bluuebunny/crossref_metadata_2025_split huggingface dataset:
Host-derivedLactobacillus plantarumalleviates hyperuricemia by improving gut microbial community and hydrolase-mediated degradation of purine nucleosides
Authors: Fu Yang , Luo Xiao-Dan , Li Jin-Ze , Mo Qian-Yuan , Wang Xue , Zhao Yue , Zhang You-Ming , Luo Hao-Tong , Xia Dai-Yang , Ma Wei-Qing , Chen Jian-Ying , Wang Li-Hua , Deng Qiu-Yi , Ben Lukuyu , Saleemi Muhammad Kashif , Jiang Xian-Zhi , Chen Juan , Miao Kai , Lin Zhen-Ping , Zhang Peng , Ye Hui , Cao Qing-Yun , Zhu Yong-Wen , Yang Lin , Tu Qiang , Wang Wen-Ce 
Published: August 2024

AbstractGut microbiota has been considered to be associated with the development of hyperuricemia (HUA) and gout. Some prebiotics from different sources had the potential alleviation function in HUA. However, whether host-derived potential probiotics, like Lactobacillus, from the gut of HUA animals could prevent the development of HUA is still uncertain.In this study, we established a HUA geese model and isolated aLactobacillus plantarumstrain, SQ001, from the cecum of HUA geese. Nucleoside co-culture and metabolomics analyses revealed thatL. plantarumSQ001 is involved in the absorption and hydrolysis of nucleosides. Whole genome analysis showed there are four genes related with nucleosides hydrolysis inL. plantarumSQ001. The function of iunH, a nucleoside hydrolase gene, was further confirmed by heterologous expression and knockout assays. Oral administration ofL. plantarumSQ001 in HUA geese models upregulated theLactobacillusabundance and reduced serum uric acid (UA) levels. Likewise, in mouse models,L. plantarumSQ001 increased theL. plantarumabundance and lowered serum UA levels by reducing hepatic UA synthesis protein XO and renal UA reabsorption protein GLUT9 expression, while increasing renal UA excretion protein ABCG2 expression.Our findings demonstrated that host-derived probiotics from geese could alleviate gut microbe dysbiosis and HUA in its host and mouse model. This study provides insights into the potential probiotic role ofLactobacillus plantarumin HUA or gout therapy.Graphical abstract
Enhanced Sampling Simulations of RNA-peptide Binding using Deep Learning Collective Variables
Authors: Kumari Nisha , Dhull Sonam , Karmakar Tarak 
Published: August 2024

AbstractEnhanced sampling (ES) simulations of biomolecular recognition such as binding of small molecules to proteins and nucleic acids targets, protein-protein association, and protein-nucleic acids interactions have been gaining significant attention in the simulation community due to their ability to sample long timescale processes. However, a key challenge in implementing collective variable (CV)-based enhanced sampling methods is the selection of appropriate CVs that can distinguish the system’s metastable states and, when biased, can effectively sample these states. This challenge is particularly acute when simulating the binding of a flexible molecule to a conformationally rich host molecule, such as the binding of a peptide to an RNA. In such cases, a large number of CVs are required to capture the conformations of both the host and the guest, as well as the binding process. In our work, we employed the recently developed Deep Targeted Discrimination Analysis (DeepTDA) method to design CVs for the study of the binding of a cyclic peptide, L22 to a TAR RNA of HIV as a prototypical system. These CVs were used in the on-the-fly probability-based enhanced sampling and well-tempered metadynamics simulations to sample reversible binding and unbinding of L22 peptide to the TAR RNA target. The enhanced sampling simulations revealed multiple binding and unbinding events, which enabled the calculation of the free energy surface for the peptide binding process. Our results demonstrate the potential of the DeepTDA method for designing CVs to study complex biomolecular recognition processes.
Can digital transformation improve sustainable performance of manufacturing firms? Empirical evidence based on core technology capabilities
Authors: Xi Xi , Yang Jing , Wang Ce 
Published: August 2024

Purpose
The purpose of this study is to solve the problem that existing researches ignore the long-term and staged nature of digital transformation, failing to conduct specific discussions for different stages. It responded the call by constructing a three-stage evolutionary model to analyze the impact of digital transformation at different stages on the sustainable performance of manufacturing enterprises. The moderating effect of core technology capabilities is also explored, guided by the theory of assimilation innovation.

Design/methodology/approach
Based on the panel data of Chinese listed manufacturing companies from 2012 to 2020, this study empirically investigate the impact of digital transformation (digital process, digital operation and digital ecology) on sustainability performance (economic performance and environmental performance).

