Sustainable luxury: from an oxymoron to a tautology – the case of the Indian luxury hospitality industry

PurposeIn the context of heightened awareness and understanding of responsible tourism, it has become increasingly imperative for luxury hotels to introspect on the impact of their operations on environmental and social sustainability. This study aims at examining the prevalence and growth of sustainability practices in the Indian luxury hotel segment as it increasingly competes on a global platform.Design/methodology/approachThe research approach adopted in this paper is qualitative and emic. Primary data is gathered for the study through semi-structured interviews with select luxury hotel general managers from four hotel chains of Indian origin – ITC Luxury Hotels, Oberoi Hotels and Resorts, Taj Hotels and Palaces and The Leela – to gain an insight into sustainability initiatives adopted in the Indian hospitality industry. Secondary research data regarding the eco-friendly, green measures implemented in these hotels is collected primarily from the websites of the respective hotel chains and supplemented by review of academic literature, media articles, industry reports and company press releases.FindingsThe findings of the study reveal that Indian luxury hotel brands are increasingly adopting green products and integrating innovative sustainability practices in their day-to-day operations. However, several of these initiatives are in the non-customer-facing domains such as the engineering and back-of-the-house operations. Very few Indian hotel chains are building these sustainability initiatives into their core philosophy and embedding it in the front-line customer service experience at their properties to reinforce the green image of the hotels.Originality/valueThe paper also proposes the 6Cs Framework of Sustainability that can be utilised to categorise the green sustainable practices adopted in responsible hotels in a simplistic manner under six broad verticals. Practitioners, researchers and educationists in the hospitality industry would find the implications of this study useful in the context of a post-pandemic world where sustainability is influencing consumer choices across industries in today's eco-conscious and enlightened business and marketing environment.

Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity.

Messenger RNA (mRNA) vaccines are being used to contain COVID-19 (1, 2, 3), but still suffer from the critical limitation of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine products (4). Previous work showed that increasing secondary structure lengthens mRNA half-life, which, together with optimal codons, improves protein expression (5). Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage. However, due to synonymous codons, the mRNA design space is prohibitively large (e.g., ~10632 candidates for the SARS-CoV-2 Spike protein), which poses insurmountable computational challenges. Here we provide a simple and unexpected solution using a classical concept in computational linguistics, where finding the optimal mRNA sequence is akin to identifying the most likely sentence among similar sounding alternatives (6). Our algorithm LinearDesign takes only 11 minutes for the Spike protein, and can jointly optimize stability and codon usage. On both COVID-19 and varicella-zoster virus mRNA vaccines, LinearDesign substantially improves mRNA half-life and protein expression, and dramatically increases antibody titer by up to 128× in vivo, compared to the codon-optimization benchmark. This surprising result reveals the great potential of principled mRNA design, and enables the exploration of previously unreachable but highly stable and efficient designs. Our work is a timely tool not only for vaccines but also for mRNA medicine encoding all therapeutic proteins (e.g., monoclonal antibodies and anti-cancer drugs (7, 8)).

In vivo secondary structural analysis of Influenza A virus genomic RNA.

Influenza A virus (IAV) is a respiratory virus that causes epidemics and pandemics. Knowledge of IAV RNA secondary structure in vivo is crucial for a better understanding of virus biology. Moreover, it is a fundament for the development of new RNA-targeting antivirals. Chemical RNA mapping using selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) coupled with Mutational Profiling (MaP) allows for the thorough examination of secondary structures in low-abundance RNAs in their biological context. So far, the method has been used for analyzing the RNA secondary structures of several viruses including SARS-CoV-2 in virio and in cellulo. Here, we used SHAPE-MaP and dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) for genome-wide secondary structure analysis of viral RNA (vRNA) of the pandemic influenza A/California/04/2009 (H1N1) strain in both in virio and in cellulo environments. Experimental data allowed the prediction of the secondary structures of all eight vRNA segments in virio and, for the first time, the structures of vRNA5, 7, and 8 in cellulo. We conducted a comprehensive structural analysis of the proposed vRNA structures to reveal the motifs predicted with the highest accuracy. We also performed a base-pairs conservation analysis of the predicted vRNA structures and revealed many highly conserved vRNA motifs among the IAVs. The structural motifs presented herein are potential candidates for new IAV antiviral strategies.

Generation and Functional Analysis of Defective Viral Genomes during SARS-CoV-2 Infection.

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from transcriptome sequencing (RNA-seq) data sets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hot spots were identified for DVG recombination, and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single-cell RNA-seq analysis indicated the interferon (IFN) stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the next-generation sequencing (NGS) data set from a published cohort study and observed a significantly higher amount and frequency of DVG in symptomatic patients than those in asymptomatic patients. Finally, we observed exceptionally diverse DVG populations in one immunosuppressive patient up to 140 days after the first positive test of COVID-19, suggesting for the first time an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and into how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection. IMPORTANCE Defective viral genomes (DVGs) are generated ubiquitously in many RNA viruses, including SARS-CoV-2. Their interference activity to full-length viruses and IFN stimulation provide the potential for them to be used in novel antiviral therapies and vaccine development. SARS-CoV-2 DVGs are generated through the recombination of two discontinuous genomic fragments by viral polymerase complex, and this recombination is also one of the major mechanisms for the emergence of new coronaviruses. Focusing on the generation and function of SARS-CoV-2 DVGs, these studies identify new hot spots for nonhomologous recombination and strongly suggest that the secondary structures within viral genomes mediate the recombination. Furthermore, these studies provide the first evidence for IFN stimulation activity of de novo DVGs during natural SARS-CoV-2 infection. These findings set up the foundation for further mechanism studies of SARS-CoV-2 recombination and provide evidence to harness the immunostimulatory potential of DVGs in the development of a vaccine and antivirals for SARS-CoV-2.

