Splitting Arabic Texts into Elementary Discourse Units

International audienceIn this article, we propose the first work that investigates the feasibility of Arabic discourse segmentation into elementary discourse units within the segmented discourse representation theory framework. We first describe our annotation scheme that defines a set of principles to guide the segmentation process. Two corpora have been annotated according to this scheme: elementary school textbooks and newspaper documents extracted from the syntactically annotated Arabic Treebank. Then, we propose a multiclass supervised learning approach that predicts nested units. Our approach uses a combination of punctuation, morphological, lexical, and shallow syntactic features. We investigate how each feature contributes to the learning process. We show that an extensive morphological analysis is crucial to achieve good results in both corpora. In addition, we show that adding chunks does not boost the performance of our system

Knowledge, practice and attitude associated with SARS-CoV-2 Delta Variant among adults in Jordan

COVID-19 infection is a global pandemic health emergency. This contagious disease was caused by the Severe Acute Respiratory Syndrome Coronavirus‑2 (SARS‑CoV-2) which is mutating over time. In 2021, the Delta variant became the most dominant transmissible form. During the crisis, human practice and knowledge were critical in the overall efforts to encompass the outbreak. A cross-sectional, web-based approach was conducted among adults in Jordan to quantify knowledge, attitude, and practices towards SARS-CoV-2 (Delta variant). This research was carried out between 15th April and 15th of May 2021. The study questionnaire consisted of four sections including the participant’s demographics, knowledge, practices and attitude. Comparative evaluation of responses was accomplished using a scoring system. Respondents who scored above the mean score (60%) on the item measured were categorized as knowledgeable, having a positive attitude, and good practices. Participants were allocated to one of the three groups; medical, non-medical and others (unemployed and housewives). Data collected was analyzed using Statistical Package for Social Sciences (SPSS) version 23.0 software. A variance test to assess the statistical difference between groups was used. Pearson’s chi-squared test was applied to compare the variables and identify significant predictors. Of the participants, 308 (66%) were in the age group of 18-25yrs, 392 (84.1%) females, 120 (25.8%) employed and 346 (74.2%) unemployed. The principle source of knowledge was social media (291, 62.4%). Interestingly, participants had adequate overall knowledge. The mean knowledge score was 22.6 (± 0.19), 20.6 (± 0.19), and 21.3 (± 0.18) for the medical, the non-medical and the others group, respectively. Also, participants showed a positive attitude and good practices towards SARS-CoV-2 (Delta variant). The mean practice score for medical, the non-medical and the others groups was 7.35 (± 0.25), 7.38 (± 0.24), 7.35 (± 0.24) and the mean attitude score was 10.8 (± 0.16), 9.4 (± 0.21), 9.5 (± 0.22), respectively. The studied groups generally had good knowledge, positive attitudes and good practices about SARS-CoV-2 (Delta variant). This was expected due to the authorities’ successful management of the pandemic and the high educational level of the Jordanian society, bearing in mind the economic and social impact of COVID-19 disease

Antibiotic—Lysobacter enzymogenes proteases combination as a novel virulence attenuating therapy

Minimizing antibiotic resistance is a key motivation strategy in designing and developing new and combination therapy. In this study, a combination of the antibiotics (cefixime, levofloxacin and gentamicin) with Lysobacter enzymogenes (L. enzymogenes) bioactive proteases present in the cell- free supernatant (CFS) have been investigated against the Gram-positive methicillin-sensitive Staphylococcus aureus (MSSA), methicillin-resistant Staphylococcus aureus (MRSA) and the Gram-negative Escherichia coli (E. coli O157:H7). Results indicated that L. enzymogenes CFS had maximum proteolytic activity after 11 days of incubation and higher growth inhibitory properties against MSSA and MRSA compared to E. coli (O157:H7). The combination of L. enzymogenes CFS with cefixime, gentamicin and levofloxacin at sub-MIC levels, has potentiated their bacterial inhibition capacity. Interestingly, combining cefixime with L. enzymogenes CFS restored its antibacterial activity against MRSA. The MTT assay revealed that L. enzymogenes CFS has no significant reduction in human normal skin fibroblast (CCD-1064SK) cell viability. In conclusion, L. enzymogenes bioactive proteases are natural potentiators for antimicrobials with different bacterial targets including cefixime, gentamicin and levofloxacin representing the beginning of a modern and efficient era in the battle against multidrug-resistant pathogens

Antibiotic-Lysobacter enzymogenes proteases combination as a novel virulence attenuating therapy.

Minimizing antibiotic resistance is a key motivation strategy in designing and developing new and combination therapy. In this study, a combination of the antibiotics (cefixime, levofloxacin and gentamicin) with Lysobacter enzymogenes (L. enzymogenes) bioactive proteases present in the cell- free supernatant (CFS) have been investigated against the Gram-positive methicillin-sensitive Staphylococcus aureus (MSSA), methicillin-resistant Staphylococcus aureus (MRSA) and the Gram-negative Escherichia coli (E. coli O157:H7). Results indicated that L. enzymogenes CFS had maximum proteolytic activity after 11 days of incubation and higher growth inhibitory properties against MSSA and MRSA compared to E. coli (O157:H7). The combination of L. enzymogenes CFS with cefixime, gentamicin and levofloxacin at sub-MIC levels, has potentiated their bacterial inhibition capacity. Interestingly, combining cefixime with L. enzymogenes CFS restored its antibacterial activity against MRSA. The MTT assay revealed that L. enzymogenes CFS has no significant reduction in human normal skin fibroblast (CCD-1064SK) cell viability. In conclusion, L. enzymogenes bioactive proteases are natural potentiators for antimicrobials with different bacterial targets including cefixime, gentamicin and levofloxacin representing the beginning of a modern and efficient era in the battle against multidrug-resistant pathogens

Identifying similar opinions in news comments using a community detection algorithm

Despite playing many important roles in society, the news media have been frequently criticised for failing to represent a wide range of viewpoints. Online news systems have the potential to allow readers to add additional information and perspectives. However, due to the simplicity of the filtering mechanisms typically employed, these systems can themselves be prone to over-promoting popular viewpoints at the expense of others. Previous research has attempted to diversify news comments through the use of content similarity, sentiment analysis, named entity recognition, and other factors. In this paper we propose the use of a commonly used community detection algorithm on a network of voting data to identify sentiment groups in news discussion threads, with the eventual goal that these groups may be used to present diverse content. In a controlled experiment with 154 participants, we verify that the Louvain Community Detection algorithm is able to group users with accuracy comparable to an average human. This produces groups containing users who share similar sentiment on a given topic. This is an important step towards ensuring that each group is represented, as by using this method future news systems can ensure that more diverse views are represented in open comment threads