bluuebunny/crossref_metadata_2025 huggingface dataset:
Bacterial profile of wound site infections and evaluation of risk factors for sepsis among road traffic accident patients from Apex Trauma Centre, Northern India
Authors: Singh Aparna , Singh Patel Sangram , Sahu Chinmoy , Singh Amit Kumar , Tejan Nidhi , Varghese Gerlin , Jamwal Ashima , Singh Pooja , Ghar Malay
Published: October 2024

Background. Among the most significant yet often ignored health issues worldwide are trauma and accidental injuries. India accounts for 11% of global deaths in road accidents, the highest in the world, according to the World Bank report. There are limited data about the bacterial contamination of road traffic accident (RTA) wounds and their antibiotic susceptibility patterns.

Materials and Methods. This prospective study was conducted in a tertiary care centre in northern India from January 2023 to January 2024. Wound deep swabs or aspirates were collected from RTA patients with traumatic injuries at different time intervals. Gram stain and culture were performed, and positive aerobic culture was subjected to antibiotic susceptibility testing. Organism identification was done using MALDI-TOF MS and routine biochemical tests. Blood samples were also collected to rule out bloodstream infections during follow-up if the patient became febrile or showed symptoms of systemic infection. Sepsis was defined in those patients who had two or more scores in the systemic inflammatory response syndrome criteria with a positive microbiological culture. Risk factors were evaluated for sepsis on the basis of the patient’s vitals, injury characteristics, procalcitonin, Glasgow Coma Scale (GCS) score, need for mechanical ventilation and complete blood count, which were obtained from the patient’s admission file.

Results. A total of 189 wound samples were collected, of which 99 (52.38%) samples showed the growth of microorganisms. The aerobic isolates included 69 (69.69%) Gram-negative bacilli, of which the majority were Klebsiella pneumoniae, 28 (28.28%) Gram-positive cocci, of which the majority were Staphylococcus aureus and 2 (2.02%) anaerobic isolates. Among the Gram-negative isolates, none of the isolates were resistant to colistin. All S. aureus isolates were susceptible to vancomycin, teicoplanin and levonadifloxacin. Sepsis developed in 50 (26.45 %) patients. Significant risk factors evaluated for sepsis were a raised procalcitonin level, a low GCS score, a higher injury severity score, the need for mechanical ventilation and a raised quick sequential organ failure assessment score.

Conclusion. It is essential to ascertain the profile of microorganisms isolated from RTA wounds in order to reduce antibiotic resistance and deliver efficient treatment.
Cholesterol differentially modulates the activity of opioid and muscarinic receptors via common non-canonical binding site
Authors: Chetverikov Nikolai , Janoušková-Randáková Alena , Nelic Dominik , Jakubík Jan
Published: October 2024

G protein-coupled receptors (GPCRs) are membrane proteins representing the largest and most therapeutically targeted receptor class for 30% of currently marketed drugs. Two binding motifs for membrane cholesterol, the ‘cholesterol recognition amino acid consensus (CRAC) domain and ‘cholesterol consensus motif’ (CCM), were postulated. Using a simulation of molecular dynamics we demonstrate the binding of membrane cholesterol to non-canonical sites, distinct from CRAC and CCM, at muscarinic and opioid receptors. We identified a binding site common for muscarinic and opioid receptors at TM6, arginine 6.35 being the principal residue. Effects of depletion of membrane cholesterol on the functional responses of these receptors correlated with effects of mutations arginine 6.35 on functional responses. Exploiting cholesterol-binding sites and variations in receptor-membrane interactions brings novel pharmacotherapeutic opportunities including tissue-specific targeting for sterol-based modulators.
Structural insights into inhibitor mechanisms on immature HIV-1 Gag lattice revealed by high-resolutionin situsingle-particle cryo-EM
Authors: Wu Chunxiang , Meuser Megan E. , Rey Juan S. , Meshkin Hamed , Yang Rachel , Devarkar Swapnil Chandrakant , Freniere Christian , Shi Jiong , Aiken Christopher , Perilla Juan R. , Xiong Yong
Published: October 2024

HIV-1 inhibitors, such as Bevirimat (BVM) and Lenacapavir (LEN), block the production and maturation of infectious virions. However, their mechanisms remain unclear due to the absence of high-resolution structures for BVM complexes and LEN’s structural data being limited to the mature capsid. Utilizing perforated virus-like particles (VLPs) produced from mammalian cells, we developed an approach to determinein situcryo-electron microscopy (cryo-EM) structures of HIV-1 with inhibitors. This allowed for the first structural determination of the native immature HIV-1 particle with BVM and LEN bound inside the VLPs at high resolutions. Our findings offer a more accurate model of BVM engaging the Gag lattice and, importantly, demonstrate that LEN not only binds the mature capsid but also targets the immature lattice in a distinct manner. The binding of LEN induces a conformational change in the capsid protein (CA) region and alters the architecture of the Gag lattice, which may affect the maturation process. These insights expand our understanding of the inhibitory mechanisms of BVM and LEN on HIV-1 and provide valuable clues for the design of future inhibitors.
Toxicokinetics for organ-on-chip devices
Authors: Hermann Nathaniel G. , Ficek Richard A. , Markov Dmitry A. , McCawley Lisa J. , Hutson M. Shane
Published: October 2024

