bluuebunny/crossref_metadata_2025_split huggingface dataset:
RelQ-mediated alarmone signaling regulates growth, sporulation, and stress-induced biofilm formation inClostridioides difficile
Authors: Malik Areej , Oludiran Adenrele , Poudel Asia , Alvarez Orlando Berumen , Woodward Charles , Purcell Erin B. 
Published: February 2024

ABSTRACTThe bacterial stringent response (SR) is a conserved transcriptional reprogramming pathway mediated by the nucleotide signaling alarmones, (pp)pGpp. The SR has been implicated in antibiotic survival inClostridioides difficile, a biofilm- and spore-forming pathogen that causes resilient, highly recurrentC. difficileinfections. The role of the SR in other processes and the effectors by which it regulatesC. difficilephysiology are unknown.C. difficileRelQ is a clostridial alarmone synthetase. Deletion ofrelQdysregulatesC. difficilegrowth in unstressed conditions, affects susceptibility to antibiotic and oxidative stressors, and drastically reduces biofilm formation. While wild-typeC. difficiledisplays increased biofilm formation in the presence of sub-lethal stress, the ΔrelQstrain cannot upregulate biofilm production in response to stress. Deletion ofrelQslows spore accumulation in planktonic cultures but accelerates it in biofilms. This work establishes biofilm formation and sporulation as alarmone-mediated processes inC. difficileand reveals the importance of RelQ in stress-induced biofilm regulation.
A quantitative ultrastructural timeline of nuclear autophagy reveals a role for dynamin-like protein 1 at the nuclear envelope
Authors: Mannino Philip J. , Perun Andrew , Surovtsev Ivan V. , Ader Nicholas R. , Shao Lin , Rodriguez Elisa C. , Melia Thomas J. , King Megan C. , Lusk C. Patrick 
Published: February 2024

AbstractAutophagic mechanisms that maintain nuclear envelope homeostasis are bulwarks to aging and disease. By leveraging 4D lattice light sheet microscopy and correlative light and electron tomography, we define a quantitative and ultrastructural timeline of nuclear macroautophagy (nucleophagy) in yeast. Nucleophagy begins with a rapid accumulation of the selective autophagy receptor Atg39 at the nuclear envelope and finishes in ∼300 seconds with Atg39-cargo delivery to the vacuole. Although there are several routes to the vacuole, at least one pathway incorporates two consecutive membrane fission steps: inner nuclear membrane (INM) fission to generate an INM-derived vesicle in the perinuclear space and outer nuclear membrane (ONM) fission to liberate a double membraned vesicle to the cytosol. ONM fission occurs independently of phagophore engagement and instead relies surprisingly on dynamin like 1 (Dnm1), which is recruited to sites of Atg39 accumulation by Atg11. Loss of Dnm1 compromises nucleophagic flux by stalling nucleophagy after INM fission. Our findings reveal how nuclear and INM cargo are removed from an intact nucleus without compromising its integrity, achieved in part by a non-canonical role for Dnm1 in nuclear envelope remodeling.
Chrono-optimization of influenza vaccine administration: A systematic review and meta-analysis
Authors: Vink Koen , Kusters Jeroen , Wallinga Jacco 
Published: February 2024

AbstractBackgroundThere is growing evidence that the strength of vaccine responses depends on the time of day of vaccine administration. This systematic review provides an overview of the literature regarding the effect of the timing of influenza vaccination on the vaccine response. To estimate the extent of this effect, we conducted a meta-analysis of randomized controlled trials (RCTs) in which antigen-specific antibody titers were monitored following either morning or afternoon administration of the influenza vaccine.Methods and resultsA systematic literature search identified five relevant studies that reported antigen-specific titers against multiple influenza vaccine strains after both morning and afternoon vaccination. Four of the five studies reported higher antibody titers for at least one vaccine strain following morning vaccination. Two RCTs were included in the meta-analysis, each of which reported the response to three vaccine strains, resulting in a total of six responses. The meta-analysis revealed that morning vaccination elicited a stronger antibody response than afternoon vaccination, with a pooled standardized mean difference (SMD) of 0.24 (95% CI=0.01–0.47). The between-study heterogeneity (I2=66%) was mainly due to the significantly 01greater effect of morning vaccination among adults aged 65 years or older than among adults aged 60 years or younger (SMD=0.32, 95% CI=0.21–0.43 versus SMD=0.00, 95% CI=−0.16–0.16, respectively).ConclusionInfluenza vaccinations administered in the morning induced a stronger antibody response in adults aged 65 years or older, who represent a major target group for influenza vaccination programs. Therefore, chrono-optimization of influenza vaccination could offer a safe and simple strategy for enhancing vaccine effectiveness. The paucity of relevant studies suggests that accounting for the time of vaccine administration in future vaccination trials could provide valuable insights into the potential benefits of chrono-optimization strategies.
Long-distance optical-conveyor-belt transport of ultracold
Cs133
and
Rb87
atoms
Authors: Matthies Alex J. , Mortlock Jonathan M. , McArd Lewis A. , Raghuram Adarsh P. , Innes Andrew D. , Gregory Philip D. , Bromley Sarah L. , Cornish Simon L. 
Published: February 2024

