bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cannabidiol (CBD) modulates the transcriptional profile of ethanol-exposed human dermal fibroblast cells
Authors: Gurgul Artur , Żurowski Jakub , Szmatoła Tomasz , Kucharski Mirosław , Sawicki Sebastian , Semik-Gurgul Ewelina , Ocłoń Ewa 
Published: October 2024

AbstractCannabidiol (CBD) is abundant in the Cannabis sativa plant and exhibits complex immunomodulatory, anxiolytic, antioxidant, and antiepileptic properties. Several studies suggest that CBD could be used for different purposes in alcohol use disorder (AUD) and alcohol-related injuries to the brain and the liver. In this study, we focused on analyzing transcriptional alterations in human dermal fibroblasts (HDFs) cell line challenged simultaneously with ethanol and CBD as an ethanol-protective agent. We aimed to expose the genes and pathways responsible for at least some of the CBD effects in those cells that can be related to the AUD. Transcriptome analysis was performed using HDFs cell line that expresses both cannabinoid receptors and can metabolize ethanol through alcohol dehydrogenase activity. Fibroblasts are also responsible for the progression of liver fibrosis, a common comorbidity in AUD. With the use of a cellular test, we found that CBD at the lowest applied concentration (0.75 μM) was able to stimulate depressed metabolism and reduce the level of apoptosis of cells treated with different concentrations of ethanol to the level observed in the control cells. Similar observations were made at the transcriptome level, in which cells treated with ethanol and CBD had similar expression profiles to the control cells. CBD also affects several genes connected with extracellular matrix formation (especially its collagen constituent), which can have potential implications for, e.g., fibrosis process.
Polymorphic insertions of DcSto miniature inverted-repeat transposable elements reveal genetic diversity structure within the cultivated carrot
Authors: Hadagali Santosh , Stelmach-Wityk Katarzyna , Macko-Podgórni Alicja , Cholin Sarvamangala , Grzebelus Dariusz 
Published: October 2024

AbstractMiniature inverted-repeat transposable elements (MITEs) are a potent source of polymorphisms in plant genomes. A genotyping system, named DcS-ILP, based on polymorphic insertions of Stowaway MITEs (DcStos) localized in introns and identified in the reference genome DH1, has been developed for carrot. Here, we report an extension of the DcS-ILP genotyping system by incorporation of non-reference insertions identified in resequenced genomes representing the eastern gene pool. We genotyped 52 carrot accessions representing the eastern and western carrot gene pools with 92 markers developed previously (western DcS-ILP panel) together with 84 newly developed markers (eastern DcS-ILP panel). Overall, the DcS-ILP markers revealed a highly structured genetic diversity separating the eastern and the western carrot accessions at K = 2 and differentiating Indian breeding lines from the eastern accessions at K = 3. The eastern DcS-ILP panel proved to be more robust with respect to the eastern carrot gene pool, while it provided little information on the western accessions, as many of the DcSto insertions present in the eastern gene pool were absent in the western gene pool. As the western carrot accessions represent improved cultivars, DcSto insertional polymorphisms allowed detection of a selection-driven bottleneck at the improvement stage. Selection in the course of the improvement stage generally operated on standing variation, as the subset of DcSto insertions present in the western carrot likely originated from transposition events preceding the separation of both gene pools. However, occasional frequency shifts in the opposite direction were also revealed, possibly indicating selection for favorable variants associated with DcSto insertions.
Towards ML Models’ Recommendations
Authors: Kallab Lara , Mansour Elio , Chbeir Richard 
Published: October 2024

AbstractArtificial Intelligence encompasses a range of technologies that replicate human-like cognitive abilities through computer systems, enabling the execution of tasks associated with intelligent beings. A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. Such diversity complicates the selection of an appropriate model for a specific use case, answering user demands. This paper presents an approach for ML models retrieval based on the matching between user inputs and ML models criteria, all described in a semantic ML ontology named SML model (Semantic Machine Learning model), which facilitates the process of ML models selection. Our approach is based on similarities measures that we tested and experimented to score the ML models and retrieve the ones matching, at best, user inputs.
Towards Human Sensory Augmentation: A Cognitive Neuroscience Framework for Evaluating Integration of New Signals within Perception, Brain Representations, and Subjective Experience
Authors: Nardini Marko , Scheller Meike , Ramsay Melissa , Kristiansen Olaf , Allen Chris 
Published: October 2024

AbstractNew wearable devices and technologies provide unprecedented scope to augment or substitute human perceptual abilities. However, the flexibility to reorganize brain processing to use novel sensory signals during early sensitive periods in infancy is much less evident at later ages, making integration of new signals into adults’ perception a significant challenge. We believe that an approach informed by cognitive neuroscience is crucial for maximizing the true potential of new sensory technologies. Here, we present a framework for measuring and evaluating the extent to which new signals are integrated within existing structures of perception and experience. As our testbed, we use laboratory tasks in which healthy volunteers learn new, augmented perceptual-motor skills. We describe a suite of measures of (i) perceptual function (psychophysics), (ii) neural representations (fMRI/decoding), and (iii) subjective experience (qualitative interview/micro-phenomenology) targeted at testing hypotheses about how newly learned signals become integrated within perception and experience. As proof of concept, we provide example data showing how this approach allows us to measure changes in perception, neural processing, and subjective experience. We argue that this framework, in concert with targeted approaches to optimizing training and learning, provides the tools needed to develop and optimize new approaches to human sensory augmentation and substitution.
»Tiefe Lektüren«
Authors: Albrecht Andrea , Mateescu Kristina , Semmler Louisa 
Published: October 2024