Organ-on-chip (OOC) devices are an emerging New Approach Method in both pharmacology and toxicology. Such devices use heterotypic combinations of human cells in a micro-fabricated device to mimicin vivoconditions and better predict organ-specific toxicological responses in humans. One drawback of these devices is that they are typically made from polydimethylsiloxane (PDMS), a polymer known to interact with hydrophobic chemicals. Due to this interaction, the actual dose experienced by cells inside OOC devices can differ strongly from the nominal dose. To account for these effects, we have developed a comprehensive toxicokinetic approach to measure and model chemical-PDMS interactions, including partitioning into and diffusion through PDMS. We use these methods to characterize PDMS interactions for ten chemicals, ranging from fluorescent dyes to persistent organic pollutants to organophosphate pesticides. We further show that these methods return physical interaction parameters that can be used to accurately predict time-dependent doses under continuous-flow conditions, as would be present in an OOC device. These results demonstrate the validity of the methods and model across geometries and flow rates.
Glucose-dependent glycosphingolipid biosynthesis fuels CD8+T cell function and tumor control
Authors: Longo Joseph , DeCamp Lisa M. , Oswald Brandon M. , Teis Robert , Reyes-Oliveras Alfredo , Dahabieh Michael S. , Ellis Abigail E. , Vincent Michael P. , Damico Hannah , Gallik Kristin L. , Compton Shelby E. , Capan Colt D. , Williams Kelsey S. , Esquibel Corinne R. , Madaj Zachary B. , Lee Hyoungjoo , Roy Dominic G. , Krawczyk Connie M. , Haab Brian B. , Sheldon Ryan D. , Jones Russell G.
Published: October 2024

Glucose is essential for T cell proliferation and function, yet its specific metabolic rolesin vivoremain poorly defined. Here, we identify glycosphingolipid (GSL) biosynthesis as a key pathway fueled by glucose that enables CD8+T cell expansion and cytotoxic functionin vivo. Using13C-based stable isotope tracing, we demonstrate that CD8+effector T cells use glucose to synthesize uridine diphosphate-glucose (UDP-Glc), a precursor for glycogen, glycan, and GSL biosynthesis. Inhibiting GSL production by targeting the enzymes UGP2 or UGCG impairs CD8+T cell expansion and cytolytic activity without affecting glucose-dependent energy production. Mechanistically, we show that glucose-dependent GSL biosynthesis is required for plasma membrane lipid raft integrity and aggregation following TCR stimulation. Moreover, UGCG-deficient CD8+T cells display reduced granzyme expression and tumor controlin vivo. Together, our data establish GSL biosynthesis as a critical metabolic fate of glucose—independent of energy production—required for CD8+T cell responsesin vivo.
Shaping Kale Morphology and Physiology Using Precision LED Light Recipes
Authors: Scandola Sabine , Grubb Lauren E. , Castillo Brigo , Iliscupidez Lexyn , Kennedy Curtis , Boyce Nicholas , Talasila Mohana , Uhrig R. Glen
Published: October 2024

Light emitting diodes (LEDs) present an innovative opportunity for manipulating plant growth and development. Light serves as a fundamental energy source and an environmental cue, providing an ability to control plant traits through the precise manipulation of light intensity and quality. Here, we assess effects of light intensity and spectral composition on the growth and physiology of the important model plant: Kale (Brassica oleracea), known for its phenotypic plasticity and nutritional composition, kale is a crop well-suited for indoor cultivation. Here, we employ a combination of advanced phenotyping, computer vision, gas chromatography-mass spectrometry (GC-MS) metabolomics, and liquid chromatography-mass spectrometry (LC-MS)-based quantitative proteomics to characterize the molecular changes that underpin light-dictated differences in the growth and metabolism of two different kale cultivars under different light intensity and spectral composition scenarios. We identify time-of-day and cultivar-specific light intensity and spectral composition-induced changes related to growth, shade avoidance, photosynthesis and several nutritional components including amino acids, glucosinolates and carotenoids. Our results offer a key resource to the plant community and demonstrate the translational potential of light manipulation in tailoring kale growth and nutritional content for enhanced crop productivity and/or health benefits, while simultaneously offering a more cost-effective solution for contemporary agricultural challenges.
PhenoVision: A framework for automating and delivering research-ready plant phenology data from field images
Authors: Dinnage Russell , Grady Erin , Neal Nevyn , Deck Jonn , Denny Ellen , Walls Ramona , Seltzer Carrie , Guralnick Robert , Li Daijiang
Published: October 2024

