Twitter (X), Fast Fashion and Backlash: Argumentation and Ethics on Social Media

Social media backlashes have emerged as important phenomena complicating how businesses communicate online and representing significant brand risk. This article demonstrates the value of content analysis and argumentation theory for understanding and responding to social media backlash events, using two examples from the UK fashion industry (hashtags #ThanksItsASOS and #boycottboohoo). The results provide lessons about the way backlashes operate in practice, how to analyze these effectively, and have implications for business approaches to communicating about Corporate Social Responsibility and managing social media. The authors conclude with suggestions for training on social media and CSR for businesses. </jats:p

Sampling-based adaptive Bayesian quadrature for probabilistic model updating

Bayesian (probabilistic) model updating is a fundamental concept in computational science, allowing for the incorporation of prior beliefs with observed data to reduce prediction uncertainty of a computer simulator. However, the efficient evaluation of posterior probability density functions (PDFs) of model parameters poses challenges, particularly for computationally expansive simulators. This work presents a sampling-based adaptive Bayesian quadrature method to fill this gap. The method is based on approximating the simulator under investigation with a Gaussian process (GP) model, and then a conditional sampling procedure is introduced for generating sample paths, this way to infer a probability distribution for the evidence term. This inferred probability distribution indeed measures the prediction uncertainty of the evidence term, and thus based on which, an acquisition function is proposed to identify the site at which the prediction uncertainty of the GP model contributes the most to that of the evidence term. All the above ingredients finally form an adaptive algorithm for updating the posterior PDFs of model parameters with pre-specified accuracy tolerance. Case studies across numerical examples and engineering applications validate the ability of the proposed method to deal with multi-modal problems, and demonstrate its superiority in terms of computational efficiency and precision for estimating model evidence and posterior PDFs

Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.

As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation

Form Follows Fengshui: Aparametric approach to investigate the influence of Fengshui on traditional Chinese courtyard dwelling design

The traditional Chinese courtyard dwelling type- Siheyuan, is considered a cultural artefact where the ancient Chinese superstitious geomancy- Fengshui, is embedded. This paper focuses on a computational interpretation of the Li Qi, a Fengshui theory, and presents a parametric approach to develop its principles into an algorithm, which is used to assess Siheyuan houses' fortune status according to their layout in three aspects: room orientation, room size ratio, and room dimensions. A parametric tool is proposed to verify the Siheyuan historical precedents from the ancient Beijing map and measuring surveys on extant buildings, the verification results proved that most Siheyuan houses were designed to be auspicious even though other forces were working against them

Clinical associations and prognosis in Asian and European patients with symptom-controlled atrial fibrillation: Insights from two prospective registries in Europe and Asia.

BackgroundClinical associations and prognosis of patients with symptom-controlled AF (scAF) remain poorly understood.MethodsWe analysed data from the Asian-Pacific Heart Rhythm Society and EURObservational Research Programme registries. Based on the European Heart Rhythm Association (EHRA) score, patients were classified as scAF (EHRA I or II) or symptomatic AF (EHRA III or IV). Clinical characteristics were examined by logistic regression, and prognosis was assessed by Cox models. The primary outcome was composed of all-cause death and major cardiovascular events. Interaction analyses were performed to investigate ethnic differences.ResultsAmong 13,577 AF patients (mean age 69.0 ± 11.6 years; 38.7% female), 11,470 (84.5%) had scAF. Asians were more likely to be scAF, characterised by younger age and lower cardiovascular burden compared to Europeans. Diabetes mellitus was significantly associated with scAF only in Asians (adjusted odd ratio [aOR] 1.43, 95% confidence interval [CI] 1.03-2.04, pinteraction = 0.021). The associations with hypertension (aOR 1.29, 95% CI 0.98-1.70, pinteraction = 0.004) and prior ischemic stroke (aOR 1.75, 95% CI 0.96-3.58, pinteraction = 0.045) were more evident in Asians. Patients with scAF showed a notable association with increased likelihood of using vitamin K antagonists (aOR 1.19, 95% CI 1.07-1.33), which was more prominent in Asians. In both Asians and Europeans, scAF was associated with reduced rhythm control management. Compared to non-scAF, European patients with scAF had a reduced risk of the composite outcome, but the association was non-significant in Asians (pinteraction = 0.594).ConclusionAsians and Europeans with scAF demonstrate clinically relevant differences in terms of overall prevalence, related risk factors, and clinical management

One health surveillance: linking human and animal rabies surveillance data in Kenya.

