bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatial dynamics of peripheral and central nervous system infection by an interferon-inducing neuroinvasive virus
Authors: Laghi Valerio , Boucontet Laurent , Wiggett Hannah , Banerjee Payel , Simion Matthieu , Maggi Ludovico , Ciura Sorana , Guedj Jérémie , Colucci-Guyon Emma , Levraud Jean-Pierre 
Published: May 2024

AbstractOrgan-to-organ dissemination of viruses is a critical feature of host-virus interactions. In particular, neuroinvasive viruses are able to enter the central nervous systems (CNS), which may result in death or permanent neurological impairment. The complex mechanisms underpinning this spread are poorly understood, as they depend on a variety of parameters, including initial site of entry, route of access to the CNS, and immune responses. To better understand these phenomena, we analyzed the spatial dynamics of Sindbis virus (SINV) dissemination in transparent zebrafish larvae. Using fluorescent reporter viruses, we observed that SINV readily invaded the CNS after inoculation at various peripheral sites. From tail muscle, the virus used dorsal root ganglia (DRG) sensory neurons as a gateway to the spinal cord and further propagation to the brain. While peripheral infection was systematically transient, due to the key protective role of the strong and rapid type I interferon (IFN) response, CNS infection was persistent and more variable. Within the CNS, viral dissemination resulted both from long-distance axonal transport and short distance shedding, and IFN response was local, while it was systemic in the periphery. A mathematical model was built on this quantitative imaging foundation, that provided additional insight on the parameters of this infection, such as the rate of new virion production, estimated around 1 to 2 infective virions per productively infected cell per hour; the occurrences of CNS entry events, which was 2 to 3 per larva; or the impact of the IFN response, which did not only prevent new infections but accelerated the death of infected cells.
Aspartate/alanine aminotransferase ratio and development of chronic kidney disease in non-diabetic Japanese men and women
Authors: Okawa Yukari , Mitsuhashi Toshiharu , Suzuki Etsuji 
Published: May 2024

AbstractAimThe objective of this study was to explore the relationship between serum aspartate/alanine aminotransferase ratio (AST/ALT) and subsequent development of chronic kidney disease (CKD) in non-diabetic Japanese men and women in a 25-year follow-up study.MethodsThe study included all middle-aged and older non-diabetic Japanese citizens who received health check-ups in Zentsuji, Kagawa, Japan (1998–2023). AST/ALT was classified into three categories: <1.0 (reference), 1.0–<1.5, and ≥1.5. Participant characteristics were compared by AST/ALT categories. The Weibull accelerated failure time model was used to examine the association between AST/ALT categories and subsequent CKD onset because the proportional hazards assumption was violated.ResultsOf 6309 men and 9192 women, 2966 men and 4395 women remained in the final cohort. After a mean follow-up of 7.50 years for men and 8.34 years for women, 33.7% of men and 34.0% of women developed CKD. Women had higher AST/ALT than men. In women, a dose-response relationship was observed, with a 9% shorter survival time to CKD onset for AST/ALT ≥1.5 compared with AST/ALT <1.0. In contrast, men had a shorter survival time to CKD onset by point estimates, but the 95% confidence intervals crossed 1 in all models.ConclusionsIn this study comparing the risks of CKD development in non-diabetic men and women by AST/ALT levels, a dose-response relationship was only observed in women. Differences in the distribution of AST/ALT by sex may have affected the results. Therefore, in non-diabetic Japanese women, AST/ALT may be used as an indicator of future CKD development.
Clinical Decision Support Opportunities among Clinicians Caring for People Receiving Invasive Mechanical Ventilation: A Cross-sectional Survey
Authors: Weissman Gary E. , Bishop Nicholas S. , Schmid Benjamin E. , Madden Vanessa , Pirracchio Romain , Jentzer Jacob C. , Riman Kathryn A. , Krutsinger Dustin C. , DiGiovine Bruno , Ungar Lyle H. , Halpern Scott D. , Auriemma Catherine L. , Valley Thomas S. , Kerlin Meeta Prasad 
Published: May 2024