Plant phenology plays a fundamental role in shaping ecosystems, and global change-induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high-quality observations of when plants undergo seasonal transitions such as leaf-out, flowering, and fruiting are critical for tracking causes and consequences of phenology shifts, but these data are often sparse and biased globally. These data gaps limit broader generalizations and forecasting improvements in the face of continuing disturbance. One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global scale research-grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist – data spanning all angiosperms and drawn from across the globe – to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. PhenoVision utilizes a vision transformer architecture pretrained with a masked autoencoder to improve classification success, and it achieves high accuracy for flower (98.5%) and fruit presence (95%). Key to producing research-ready phenology data is post-calibration tuning and validation focused on reducing noise inherent in field photographs, and maximizing the true positive rate. We also develop a standardized set of quality metrics and metadata so that results can be used effectively by the community. Finally, we showcase how this effort vastly increases phenology data coverage, including regions of the globe where data have been limited before. Our end products are tuned models, new data resources, and an application streamlining discovery and use of those data for the broader research and management community. We close by discussing next steps, including automating phenology annotations, adding new phenology targets, e.g., leaf phenology, and further integration with other resources to form a global central database integrating all in-situ plant phenology resources.
Semi-supervised Omics Factor Analysis (SOFA) disentangles known and latent sources of variation in multi-omic data
Authors: Capraz Tümay , Vöhringer Harald , Kruger Serrano Klaus Sebastian Augusto , Ramirez Flores Ricardo Omar , Saez-Rodriguez Julio , Huber Wolfgang
Published: October 2024

A fundamental design pattern in biomolecular studies is to assay the same set of samples (organisms, tissue biopsies, or individual cells) by multiple different ‘omics assays. Group Factor Analysis (GFA) and its adaptation to high-dimensional settings, Multi-Omics Factor Analysis (MOFA), are widely used as a first-line approach to analyse such data and are effective in detecting patterns of correlation, organize them into so-called latent factors, and identify common and assay-specific factors. However, in many applications a subset of the found factors just rediscovers already known covariates (e.g., disease subtypes, environmental covariates) while others may represent genuine novelty.

Here, we present Semi-supervised Omics Factor Analysis (SOFA), a method that incorporates known covariates into the model upfront and focuses the factor discovery on novel sources of variation. We show SOFA’s effectiveness for discovering novel patterns by applying it to cancer, brain development and heart failure multi-omic data sets.
A Suite of Foundation Models Captures the Contextual Interplay Between Codons
Authors: Naghipourfar Mohsen , Chen Siyu , Howard Mathew , Macdonald Christian , Saberi Ali , Hagen Timo , Mofrad Mohammad , Coyote-Maestas Willow , Goodarzi Hani
Published: October 2024

In the canonical genetic code, many amino acids are assigned more than one codon. Work by us and others has shown that the choice of these synonymous codon is not random, and carries regulatory and functional consequences. Existing protein foundation models ignore this context-dependent role of coding sequence in shaping the protein landscape of the cell. To address this gap, we introduce cdsFM, a suite of codon-resolution large language models, including both EnCodon and DeCodon models, with up to 1B parameters. Pre-trained on 60 million protein-coding sequences from more than 5,000 species, our models effectively learn the relationship between codons and amino acids, recapitualing the overall structure of the genetic code. In addition to outperforming state-of-the-art genomic foundation models in a variety of zero-shot and few-shot learning tasks, the larger pre-trained models were superior in predicting the choice of synonymous codons. To systematically assess the impact of synonymous codon choices on protein expression and our models' ability to capture these effects, we generated a large dataset measuring overall and surface expression levels of three proteins as a function of changes in their synonymous codons. We showed that our EnCodon models could be readily fine-tuned to predict the contextual consequences of synonymous codon choices. Armed with this knowledge, we applied EnCodon to existing clinical datasets of synonymous variants, and we identified a large number of synonymous codons that are likely pathogenic, several of which we experimentally confirmed in a cell-based model. Together, our findings establish the cdsFM suite as a powerful tool for decoding the complex functional grammar underlying the choice of synonymous codons.
Small amounts of misassembly can have disproportionate effects on pangenome-based metagenomic analyses
Authors: Majernik Stephanie N. , Beaver Larry , Bradley Patrick H.
Published: October 2024

Individual genes from microbiomes can drive host-level phenotypes. To help identify such candidate genes, several recent tools estimate microbial gene copy numbers directly from metagenomes. These tools rely on alignments to pangenomes, which in turn are derived from the set of all individual genomes from one species. While large-scale metagenomic assembly efforts have made pangenome estimates more complete, mixed communities can also introduce contamination into assemblies, and it is unknown how robust pangenome-based metagenomic analyses are to these errors. To gain insight into this problem, we re-analyzed a case-control study of the gut microbiome in cirrhosis, focusing on commensal Clostridia previously implicated in this disease. We tested for differentially prevalent genes in theLachnospiraceae, then investigated which were likely to be contaminants using sequence similarity searches. Out of 86 differentially prevalent genes, we found that 33 (38%) were probably contaminants originating in taxa such asVeillonellaandHaemophilus, unrelated genera that were independently correlated with disease status. Our results demonstrate that even small amounts of contamination in metagenome assemblies, below typical quality thresholds, can threaten to overwhelm gene-level metagenomic analyses. However, we also show that such contaminants can be accurately identified using a method based on gene-to-species correlation. After removing these contaminants, we observe that several flagellar motility gene clusters in theLachnospira eligenspangenome are associated with cirrhosis status. We have integrated our analyses into an analysis and visualization pipeline, PanSweep, that can automatically identify cases where pangenome contamination may bias the results of gene-resolved analyses.