We report on the transport of a thermal cloud of ultracold cesium and rubidium atoms over about 37 cm in under 25 ms using an optical conveyor belt formed by two counterpropagating beams with a controllable frequency difference that generate a movable optical lattice. By carefully selecting the waists and focus positions, we are able to use two static Gaussian beams for the transport, avoiding the need for a Bessel beam or variable-focus lenses. We characterize the transport efficiency for both species, including a comparison of different transport trajectories, gaining insight into the loss mechanisms and finding the minimum jerk trajectory to be optimum. Using the optimized parameters, we are able to transport up to 7×106 cesium or rubidium atoms with an efficiency up to 75%. To demonstrate the viability of our transport scheme for experiments employing quantum gas microscopy, we produce Bose-Einstein condensates of either species after transport and present measurements of the simultaneous transport of both species.

Published by the American Physical Society
2024
Natural anomaly mediation from the landscape with implications for LHC SUSY searches
Authors: Baer Howard , Barger Vernon , Bolich Jessica , Dutta Juhi , Sengupta Dibyashree 
Published: February 2024

Supersymmetric models with the anomaly-mediated supersymmetry (SUSY) breaking (AMSB) form for soft SUSY breaking terms arise in two different settings: (1) extra-dimensional models where SUSY breaking occurs in a sequestered sector, and (2) 4−d models with dynamical SUSY breaking in a hidden sector where scalars gain masses of order the gravitino mass m3/2, but gaugino masses and trilinear soft terms are assumed to be of the AMSB form. Both models run into serious conflicts with (1) LHC sparticle and Higgs mass constraints, (2) constraints from winolike weakly interacting massive particle dark matter searches, and (3) bounds from naturalness. These conflicts may be avoided by introducing minor changes to the underlying phenomenological models consisting of nonuniversal bulk scalar Higgs masses and A terms, providing a setting for natural anomaly mediation (nAMSB). In nAMSB, the wino is still expected to be the lightest of the gauginos, but the Higgsinos are expected to be the lightest electroweakinos (EWinos) in accord with naturalness. We examine what sorts of spectra are expected to emerge when nAMSB arises from a string landscape setting: While model 2 can only be natural for a Higgs mass mh≲123  GeV, model 1 can accommodate naturalness along with mh∼125  GeV while still respecting LHC bounds on sparticle masses. We explore the LHC phenomenology of nAMSB models where we find that for Higgsino pair production, typically larger dilepton mass gaps arise from the soft dilepton-plus-jets signature than in models with gaugino mass unification. For wino-pair production, the higher m3/2 portion of nAMSB parameter space is excluded by recent LHC bounds from gaugino pair production searches. We characterize the dominant LHC signatures arising from the remaining lower m3/2∼90–200  TeV range of parameter space, which should be fully testable at high-luminosity LHC via EWino pair production searches.

