bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rostral ventromedial medulla (RVM) projects to the lateral hypothalamic area (LHA) to drive aversion and anxiety
Authors: Rani Mousmi , Shah Devanshi Piyush , Barik Arnab 
Published: November 2024

AbstractNeurons in the LHA are critical drivers of behavioral and physiological responses to acute and chronic stress. However, the roles of the specific pre-synaptic inputs to the LHA in driving stress and resultant physiological effects are yet to be fully understood. Here, taking advantage of mouse viral genetics, rabies tracing, optogenetics, chemogenetics, and fiber photometry, we show that the excitatory projections from the RVM to LHA drive stress-induced anxiety. This is a surprising finding since, traditionally, RVM has been studied in the context of opioidergic pain modulation through its inhibitory projections to the spinal cord. We find that the LHA neurons receiving inputs from the RVM, when activated, do not alter the nociceptive thresholds yet are sufficient to drive anxiety-like behaviors. These LHA neurons are recruited by acute restraint, which is known to cause stress. On the other hand, the LHA-projecting RVM neurons are responsive to both noxious thermal stimuli and acute restraint, promoting stress-induced anxiety, yet with no effect on pain thresholds. Together, we found an ascending neural pathway between RVM and LHA that mediates stress-induced anxiety.Significance statementThere is a strong correlation between pain and anxiety. However, the underlying neural mechanisms are poorly understood. Here, we reveal a novel neural pathway between the rostral ventromedial medulla (RVM) and lateral hypothalamus (LHA) that can potentially convert painful experiences into stress and anxiety. The traditional role of RVM is opioidergic modulation of pain. However, here we show that in addition to nociception, RVM neurons can play an essential role in the affective-motivational components of pain.
The impact of systemic blockade of dopamine receptors on the acquisition of two-way active avoidance in rats
Authors: Vercammen L. , Lopez-Moraga A. , Beckers T. , Vervliet B. , Luyten L. 
Published: November 2024

AbstractActive threat avoidance is a core aspect of adaptive and maladaptive behavior, yet its underlying mechanisms are not fully understood. Prior studies concluded that pharmacologically blocking dopaminergic receptors (DRs) disrupted avoidance acquisition, but it remains unclear whether such effects on learning persist during a drug-free follow-up test. To assess the involvement of D1R and D2R in avoidance acquisition, we conducted two experiments. In Experiment 1, thirty-six male Wistar rats underwent a single avoidance training session involving 30 tone-shock pairings. Rats could avoid the shock by moving to the opposite compartment of the shuttle box. Twenty minutes before training, rats received either D1R antagonist SCH 23390 (0.05 mg/kg), D2R antagonist sulpiride (20 mg/kg), or vehicle. While sulpiride did not affect avoidance, 0.05 mg/kg SCH 23390 significantly reduced the number of avoidance responses. In a separate test, 0.05 mg/kg SCH 23390 also reduced locomotor activity. In Experiment 2 (N = 24), a lower dose of SCH 23390 (0.025 mg/kg) was administered, and a drug-free avoidance test under continued reinforcement was added 24 hours later to test for sustained effects of D1R blockade on avoidance in the absence of acute drug effects. Although animals avoided less with SCH 23390 in the system, this effect did not persist 24 hours later, suggesting that effects of D1R blockade during avoidance training might reflect an acute disruption of secondary processes involved in the performance of avoidance behavior rather than an actual impairment of avoidance learning.
From phenotype to receptor: validating physiological clustering ofEscherichia coliphages through comprehensive receptor analysis
Authors: Kaneko Tomoyoshi , Osaka Toshifumi , Inagaki Minoru , Tsuneda Satoshi 
Published: November 2024

AbstractUnderstanding the relationship between bacteriophage (phage) classification and target receptors is crucial for phage ecology and applied research. In this study, we compared 13 previously isolatedEscherichia coliphages based on physiological characteristics, whole-genome sequences, and tail fiber protein phylogenetics. We combined genomic analysis and lipopolysaccharide (LPS) structural analysis of phage-resistantE. colistrains to identify target receptors of the phages. The results revealed that phylogenetically distinctE. coliphages target different sites in the LPS R-core region (modified by WaaV, WaaW, WaaT, and WaaY), membrane proteins (NfrB, TolA, YhaH), or flagella. Notably, subtle chemical modifications of LPS (such as Heptose phosphorylation) were shown to be important forE. coliphage recognition. Furthermore, physiological characteristics, tail fiber phylogenetics, and whole genome analysis independently classified the phages into five identical groups. Meanwhile, classification based on target receptors placed them into six groups, which was very similar to the grouping pattern based on the three independent parameters described above. Our results suggest that phage phenotypes and genotypes are closely related to target receptor specificity and the physiological characteristics ofE. coliphages. Here, we elucidated the diversity and specificity ofE. coliphage target receptors, providing new insights into the classification ofE. coliphages and phage-host interactions.
Integrating Ideal Bayesian Searcher and Neural Networks Models for Eye Movement Prediction in a Hybrid Search Task
Authors: Ruarte Gonzalo , Care Damián , Bujia Gaston , Ison Matias J , Kamienkowski Juan E. 
Published: November 2024