Importance

Metagenome-assembled genomes, or MAGs, can be constructed without pure cultures of microbes. Large scale efforts to build MAGs have yielded more complete pangenomes (i.e., sets of all genes found in one species). Pangenomes allow us to measure strain variation in gene content, which can strongly affect phenotype. However, because MAGs come from mixed communities, they can contaminate pangenomes with unrelated DNA, and how much this impacts downstream analyses has not been studied. Using a metagenomic study of gut microbes in cirrhosis as our test case, we investigate how contamination affects analyses of microbial gene content. Surprisingly, even small, typical amounts of MAG contamination (<5%) result in disproportionately high levels of false positive associations (38%). Fortunately, we show that most contaminants can be automatically flagged, and provide a simple method for doing so. Furthermore, applying this method reveals a new association between cirrhosis and gut microbial motility.
Identification of key genes and enriched biological processes in diabetes-associated traumatic brain injury through weighted gene correlation network and bioinformatics analysis
Authors: Saidu Umar Faruk , Bulama Ibrahim
Published: October 2024

Traumatic brain injury (TBI) is a significant cause of morbidity and mortality worldwide, with long-term neurological and psychological impacts. Recent studies have indicated that diabetes (T1DM) exacerbates the outcomes of TBI, leading to more severe cognitive deficits and increased risk of complications. This study investigated the underlying molecular mechanisms and potential therapeutic targets for T1DM-associated TBI. Four mRNA datasets (GSE4745, GSE125451, GSE173975, and GSE80174) downloaded from GEO repository were used in this study. Using limma, a total of 284 differentially expressed genes (DEGs) were identified in T1DM, of which 11 were upregulated and 9 were downregulated. GSEA showed that these DEGs were significantly enriched in cell communication, lipid metabolic process, and PPAR signaling. A total of 584 DEGs were identified in TBI, of which 186 were upregulated and 9 were downregulated. GSEA showed that these DEGs were mainly enriched in immune response-regulating signaling pathway. WGCNA identified 122 significant genes in TIDM-related modules and 368 significant genes in TBI-related module. GO and KEGG enrichment analysis showed that T1DM module genes were significantly correlated with lipid metabolic process and ribosome biogenesis, while TBI module genes were significantly correlated with inflammation and immune response, including leukocyte mediated immunity, lymphocyte mediated immunity, and cytokine mediated receptor activity. PPI network analysis of T1DM module genes identified 20 hub genes, including 14 ribosomal genes: Rpl23, Rps3a, Rps6, Rpl5, Rpl17, Rps24, Rpl23a, Rps4x, Rpl9, Rps15a14, Rpl30, Rpl31, Rps25, and Rps27a-2. The hub genes were primarily related to ribosome biogenesis and RNA post-transcriptional regulation. PPI network analysis of TBI module genes identified 20 hub genes: Ptprc, Tp53, Stat1, Stat3, Tyrobp, Itgad, Csf1r, Itgb2, Rac2, Icam1, Myd88, Cd44, Vav1, Aif1, C1qa, Laptm5, B2m, Fcer1g, and Lyn. The hub genes were primarily related to inflammatory mediators and immune response. Based on the overlap of T1DM module genes and TBI module genes, Cmklr1, Mgst1, and Plin2, were identified as key genes of T1DM-associated TBI. Functional enrichment analysis showed that they were primarily enriched in the cellular response to hydroperoxide, cytokine-mediated receptor signaling activity, regulation of sequestering of triglyceride, negative regulation of IL-12 production, and positive regulation of macrophage chemotaxis. Using Reactome, Cmklr1, Mgst1, and Plin2 were related to cytokine signaling activity, neutrophil degranulation, and lipid storage, respectively. We concluded that Lipid droplet dysregulation and Neuroinflammation are the potential molecular mechanisms of T1DM-associated TBI. Cmklr1, Mgst1, and Plin2 are positively correlated with T1DM-associated TBI and may be important biomarkers and potential treatment targets for diabetic TBI.
A versatile protocol for purifying recombinant proteins fromNicotiana benthamianafor structural studies
Authors: Lawson Aaron W. , Macha Arthur , Neumann Ulla , Gunkel Monika , Chai Jijie , Behrmann Elmar , Schulze-Lefert Paul
Published: October 2024

