bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is Some Politics Still Local? Voter Preferences for Local Candidates
Authors: Hunt Charles R. , Fontana David 
Published: June 2024

Abstract
Previous literature has demonstrated electoral advantages for candidates with backgrounds in the communities they represent, even amidst the trends of partisan polarization and the nationalization of elections. Here, we consider voter preferences for local candidates using a broader range of methods, jurisdictions, time periods, and populations than has previously been utilized. Our results leverage a survey experiment presenting respondents with pairs of fictional open-seat U.S. Senate candidates with varying degrees of local backgrounds. Voters consistently responded more favorably to locally-rooted candidates across different states. We also present novel evidence that local candidate effects are due largely to voters crossing party lines, and that the parties are asymmetrical in their responses to localism and carpetbagging, with Democrats benefitting from local ties more than Republicans. Observational data on real-life Senate candidates from 1960 to 2020 largely mirror our main experimental findings. Together, these findings demonstrate that local connections matter in more nuanced ways than have been previously considered.
How Selective Mobility, Social and Ecological Influence may Impact Geographic Variations in Life Satisfaction Scores: An Australian Longitudinal Study
Authors: Lignier Phil , Jarvis Diane , Grainger Daniel , Chaiechi Taha 
Published: June 2024

AbstractThe spatial clustering of life satisfaction scores noted in recent empirical research suggests that ‘happier’ people may live in specific neighbourhoods or regions. This prompts the questions: Do ‘happier’ people choose to move to specific places? Does living in specific places make people ‘happier’? To answer these questions, this paper explores possible occurrences of selective mobility, and social and ecological influence. Using panel data collected in Australia from 2013 to 2021, we examine the association between life satisfaction scores and selective geographic mobility, and the possible influence that living at specific locations may have on individual life satisfaction trajectory, while controlling for individual personality traits and socio-demographic factors. Our results indicate that urban residents reporting lower life satisfaction scores before the move have a higher probability of moving to a rural area. Similarly, lower life satisfaction scores are associated with a higher probability of moving to a region with a different climate. We also find evidence that moving from the city to the country is associated with an uplift of the life satisfaction trajectory for the individual. A similar conclusion is reached for people who moved to a warmer climate, but not for a move to a cooler climate. To our knowledge, this is the first time the concepts of selective mobility and social and ecological influence have been applied in life satisfaction research. Our work provides an indicator that can be important to demographers predicting population movements. It can also inform policy development around assisting regional and rural areas attract/ retain residents to support regional sustainability.
Bounded-memory adjusted scores estimation in generalized linear models with large data sets
Authors: Zietkiewicz Patrick , Kosmidis Ioannis 
Published: June 2024

AbstractThe widespread use of maximum Jeffreys’-prior penalized likelihood in binomial-response generalized linear models, and in logistic regression, in particular, are supported by the results of Kosmidis and Firth (Biometrika 108:71–82, 2021. https://doi.org/10.1093/biomet/asaa052), who show that the resulting estimates are always finite-valued, even in cases where the maximum likelihood estimates are not, which is a practical issue regardless of the size of the data set. In logistic regression, the implied adjusted score equations are formally bias-reducing in asymptotic frameworks with a fixed number of parameters and appear to deliver a substantial reduction in the persistent bias of the maximum likelihood estimator in high-dimensional settings where the number of parameters grows asymptotically as a proportion of the number of observations. In this work, we develop and present two new variants of iteratively reweighted least squares for estimating generalized linear models with adjusted score equations for mean bias reduction and maximization of the likelihood penalized by a positive power of the Jeffreys-prior penalty, which eliminate the requirement of storing O(n) quantities in memory, and can operate with data sets that exceed computer memory or even hard drive capacity. We achieve that through incremental QR decompositions, which enable IWLS iterations to have access only to data chunks of predetermined size. Both procedures can also be readily adapted to fit generalized linear models when distinct parts of the data is stored across different sites and, due to privacy concerns, cannot be fully transferred across sites. We assess the procedures through a real-data application with millions of observations.
Characterization of the proteins encoded by a recently emerged cotton-infecting Polerovirus
Authors: Akinyuwa Mary F. , Price Bailee K. , Kang Sung-Hwan 
Published: June 2024