LazySampling and LinearSampling: fast stochastic sampling of RNA secondary structure with applications to SARS-CoV-2.

Many RNAs fold into multiple structures at equilibrium, and there is a need to sample these structures according to their probabilities in the ensemble. The conventional sampling algorithm suffers from two limitations: (i) the sampling phase is slow due to many repeated calculations; and (ii) the end-to-end runtime scales cubically with the sequence length. These issues make it difficult to be applied to long RNAs, such as the full genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address these problems, we devise a new sampling algorithm, LazySampling, which eliminates redundant work via on-demand caching. Based on LazySampling, we further derive LinearSampling, an end-to-end linear time sampling algorithm. Benchmarking on nine diverse RNA families, the sampled structures from LinearSampling correlate better with the well-established secondary structures than Vienna RNAsubopt and RNAplfold. More importantly, LinearSampling is orders of magnitude faster than standard tools, being 428× faster (72 s versus 8.6 h) than RNAsubopt on the full genome of SARS-CoV-2 (29 903 nt). The resulting sample landscape correlates well with the experimentally guided secondary structure models, and is closer to the alternative conformations revealed by experimentally driven analysis. Finally, LinearSampling finds 23 regions of 15 nt with high accessibilities in the SARS-CoV-2 genome, which are potential targets for COVID-19 diagnostics and therapeutics.

Specific length and structure rather than high thermodynamic stability enable regulatory mRNA stem-loops to pause translation.

Translating ribosomes unwind mRNA secondary structures by three basepairs each elongation cycle. Despite the ribosome helicase, certain mRNA stem-loops stimulate programmed ribosomal frameshift by inhibiting translation elongation. Here, using mutagenesis, biochemical and single-molecule experiments, we examine whether high stability of three basepairs, which are unwound by the translating ribosome, is critical for inducing ribosome pauses. We find that encountering frameshift-inducing mRNA stem-loops from the E. coli dnaX mRNA and the gag-pol transcript of Human Immunodeficiency Virus (HIV) hinders A-site tRNA binding and slows down ribosome translocation by 15-20 folds. By contrast, unwinding of first three basepairs adjacent to the mRNA entry channel slows down the translating ribosome by only 2-3 folds. Rather than high thermodynamic stability, specific length and structure enable regulatory mRNA stem-loops to stall translation by forming inhibitory interactions with the ribosome. Our data provide the basis for rationalizing transcriptome-wide studies of translation and searching for novel regulatory mRNA stem-loops.

Secondary Structure of Influenza A Virus Genomic Segment 8 RNA Folded in a Cellular Environment.

Influenza A virus (IAV) is a member of the single-stranded RNA (ssRNA) family of viruses. The most recent global pandemic caused by the SARS-CoV-2 virus has shown the major threat that RNA viruses can pose to humanity. In comparison, influenza has an even higher pandemic potential as a result of its high rate of mutations within its relatively short (<13 kbp) genome, as well as its capability to undergo genetic reassortment. In light of this threat, and the fact that RNA structure is connected to a broad range of known biological functions, deeper investigation of viral RNA (vRNA) structures is of high interest. Here, for the first time, we propose a secondary structure for segment 8 vRNA (vRNA8) of A/California/04/2009 (H1N1) formed in the presence of cellular and viral components. This structure shows similarities with prior in vitro experiments. Additionally, we determined the location of several well-defined, conserved structural motifs of vRNA8 within IAV strains with possible functionality. These RNA motifs appear to fold independently of regional nucleoprotein (NP)-binding affinity, but a low or uneven distribution of NP in each motif region is noted. This research also highlights several accessible sites for oligonucleotide tools and small molecules in vRNA8 in a cellular environment that might be a target for influenza A virus inhibition on the RNA level.

Social Norms and Engagement in Protective Health Behaviors Among Rural Health Providers.

The COVID-19 pandemic has had far-reaching effects for individuals and healthcare systems in the United States. Increasing and sustaining behavioral changes to reduce transmission of disease among medical providers is essential for the protection of the community at large. Using a social norms perspective, this study aimed to (a) examine the accuracy of perceptions of engagement in protective health behaviors among a sample of rural health providers, and (b) determine whether greater self-other discrepancies were associated with engagement in these behaviors. Electronic surveys were completed by 214 rural medical providers. Findings suggested that rural healthcare providers had exaggerated perceptions of peer engagement in several COVID-19-related protective health behaviors. As expected, positive self-other differences were positively associated with providers' own behaviors, and perceived descriptive norms were associated with providers' engagement in these behaviors. Future studies using normative interventions might examine how positive self-other differences increase the use of protective health behaviors over time.

LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2.

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.