Published by the American Physical Society
2024
Predicting construction equipment resale price: machine learning model
Authors: Toma Hossam Mohamed , Abdeen Ahmed H. , Ibrahim Ahmed 
Published: February 2024

Purpose The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price.Design/methodology/approach A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment.Findings Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier.Originality/value The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.
Harvesting sustainability: assessing Industry 4.0 in agri-food supply chains
Authors: Sonar Harshad , Sharma Isha , Ghag Nikhil , Raje Bhagyashri 
Published: February 2024

PurposeThe agri-food industry is experiencing a revolutionary shift due to the introduction of Industry 4.0 technologies to improve efficiency, transparency and sustainability. The importance of agri-food supply chains (AFSC) in promoting sustainability is expanding as the globe struggles with issues including resource scarcity, climate change and population growth. In order to better understand how Industry 4.0 might improve sustainability in a world that is changing quickly, this work aims to focus on identifying various sustainability assessment factors influencing AFSC to increase overall sustainability, minimize resource consumption, cut waste and streamline operations.Design/methodology/approachImportant sustainability assessment factors are identified from the past academic literature and are then validated using the fuzzy-Delphi method. A method called decision-making trial and evaluation laboratory (DEMATEL) is used to examine and analyze structural models with complex causal linkages. The results are then validated using sensitivity analysis.FindingsThe factors that emerged as the highest ranked for evaluating the sustainability of Industry 4.0 in AFSC are market competitiveness, and knowledge and skill development, followed by resource efficiency. Industry 4.0 technologies are essential for increasing the marketability of agricultural products because of the major implications of market competitiveness. The significance of knowledge and skill development draws attention to Industry 4.0’s contribution to the promotion of chances for farmers and agricultural employees to increase their capability.Practical implicationsBy outlining the nexus between Industry 4.0 technologies and sustainability, the study presents a comprehensive framework that would be relevant for researchers, policymakers and industry stakeholders who want to leverage Industry 4.0 technology to build more sustainable AFSC in the future. The study findings can help the farmers or producers make sensible choices that adhere to sustainability standards and guarantee long-term financial viability.Originality/valueThe originality of this work lies in the identification of sustainability assessment factors especially for AFSC in the era of digitalization which has not been discussed previously.
Roles of lean learners for successful lean implementation in the construction industry: a force-directed graph
Authors: Parameswaran Agana , Ranadewa K.A.T.O. , Rathnasinghe Akila Pramodh 
Published: February 2024

PurposeThe proliferation of lean principles in the construction industry is offset by the enduring uncertainty among industry stakeholders regarding their respective roles in lean implementation. This uncertainty is further compounded by the scarcity of empirical investigations in this area. Consequently, this study undertakes the task of bridging this knowledge gap by identifying the critical roles of lean learners and their indispensable contributions to achieving successful lean implementation.Design/methodology/approachA qualitative exploratory approach informed by an interpretivism perspective was adopted. The case study strategy was employed to gather data from three contracting organisations that had implemented lean practices. Empirical data was collected through in-depth semi-structured interviews with fifteen industry experts and complemented by document reviews. To analyse the data, a code-based content analysis approach was employed using NVivo software, while Power BI software was utilised to develop a comprehensive force-directed graph visualisation.FindingsThe research findings substantiated nine lean learners and unveiled a set of seventy-three roles associated with them. The force-directed graph facilitated the identification of lean learners and their connections to the emerged roles. Notably, the graph highlighted the pivotal role played by project managers and internal lean trainers in ensuring the success of lean implementation, surpassing the contributions of other lean learners.Originality/valueThe implications of findings extend to industry professionals seeking to establish a robust lean learning framework to expedite lean implementation within the construction sector. This study not only provides a comprehensive definition of lean learners’ roles but also transcends specific construction types, making it a significant catalyst for global impact.
Optimal estimation of reliability parameter for inverse Pareto distribution with application to insurance data
Authors: Joshi Neeraj , Bapat Sudeep R. , Sengupta Raghu Nandan 
Published: February 2024

PurposeThe purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).Design/methodology/approachWe estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.FindingsWe state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.Originality/valueThis study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.
A bibliometric review of Internet of Things (IoT) on cybersecurity issues
Authors: Ganji Kashmira , Afshan Nikhat 
Published: February 2024

Purpose
In response to the growing interest in Internet of Things (IoT) technology and its profound implications for businesses and individuals, this bibliometric study focuses on a critical yet understudied aspect, i.e. cybersecurity. As IoT adoption grows, so do concerns regarding user privacy and data security. This study aims to provide a comprehensive understanding of the current research in this vital area, shedding light on research trends, gaps and emerging themes.