AbstractThe cotton leafroll dwarf virus (CLDV), an important viral pathogen responsible for substantial losses in cotton crops, has recently emerged in the United States (US). Although CLDV shares similarities with other members of the genus Polerovirus in terms of encoded proteins, their functional characteristics remain largely unexplored. In this study, we expressed and analyzed each protein encoded by CLDV to determine its intracellular localization using fluorescence protein fusion. We also evaluated their potential to induce plant responses, such as the induction of hypersensitive response-like necrosis and the generation of reactive oxygen species. Our findings show that the proteins encoded by CLDV exhibit comparable localization patterns and elicit similar robust plant responses as observed with cognate proteins from other viruses within the genus Polerovirus. This study contributes to our understanding of the functional repertoire of genes carried by Polerovirus members, particularly to CLDV that has recently emerged as a widespread viral pathogen infecting cotton in the US.
Preparation of Eco-Friendly Composite Material for Mercury (II) Adsorption Including Non-Wood Content From Walnut Green Husk (Juglon Regia L.): Experimental and Theoretical Studies
Authors: Koçak Nurdan , Çoktaş Fatma , Şimşek Selçuk , Kaya Savaş , Maslow M. 
Published: June 2024

AbstractIn this study, the adsorption properties of a composite material consisting of polyacrylamide, an inert polymer, and an extract obtained from the water-soluble part of a green walnut shell were investigated for Hg(II) ions. SEM, EDX, FTIR, and PZC analyses were performed to characterize the newly synthesized material. SEM and EDX analyses confirmed that the surface of the synthesized adsorbent became softer and smoother after adsorption, indicating the presence of Hg in its elemental composition. FTIR analysis showed that mercury enters the structure through chemical interactions, and there are changes in bond vibration frequencies in the presence of Hg(II). According to the PZC point analysis, the point at which the surface charge was zero was found to be pH 4. The Langmuir model was used to calculate the adsorption capacity after investigating the effect of concentration on adsorption. The adsorption capacity was found to be 1.808 molkg−1 (362,67 mgg−1) from the Langmuir model, which is very high compared to similar adsorbents. PFO model was used to explain the adsorption kinetics and very fast adsorption kinetics were observed. The adsorption entropy increased, free enthalpy of adsorption was negative, and heat of adsorption was in the energy-consuming direction.
Shor’s algorithm does not factor large integers in the presence of noise
Authors: Cai Jin-Yi 
Published: June 2024

AbstractWe consider Shor’s quantum factoring algorithm in the setting of noisy quantum gates. Under a generic model of random noise for (controlled) rotation gates, we prove that the algorithm does not factor integers of the formpqwhen the noise exceeds a vanishingly small level in terms ofn—the number of bits of the integer to be factored, wherepandqare from a well-defined set of primes of positive density. We further prove that with probability 1 −o(1) over random prime pairs (p, q), Shor’s factoring algorithm does not factor numbers of the formpq, with the same level of random noise present.
Microglial EPOR Contribute to Sevoflurane-induced Developmental Fine Motor Deficits Through Synaptic Pruning in Mice
Authors: He Danyi , Shi Xiaotong , Liang Lirong , Zhao Youyi , Ma Sanxing , Cao Shuhui , Liu Bing , Gao Zhenzhen , Zhang Xiao , Fan Ze , Kuang Fang , Zhang Hui 
Published: June 2024

AbstractClinical researches including the Mayo Anesthesia Safety in Kids (MASK) study have found that children undergoing multiple anesthesia may have a higher risk of fine motor control difficulties. However, the underlying mechanisms remain elusive. Here, we report that erythropoietin receptor (EPOR), a microglial receptor associated with phagocytic activity, was significantly downregulated in the medial prefrontal cortex of young mice after multiple sevoflurane anesthesia exposure. Importantly, we found that the inhibited erythropoietin (EPO)/EPOR signaling axis led to microglial polarization, excessive excitatory synaptic pruning, and abnormal fine motor control skills in mice with multiple anesthesia exposure, and those above-mentioned situations were fully reversed by supplementing EPO-derived peptide ARA290 by intraperitoneal injection. Together, the microglial EPOR was identified as a key mediator regulating early synaptic development in this study, which impacted sevoflurane-induced fine motor dysfunction. Moreover, ARA290 might serve as a new treatment against neurotoxicity induced by general anesthesia in clinical practice by targeting the EPO/EPOR signaling pathway.
A novel mitochondrial-related lncRNA signature mediated prediction of overall survival, immune landscape, and the chemotherapeutic outcomes for bladder cancer patients
Authors: Xiong Hao , Lin Cheng , Huang Xiang , Wang Hao 
Published: June 2024

Abstract
Objective
To develop a prognostic risk model for Bladder Cancer (BLCA) based on mitochondrial-related long non-coding RNAs (lncRNAs).

Methods
Transcriptome and clinical data of BLCA patients were retrieved from the TCGA database. Mitochondrial-related lncRNAs with independent prognostic significance were screened to develop a prognostic risk model. Patients were categorized into high- and low-risk groups using the model. Various methods including Kaplan–Meier (KM) analysis, ROC curve analysis, Gene Set Enrichment Analysis (GSEA), immune analysis, and chemotherapy drug analysis were used to verify and evaluate the model.