AbstractTreatment decisions for patients receiving invasive mechanical ventilation (IMV) are complex and depend simultaneously on the current ventilator settings, the function of multiple interrelated organ systems, and other treatments. An artificial intelligence (AI)-based clinical decision support system (CDSS) offers a promising approach to alleviate uncertainty in this complex management and provide personalized treatment recommendations. However, little is known about clinician preferences for which treatment decisions clinicians think would most benefit from a CDSS. Therefore, we conducted a cross-sectional electronic survey of practicing physicians, nurses, advanced practice providers, and respiratory therapists to identify key treatment decisions in IMV care. We sent the survey instrument to 132 clinicians across six geographically diverse health systems. Among 51 respondents (39% response rate), there were 24 (47%) physicians, 7 (14%) registered nurses, 8 (16%) advanced practice providers, and 12 (24%) respiratory therapists. Participants were from five US states including Pennsylvania, California, Michigan, Minnesota, and Nebraska. At least 50% of participants identified 18 distinct treatment decisions for IMV care asvery importantorabsolutely essential, including many that were outside of the ventilator settings themselves. The highest agreement about importance was for the decision to extubate (N=51, 100%), and the decisions to conduct a spontaneous awakening trial (N=48, 94%) and spontaneous breathing trial (N=48, 94%). The highest agreement about a decision beingnot importantor onlyslightly importantwas for the shape of the inspiratory flow pattern (N=15, 29%). These findings underscore the scope and complexity of clinical decision making across multidisciplinary teams in caring for people receiving IMV. Furthermore, they underscore the gap between important clinician-identified decisions and existing IMV CDSSs that provide suggestions for at most 5 ventilator settings without support for other related decisions. Future work is needed to identify which of these decisions might be most appropriate for inclusion in a CDSS to support IMV management.
Fine-mapping a genome-wide meta-analysis of 98,374 migraine cases identifies 181 sets of candidate causal variants
Authors: Hautakangas Heidi ,   FinnGen,   International Headache Genetics Consortium,   HUNT All-in Headache, Palotie Aarno , Pirinen Matti 
Published: May 2024

AbstractMigraine is a highly prevalent neurovascular disorder for which genome-wide association studies (GWAS) have identified over one hundred risk loci, yet the causal variants and genes remain mostly unknown. Here, we meta-analyzed three migraine GWAS including 98,374 cases and 869,160 controls and identified 122 independent risk loci of which 35 were new. Fine-mapping of a meta-analysis is challenging because some variants may be missing from some participating studies and accurate linkage disequilibrium (LD) information of the variants is often not available. Here, using the exact in-sample LD, we first investigated which statistics could reliably capture the quality of fine-mapping when only reference LD was available. We observed that the posterior expected number of causal variants best distinguished between the high- and low-quality results. Next, we performed fine- mapping for 102 autosomal risk regions using FINEMAP. We produced high-quality fine-mapping for 93 regions and defined 181 distinct credible sets. Among the high- quality credible sets were 7 variants with very high posterior inclusion probability (PIP > 0.9) and 2 missense variants with PIP > 0.5 (rs6330 inNGFand rs1133400 inINPP5A). For 35 association signals, we managed to narrow down the set of potential risk variants to at most 5 variants.
Spontaneous breaking of the
SO(2N)
symmetry in the Gross-Neveu model
Authors: Han SangEun , Herbut Igor F. 
Published: May 2024

The canonical Gross-Neveu model for N two-component Dirac fermions in 2+1 dimensions suffers a continuous phase transition at a critical interaction gc1∼1/N at large N, at which its continuous symmetry SO(2N) is preserved and a discrete (Ising) symmetry becomes spontaneously broken. A recent mean-field calculation, however, points to an additional transition at a different critical gc2∼−Ngc1, at which SO(2N)→SO(N)×SO(N). To study the latter phase transition we rewrite the Gross-Neveu interaction g(ψ¯ψ)2 in terms of three different quartic terms for the single (L=1) 4N-component real (Majorana) fermion, and then extend the theory to L>1. This allows us to track the evolution of the fixed points of the renormalization group transformation starting from L≫1, where one can discern three distinct critical points which correspond to continuous phase transitions into (1) SO(2N)-singlet mass-order-parameter, (2) SO(2N)-symmetric-tensor mass-order-parameters, and (3) SO(2N)-adjoint nematic-order-parameters, down to L=1 value that is relevant to the standard Gross-Neveu model. Below the critical value of Lc(N)≈0.35N for N≫1 only the Gross-Neveu critical point (1) still implies a diverging susceptibility for its corresponding (SO(2N)-singlet) order parameter, whereas the two new critical points that existed at large L ultimately become equivalent to the Gaussian fixed point at L=1. We interpret this metamorphosis of the SO(2N)-symmetric-tensor fixed point from critical to spurious as an indication that the transition at gc2 in the original Gross-Neveu model is turned first-order by fluctuations.