Structural biology is an essential tool for understanding the molecular basis of biological processes. Although predicting protein structures by fold recognition algorithms has become increasingly powerful, especially with the integration of deep-learning approaches, experimentally resolved structures are indispensable for guiding structure-function studies and for improving modelling. However, experimental structural studies of protein complexes are still challenging, owing to, for example, the necessity for high protein concentrations and purity for downstream analyses such as cryogenic electron microscopy (cryo-EM). The use ofNicotiana benthamianaleaves as a transient expression system for recombinant proteins has become an increasingly attractive approach as the plant is inexpensive to cultivate, grows rapidly, allows fast experimental turnaround and is easily scalable compared to other established systems such as insect cell cultures. UsingN. benthamianaas an expression system, we present here a robust and versatile protocol for the purification of five heterocomplexes with sizes ranging from ∼140 kDa to ∼660 kDa consisting of immunoreceptors and their associated pathogen effectors, followed by electron microscopy. The plant-based protocol was applied to verify the structure of the insect cell-derived wheat Sr35 resistosome and to co-purify and co-resolve a ∼140 kDa homodimer of the AvrSr35 effector from the fungusPuccinia graminisf sptritici(Pgt). In several cases, only a single epitope tag is needed for complex purification, reducing complications that come with multiple epitope tags and two-step affinity purifications. We identify codon usage, signal peptide fusion, epitope tag choice and detergents as critical factors for expression and purification of recombinant protein fromN. benthamianaleaves.
X-ray Diffraction Reveals Periodicity in Murine Neocortex
Authors: Murokh Sasha , Willerson Ezekiel , Lazarev Alexander , Lazarev Pavel , Brumberg Joshua C.
Published: October 2024

Background

Sensory experience impacts brain development. In the mouse somatosensory cortex, sensory deprivation via whisker trimming induces reductions in the perineuronal net (PNN), the size of neuronal cell bodies, the size and orientation of dendritic arbors, the density of dendritic spines, and the level of myelination, among other effects.

New Methods

Here, we measured the X-ray diffraction patterns of mouse brain tissue to establish a novel method for examining nanoscale brain structures. Two groups of mice were examined: a control group and one that underwent 30 days of whisker-trimming from birth - an established method of sensory deprivation that affects the mouse barrel cortex (whisker sensory processing region of the primary somatosensory cortex). Mice were perfused, and primary somatosensory cortices (barrel cortex) were isolated for immunocytochemistry and X-ray diffraction imaging.

Results

X-ray images were characterized using a specially developed machine-learning approach, and the clusters that correspond to the two groups are well separated in the space of the principal components. The obtained values for sensitivity/specificity are 1/0.93, and the receiver operator curve classifier is 0.99.

Conclusions

We hypothesize that such separation is related to the development of different nanoscale structural components in the brains of control and sensory deprived mice. The effects of these nanoscale structural formations can be seen in PNN and other micro- and macro-scale structures and assemblies.
Suppression of Visceral Nociception by Selective C-Fiber Transmission Block Using Temporal Interference Sinusoidal Stimulation
Authors: Zhang Shaopeng , Chen Longtu , Woon Eric , Liu Jia , Ryu Jaehyeon , Chen Hsin , Fang Hui , Feng Bin
Published: October 2024

Chronic visceral pain management remains challenging due to limitations in selective targeting of C-fiber nociceptors. This study investigates temporal interference stimulation (TIS) on dorsal root ganglia (DRG) as a novel approach for selective C-fiber transmission block. We employed (1) GCaMP6 recordings in mouse whole DRG using a flexible, transparent microelectrode array for visualizing L6 DRG neuron activation, (2) ex vivo single-fiber recordings to assess sinusoidal stimulation effects on peripheral nerve axons, (3) in vivo behavioral assessment measuring visceromotor responses (VMR) to colorectal distension in mice, including a TNBS-induced visceral hypersensitivity model, and (4) immunohistological analysis to evaluate immediate immune responses in DRG following TIS. We demonstrated that TIS (2000 Hz and 2020 Hz carrier frequencies) enabled tunable activation of L6 DRG neurons, with the focal region adjustable by altering stimulation amplitude ratios. Low-frequency (20-50 Hz) sinusoidal stimulation effectively blocked C-fiber and Aδ-fiber transmission while sparing fast-conducting A-fibers, with 20 Hz showing highest efficacy. TIS of L6 DRG reversibly suppressed VMR to colorectal distension in both control and TNBS-induced visceral hypersensitive mice. The blocking effect was fine-tunable by adjusting interfering stimulus signal amplitude ratios. No apparent immediate immune responses were observed in DRG following hours-long TIS. In conclusion, TIS on lumbosacral DRG demonstrates promise as a selective, tunable approach for managing chronic visceral pain by effectively blocking C-fiber transmission. This technique addresses limitations of current neuromodulation methods and offers potential for more targeted relief in chronic visceral pain conditions.
STOPS and multivariate hierarchical aglomerative clustering: segmentation of the public regarding children’s vaccinaton communication in Slovenia
Authors: Kropivnik Samo , Vrdelja Mitja
Published: October 2024

Purpose

From a public health perspective, vaccination programmes significantly add to long-term, safe co-existence. However, because there is no social consensus about their benefits and risks, the promotion of vaccinations is difficult. Based on Kim and Grunig’s situational theory of problem solving (STOPS), including communicative action in problem solving (CAPS), this paper both proposes a model for identifying the involvement of mothers of young children in communication regarding vaccination and advocates for a novel approach to STOPS and CAPS data analyses.

Design/methodology/approach

The methodological design develops empirical analyses of the data yielded by the STOPS model. Two approaches to determining associations between situational-motivational variables and communicative-action variables in random-sample survey data obtained in Slovenia in 2016 (N = 1704) are implemented – i.e. visual methods and multivariate agglomerative clustering algorithm.

