objects in a phrase or a sentence.Results: We found that the consistency of activation in language regions was greater for naming two objects per picture than one object per picture, even when controlling for the number of names produced in 5 s.Conclusion: More consistent activation in language areas for naming two objects compared to one object suggests that two-object naming tasks may be more suitable for delimiting language eloquent regions with pre- and intra-operative language testing. More broadly, we propose that the functional specificity of brain mapping paradigms for a whole range of different linguistic and non-linguistic functions could be enhanced by referring to databased models of inter-subject consistency and variability in typical and atypical brain responses.