AbstractVisual search, where observers search for a specific item, is a crucial aspect of daily human interaction with the visual environment. Hybrid search extends this by requiring observers to search for any item from a given set of objects. While there are models proficient at simulating human eye movement in visual search tasks within natural scenes, none are able to do so in Hybrid search tasks within similar environments. In this work, we present an enhanced version of the neural network Entropy Limit Minimization (nnELM) model, which is based on a Bayesian framework and decision theory. We also present the Hybrid Search Eye Movements (HSEM) Dataset, comprising several thousands of human eye movements during hybrid search tasks in natural scenes. A key challenge in Hybrid search, absent in visual search, is that participants might search for different objects at different time points. To address this, we developed a strategy based on the posterior probability distribution generated after each fixation. By adjusting the model’s peripheral visibility, we made early search stages more efficient, aligning it closer to human behaviour. Additionally, limiting the model’s memory capacity reduced its success in longer searches, mirroring human performance. To validate these improvements, we compared our model against participants from the HSEM dataset and against existing models in a visual search benchmark. Altogether, the new nnELM model not only successfully explains Hybrid search tasks, but also closely replicates human behaviour in natural scenes. This work advances our understanding of complex processes underlying visual and Hybrid search while maintaining model interpretability.
Mutations in the microexon splicing regulatorsrrm4have minor phenotypic effects on zebrafish neural development
Authors: Gupta Tripti , Margolin Gennady , Burgess Harold A. 
Published: November 2024

AbstractAchieving a diversity of neuronal cell types and circuits during brain development requires alternative splicing of developmentally regulated mRNA transcripts. Microexons are a type of alternatively spliced exon that are 3–27 nucleotides in length and are predominantly expressed in neuronal tissues. A key regulator of microexon splicing is the RNA-binding protein Serine/arginine repetitive matrix 4 (Srrm4).Srrm4is a highly conserved, vertebrate splicing factor that is part of an ancient family of splicing proteins. To better understand the function ofSrrm4during brain development, we examined neural expression of zebrafishsrrm4from days 1–5 of development using fluorescencein situhybridization. We found thatsrrm4has a dynamically changing expression pattern, with expression in diverse cell types and stages during development. We then used CRISPR-based mutagenesis to generate zebrafishsrrm4mutants. Unlike previously described morphant phenotypes,srrm4mutants did not show overt morphological defects. Moreover, sequencing of wild-type and mutant transcriptomes revealed only minor changes in splicing.srrm4thus appears to have a limited role in zebrafish neural development.
Red means heavy: Action intentions reactivate representations of task-relevant cognitive cues
Authors: Lee Nina , Guo Lin Lawrence , Nestor Adrian , Niemeier Matthias 
Published: November 2024

AbstractRecent research shows that the intention to act on an object alters its neural representation in ways as afforded by underlying sensorimotor processes. For example, the intention to grasp and pick up an object results in representations of the object’s weight. But these representations become grasp-specific only immediately before object lift if weight information is relayed through object material. This feature triggers earlier representations regardless of intention probably because material-weight contingencies are overlearned. By contrast, recently learned weight cues should be recalled deliberately during grasp planning resulting in early grasp-specific representations. Here, we examined how action intentions affect the representation of newly acquired colour-weight contingencies. We recorded electroencephalography while human participants grasped or reached for objects that varied in shape and density as indicated by their colour. Multivariate analyses revealed a grasp-specific reactivation of colour during planning that was mirrored in beta band. This suggests that task-relevancy influences the representation of colour such that previously encoded colour-weight contingencies may be reactivated as required for grasping, mediated top-down via working memory. Grasp-specific representations of shape and colour were also present in theta band, perhaps reflecting attentional activity. These results provide novel insights into the interplay between cognition and motor planning processes.
Does financing startups impact the profitability of microfinancing institutions?
Authors: Singh Kuldeep , Sharma Arpita 
Published: November 2024

