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\"Brings together writers originally from Mexico, Bosnia, Iran, Afghanistan, Soviet Ukraine, Hungary, Chile, Ethiopia, and others to make their stories heard ... Their 17 contributions are as diverse as their own lives have been, and yet hold just as many themes in common\"--Amazon.com.

Purpose The purpose of this paper is to undertake a “real-time” assessment of the impact of the COVID-19 pandemic on the right to participate in hospitality and tourism and to illustrate where such rights are under threat. Design/methodology/approach This discussion is based on a review of current events, assessed through interpretation of a human rights lens. Findings Rights to participate in hospitality and tourism, particularly in parts of Asia, Europe and North America, were affected on a scale unprecedented in peacetime. Research limitations/implications The rights to participate in hospitality and tourism have been challenged as never before. The big questions that will need to be answered going forward are the extent to which such rights will be restored, post-COVID-19. Originality/value This is a “real-time” assessment and will require re-visiting as events unfold over the coming months and years.

Crinum latifolium (CL) leaf is a source of various biologically active compounds such as alkaloid and phenolic compounds, which exhibit anti-inflammatory, antitumor, and antimicrobial effects. In the purpose of expanding applications for the field of bionanotechnology, we report biosynthesis of silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) by using aqueous extract from C. latifolium leaf and explore antibacterial activity and catalytic performance for degradation of pollutants. The formation of CL-AgNPs and CL-AuNPs is confirmed and optimized by UV-visible spectroscopy with surface plasmon resonance (SPR) peaks at around 402 and 539 nm, respectively. The spherical CL-AgNPs have an average diameter of 20.5 nm and the multishaped CL-AuNPs possess an average size of 17.6 nm. The actions of four bacterial strains were strongly inhibited by using the CL-AgNPs. Furthermore, the biosynthesized metallic nanoparticles (MNPs) exhibited the excellent catalytic degradation performance of pollutants.

To investigate the effect of environmental stresses on the exopolysaccharide biosynthesis, after 24 h of culture at 37 °C with pH 6.8 and without sodium chloride, Lactobacillus plantarum VAL6 was exposed to different stress conditions, including pH (pHs of 3 and 8) and high sodium chloride concentration treatments. The results found that Lactobacillus plantarum VAL6 exposed to stress at pH 3 for 3 h gives the highest exopolysaccharide yield (50.44 g/L) which is 6.4 fold higher than non-stress. Under pH and sodium chloride stresses, the mannose content in exopolysaccharides decreased while the glucose increased in comparison with non-stress condition. The galactose content was highest under stress condition of pH 8 meantime rhamnose content increased sharply when Lactobacillus plantarum VAL6 was stressed at pH 3. The arabinose content in exopolysaccharides was not detected under non-stress condition but it was recorded in great amounts after 3 h of stress at pH 3. In addition, stress of pH 8 triggered the mRNA expression of eps F gene resulting in galactose-rich EPS synthesis. According to our results, the stresses of pH and sodium chloride enhance the production and change the mRNA expression of eps F gene, leading to differences in the monosaccharide composition of exopolysaccharides.

This study presents an efficient and facile method for biosynthesis of silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) using aqueous extract of burdock root (BR), A . lappa , and their applications. The nanoparticles were characterized by ultraviolet-visible spectrophotometry, X-ray diffraction, transmission electron microscopy, energy dispersive X-ray, thermogravimetry, and differential thermal analysis. AgNPs capped the BR extract (BR-AgNPs) possessed roughly spherical geometry with an average diameter of 21.3 nm while uneven geometry of AuNPs capped the BR extract (BR-AuNPs) showed multi shapes in average size of 24.7 nm. The BR-AgNPs strongly inhibited five tested microorganism strains. In particular, the nanoparticles showed excellent catalytic activity for the conversion of pollutants within wastewater. Pseudo-first-order rate constants for the degradation of 4-nitrophenol, methyl orange, and rhodamine B were respectively found 6.77 × 10 −3 , 3.70 × 10 −3 , and 6.07 × 10 −3  s −1 for BR-AgNPs and 6.87 × 10 −3 , 6.07 × 10 −3 , and 7.07 × 10 −3  s −1 for BR-AuNPs. Graphical abstract ᅟ

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

Mpox was diagnosed in 2 women returning to Vietnam from the United Arab Emirates. The monkeypox viruses belonged to an emerging sublineage, A.2.1, distinct from B.1, which is responsible for the ongoing multicountry outbreak. Women could contribute to mpox transmission, and enhanced genomic surveillance is needed to clarify pathogen evolution.

