An analysis of customer perception using lexicon-based sentiment analysis of Arabic Texts framework.

Sentiment Analysis (SA) employing Natural Language Processing (NLP) is pivotal in determining the positivity and negativity of customer feedback. Although significant research in SA is focused on English texts, there is a growing demand for SA in other widely spoken languages, such as Arabic. This is predominantly due to the global reach of social media which enables users to express opinions on products in any language and, in turn, necessitates a thorough understanding of customers' perceptions of new products based on social media conversations. However, the current research studies demonstrate inadequacies in furnishing text analysis for comprehending the perceptions of Arabic customers towards coffee and coffee products. Therefore, this study proposes a comprehensive Lexicon-based Sentiment Analysis on Arabic Texts (LSAnArTe) framework applied to social media data, to understand customer perceptions of coffee, a widely consumed product in the Arabic-speaking world. The LSAnArTe Framework incorporates the existing AraSenTi dictionary, an Arabic database of sentiment scores for Arabic words, and lemmatizes unknown words using the Qalasadi open platform. It classifies each word as positive, negative or neutral before conducting sentence-level sentiment classification. Data collected from X (formerly known as Twitter, resulted in a cleaned dataset of 10,769 tweets, is used to validate the proposed framework, which is then compared with Amazon Comprehend. The dataset was annotated manually to ensure maximum accuracy and reliability in validating the proposed LSAnArTe Framework. The results revealed that the proposed LSAnArTe Framework, with an accuracy score of 93.79 %, outperformed the Amazon Comprehend tool, which had an accuracy of 51.90 %

Sentiment analysis of Arabic social media texts: A machine learning approach to deciphering customer perceptions

entiment analysis (SA) is a subfield of artificial intelligence that entails natural language processing. This has become increasingly significant because it discerns the emotional tone of reviews, categorising them as positive, neutral, or negative. In the highly competitive coffee industry, understanding customer sentiment and perception is paramount for businesses seeking to optimise their product offerings. Traditional methods of market analysis often fall short of capturing the nuanced views of consumers, necessitating a more sophisticated approach to sentiment analysis. This research is motivated by the need for a nuanced understanding of customer sentiments across various coffee products, enabling companies to make informed decisions regarding product promotion, improvement, and discontinuation. However, sentiment analysis faces a challenge when it comes to analysing Arabic text due to the language's extraordinarily complex inflectional and derivational morphology. Consequently, to address this challenge, we have developed a new method designed to improve the precision and effectiveness of Arabic sentiment analysis, specifically focusing on understanding customer opinions about various coffee products on social media platforms like Twitter. We gathered 10,646 various coffee products' Twitter reviews and applied feature extraction techniques using the term frequency-inverse document frequency (TF-IDF) and minimum redundancy maximum relevance (MRMR). Subsequently, we performed sentiment analysis using four supervised learning algorithms: k-nearest neighbor, support vector machine, decision tree, and random forest. All the classification statements derived in the analysis were aggregated via ensemble learning to convey the final results. Our results demonstrated an increase in prediction accuracy, with our method achieving over 95.95% accuracy in the Hard voting and soft voting at 94.51 %

Recalcitrant chronic bladder pain and recurrent cystitis but negative urinalysis - what should we do?

Purpose: Lower urinary tract symptoms (LUTS) may be associated with chronic urinary tract infection (UTI) undetected by routine diagnostic tests. Antimicrobial therapy might confer benefit for these patients. Materials and Methods: Over ten years, we treated patients with chronic LUTS. Pyuria was adopted as the principal biomarker of infection. Urinary leucocyte counts were recorded from microscopy of fresh midstream urine (MSU) samples. Antibiotics were prescribed and the prescription adjusted to achieve a measurable clinical response and a reduction in pyuria. Results: We treated 624 women (mean age=53.4 years; sd=18) with chronic LUTS and pyuria. The mean duration of symptoms prior to presentation was 6.5 years. Only 16% of MSU cultures submitted were positive (≥105 cfu ml-1). Mean treatment length was 383 days (SD=347; 95% CI=337-428). Treatment was associated with a reduction in total LUTS (F=98; p=.0001), 24-hour frequency (F=75; p=.0001), urinary 3 urgency (F=90; p=.0001), lower urinary tract pain (F=108; p=.0001), voiding symptoms (F=10; p=.002) and pyuria (F=15.4; p=.0001). Full-dose first-generation urinary antibiotic (such as cefalexin, nitrofurantoin, or trimethoprim) was combined with Methenamine Hippurate. We recorded 475 adverse events (AEs) during 273,762 treatment days. There was only one serious adverse event (SAE). We observed no increase in the proportion of resistant bacterial isolates. Conclusion: This large case series demonstrates that patients with chronic LUTS and pyuria experience symptom regression and a reduction in urinary tract inflammation associated with antimicrobial therapy. Disease regression was achieved with a low frequency of AEs. These results provide preliminary data to inform a future RCT

Impact of Renal Impairment on Beta-Blocker Efficacy in Patients With Heart Failure.