Findings
The findings indicate that digital operations and digital ecology significantly improve economic performance and environmental performance. Furthermore, the core technological capacity of the enterprise serves to modify the positive correlation between digital transformation at each stage and sustainable performance to some extent. In other words, when an enterprise is equipped with the requisite technological capacity, the digital transformation at each stage accelerates both economic performance and environmental performance, which in turn is conducive to an improvement in the enterprise’s sustainable development performance.

Originality/value
The findings contribute to the theoretical framework of digital transformation and sustainable development in all stages of enterprises. Furthermore, they provide guidance for achieving sustainable development through the implementation of digital transformation and the enhancement of core technological capacity.
Short-term study abroad (STSA): a review of key risks and challenges
Authors: Bradly Andrew , Iskhakova Marina , Ott Dana L. 
Published: August 2024

Purpose
The purpose of this paper is to provide a comprehensive overview of the key risks and challenges of Short-Term Study Abroad (STSA), so higher education practitioners and global mobility providers can design effective and safe STSA programmes. The Institute of International Education defines STSA as programmes of up to eight weeks in length, with this standard now widely accepted by both practitioners and scholars (Iskhakova and Bradly, 2022).

Design/methodology/approach
This study draws from a comprehensive review of STSA risks literature, cases of published “incidents”, “safety practices”, “risk lists” and examples by global mobility professionals. The paper also draws upon the extensive practical experiences that the authors have acquired through their management of risks while delivering numerous STSA international business programmes for undergraduate students across seven different global destinations (India, USA, UAE, Russia, Croatia, Slovenia and Vietnam).

Findings
Through the analysis, the authors identify 42 types of risks and challenges that may be encountered in the delivery of STSA programmes that we group into 12 categories.

Practical implications
The results serve as an effective guide for planning, designing and delivering effective and safe STSA programmes for global mobility practitioners worldwide. The authors also provide an agenda and specific directions for future research on the topic to global mobility scholars.

Originality/value
For those designing STSA programmes, an in-depth understanding of the nature and range of risks during study tours is needed, including how to ensure in-country experiences are safe for all participants while achieving the intended learning outcomes.
I can’t get satisfaction: examining the moderating role of service brand authenticity in the relationship of imperfect experiences and satisfaction
Authors: Anninou Ioanna , Stavraki Georgia , Floh Arne 
Published: August 2024

Purpose
This study aims to examine the moderating role of dimensions of brand authenticity (continuity, credibility, integrity and symbolism) in the relationship between evaluations of imperfect experiences and satisfaction within a service experience context. Building on prior research arguing for the subjective evaluation of service experiences as well as for the use of signals during dynamic experiences that build a shield to protect a brand, it assumes that service experiences range across a continuum of experiential perfection/imperfection.

Design/methodology/approach
Two Web-based surveys, one pilot (N = 231) and one main (N = 349) have been conducted. The survey methodology was complemented by a (moderated) structural equation modelling approach taken for the data analysis.

Findings
The findings indicate that imperfect experiences in a restaurant context have a negative influence on satisfaction. Brand continuity, credibility, integrity and symbolism authenticity weaken the negative relationship between imperfect experiences and satisfaction, but this effect seems to depend on the type of experiential imperfections.

Originality/value
The current study provides theoretical and preliminary empirical evidence explaining how dynamic interactions with customers can impact subsequent static experiences. The authors found that the strength of the relationship between imperfect experiential elements and satisfaction does not only depend on perceived brand authenticity and does not always weaken when brand authenticity is present.
Unlocking resilience: the impact of Industry 4.0 technologies on manufacturing firms' response to the COVID-19 pandemic
Authors: Madrid-Guijarro Antonia , Maldonado-Guzmán Gonzalo , Rodríguez-González Rubén 
Published: August 2024