Peptide-based strategies have received an enormous amount of attention because of their specificity and applicability. Their specificity and tumor-targeting ability are applied to diagnosis and treatment for cancer patients. In this review, we will summarize recent advancements and future perspectives on peptide-based strategies for cancer treatment. The literature search was conducted to identify relevant articles for peptide-based strategies for cancer treatment. It was performed using PubMed for articles in English until June 2023. Information on clinical trials was also obtained from ClinicalTrial.gov. Given that peptide-based strategies have several advantages such as targeted delivery to the diseased area, personalized designs, relatively small sizes, and simple production process, bioactive peptides having anti-cancer activities (anti-cancer peptides or ACPs) have been tested in pre-clinical settings and clinical trials. The capability of peptides for tumor targeting is essentially useful for peptide–drug conjugates (PDCs), diagnosis, and image-guided surgery. Immunomodulation with peptide vaccines has been extensively tested in clinical trials. Despite such advantages, FDA-approved peptide agents for solid cancer are still limited. This review will provide a detailed overview of current approaches, design strategies, routes of administration, and new technological advancements. We will highlight the success and limitations of peptide-based therapies for cancer treatment.

Real estate markets are inherently dynamic, influenced by economic fluctuations, policy changes and socio-demographic shifts, often leading to emergence of anomalous—regions, where market behavior significantly deviates from expected trends. Traditional forecasting models struggle to handle such anomalies, resulting in higher errors and reduced prediction stability. In order to address this challenge, we propose EGCN, a novel cluster-specific forecasting framework that first detects and clusters anomalous regions separately from normal regions, and then applies forecasting models. This structured approach enables predictive models to treat normal and anomalous regions independently, leading to enhanced market insights and improved forecasting accuracy. Our evaluations on the UK, USA, and Australian real estate market datasets demonstrates that the EGCN achieves the lowest error both anomaly-free (baseline) methods and alternative anomaly detection methods, across all forecasting horizons (12, 24, and 48 months). In terms of anomalous region detection, our EGCN identifies 182 anomalous regions in Australia, 117 in the UK and 34 in the US, significantly more than the other competing methods, indicating superior sensitivity to market deviations. By clustering anomalies separately, forecasting errors are reduced across all tested forecasting models. For instance, when applying Neural Hierarchical Interpolation for Time Series Forecasting, the EGCN improves accuracy across forecasting horizons. In short-term forecasts (12 months), it reduces MSE from 1.3 to 1.0 in the US, 9.7 to 6.4 in the UK and 2.0 to 1.7 in Australia. For mid-term forecasts (24 months), EGCN achieves the lowest errors, lowering MSE from 3.1 to 2.3 (US), 14.2 to 9.0 (UK), and 4.5 to 4.0 (Australia). Even in long-term forecasts (48 months), where error accumulation is common, EGCN remains stable; decreasing MASE from 6.9 to 5.3 (US), 12.2 to 8.5 (UK), and 16.0 to 15.2 (Australia), highlighting its robustness over extended periods. These results highlight how separately clustering anomalies allows forecasting models to better capture distinct market behaviors, ensuring more precise and risk-adjusted predictions.

STEM education has been garnering increasing global attention. Implementing STEM education for primary school students serves as a critical starting point to ignite their interest and enthusiasm for learning, while simultaneously fostering the development of essential skills from an early age. To provide an overview of the issues related to STEM in primary schools as represented in publications indexed in the Scopus database, a bibliometric analysis of 967 publications from 2004 to 2024 was conducted. The findings indicate a significant surge in publications on STEM in primary schools from 2019 to 2023. The USA emerges as the most prominent country in the collection, excelling in the number of publications, citations, affiliations, and authors with the highest contributions. The ASEE Annual Conference and Exposition is identified as the leading conference proceeding in terms of publication volume. By analyzing keywords and examining the content of the most influential publications in the collection, four main research trends in STEM education for primary schools were identified: integration of STEM into the curriculum; development of self-directed learning skills in primary education; exploration of the potential of technology in teaching; and program development and teacher training, with a focus on addressing gender and social biases related to STEM. Differences in research trends across various groups were also highlighted. The study’s findings provide valuable insights for individuals and institutions while contributing significantly to advancing STEM education, addressing current challenges, and fostering comprehensive societal development in the future.