BACKGROUND: Moderate and moderately severe renal impairment are common in patients with heart failure and reduced ejection fraction, but whether beta-blockers are effective is unclear, leading to underuse of life-saving therapy. OBJECTIVES: This study sought to investigate patient prognosis and the efficacy of beta-blockers according to renal function using estimated glomerular filtration rate (eGFR). METHODS: Analysis of 16,740 individual patients with left ventricular ejection fraction <50% from 10 double-blind, placebo-controlled trials was performed. The authors report all-cause mortality on an intention-to-treat basis, adjusted for baseline covariates and stratified by heart rhythm. RESULTS: Median eGFR at baseline was 63 (interquartile range: 50 to 77) ml/min/1.73 m2; 4,584 patients (27.4%) had eGFR 45 to 59 ml/min/1.73 m2, and 2,286 (13.7%) 30 to 44 ml/min/1.73 m2. Over a median follow-up of 1.3 years, eGFR was independently associated with mortality, with a 12% higher risk of death for every 10 ml/min/1.73 m2 lower eGFR (95% confidence interval [CI]: 10% to 15%; p < 0.001). In 13,861 patients in sinus rhythm, beta-blockers reduced mortality versus placebo; adjusted hazard ratio (HR): 0.73 for eGFR 45 to 59 ml/min/1.73 m2 (95% CI: 0.62 to 0.86; p < 0.001) and 0.71 for eGFR 30 to 44 ml/min/1.73 m2 (95% CI: 0.58 to 0.87; p = 0.001). The authors observed no deterioration in renal function over time in patients with moderate or moderately severe renal impairment, no difference in adverse events comparing beta-blockers with placebo, and higher mortality in patients with worsening renal function on follow-up. Due to exclusion criteria, there were insufficient patients with severe renal dysfunction (eGFR <30 ml/min/1.73 m2) to draw conclusions. In 2,879 patients with atrial fibrillation, there was no reduction in mortality with beta-blockers at any level of eGFR. CONCLUSIONS: Patients with heart failure, left ventricular ejection fraction <50% and sinus rhythm should receive beta-blocker therapy even with moderate or moderately severe renal dysfunction

Strengthen causal models for better conservation outcomes for human well-being

This is the final version. Available from the publisher via the DOI in this record.All data files are available on github at www.github.com/scheng87/ toc.Background Understanding how the conservation of nature can lead to improvement in human conditions is a research area with significant growth and attention. Progress towards effective conservation requires understanding mechanisms for achieving impact within complex social-ecological systems. Causal models are useful tools for defining plausible pathways from conservation actions to impacts on nature and people. Evaluating the potential of different strategies for delivering co-benefits for nature and people will require the use and testing of clear causal models that explicitly define the logic and assumptions behind cause and effect relationships. Objectives and methods In this study, we outline criteria for credible causal models and systematically evaluated their use in a broad base of literature (~1,000 peer-reviewed and grey literature articles from a published systematic evidence map) on links between nature-based conservation actions and human well-being impacts. Results Out of 1,027 publications identified, only ~20% of articles used any type of causal models to guide their work, and only 14 total articles fulfilled all criteria for credibility. Articles rarely tested the validity of models with empirical data. Implications Not using causal models risks poorly defined strategies, misunderstanding of potential mechanisms for affecting change, inefficient use of resources, and focusing on implausible efforts for achieving sustainability.Science for Nature and People Partnership (SNAPP)National Institute for Health Research (NIHR

Urinary ATP as an indicator of infection and inflammation of the urinary tract in patients with lower urinary tract symptoms

BACKGROUND: Adenosine-5'-triphosphate (ATP) is a neurotransmitter and inflammatory cytokine implicated in the pathophysiology of lower urinary tract disease. ATP additionally reflects microbial biomass thus has potential as a surrogate marker of urinary tract infection (UTI). The optimum clinical sampling method for ATP urinalysis has not been established. We tested the potential of urinary ATP in the assessment of lower urinary tract symptoms, infection and inflammation, and validated sampling methods for clinical practice. METHODS: A prospective, blinded, cross-sectional observational study of adult patients presenting with lower urinary tract symptoms (LUTS) and asymptomatic controls, was conducted between October 2009 and October 2012. Urinary ATP was assayed by a luciferin-luciferase method, pyuria counted by microscopy of fresh unspun urine and symptoms assessed using validated questionnaires. The sample collection, storage and processing methods were also validated. RESULTS: 75 controls and 340 patients with LUTS were grouped as without pyuria (n = 100), pyuria 1-9 wbc ?l(-1) (n = 120) and pyuria ?10 wbc ?l(-1) (n = 120). Urinary ATP was higher in association with female gender, voiding symptoms, pyuria greater than 10 wbc ?l(-1) and negative MSU culture. ROC curve analysis showed no evidence of diagnostic test potential. The urinary ATP signal decayed with storage at 23°C but was prevented by immediate freezing at ??-20°C, without boric acid preservative and without the need to centrifuge urine prior to freezing. CONCLUSIONS: Urinary ATP may have a role as a research tool but is unconvincing as a surrogate, clinical diagnostic marker