IntroductionRabies poses a significant public health and economic challenge in Kenya. The Kenya rabies elimination strategy identifies surveillance as a key pillar to achieve the targets of ending human deaths from rabies by 2030. Here we investigated the utility of the national human and animal rabies surveillance data to provide robust surveillance data to guide the Kenya rabies elimination program.MethodsWe conducted a retrospective analysis of the official rabies data obtained from the national human and animal health surveillance systems between 2017 and 2023. We obtained data on bites, cases, and deaths in dogs and humans due to rabies. We estimated incidences and tested the relationships between rabies variables in human and dogs as a proxy for robust data availability.ResultsOn average, there were 162 cases and 84 deaths in dogs, while in humans, there were 53 cases and 6 deaths. We found positive correlations between dog bites and cases of dog rabies [RR = 1.33, 95% credible interval (CI): 1.16, 1.54], deaths and rabies cases in dogs (RR = 1.09, 95% CI: 1.05, 1.14) and death cases and dog bites (RR = 1.46, 95% CI: 1.06, 1.98). However, relationships between rabies cases and dog bites in humans were not statistically significant (RR = 1.00, 95% CI: 0.98, 1.03), whereas rabies cases in dogs and humans were negatively correlated (RR = 0.82, 95% CI: 0.68, 0.94).DiscussionThe findings indicate that Kenya's rabies surveillance system effectively captures trends in dog rabies but has gaps in human rabies case reporting. The weak relationship between rabies cases and dog bites in humans and the negative correlation between rabies cases in dogs and humans point to potential underreporting of human cases, that could be possibly driven by misdiagnosis or limited access to healthcare, or effective post-exposure treatment.ConclusionUnderstanding these relationships is critical for improving the surveillance systems that can effectively support the rabies elimination program

Monolaurin inhibits antibiotic-resistant Staphylococcus aureus in patients with atopic dermatitis.

Frequent use of antibiotics increases the incidence of antimicrobial-resistant Staphylococcus aureus in atopic dermatitis (AD), which prompts the search for new treatments. Monolaurin is a chemical byproduct found in coconut oil and has anti-bacterial properties. This study aimed to investigate the inhibitory effect of monolaurin on antimicrobial-resistant S. aureus. Thirty children and thirty adults diagnosed with AD were recruited and swabbed at three different sites: lesion, non-lesion, and nasal mucosa. Methicillin resistance and high-level mupirocin resistance in S. aureus were identified using mecA and mupA PCR, respectively, whilst fusidic acid resistance were detected by fusA gene sequencing. The broth microdilution method and tetrazolium bromide assays were used for monolaurin susceptibility and cellular cytotoxicity, respectively. We show that S. aureus was frequently isolated from lesions of both children and adults with AD. One isolate of methicillin-resistant S. aureus (MRSA) harboring mecA, one isolate of mupirocin-resistant S. aureus harboring mupA, and four isolates of fusidic acid-resistant S. aureus with novel point mutations of fusA were found in the children group. In silico molecular docking showed that these mutants interacted weakly with fusidic acid, explaining the mechanism of drug resistance. Monolaurin inhibited these antimicrobial-resistant S. aureus isolates with a minimal inhibitory concentration of 2 µg/mL without cytotoxicity to cultured epidermal and dermal cells. These data show that monolaurin could potentially be used to inhibit antimicrobial-resistant S. aureus in AD patients

Food marketing, eating and health outcomes in children and adults: a systematic review and meta-analysis.