PurposeThis research investigates the impact of Industry 4.0 technologies (I4.0) on the resilience of manufacturing firms against the COVID-19 pandemic. The study explores the mediating effects linked to the firm’s supply chain resilience and absorptive capacity in this relationship.Design/methodology/approachThe empirical analysis involves 304 manufacturing firms and uses Partial Least Squares Structural Equation Modeling (PLS-SEM). A two-step hierarchical component model has been employed, considering the statistical analysis validation (reliability and validity) of the LOC and HOC models. The choice of the manufacturing industry is justified due to its degree of automation in emerging economies, such as Mexico, and its significant impact on job creation and the national gross domestic product.FindingsThe results confirm the positive impact of I4.0 technologies on companies’ resilience to COVID-19. They also support the indirect effects of the firm’s supply chain resilience and absorptive capacity. Absorptive capacity was found to have a higher significant indirect effect than supply chain resilience. Consequently, the study accentuates the significance of leveraging external knowledge and highlights the role of acquisition, assimilation, transformation, and exploitation capabilities in enhancing absorptive capacity.Research limitations/implicationsThe implications of this research extend to both manufacturing companies and public administrations, suggesting the need for I4.0 technologies implementation and supportive policies aimed at fostering absorptive capacity.Originality/valueThis research contributes to the literature by filling gaps in empirical studies in the context of developing economies. It provides valuable insights into the effects of I4.0 on absorptive capacity, supply chain resilience, and COVID-19 resilience, particularly in non-essential supply chains. It enriches the understanding of how I4.0 impacts the absorptive capacity and resilience of the supply chain during the COVID-19 crisis.
How big data analytics and artificial intelligence facilitate digital supply chain transformation: the role of integration and agility
Authors: Ma Lina , Chang Ruijie 
Published: August 2024

PurposeUnder the digital wave and the new industrial competition pattern, the automobile industry is facing multiple challenges such as the redefinition of new technologies and supply chain changes. The purpose of this study is to link big data analytics and artificial intelligence (BDA-AI) with digital supply chain transformation (DSCT) by taking Chinese automobile industry firms as a sample and to consider the role of supply chain internal integration (SCII), supply chain external integration (SCEI) and supply chain agility (SCA) between them.Design/methodology/approachData were collected from 192 Chinese firms in the automotive industry and analyzed using partial least squares structural equation modeling (PLS-SEM). Importance-performance map analysis is used to extend the standard results reporting of path coefficient estimates in PLS-SEM.FindingsThe results indicate that BDA-AI, SCII, SCEI and SCA positively influence DSCT. In addition, this study found that SCII, SCEI and SCA play an intermediary role in BDA-AI and DSCT.Originality/valueThe paper enriches the research on the mechanism of digital resources affecting DSCT and expands the research of organizational information processing theory in the context of digital transformation. The paper explores how the resources deployed by firms change the strategic measures of firms from the perspective of responsiveness. By exploring the positive impact of SCA as a response capability on the DSCT strategy and its intermediary role between digital resources and DSCT, which is helpful to the further theoretical development of logistics and supply chain disciplines.
The impact of market competitors on the demand for reinsurance
Authors: Chang Vincent Y.L. 
Published: August 2024

PurposeAccording to the market competition theory, a firm’s decision-making is influenced by the behaviors or strategies of its competitors. The repercussions of competition include market-stealing and spillover effects. Relatively few studies in the reinsurance literature discuss the effect of competitors on an insurer’s decision-making. This study aims to fill a gap in the reinsurance literature by comparing insurers' reinsurance demand to their competitors' reinsurance purchases.Design/methodology/approachThis study uses unbalanced panel data for the US property-liability insurance industry from 2006 to 2017 to determine the impact of competitors' reinsurance purchases on insurers' reinsurance demand. This study employs the Mixed Effect Model and the Quantile Regression to test the proposed hypotheses.FindingsThe evidence suggests that the affiliated reinsurance purchases of competitors have a positive and substantial effect on the affiliated reinsurance demand of insurers, crediting mimicking the reinsurance strategy. Interestingly, the market-stealing effect is supported while the non-affiliated reinsurance metric is used. Remarkably, given insurers with low non-affiliated reinsurance purchases, the finding sustains the mimicking reinsurance strategy. Nevertheless, the market-stealing effect remains a concern for insurers with a high non-affiliated reinsurance purchase.Originality/valueThe new findings concerning competitor effects analysis fill a void in the reinsurance literature. Risk diversification, capital substitution, and real services demand may play a crucial role in determining the market-stealing effect, leading to a decrease in market share. Insurers can mitigate the market-stealing effect of competitors by accessing expertise and capital substitution through non-affiliated reinsurance purchases.
Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda
Authors: Elghaish Faris , Matarneh Sandra , Hosseini M. Reza , Tezel Algan , Mahamadu Abdul-Majeed , Taghikhah Firouzeh 
Published: August 2024

PurposePredictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.Design/methodology/approachThis study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.FindingsFindings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.Practical implicationsThe results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.Originality/valueThis paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.
Soil nitrogen regulates root carbon secretion in the context of global change: A global meta‐analysis
Authors: Zeng Jia , Li Xiangyang , Xing Liheng , Chen Ling , Li Yu , Wang Xing , Zhang Qi , Yang Gang , Ren Chengjie , Yang Gaihe , Han Xinhui 
Published: August 2024

Abstract

The secretion of root exudates plays a crucial regulatory role in the process of soil carbon cycling. It is also significantly affected by global change factors (GCFs), yet the general patterns of these changes have not been fully explored, which greatly limits the accurate establishment of carbon cycling models under the background of global change.