Design/methodology/approach
The study conducted a bibliometric analysis and systematic review of literature spanning over two decades (2013–2023). Bibliometric analysis is conducted using Biblioshiny which is R-software-based advanced analytical tool. Further, VOSviewer is used to conduct network analysis. The study highlights the evolving landscape of IoT cybersecurity, emphasizing interdisciplinary intersections and the ethical dimensions of IoT technologies.

Findings
The study uncovers crucial concerns related to IoT adoption, emphasizing the urgent need for comprehensive cybersecurity protocols. It identifies emerging themes such as artificial intelligence and blockchain integration, indicating a shift toward interdisciplinary solutions. Furthermore, the research highlights ethical gaps in current IoT discussions, emphasizing the importance of responsible innovation.

Research limitations/implications
Businesses can bolster their cybersecurity strategies, policymakers can craft informed regulations and researchers are encouraged to explore IoT’s ethical dimensions.

Originality/value
This study pioneers a nuanced analysis of IoT cybersecurity, filling a crucial gap in the existing business and management literature. By synthesizing a decade of scholarly work, it provides foundational insights for researchers, businesses and policymakers. The research not only informs academic discourse but also offers practical guidance for enhancing IoT security measures and fostering ethical innovation.
Impact of patient‐reported salpingitis on the outcome of hysterectomy and adnexal surgery: A national register‐based cohort study in Sweden
Authors: Jännebring Josefin , Liv Per , Knuts Malin , Idahl Annika 
Published: February 2024

AbstractIntroductionSalpingitis is caused by ascending microbes from the lower reproductive tract and contributes to tubal factor infertility, ectopic pregnancy, and chronic pelvic pain. The aim of this study was to analyze if the risk for complications and dissatisfaction after hysterectomy and adnexal surgery was increased in women reporting previous salpingitis.Material and methodsThis is an observational cohort study including women undergoing gynecologic surgery from 1997 to 2020, registered in the Swedish National Quality Register of Gynecologic Surgery (GynOp). Patient‐reported previous salpingitis was the exposure. Complications up to 8 weeks and satisfaction at 1 year postoperatively were the outcomes. Multivariable logistic regression and ordinal regression were performed. Results were adjusted for potential confounders including age, body mass index, smoking and year of procedure as well as endometriosis and previous abdominal surgery. Multiple imputation was used to handle missing data.ResultsIn this study, 61 222 women were included, of whom 5636 (9.2%) women reported a previous salpingitis. There was an increased risk for women reporting previous salpingitis in both the unadjusted and fully adjusted models to have complications within 8 weeks of surgery (adjusted odds ratio [aOR] 1.22, 95% confidence interval [CI] 1.14–1.32). The highest odds ratios were found for bowel injury (aOR 1.62, 95% CI 1.29–2.03), bladder injury (aOR 1.52, 95% CI 1.23–1.58), and postoperative pain (aOR 1.37, 95% CI 1.22–1.54). Women exposed to salpingitis were also more likely to report a lower level of satisfaction 1 year after surgery compared with unexposed women (aOR 0.87, 95% CI 0.81–0.92).ConclusionsSelf‐reported salpingitis appears to be a risk factor for complications and dissatisfaction after gynecologic surgery. This implies that known previous salpingitis should be included in the risk assessment before gynecologic procedures.
Empirical challenges in the study of employer associations and their representativeness
Authors: Breda Thomas 
Published: February 2024

AbstractThe article examines the quality and appropriateness of the data available to measure firms' affiliation to employer associations (EAs). We find large discrepancies in affiliation rates obtained from the five different data sources available for France, leading us in particular to discard tax data. Focusing on survey data, we show that asking managers about affiliation to EAs in general or affiliation to a list of specific EAs can lead to large differences in affiliation rates, highlighting the importance of the framing of survey questions. We then provide methods to estimate an aggregate firm‐level affiliation rate from surveys covering workplaces with 11 or more employees. Exploiting (i) conflicting survey responses regarding EA affiliation between distinct establishments in the same firm and (ii) survey responses for firms that report paying contributions to EAs in their financial statements, we finally estimate the shares of employers that wrongly declare being or not being affiliated to EAs, and provide a rate of affiliation corrected for such errors. The implications for econometric analysis of the high observed error rates are discussed.