The marketing of unhealthy foods has been implicated in poor diet and rising levels of obesity. Rapid developments in the digital food marketing ecosystem and associated research mean that contemporary review of the evidence is warranted. This preregistered (CRD420212337091) systematic review and meta-analysis aimed to provide an updated synthesis of the evidence for behavioural and health impacts of food marketing on both children and adults, using the 4Ps framework (Promotion, Product, Price, Place). Ten databases were searched from 2014 to 2021 for primary data articles of quantitative or mixed design, reporting on one or more outcome of interest following food marketing exposure compared with a relevant control. Reviews, abstracts, letters/editorials and qualitative studies were excluded. Eighty-two studies were included in the narrative review and twenty-three in the meta-analyses. Study quality (RoB2/Newcastle-Ottawa scale) was mixed. Studies examined 'promotion' (n 55), 'product' (n 17), 'price' (n 15) and 'place' (n 2) (some > 1 category). There is evidence of impacts of food marketing in multiple media and settings on outcomes, including increased purchase intention, purchase requests, purchase, preference, choice, and consumption in children and adults. Meta-analysis demonstrated a significant impact of food marketing on increased choice of unhealthy foods (OR = 2·45 (95 % CI 1·41, 4·27), Z = 3·18, P = 0·002, I2 = 93·1 %) and increased food consumption (standardised mean difference = 0·311 (95 % CI 0·185, 0·437), Z = 4·83, P 2 = 53·0 %). Evidence gaps were identified for the impact of brand-only and outdoor streetscape food marketing, and for data on the extent to which food marketing may contribute to health inequalities which, if available, would support UK and international public health policy development

“Latent archangels”: The archangel Michael in Romanian fascism

This open access book explores the various manifestations of the archangel Michael in history, politics, and popular culture. One of the most venerated intermediate beings of the belief systems of all Christian traditions, the archangel Michael has assumed multiple roles that go far beyond the ways in which he has been defined by his major cults. Chapters explore how the archangel Michael has often accompanied processes of Christianization as well as being the divine messenger par excellence. Covering a broad variety of academic perspectives and historical contexts, the book explores how the archangel Michael has subsumed ancient cults, been endowed with magical and ritual powers and guided religious and secular leaders in their exploits. The figure of the archangel Michael is shown to have even inspired educational systems, and in more recent times become a “commercial brand” of blessings and protections. Going beyond orthodoxies, this book reveals how the history and interpretations of the Archangel’s manifestations have been redefined by Christians of Adventist beliefs, by politicians using religious apocalypse in their rhetoric, and by New Age leaders in highly innovative and individualistic manners. The ebook editions of this book are available open access under a CC BY 4.0 licence on bloomsburycollections.com. Open access was funded by the University of Bergen, University of Oslo, Austrian Academy of Sciences, University College Cork, University of Liverpool and Fundatia Noua Europa

Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning

This article investigates the application of Deep Reinforcement Learning (DRL) for attributing malware to specific Advanced Persistent Threat (APT) groups through detailed behavioural analysis. By analysing over 3,500 malware samples from 12 distinct APT groups, the study utilises sophisticated tools like Cuckoo Sandbox to extract behavioural data, providing a deep insight into the operational patterns of malware. The research demonstrates that the DRL model significantly outperforms traditional machine learning approaches such as SGD, SVC, KNN, MLP and Decision Tree Classifiers, achieving an impressive test accuracy of 94.12%. It highlights the model’s capability to adeptly manage complex, variable and elusive malware attributes. Furthermore, the article discusses the considerable computational resources and extensive data dependencies required for deploying these advanced AI models in cybersecurity frameworks. Future research is directed towards enhancing the efficiency of DRL models, expanding the diversity of the datasets, addressing ethical concerns and leveraging Large Language Models (LLMs) to refine reward mechanisms and optimise the DRL framework. By showcasing the transformative potential of DRL in malware attribution, this research advocates for a responsible and balanced approach to AI integration, with the goal of advancing cybersecurity through more adaptable, accurate and robust systems.</jats:p