Published by the American Physical Society
2024
New Constraint for Isotropic Lorentz Violation from LHC Data
Authors: Amram David , Bouzoud Killian , Chanon Nicolas , Hansen Hubert , Ribeiro Marcos R. , Schreck Marco 
Published: May 2024

New calculations for the kinematics of photon decay to fermions under an isotropic violation of Lorentz invariance (LV), parametrized by the standard-model extension, are presented in this Letter and used to interpret prompt photon production in LHC data. The measurement of inclusive prompt photon production at the LHC Run 2, with photons observed up to a transverse energy of 2.5 TeV, provides the lower bound κ˜tr>−1.06×10−13 on the isotropic coefficient κ˜tr at 95% confidence level. This result improves over the previous bound from hadron colliders by a factor of 55. The calculations for the kinematics of photon decay have further potential use to constrain LV coefficients from the appearance of fermion pairs.

Published by the American Physical Society
2024
Can board funds, bank credit, and economic development improve food production? Evidence from South Asia
Authors: Farooq Usman , Chandio Abbas Ali , Guan Zhenzhong 
Published: May 2024

PurposeThis study investigates the impact of board funds, banking credit, and economic development on food production in the context of South Asian economies (India, Pakistan, Bangladesh, Sri Lanka, and Nepal).Design/methodology/approachThis study used data from the World Development Indicators covering the years 1991–2019. To investigate the relationship between the variables of the study, we employed the panel unit root test, panel cointegration test, cross-sectional dependence test, fully modified least squares (FMOLS), and panel dynamic least squares (DOLS) estimators.FindingsThe empirical results indicate that board funding significantly increase food production; however, banking credit had a negative impact. Furthermore, the findings indicate that economic development, Arable land, fertilizer consumption, and agricultural employment play a leading role in enhancing food production. The results of the Dumitrescu-Hurlin causality test also show substantiated the significance of the causal relationship among all variables.Practical implicationsSouth Asian countries should prioritize board funding, bank credit, and economic development in their long-term strategies. Ensuring financial access for farmers through micro-credit and public bank initiatives can spur agricultural productivity and economic growth.Originality/valueThis study is the first to combine board funding, banking credit, and economic development to better comprehend their potential impact on food production. Instead of using traditional approaches, this study focuses on these financial and developmental aspects as critical determinants for increasing food production, using evidence from South Asia.
Assessing urban growth through morphological spatial pattern analysis in cloud computing platform
Authors: Shohan Ahmed Ali A. , Bindajam Ahmed , Al-Shayeb Mohammed , Thi Hang 
Published: May 2024

PurposeThis study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).Design/methodology/approachUsing the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.FindingsThe study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.Originality/valueThe novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.
Spatiotemporal climate variability and extremes in Middle Awash Afar region Ethiopia: implications to pastoralists and agro-pastoralists food security
Authors: Aytenfisu Ameha Tadesse , Tolossa Degefa , Feleke Solomon Tsehay , Ayal Desalegn Yayeh 
Published: May 2024

Purpose
This study aims to examine the phenomenon of climate variability and its implications for pastoralists and agro-pastoralists food security in Amibara and Awash Fentale districts of the Afar region, Ethiopia.

Design/methodology/approach
The study relied on meteorological records of temperature and rainfall in the study area between 1988 and 2018. Besides, literature on the topic was reviewed to make caveats on the literal picture that comes from quantitative data, and that is the contribution of this study to the existing debate on climate change and variability. The spatiotemporal trend was determined using the Mann–Kendall test and Sen’s slope estimator, while variability was analyzed using the coefficient of variation and standardized anomaly index, and standardized precipitation index/standardized precipitation evapotranspiration index were applied to determine the drought frequency and severity.

