Draft genome sequence of a Salmonella enterica serovar Typhi strain resistant to fourth-generation cephalosporin and fluoroquinolone antibiotics

ABSTRACT Typhoid is endemic in developing countries. We report here the first draft genome sequence of a Salmonella enterica serovar Typhi clinical isolate from Pakistan exhibiting resistance to cefepime (a fourth-generation cephalosporin) and fluoroquinolone antibiotics, two of the last-generation therapies against this pathogen. The genome is ~4.8 Mb, with two putative plasmids. </jats:p

Toward a New Paradigm for Author Name Disambiguation

Author Name Disambiguation (AND) has emerged as a significant challenge in the bibliometric context with the growing volume of scientific literature. When citations written by different authors have the same names (polysemy or homonym names), and when an author has different names, there is ambiguity (synonyms or name variants). It is difficult to associate a citation with the correct author. Polysemy and synonyms cause merging and splitting anomalies in the citations. These anomalies affect the quantification of an author&#x2019;s productivity (bibliometric analysis) and the reliability and quality of the information retrieved. Many techniques for AND have been proposed in the literature; most of them do not go beyond string matching or text matching. Most of the existing work do not consider the context or semantics of the terms used in the citations. In this study, the AND problem is resolved semantically using the deep learning technique on the PubMed dataset. The experimental results show that the proposed method achieves overall (11.72 &#x0025;, 12.5 &#x0025;, and 12.1 &#x0025;) higher precision, recall, and f-measure than the pairwise class classification

Dermo-Seg: ResNet-UNet Architecture and Hybrid Loss Function for Detection of Differential Patterns to Diagnose Pigmented Skin Lesions

Convolutional neural network (CNN) models have been extensively applied to skin lesions segmentation due to their information discrimination capabilities. However, CNNs’ struggle to capture the connection between long-range contexts when extracting deep semantic features from lesion images, resulting in a semantic gap that causes segmentation distortion in skin lesions. Therefore, detecting the presence of differential structures such as pigment networks, globules, streaks, negative networks, and milia-like cysts becomes difficult. To resolve these issues, we have proposed an approach based on semantic-based segmentation (Dermo-Seg) to detect differential structures of lesions using a UNet model with a transfer-learning-based ResNet-50 architecture and a hybrid loss function. The Dermo-Seg model uses ResNet-50 backbone architecture as an encoder in the UNet model. We have applied a combination of focal Tversky loss and IOU loss functions to handle the dataset’s highly imbalanced class ratio. The obtained results prove that the intended model performs well compared to the existing models. The dataset was acquired from various sources, such as ISIC18, ISBI17, and HAM10000, to evaluate the Dermo-Seg model. We have dealt with the data imbalance present within each class at the pixel level using our hybrid loss function. The proposed model achieves a mean IOU score of 0.53 for streaks, 0.67 for pigment networks, 0.66 for globules, 0.58 for negative networks, and 0.53 for milia-like-cysts. Overall, the Dermo-Seg model is efficient in detecting different skin lesion structures and achieved 96.4% on the IOU index. Our Dermo-Seg system improves the IOU index compared to the most recent network

VARIATION IN CITATION BASED FRACTIONAL COUNTING OF AUTHORSHIP

Existing indexing methods do not consider the variation among number of citations received by publications of authors. In this paper, we propose variation in citation based fractional Vf -index which not only consider the number of authors but also the variation factor in the number of citations. Vf -Index considers the consistency in received citations of publication in addition to their quality and quantity for indexing. We have used Co-efcient of quartile deviation for calculation of variation in received citations because it is sensitive for both skewed and un-skewed data. We have used real world data for validation purpose and have used fractional h- and g-index as our baseline indexing methods. We compared the results of our proposed method with baseline methods and have analyzed that our intuition has clear impact on the authors indexing. Author on higher index in fractional index gets impacted by Vf-index and its rank changes accordingly. Baseline methods do not considers variation factor and it is possible that authors with inconsistent citations receive high index value but if we use variation factor then our results will be more consistent. More the Co-efcient of quartile deviation lower the consistency and thus lower indexing

Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization

In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%

Named Entity Recognition Using Conditional Random Fields

Named entity recognition (NER) is an important task in natural language processing, as it is widely featured as a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection in Western and Asian languages. However, little effort has been made to develop techniques for the Urdu language, which is a prominent South Asian language with hundreds of millions of speakers across the globe. NER in Urdu is considered a hard problem owing to several reasons, including the paucity of large, annotated datasets; an inaccurate tokenizer; and the absence of capitalization in the Urdu language. To this end, this study proposed a conditional-random-field-based technique with both language-dependent and language-independent features, such as part-of-speech tags and context windows of words, respectively. As a second contribution, we developed an Urdu NER dataset (UNER-I) in which a large number of NE types were manually annotated. To evaluate the effectiveness of the proposed approach, as well as the usefulness of the dataset, experiments were performed using the dataset we developed and an existing dataset. The results of the experiments showed that our proposed technique outperformed the baseline technique for both datasets by improving the F1 scores by 1.5% to 3%. Furthermore, the results demonstrated that the enhanced dataset was useful for learning and prediction in a supervised learning approach