In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.
IMPORTANCEThe monoclonal antibody combination of casirivimab and imdevimab reduced viral load, hospitalization, or death when administered as a 1200-mg or greater intravenous (IV) dose in a phase 3 COVID-19 outpatient study. Subcutaneous (SC) and/or lower IV doses should increase accessibility and/or drug supplies for patients. OBJECTIVE To assess the virologic efficacy of casirivimab and imdevimab across different IV and SC doses compared with placebo. DESIGN, SETTING, AND PARTICIPANTS This phase 2, randomized, double-blind, placebocontrolled, parallel-group, dose-ranging study included outpatients with SARS-CoV-2 infection at 47 sites across the United States. Participants could be symptomatic or asymptomatic; symptomatic patients with risk factors for severe COVID-19 were excluded. Data were collected from December 15, 2020, to March 4, 2021. INTERVENTIONS Patients were randomized to a single IV dose (523 patients) of casirivimab and imdevimab at 300, 600, 1200, or 2400 mg or placebo; or a single SC dose (292 patients) of casirivimab and imdevimab at 600 or 1200 mg or placebo. MAIN OUTCOMES AND MEASURES The primary end point was the time-weighted average daily change from baseline (TWACB) in viral load from day 1 (baseline) through day 7 in patients seronegative for SARS-CoV-2 at baseline. RESULTS Among 815 randomized participants, 507 (282 randomized to IV treatment, 148 randomized to SC treatment, and 77 randomized to placebo) were seronegative at baseline and included in the primary efficacy analysis. Participants randomized to IV had a mean (SD) age of 34.6 (9.6) years (160 [44.6%] men; 14 [3.9%] Black; 121 [33.7%] Hispanic or Latino; 309 [86.1%] White); those randomized to SC had a mean age of 34.1 (10.0) years (102 [45.3%] men; 75 [34.7%] Hispanicor Latino; 6 [2.7%] Black; 190 [84.4%] White). All casirivimab and imdevimab treatments showed significant virologic reduction through day 7. Least-squares mean differences in TWACB viral load for casirivimab and imdevimab vs placebo ranged from -0.56 (95% CI; -0.89 to -0.24) log 10 copies/mL for the 1200-mg IV dose to -0.71 (95% CI, -1.05 to -0.38) log 10 copies/mL for the 2400-mg IV dose. There were no adverse safety signals or dose-related safety findings, grade 2 or greater infusionrelated or hypersensitivity reactions, grade 3 or greater injection-site reactions, or fatalities. Two serious adverse events not related to COVID-19 or the study drug were reported.
Aims: To characterize the mechanisms by which bacteria in the peanut rhizosphere promote plant growth and suppress Aspergillus niger, the fungus that causes collar rot of peanut. Methods and Results: In all, 131 isolates cultured from the peanut rhizosphere were assayed for growth promotion in a seedling germination assay. The most effective isolate, RR18, was identified as Burkholderia sp. by 16S sequencing analysis. RR18 reduced collar rot disease incidence and increased the germination rate and biomass of peanut seeds, and had broad-spectrum antifungal activity. Quantitative analyses showed that RR18 induced long-lasting accumulation of jasmonic acid, salicylic acid and phenols, and triggered the activity of six defence enzymes related to these changes. Comparative proteomic analysis of treated and untreated seedlings revealed a clear induction of four abundant proteins, including a member of the pre-chorismate pathway, a regulator of clathrin-coated vesicles, a transcription factor and a hypothetical protein.Conclusion: Burkholderia sp. RR18 promotes peanut growth and disease resistance, and stably induces two distinct defence pathways associated with systemic resistance. Significance and Impact of the Study: This study demonstrates that a strain of the Burkholderia cepacia complex can elicit both salicylic-and jasmonic-acidmediated defences, in addition to having numerous other beneficial properties.
Antimicrobial treatment of bacteria often results in a small population of surviving tolerant cells, or persisters, that may contribute to recurrent infection. Antibiotic persisters are metabolically dormant, but the basis of their persistence in the presence of membrane-disrupting biological compounds is less well understood. We previously found that the model plant pathogen Pseudomonas syringae pv. phaseolicola 1448A (Pph) exhibits persistence to tailocin, a membrane-disrupting biocontrol compound with potential for sustainable disease control. Here, we compared physiological traits associated with persistence to tailocin and to the antibiotic streptomycin and established that both treatments leave similar frequencies of persisters. Microscopic profiling of treated populations revealed that while tailocin rapidly permeabilizes most cells, streptomycin treatment results in a heterogeneous population in the redox and membrane permeability state. Intact cells were sorted into three fractions according to metabolic activity, as indicated by a redox-sensing reporter dye. Streptomycin persisters were cultured from the fraction associated with the lowest metabolic activity, but tailocin persisters were cultured from a fraction associated with an active metabolic signal. Cells from culturable fractions were able to infect host plants, while the nonculturable fractions were not. Tailocin and streptomycin were effective in eliminating all persisters when applied sequentially, in addition to eliminating cells in other viable states. This study identifies distinct metabolic states associated with antibiotic persistence, tailocin persistence, and loss of virulence and demonstrates that tailocin is highly effective in eliminating dormant cells. IMPORTANCE Populations of genetically identical bacteria encompass heterogeneous physiological states. The small fraction of bacteria that are dormant can help the population survive exposure to antibiotics and other stresses, potentially contributing to recurring infection cycles in animal or plant hosts. Membrane-disrupting biological control treatments are effective in killing dormant bacteria, but these treatments also leave persister-like survivors. The current work demonstrates that in Pph, persisters surviving treatment with membrane-disrupting tailocin proteins have an elevated redox state compared to that of dormant streptomycin persisters. Combination treatment was effective in killing both persister types. Culturable persisters corresponded closely with infectious cells in each treated population, whereas the high-redox and unculturable fractions were not infectious. In linking redox states to heterogeneous phenotypes of tailocin persistence, streptomycin persistence, and infection capability, this work will inform the search for mechanisms and markers for each phenotype.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.