PurposeFunding startups is usually perceived as a risky investment for money lenders and investors due to the high failure rate of such early-stage firms. Therefore, funding startups may negatively impact the profitability of the lending institutions. The current study aims to test how financing startups (the percentage of financed enterprises that are startups) affects the profitability of microfinancing institutions (MFIs). It also attempts to determine the conditions that allow MFIs to limit the negative effects of such risky investments on their profitability.Design/methodology/approachThe study considers panel data of 2,694 global MFIs hosted on the Microfinance Information Exchange Market on the World Bank website. Dynamic panel data methodology (generalized methods of moment) is applied to conduct the econometric analysis. In addition to regression analysis, to test the main and interaction effects, interaction graphs are plotted to elaborate on the findings.FindingsDirect evidence implies that financing startups negatively impacts the profitability of MFIs. Therefore, MFIs should act per two necessary conditions to reduce risks associated with startups financing. First, MFIs should optimize their risk cover and use these funds to finance startups. Second, an increased use of non-earning liquid funds to finance startups is recommended to maintain profitability.Practical implicationsThe findings of our study will help MFIs continue lending to startups while protecting their profitability.Originality/valueThe findings contribute to the institutionalization approach of MFIs while providing favorable conditions for financing startups and protecting profitability at the same time.
Analysis methodology for dynamic structural behaviour modelling of highway bridges based on Monte Carlo simulations (MCS) considering vehicle-pavement-bridge interaction
Authors: Cardoso Marilene Lobato , Alencar Guilherme , Santos da Silva José Guilherme 
Published: November 2024

PurposeTo develop an analysis methodology to evaluate the dynamic behaviour and fatigue assessment of highway bridges due to vehicle passage on irregular pavement surfaces. The approach considers the non-deterministic nature of the parameters of the vehicle-bridge system using Monte Carlo simulations.Design/methodology/approachIn the proposed approach, 11 vehicle-bridge system parameters are modelled as random variables with predefined probability distributions. The dynamic analysis considers the vehicle-structure-pavement interaction, in which road surface roughness is defined based on the use of the power spectral density function, as an expression of the road surface random irregularities. Based on the results of the dynamic analysis, a fatigue assessment is performed. To demonstrate the applicability of the methodology, a case study of a 40-m-span steel-concrete composite highway bridge was selected, considering two levels of pavement quality.FindingsThe results reveal that vehicle speed, sprung masses, bridge mass and rear axle and tire stiffness are the parameters that most influence vertical displacement, bending stress and fatigue life. Numerical simulations showed that the pavement deterioration reduced fatigue life by up to 98.6%, increasing the failure probability.Originality/valueThe dynamic behaviour and fatigue of a highway bridge are evaluated by means of vehicle-structure interaction analyses, in which the randomness of 11 parameters of the vehicle-bridge system is simulated. This includes dynamic parameters of the suspension and tires, whose variability is rarely considered due to the difficulty in obtaining sample data.
Dilemmatic commitments following psychological contract breach and its contrasting implications for voluntary turnover and organizational citizenship behaviour
Authors: Conway Neil , Clinton Michael E. 
Published: November 2024

PurposeWe introduce the notion of dilemmatic commitments to psychological contract theory and research – where breach lowers affective commitment while raising continuance commitment – and present its contrasting implications for explaining outcomes.Design/methodology/approachWe draw on longitudinal survey data, along with objective data on actual employee turnover from organizational records.FindingsWe find opposing mediating pathways between psychological contract breach and employee turnover, via dilemmatic commitments where the effect of breach on employee turnover is positive via affective commitment and negative via continuance commitment.Originality/valueThe dual opposing commitment pathways following breach may explain why previous research finds small associations between breach and employee turnover. While dilemmatic commitment suggests that continuance commitment can offset the affective commitment pathway in limiting actual employee turnover, it represents a “mixed blessing” as both pathways reduced employee engagement in organizational citizenship behaviours.
Land coverage prediction using convolutional neural network for enhanced wind loading estimation
Authors: Alinejad Nasrollah , Jung Sungmoon , Cai Jinglun , Liu Xiuwen 
Published: November 2024