Findings

The STOPS model has been confirmed and both data-analyses approaches have shown potential by clearly demonstrating associations and patterns in the data. Based on these findings, we conclude that they have the potential to be the norm in analysing STOPS models.

Research limitations/implications

Limitations of the study, which are still to be overcome, involve drawing on one sample in one country and testing only one set of indicators.

Practical implications

From an academic point of view, confirmation of both the model and the analytical power of the pragmatic data-analyses methods significantly add to communication studies. From practical and social points of view, relationships among attitudes and communication behaviour, as outlined in the exposed segments of the public, enable the improvement of every step in strategic-communication planning and implementation.

Originality/value

This paper fulfils an identified need to establish a theoretical framework and methodology of segmentation in vaccination-communication studies.
Knowledge towards health impacts of climate change among Amhara Sayint community, Northeastern Ethiopia
Authors: Kassaw Genanew Mulugeta , Malede Asmamaw , Gizeyatu Adinew , Adane Metadel , Embrandiri Asha , Keleb Awoke , Berihun Gete , Geto Abebe Kassa , Wogayehu Belachew Tekleyohannes , Abebe Gebremeskel Kibret , Zewde Tibebu Habtie , Debela Sisay Abebe , Seyoum Solomon , Luke Amana Ogeto , Wondaya Gashaw , Kide Atitegeb Abera , Moges Mekonnen , Metoyer Denise , Gebrehiwot Mesfin
Published: October 2024

Purpose

This study aims to assess the knowledge of Amhara Sayint district community in Northeastern Ethiopia towards the health impacts of climate change.

Design/methodology/approach

A community-based cross-sectional study was conducted from 20 July to 5 September 2022, involving 605 randomly selected households in Amhara Sayint district, Northeastern Ethiopia. Data were collected through face-to-face interviews using a pilot-tested and content-validated questionnaire administered via Kobo Collect survey tool. The data were then exported to STATA for management and analysis. A proportional odds approach of ordinal logistic regression model was fitted with a 95% confidence level to identify factors associated with the community’s knowledge towards the health impacts of climate change.

Findings

Of the total respondents, 3.47% (95% confidence interval [CI]: 2.1, 5.3) and 42.98% (95% CI: 38.9, 47) had inadequate and medium levels of knowledge towards the health impacts of climate change, respectively. Proportional odds model analysis result indicated that gender (female) (adjusted odds ratio [AOR] = 1.49; 95% CI: 1.053, 2.13), wealth status (poorest) (AOR = 1.97; 95% CI: 1.01, 3.83), educational status (unable to read and write) (AOR = 4.13; 95% CI: 1.25, 13.68) and participation status in environmental protection activities (not) (AOR = 1.78; 95% CI: 1.17, 2.71) were factors that significantly associated with inadequate and medium levels of knowledge towards the health impacts of climate change.

Research limitations/implications

The result of this research is not supported by qualitative methods.

Practical implications

Residents of the study area know the climate is changing; however, they lack adequate knowledge towards the health impacts of climate change.

Social implications

Significant number of people have inadequate level of knowledge about health impacts of climate change so relevant health education is needed.

Originality/value

This research is original in its focus on assessing public knowledge about the health impacts of climate change in the Amhara Sayint district, northeastern Ethiopia, a perspective often overlooked in existing studies. Its findings are crucial for guiding future research and developing targeted public health strategies and educational initiatives.
Exploring diabetic patients’ intention to switch POCT self-monitoring glucose devices: investigating influence of perceived usefulness and perceived ease of use
Authors: Krishnan Geetha , J.N.V. Raghuram
Published: October 2024

Purpose

The study aims to examine diabetic patients’ switching intentions toward self-monitoring blood glucose devices, incorporating perceived usefulness and perceived ease of use, with switching cost as a moderator.

Design/methodology/approach

A self-administered questionnaire was developed using established measuring scales. Data from 321 respondents was collected and analyzed using the partial least squares structural equation modeling approach.

Findings

Results indicate that perceived usefulness and perceived ease of use influence switching intention. Switching cost does not moderate the perceived usefulness, perceived ease of use and switching intention. This study reveals important information for healthcare practitioners and device manufacturers.

Originality/value

The significance and originality of this study stem from its pioneering investigation into the switching intentions of diabetic patients regarding self-monitoring glucose devices through the application of the technology acceptance model, thereby addressing a notable gap in the existing literature on diabetes management and technology adoption, which has lacked comprehensive examination of patients’ transition behaviors in this specific context.
Leadership essential to steer success within the retail sector
Authors: Bhasin Sanjay
Published: October 2024

Purpose

The purpose of this investigation was to determine the leadership necessary for the retail sector which is having to embrace complexity induced through accelerating changes in consumer behaviour and expanding information accessibility.

Design/methodology/approach

Semi-structured interviews were used for across 10 disparate retail organisations deployed as case studies safeguarding all quality considerations were fully integrated.

Findings

The findings reveal brands are coping with greater complexity than before. Loyalty is eroding whilst social media increases the complexity for brands to uphold control of their image. Developing the market without sacrificing exclusivity or alienating core consumers necessitates effective leadership. The deductions revealed that despite some leadership styles seemingly more conducive, that specific qualities are more important for the retail sector.