Results
A mitochondrial-associated lncRNA prognostic risk model with independent prognostic significance was developed. High-risk group (HRG) patients exhibited significantly shorter survival periods compared to low-risk group (LRG) patients (P < 0.01). The risk score from the model was an independent predictor of BLCA prognosis, correlating with tumor grade, pathological stage, and lymph node metastasis (P < 0.05). The HRG showed significant positive correlations with high expressions of immune checkpoints (CTLA4, LAG3, PD-1, TIGIT, PD-L1, PD-L2, and TIM-3) and lower IC50 for chemotherapy drugs (cisplatin, docetaxel, paclitaxel, methotrexate, and vinblastine) (P < 0.001).

Conclusions
The mitochondrial-related lncRNA-based prognostic risk model effectively predicts BLCA prognosis and can guide individualized treatment for BLCA patients.
Reliability analysis of railway assets considering the impact of geographical and climatic properties
Authors: Kasraei Ahmad , Garmabaki A. H. S. 
Published: June 2024

AbstractVarious factors, including climate change and geographical features, contribute to the deterioration of railway infrastructures over time. The impacts of climate change have caused significant damage to critical components, particularly switch and crossing (S&C) elements in the railway network. These components are sensitive to abnormal temperatures, snow and ice, and flooding, making them susceptible to failures. The consequences of S&C failures can have a detrimental effect on the reliability and safety of the entire railway network.It is crucial to have a reliable clustering of railway infrastructure assets based on various climate zones to make informed decisions for railway network operation and maintenance in the face of current and future climate scenarios. This study employs machine learning models to categorize S&Cs; therefore, historical maintenance data, asset registry information, inspection data, and weather data are leveraged to identify patterns and cluster failures. The analysis reveals four distinct clusters based on climatic patterns. The effectiveness of the proposed model is validated using S&C data from the Swedish railway network.By utilizing this clustering approach, the whole of Sweden railway network divided into 4 various groups. Utilizing this groups the development of model can associated with enhancing certainty of decision-making in railway operation and maintenance management. It provides a means to reduce uncertainty in model building, supporting robust and reliable decision-making. Additionally, this categorization supports infrastructure managers in implementing climate adaptation actions and maintenance activities management, ultimately contributing to developing a more resilient transport infrastructure.
GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study
Authors: Abdo Hazem Ghassan , Vishwakarma Dinesh Kumar , Alsafadi Karam , Bindajam Ahmed Ali , Mallick Javed , Mallick Suraj Kumar , Kumar Karikkathil C. Arun , Albanai Jasem A. , Kuriqi Alban , Hysa Artan 
Published: June 2024

AbstractIn light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria.
Adipose-derived stem cells modified by TWIST1 silencing accelerates rat sciatic nerve repair and functional recovery
Authors: Chen Bo , Wang Leining , Pan Xiaogui , Jiang Shuai , Hu Yihe 
Published: June 2024

AbstractThe regeneration of peripheral nerves after injury is often slow and impaired, which may be associated with weakened and denervated muscles subsequently leading to atrophy. Adipose-derived stem cells (ADSCs) are often regarded as cell-based therapeutic candidate due to their regenerative potential. The study aims to assess the therapeutic efficacy of gene-modified ADSCs on sciatic nerve injury. We lentivirally transduced ADSCs with shRNA-TWIST1 and transplanted modified cells to rats undergoing sciatic nerve transection and repair. Results showed that TWIST1 knockdown accelerated functional recovery of rats with sciatic nerve injury as faster nerve conduction velocity and higher wire hang scores obtained by rats transplanted with TWIST1-silenced ADSCs than scramble ADSCs. Although the rats experienced degenerated axons and decreased myelin sheath thickness after sciatic nerve injury 8 weeks after operation, those transplanted with TWIST1-silenced ADSCs exhibited more signs of regenerated nerve fibers surrounded by newly formed myelin sheaths than those with scramble ADSCs. The rats transplanted with TWIST1-silenced ADSCs presented increased expressions of neurotrophic factors including neurotrophin-3 (NT-3), brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), and glial cell line-derived neurotrophic factor (GDNF) in the sciatic nerves than those with scramble ADSCs. These results suggest that genetically modifying TWIST1 in ADSCs could facilitate peripheral nerve repair after injury in a more efficient way than that with ADSCs alone.
LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review
Authors: Balestra Mattia , Marselis Suzanne , Sankey Temuulen Tsagaan , Cabo Carlos , Liang Xinlian , Mokroš Martin , Peng Xi , Singh Arunima , Stereńczak Krzysztof , Vega Cedric , Vincent Gregoire , Hollaus Markus 
Published: June 2024

Abstract
Purpose of the Review
Many LiDAR remote sensing studies over the past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in the final data products. Here, we performed a structured literature review to analyze relevant studies on these topics published in the last decade and the main motivations and applications for fusion, and the methods used. We discuss the findings with a panel of experts and report important lessons, main challenges, and future directions.