Polycation-π Interactions Are a Driving Force for Molecular Recognition by an Intrinsically Disordered Oncoprotein Family

Molecular recognition by intrinsically disordered proteins (IDPs) commonly involves specific localized contacts and target-induced disorder to order transitions. However, some IDPs remain disordered in the bound state, a phenomenon coined "fuzziness", often characterized by IDP polyvalency, sequence-insensitivity and a dynamic ensemble of disordered bound-state conformations. Besides the above general features, specific biophysical models for fuzzy interactions are mostly lacking. The transcriptional activation domain of the Ewing's Sarcoma oncoprotein family (EAD) is an IDP that exhibits many features of fuzziness, with multiple EAD aromatic side chains driving molecular recognition. Considering the prevalent role of cation-π interactions at various protein-protein interfaces, we hypothesized that EAD-target binding involves polycation- π contacts between a disordered EAD and basic residues on the target. Herein we evaluated the polycation-π hypothesis via functional and theoretical interrogation of EAD variants. The experimental effects of a range of EAD sequence variations, including aromatic number, aromatic density and charge perturbations, all support the cation-π model. Moreover, the activity trends observed are well captured by a coarse-grained EAD chain model and a corresponding analytical model based on interaction between EAD aromatics and surface cations of a generic globular target. EAD-target binding, in the context of pathological Ewing's Sarcoma oncoproteins, is thus seen to be driven by a balance between EAD conformational entropy and favorable EAD-target cation-π contacts. Such a highly versatile mode of molecular recognition offers a general conceptual framework for promiscuous target recognition by polyvalent IDPs. © 2013 Song et al

Lipid peptide nanocomplexes for gene delivery and magnetic resonance imaging in the brain.

Gadolinium-labelled nanocomplexes offer prospects for the development of real-time, non-invasive imaging strategies to visualise the location of gene delivery by MRI. In this study, targeted nanoparticle formulations were prepared comprising a cationic liposome (L) containing a Gd-chelated lipid at 10, 15 and 20% by weight of total lipid, a receptor-targeted, DNA-binding peptide (P) and plasmid DNA (D), which electrostatically self-assembled into LPD nanocomplexes. The LPD formulation containing the liposome with 15% Gd-chelated lipid displayed optimal peptide-targeted, transfection efficiency. MRI conspicuity peaked at 4h after incubation of the nanocomplexes with cells, suggesting enhancement by cellular uptake and trafficking. This was supported by time course confocal microscopy analysis of transfections with fluorescently-labelled LPD nanocomplexes. Gd-LPD nanocomplexes delivered to rat brains by convection-enhanced delivery were visible by MRI at 6 h, 24 h and 48 h after administration. Histological brain sections analysed by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) confirmed that the MRI signal was associated with the distribution of Gd(3+) moieties and differentiated MRI signals due to haemorrhage. The transfected brain cells near the injection site appeared to be mostly microglial. This study shows the potential of Gd-LPD nanocomplexes for simultaneous delivery of contrast agents and genes for real-time monitoring of gene therapy in the brain

Superluminal motion of a relativistic jet in the neutron star merger GW170817

The binary neutron star merger GW170817 was accompanied by radiation across the electromagnetic spectrum and localized to the galaxy NGC 4993 at a distance of 41+/-3 Mpc. The radio and X-ray afterglows of GW170817 exhibited delayed onset, a gradual rise in the emission with time as t^0.8, a peak at about 150 days post-merger, followed by a relatively rapid decline. To date, various models have been proposed to explain the afterglow emission, including a choked-jet cocoon and a successful-jet cocoon (a.k.a. structured jet). However, the observational data have remained inconclusive as to whether GW170817 launched a successful relativistic jet. Here we show, through Very Long Baseline Interferometry, that the compact radio source associated with GW170817 exhibits superluminal motion between two epochs at 75 and 230 days post-merger. This measurement breaks the degeneracy between the models and indicates that, while the early-time radio emission was powered by a wider-angle outflow (cocoon), the late-time emission was most likely dominated by an energetic and narrowly-collimated jet, with an opening angle of <5 degrees, and observed from a viewing angle of about 20 degrees. The imaging of a collimated relativistic outflow emerging from GW170817 adds substantial weight to the growing evidence linking binary neutron star mergers and short gamma-ray bursts.Comment: 42 pages, 4 figures (main text), 2 figures (supplementary text), 2 tables. Referee and editor comments incorporate