Research limitations/implications

By selecting ten organisations every effort was made to ensure a greater degree of credibility in the findings. It is acknowledged that this investigation is sector specific. Nonetheless, there exists considerable transferability value as the role of leadership within the retail sector can mirror expectations within other environments.

Originality/value

It was validated that leaders need to foster an organisational culture promoting continuous learning whilst introducing a consumer-first perspective across the business. The findings reinforce how specific traits, behaviours and styles are imperative amongst leaders preserving the success of the retail organisation with less focus on specific leadership styles.
Exploring the dynamics of firm growth: the interplay of decision-making logic
Authors: Farokhmanesh Taraneh , Davari Ali , Baghersad Vajihe , Sajadi Seyed Mojtaba
Published: October 2024

Purpose

This paper investigates how various emergent theoretical perspectives in entrepreneurship research, representing diverse decision-making logics, influence firm growth and evolution. It explores the interaction among decision-making logics, including experimentation, affordable loss, flexibility and pre-commitment as components of effectuation, alongside causation and bricolage and their synergistic effects on firm growth.

Design/methodology/approach

This study uses a multi-phase, discovery-oriented approach. Initially, insights from existing literature on decision-making logic were combined with in-depth interviews with 10 Iranian entrepreneurs within the food sector. This phase used alternative template research to evaluate the principles of effectuation, causation and bricolage within case study data depicting firm growth. Subsequently, a self-administered survey was developed based on these insights and distributed to 205 entrepreneurs in Iran. The survey data was analysed using fuzzy-set qualitative comparative analysis (fsQCA) to identify key factors and pathways influencing firm growth.

Findings

Using a discovery-oriented approach, this study formulates a comprehensive framework detailing decision-making logics that influence firm growth. Through fsQCA, 12 distinct paths are identified, highlighting the complex interplay of causation, effectuation and bricolage in high-growth firms within the food sector.

Research limitations/implications

This study has limitations. FsQCA identifies only logically sufficient combinations, suggesting potential for exploring alternative pathways in future research. Given COVID-19’s impact on the food sector, examining decision-making logic across diverse contexts and industries is advisable. Additionally, exploring how bricolage, causation and effectuation affect outcomes like new product development and innovation is essential in a growth-focused context. It is also important to consider environmental and organizational factors influencing growth.

Originality/value

This paper pioneers the examination of emerging theoretical paradigms in entrepreneurship and their impact on firm growth. It identifies critical configurations of causation, effectuation and bricolage, providing actionable insights for navigating dynamic business environments.
Linking the trickle-down effect of supervisor incivility to turnover intentions in the context of SMEs: a serial mediation model
Authors: Theriou Georgios , Tasoulis Konstantinos , Kravariti Foteini
Published: October 2024

Purpose

Based on conservation of resources theory and incivility spiral theory, this study examines the relationship between supervisor incivility and turnover intentions by investigating the mediating roles of co-worker incivility and emotional exhaustion.

Design/methodology/approach

A serial mediation model was empirically tested with a sample of 600 employees in Greek small and medium-sized enterprises (SMEs).

Findings

Results indicate that supervisor incivility is linked to turnover intentions in three main ways. Directly, indirectly through emotional exhaustion, and indirectly through the serial mediation of co-worker incivility and emotional exhaustion.

Research limitations/implications

Possible limitations of the study include the sampling techniques and the self-reporting measures for data collection.

Practical implications

Findings attest that owners and managers of SMEs should take note of the trickle-down effect, namely that their own misbehavior will fuel co-worker incivility. Supervisor incivility has cumulative, deleterious consequences on employees, depleting their emotional resources and enhancing turnover intentions, and should therefore be restricted through appropriate practices.

Originality/value

Few empirical studies have explored the effects of supervisor incivility on the incivility manifested by others and their impact on turnover intentions. The study enriches the trickle-down effect literature by identifying an additional indirect path linking supervisor incivility and turnover intentions. Further, this study took place in SMEs, a context with limited research in abusive supervision and incivility yet highly pertinent, given that SMEs informality might leave such behavior unchecked.
Does robotic service quality determine robotic restaurant diners’ engagement behaviors? Role of customer engagement and attachment to the restaurant
Authors: Veera Raghavan Deepak Ramanan
Published: October 2024

Purpose

Robotic restaurants are very novel, and service robots in these restaurants are identified as offering unique advantages in terms of efficiency, tireless service and potentially lower operational costs. However, studying customer engagement with the robots can reveal aspects of robotic service that resonate with diners. Understanding how diners interact with robots can help create a more engaging and enjoyable atmosphere, bringing more business to restaurants. Building on the stimulus-organism-response (SOR) theory and place attachment theory, the purpose of this paper is to study the impact of the robotic service quality (RSQ) on the customer attachment to the robotic restaurant with the mediating role of the different dimensions of the customer engagement, like the Absorptive Attention, Enthusiastic Participation and Social Connection. Subsequently, the impact of the customer attachment to the robotic restaurant on different dimensions of customer engagement behaviors like augmenting, co-developing, influencing and mobilizing behaviors was also studied.