Recent Findings
LiDAR fusion with other datasets, including multispectral, hyperspectral, and radar, is found to be useful for a variety of applications in the literature, both at individual tree level and at area level, for tree/crown segmentation, aboveground biomass assessments, canopy height, tree species identification, structural parameters, and fuel load assessments etc. In most cases, gains are achieved in improving the accuracy (e.g. better tree species classifications), and spatial-temporal resolution (e.g. for canopy height). However, questions remain regarding whether the marginal improvements reported in a range of studies are worth the extra investment, specifically from an operational point of view. We also provide a clear definition of “data fusion” to inform the scientific community on data fusion, combination, and integration.

Summary
This review provides a positive outlook for LiDAR fusion applications in the decade to come, while raising questions about the trade-off between benefits versus the time and effort needed for collecting and combining multiple datasets.
Twin-tower transformer network for skeleton-based Parkinson’s disease early detection
Authors: Ma Lan , Huo Hua , Liu Wei , Zhao Changwei , Wang Jinxuan , Xu Ningya 
Published: June 2024

AbstractParkinson’s disease is a chronic neurodegenerative condition accompanied by a variety of motor and non-motor clinical symptoms. Diagnosing Parkinson’s disease presents many challenges, such as excessive reliance on subjective scale scores and a lack of objective indicators in the diagnostic process. Developing efficient and convenient methods to assist doctors in diagnosing Parkinson’s disease is necessary. In this paper, we study the skeleton sequences obtained from gait videos of Parkinsonian patients for early detection of the disease. We designed a Transformer network based on feature tensor fusion to capture the subtle manifestations of Parkinson’s disease. Initially, we fully utilized the distance information between joints, converting it into a multivariate time series classification task. We then built twin towers to discover dependencies within and across sequence channels. Finally, a tensor fusion layer was employed to integrate the features from both towers. In our experiments, our model demonstrated superior performance over the current state-of-the-art algorithm, achieving an 86.8% accuracy in distinguishing Parkinsonian patients from healthy individuals using the PD-Walk dataset.
A software for calculating coal mine gas emission quantity based on the different-source forecast method
Authors: Zhao Wei , Dong Huzi , Ren Junchen , Yuan Yuan , Wang Kai , Wang Fei 
Published: June 2024

AbstractThe ability to predict gas emissions accurately is pivotal in managing gas control and ensuring safe mining operations. Existing internationally acknowledged gas control and prediction software does not cater to the specific conditions in Chinese coal mines. Hence, this paper introduces an object-oriented programming method to design a software tool for calculating the total gas emission quantity using the MATLAB application program designer runtime environment. The software incorporates an algorithm, data structure, framework, and module functions, all of which enable seamless integration and visualization of gas emission calculation software. This software tool mitigates the inefficiencies and inaccuracies associated with manual, different-source forecast methods. Based on the field data of the Hulonggou Coal Mine in Shanxi province, this technical software was used to predict the gas emission of the mine. The research results show that the predicted value of the technical software is close to the actual measured value. The differing estimates of the working face and coal mine output primarily account for the deviation between the tool's predicted gas emission value and the field-measured value. The underlying design logic of this technical software determines that it has good adaptability to mines with clear mining technology parameters and gas geological parameters. This study provides a valuable method for researchers and engineers seeking to improve gas emission calculation efficiency.
Regulating AI-Based Medical Devices in Saudi Arabia: New Legal Paradigms in an Evolving Global Legal Order
Authors: Solaiman Barry 
Published: June 2024

AbstractThis paper examines the Saudi Food and Drug Authority’s (SFDA) Guidance on Artificial Intelligence (AI) and Machine Learning (ML) technologies based Medical Devices (the MDS-G010). The SFDA has pioneered binding requirements designed for manufacturers to obtain Medical Device Marketing Authorization. The regulation of AI in health is at an early stage worldwide. Therefore, it is critical to examine the scope and nature of the MDS-G010, its influences, and its future directions. It is argued that the guidance is a patchwork of existing international best practices concerning AI regulation, incorporates adapted forms of non-AI-based guidelines, and builds on existing legal requirements in the SFDA’s existing regulatory architecture. There is particular congruence with the approaches of the US Food and Drug Administration (FDA) and the International Medical Device Regulators Forum (IMDRF), but the SFDA goes beyond those approaches to incorporate other best practices into its guidance. Additionally, the binding nature of the MDS-G010 is complex. There are binding ‘components’ within the guidance, but the incorporation of non-binding international best practices which are subordinate to national law results in a lack of clarity about how penalties for non-compliance will operate.