PurposeThe wind loading on a building is likely to deviate further from the known wind loading due to the complexity of the real-world land coverage. To address this issue, research is needed in two separate areas. First, wind tunnel testing needs to be conducted for more complex terrains. Second, research is needed to classify real-world land coverage with high accuracy, specifically for wind engineering applications. This paper deals with this second area of research. The machine learning-based land cover prediction is a promising technique because it can remove subjectivity in human interpretation of upwind terrain.Design/methodology/approachThis paper presents a new deep neural network for land coverage prediction that can distinguish low- and mid-rise buildings in the built environment to enhance the estimation of surface roughness necessary in wind engineering. For the dataset, Landsat 8 satellite images were used. A patch-based convolutional neural network was employed and improved. The network predicted the land coverage at the center of the patch. Two different label schemes were used where the proposed network either achieved better accuracy than the conventional model or recognized additional building types while maintaining a similar level of accuracy.FindingsCompared to the validation accuracy of 78% in a previous study, the proposed method achieved the validation accuracy of 90% thanks to the improvements made in this study as well as the consolidation of labels with similar surface roughness. When additional building categories were added, the validation decreased to 80%, which is comparable to the previous study but is now able to predict different building types.Originality/valueThe improvement of the proposed method will depend on the site characteristics. For the sites tested in this paper, the error reduction in wind speed and pressure was up to about 55%. In addition to more accurate wind speed and pressure, better identification of buildings will benefit wind engineering research, as different building types cause different downwind effects. An example application would be automated recognition of areas that have a certain distance from the target building type to identify downwind areas affected by high winds.
Application of metaverse in higher education: a systematic literature review and bibliometric analysis
Authors: Panda Gayatri , Arora Monika , Ghoshal Indrajit , Garza-Reyes Jose Arturo , Kaswan Mahender Singh 
Published: November 2024

PurposeWith the evolving landscape of technology and innovations, metaverse has revealed a huge potential for changing the world. Gradually, it has expanded its wings to different sectors, including higher education. The key purpose of this research work is to synthesize the existing literature on metaverse in higher education and develop future research directions for researchers.Design/methodology/approachThe present study uses the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” technique to recognize relevant articles from the database, i.e. SCOPUS. Furthermore, it conducts a “bibliometric analysis and systematic literature review” to explore the research area of metaverse in higher education.FindingsThe findings reveal that the synthesis and analysis of existing literature led to the identification of an intellectual structure, which resulted in the formation of four clusters. These clusters are defined as attributes related to metaverse applications in higher education, sustainable management of complex learning practices, customization of the teaching–learning process through the metaverse and model development. The theoretical structure informs the co-word analysis and thematic map, identifying four themes: the basic theme, motor theme, niche theme and decline theme. Finally, the social structure is assessed in terms of the author’s network and collaboration across countries.Research limitations/implicationsThe study investigated the conceptual structure by developing a thematic map and social structure by measuring the relationship between country–author collaboration through bibliographic coupling (BC). The study also identified future research avenues based on the “TCCM (theory, context, characteristics, and methodology)” framework for future researchers and practitioners.Originality/valueThe research study thrives on enlarging the review scope to measure the metaverse’s role in higher education. Though bibliometric studies have been developed in the metaverse, no study has attempted to measure the relationship between metaverse in higher education using bibliometric and TCCM framework. Thus, developing a synthesized work provides a comprehensive picture of the research domain.
The 2024 General Election and the Rise of Reform UK
Authors: Heath Oliver , Prosser Christopher , Southall Humphrey , Aucott Paula 
Published: November 2024

AbstractThis article examines the social base of support for Reform UK. Did Nigel Farage's new party depend on the same types of ‘left behind’ voters who had previously backed UKIP? Do the results of the 2024 election suggest a hardening of the social divides that underpinned the rise of UKIP? Or has Britain's Eurosceptic and anti‐immigration movement broadened its social appeal, making these lines of conflict between different social groups less distinctive? Using a newly created dataset mapping election results from 1997–2019 onto the constituency boundaries which came into force for the 2024 election, and individual level data from the British Election Study Internet Panel, the findings presented here reveal remarkable continuity between the places that previously backed UKIP and those which now back Reform. Even though the salience of Brexit as an electoral issue has faded, there is little to indicate that the divides that underpinned support for leaving the EU have dissipated.
INTEGRATING CLIMATE POLICY OBJECTIVES INTO MUNICIPAL LAND POLICIES: From conceptualization to empirical evidence from Finland
Authors: Puustinen Tuulia , Falkenbach Heidi , Ekroos Ari , Junnila Seppo 
Published: November 2024

AbstractUrban development ties in closely with climate change mitigation and adaptation. However, little is known about how climate policy objectives are incorporated into municipal land policies. Addressing this knowledge gap, we draw from the literature on policy integration and policy design to propose a framework for conceptualizing the ways in which policy objectives driven by climate concerns are incorporated into land policy. This framework incorporates three dimensions: (1) the alignment and prioritization of climate policy objectives in land policy; (2) the concreteness of the final integrated policy design; and (3) the commitment to promoting the objectives set through choices of policy instruments. We apply this framework to explore the level of integration in 30 highly populated municipalities in Finland. We identify three levels of climate policy objective integration within this sample, with only three municipalities demonstrating high integration. Our findings suggest that raising the level of climate policy integration into land policy may require institutional support.