Findings
The result reveals that the mean seasonal rainfall varies from 111.34 mm to 518.74 mm. Although the maximum and minimum rainfall occurred in the summer and winter seasons, respectively, there has been a decrease in seasonal and annual at the rate of 2.51 mm per season and 4.12 mm per year, respectively. The study sites have been experiencing highly seasonal rainfall variability. The drought analysis result confirms that a total of nine agricultural droughts ranging from moderate to extreme years were observed. Overall, the seasonal and annual rainfall of the Amibara and Awash Fentale districts showed a decreasing trend with highly temporal variations of rainfall and ever-rising temperatures, and frequent drought events means the climate situation of the area could adversely affect pastoral and agro-pastoral households’ food security. However, analysis of data from secondary sources reveals that analyzing precipitation just based on the meteorological records of the study area would be misleading. That explains why flooding, rather than drought, is becoming the main source of catastrophe to pastoral and agro-pastoral livelihoods.

Practical implications
The analysis of temperature and rainfall dynamics in the Afar region, hence the inception of all development interventions, must take the hydrological impact of the neighboring regions which appears to be useful direction to future researchers.

Originality/value
The research is originally conducted using meteorological and existing literature, and hence, it is original. In this research, we utilized a standardized and appropriate methodology, resulting in insights that augment the existing body of knowledge within the field. These insights serve to advance scholarly discourse on the subject matter.
Affordances, constraints and collaborative potential of e-mentoring platforms and their features for K–12 educators: a systematic literature review
Authors: Bell Kristina 
Published: May 2024

PurposeThe purpose of this systematic literature review is to catalog, explore and disseminate knowledge developed related to the affordances, constraints and collaborative practices of e-mentoring in order to offer recommendations for mentoring programs.Design/methodology/approachAfter identifying studies utilizing inclusion and exclusion criteria and a variety of relevant databases, chosen studies were organized into three categories based on e-mentoring practices.FindingsResults reflect chat-room-based e-mentoring and e-mentoring with a video component both to have various affordances and collaborative features. These forms of e-mentoring can potentially be relevant to new teachers’ needs, may promote community and are disconnected from mentees’ school systems. A third form of e-mentoring, virtual reality e-mentoring, however, has the potential for accessibility issues and lack of relevancy to the mentoring process.Originality/valueChat-room-based e-mentoring and e-mentoring with a video component may benefit new teachers who do not have access to in-person mentors. However, these forms of e-mentoring may necessitate a component that ensures mentors and mentees alike remain consistently engaged in the e-mentoring process.
Intelligent crop management system for improving yield in maize production: evidence from India
Authors: Vishnoi Sakshi , Persis Jinil 
Published: May 2024

PurposeManaging weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and healthDesign/methodology/approachWe propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presentedFindingsWeed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actionsPractical implicationsThe proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize cropSocial implicationsThe proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.Originality/valueThis study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature
Examining the relationship among constraints, facilitators and ski participation in the host city of the 2022 Winter Olympics
Authors: Yang Yiqi , Macintosh Eric , Xing Xiaoyan 
Published: May 2024

PurposeThe study’s purpose is to investigate the constraints and facilitators influencing skiing participation in Beijing. This research includes three segments based on the frequency of skiing participation (i.e. non-, low-frequency-, and high-frequency skiers). By doing so, the study offers an enhanced understanding of the Chinese skiing market and unveils insights assisting industry professionals to effectively address their customers' diverse needs and expectations.Design/methodology/approachAn online survey was developed based on prior research and consisted of four sections: (1) skiing participation; (2) constraints; (3) facilitators; (4) demographics. Items in the constraint and facilitator scale were measured using a 7-point Likert scale. A total of 409 participants completed the survey. The participants included 137 non-skiers, 134 low-frequency skiers, and 138 high-frequency skiers.FindingsThrough an exploratory factor analysis, three constructs emerged: general constraints, facilitators and learning constraints. As expected, facilitators were a positive predictor of skiing participation. Importantly, the emergent construct of learning constraints was a negative predictor of skiing and yet, the construct of general constraints was insignificant. Furthermore, the three segments differ significantly in household status, income, and education level.Originality/valueThese results support previous research noting the relevance in skiing participation of the dimensions: facilitators and learning constraints. The findings point to the need for ski resorts in Beijing to offer instructional sessions for beginners so they may become familiar with skiing fundamentals and enhance their confidence, particularly among nonskiers and low-frequency skiers.
Wear particle image analysis: feature extraction, selection and classification by deep and machine learning
Authors: Vivek Joseph , Venkatesh S. Naveen , Mahanta Tapan K. , V. Sugumaran , Amarnath M. , Ramteke Sangharatna M. , Marian Max 
Published: May 2024

Purpose
This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Design/methodology/approach
Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

Findings
From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

Originality/value
The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.