Design/methodology/approach

The cross-sectional data from 786 robotic restaurant diners in India who answered the self-administered structured questionnaires is utilized for this descriptive study. The study employed a purposive sampling strategy. The SMART-PLS 4.0 program was used to run structural equation modeling and analyze the data.

Findings

The results indicate that customer engagement dimensions like Absorptive Attention, Enthusiastic Participation and Social Connection differentially mediate the relationship between RSQ and customer attachment with the robotic restaurant. Customer attachment to the restaurant and the robotic services subsequently positively impact customer engagement behaviors.

Research limitations/implications

The study relied upon cross-sectional data from the Indian population above 18 years to test the proposed model. Further studies could test the model across different populations to generalize the study results.

Originality/value

This study addresses the need to investigate robotic restaurant diners’ engagement behaviors. By testing place attachment theory and the SOR framework, this study is the first to show that RSQ will impact the customer attachment with the robotic restaurant and that different dimensions of customer engagement mediate the relationship. It also validates the previous research findings that customer engagement is not a single global construct, and different sub-dimensions are to be explored. This study is also the first to show customer attachment to the robotic restaurant will impact customer engagement behaviors differently.
Removing random noise and improving the resolution of seismic data using deep‐learning transformers
Authors: Sun Qifeng , Feng Yali , Du Qizhen , Gong Faming
Published: October 2024

Post‐stack data are susceptible to noise interference and have low resolution, which impacts the accuracy and efficiency of subsequent seismic data interpretation. To address this issue, we propose a deep learning approach called Seis‐SUnet, which achieves simultaneous random noise suppression and super‐resolution reconstruction of seismic data. First, the Conv‐Swin‐Block is designed to utilize ordinary convolution and Swin transformer to capture the long‐distance dependencies in the spatial location of seismic data, enabling the network to comprehensively comprehend the overall structure of seismic data. Second, to address the problem of weakening the effective signal during network mapping, we use a hybrid training strategy of L1 loss, edge loss and multi‐scale structural similarity loss. The edge loss function directs the network training to focus more on the high‐frequency information at the edges of seismic data by amplifying the weight. Additionally, the verification of synthetic and field seismic datasets confirms that Seis‐SUnet can effectively improve the signal‐to‐noise ratio and resolution of seismic data. By comparing it with traditional methods and two deep learning reconstruction methods, experimental results demonstrate that Seis‐SUnet excels in removing random noise, preserving the continuity of rock layers and maintaining faults as well as being strong robustness.
Addressing cycle‐skipping in full‐waveform inversion using acoustic wave energy
Authors: Li Zhonglei , Yao Gang
Published: October 2024

Limitations in acquisition technologies lead to insufficient low‐frequency signals in field seismic data. Local optimization methods are the common approaches for full‐waveform inversion. Inaccurate initial velocity models and lack of low‐frequency signals in seismic data typically cause the local‐gradient‐based full‐waveform inversion to converge to a local minimum due to cycle‐skipping. The existing energy‐based objective functions can generate artificial low‐frequency signals successfully by squaring the pressure but overlook the law of energy conservation, which may mislead model updates. To overcome this issue, we combine acoustic wave potential energy and kinetic energy to develop a new objective function that fits the acoustic wave energy. The new acoustic‐wave‐energy‐based full‐waveform inversion considers the law of energy conservation. The new system creates low‐frequency signals to avoid cycle‐skipping and produce an accurate smooth background velocity model, which provides a sufficient starting model for conventional full‐waveform inversion. Numerical examples demonstrate that the combination of acoustic‐wave‐energy‐based full‐waveform inversion and conventional full‐waveform inversion can deliver more faithful and accurate final results than conventional full‐waveform inversion alone.
Koalas, friends and foes—The application of airborne eDNA for the biomonitoring of threatened species
Authors: Frere Celine , Jackson Nicola , Moreno Jarred , Oliveros Sandino Alejandro , Ball Sarah , Powell Daniel
Published: October 2024

Curbing global wildlife population declines will necessitate the protection of their habitat, and subsequent robust baseline information about wildlife that use and occupy it. Collating such information, however, remains a challenging and costly endeavour. Little did we know that traces of wildlife presence float in the air and can be detected through traces of DNA.

Here, we deployed samplers to test the applicability of collecting airborne eDNA for the detection of a threatened species, the koala, and its co‐occurring terrestrial mammalian community.

We develop a novel species specific qPCR assay to detect the presence of koalas and applied this in concert with a meta‐barcoding approach to detect the co‐occurring mammalian community.

Through sampling of airborne particles, we successfully detected koala presence accurately to habitat patch level, alongside 16 unique taxonomic assignments, successfully assigning 11 of these to species level including detections belonging to the wallaby, antechinus, members of possum family and invasive species such as foxes, domestic dogs and hares.

Synthesis and applications: We demonstrate the potential of airborne eDNA for the detection of threatened terrestrial wildlife and their surrounding ecological community under natural conditions. With achievable optimisations we detail how airborne eDNA may be applied for the management and monitoring of threated species for enhanced conservation efforts.