To address this, we conducted a meta‐analysis that included 1926 data pairs to examine the effects of global variability on root exudates. Furthermore, we performed multi‐model inference using Akaike's information criterion (AIC) to assess the relative importance of multiple factors such as annual average temperature (AAT), type of ecosystem, intensity of treatment, duration and more.

The findings revealed that GCFs generally improved the production of root exudates. While the addition of nitrogen and its interaction with increased temperature significantly reduced the rate of root carbon secretion, all other factors significantly promoted it. Interestingly, as the intensity of warming increased, the rate of root carbon secretion caused by warming displayed a downward trend, with a threshold of 3.6°C. Furthermore, the influence of GCFs on root carbon secretion was found to be regulated by soil nitrogen, confirming the importance of nutrients from the rhizosphere in this process.

These results underscore the importance of GCFs in root exudates and emphasize the crucial role of soil total nitrogen in the regulation of root carbon secretion. They also provide valuable scientific parameters for the development of more accurate models for the global‐change carbon cycle.

Read the free Plain Language Summary for this article on the Journal blog.
Unravelling arthropod movement in natural landscapes: Small‐scale effects of body size and weather conditions
Authors: Logghe Garben , Taelman Charlotte , Van Hecke Florian , Batsleer Femke , Maes Dirk , Bonte Dries 
Published: August 2024

Abstract

Arthropod movement has been noticeably understudied compared to vertebrates. A crucial knowledge gap pertains to the factors influencing arthropod movement at habitat boundaries, which has direct implications for population dynamics and gene flow. While larger arthropod species generally achieve greater dispersal distances and large‐scale movements are affected by weather conditions, the applicability of these relationships at a local scale remains uncertain. Existing studies on this subject are not only scarce but often limited to a few species or laboratory conditions.

To address this knowledge gap, we conducted a field study in two nature reserves in Belgium, focusing on both flying and cursorial (non‐flying) arthropods. Over 200 different arthropod species were captured and released within a circular setup placed in a resource‐poor environment, allowing quantification of movement speed and direction. By analysing the relationship between these movement variables and morphological (body size) as well as environmental factors (temperature and wind), we aimed to gain insights into the mechanisms driving arthropod movement at natural habitat boundaries.

For flying species, movement speed was positively correlated with both body size and tailwind speed. In contrast, movement speed of cursorial individuals was solely positively related with temperature. Notably, movement direction was biased towards the vegetated areas where the arthropods were originally caught, suggesting an internal drive to move towards suitable habitat. This tendency was particularly strong in larger flying individuals and under tailwind conditions. Furthermore, both flying and cursorial taxa were hindered from moving towards the habitat by strong upwind.

In conclusion, movement speed and direction at patch boundaries are dependent on body size and prevailing weather conditions, and reflect an active decision‐making process.
Bioplastics in the basket of Italians: A hybrid framework for understanding the adoption of bioplastic food packaging
Authors: Califano Giovanbattista , Lombardi Alessia , Del Giudice Teresa , Caracciolo Francesco , Cembalo Luigi 
Published: August 2024

AbstractThis study provides a comprehensive understanding of the factors influencing Italian consumers' choice of biodegradable and compostable packaging by using a hybrid framework that integrates the theory of planned behaviour with a discrete choice experiment. By analysing the interplay between internal and external factors and rational consumer evaluations, the research clarifies that consumers' choices can be explained by a combination of economic and psychological models, offering a more accurate representation of the driving forces behind consumer decisions. Applying latent class analysis, we identify consumer segments based on packaging preferences for packaged salad, knowledge of waste disposal rules and the psychological characteristics of a statistically representative sample of Italian consumers. The integration of a comprehensive behavioural model allows for the identification of psychological determinants that influence consumer behaviour, outlining detailed profiles of both early adopters and those most opposed to this technology. This research contributes to the literature on sustainable packaging and offers insights to marketers and policymakers for promoting the adoption of sustainable packaging.