Zusammenfassung
Im Beitrag wird am Beispiel von Publikationen aus dem Umfeld der Zeitschrift Sezession und des Verlags Antaios argumentiert, dass sich die sogenannte Neue Rechte zur Rekrutierung ihres rechtsextremen intellektuellen Nachwuchses esoterischer Kommunikations- und Interaktionsformen bedient. Zentrale Bedeutung kommt dabei der Aktivierung der Lesenden zum neurechten Denken und Schreiben zu. Doppelt codierte Kommunikationsformen haben demnach auch eine gruppenstrukturierende Funktion, die zur Elitebildung und zur internen Durchsetzung rechtsintellektueller Führungsansprüche dient.
Conversational Agent Utilization Patterns of Individuals with Autism Spectrum Disorder
Authors: Aghakhani S. , Rousseau A. , Mizrahi S. , Tan X. , Dosovitsky G. , Mlodzianowski L. , Marshall Z. , Bunge E. L. 
Published: October 2024

AbstractIndividuals with autism spectrum disorder (ASD) face unique challenges in their social interactions. The use of conversational agents (CA) can provide support and help reduce barriers to care. However, research on CA’s by individuals with ASD is limited. The present study sought to better understand CA utilization patterns by users with ASD. A subset of data was collected from users of Wysa, a mental health CA. Engagement with the CA, utilization of offered mental health interventions (CA tools), collection of energy scores, depression (PHQ-9), and anxiety (GAD-7) outcomes were gathered and analyzed. Users engaged with Wysa on average 8.59 days and had a median of 97 conversational exchanges. Almost half of the users utilized at least one of the 230 tools offered. The most frequently used interventions focused on mindfulness, thought recording, sleep, grounding, and social support. Energy scores were reported on average 10.59 times, and the mean energy score was 42.77 out of 100. Mean baseline PHQ-9 and GAD-7 scores were 10.34 and 8.81, respectively. Overall, the current findings show that users with ASD engaged regularly with a CA, despite its targeted design for other mental health concerns such as depression and anxiety, rather than ASD. If users with ASD engage with these types of resources, this could become a new avenue of support to a population facing multiple challenges to accessing treatment.
Credible, Competent Contributors: Children and Young People as Postdigital Citizen Social Scientists
Authors: MacKenzie Alison 
Published: October 2024

AbstractI argue that participatory action research (PAR) is a valuable means by which to advance citizen social science, not by researching with and on adults, but with children and young people (CYP). Given the right opportunities, using child-friendly and child-centred research methods, CYP can be credible, competent contributors to social science, including the digital environment. The foundational goals of PAR are empowerment, transformation, and enfranchisement of the traditionally marginalised, and none are more universally and structurally marginalised than CYP. Using a child rights argument, I explain why CYP continue to be excluded from research and why there is still so much scepticism that CYP are credible contributors. One reason is childism, a system of oppression, prejudice, and discrimination against CYP on account of their perceived status as not yet human and not fully rational beings. Childism entails the belief that adults are automatically superior to children. I argue further that CYP’s participation in postdigital social science research enacts the entitlements in Convention on the Rights of the Child (CRC), even though state parties, having ratified this landmark convention, find it very challenging to do more than simply laud its aims. Using three case studies of CYP’s contribution to research and human rights activism, I make the case that they are expert knowers of their own social and digital experiences and have the skills and insights to examine their social worlds in ways that adult researchers too easily overlook, downplay, or simply ignore. As importantly is that CYP are expert users of digital and social media. Their epistemic and ontological experiences as users, creators, contributors, and disseminators of content, and as targets of malign actors, must be included in any research, policy, or law.
Multi-method soil moisture monitoring at two temperate forest stands in Germany
Authors: Fäth Julian , Kneisel Christof 
Published: October 2024

AbstractMany forests suffer increased drought stress due to climate change, particularly impacting Central Europe. However, our understanding considering drought stress and resulting tree mortality remains incomplete. For addressing this issue, fine-scale water balance assessment as well as robust and spatially integrating measures on large scales are necessary for forest ecosystem monitoring. The latter is particularly important to provide reliable data sets for verifying remote sensing and hydrological modelling products. Various traditional in-situ methods exist for assessing the water balance in forest ecosystems as for instance measuring the water content or matric potential. However, the spatial limitations of these methods have led to an increased importance of Cosmic Ray Neutron Sensing (CRNS) in the last two decades whose measuring signal is integrated over a larger area. We equipped two forest monitoring plots with ECH2O probes, Tensiomark soil systems, and installed a CRNS station at one site to measure soil water content and matric potential at different soil depths and distances to trees from November 2020 till November 2023. The results indicate distinct temporal and spatial variations influenced by meteorological conditions and soil properties, but also forest characteristics. The year 2022 is identified as exceptionally dry, causing high drought stress at one of the forest sites. The comparison between CRNS and traditional methods reveals a strong correlation, highlighting CRNS' potential in forest monitoring as a promising tool for an area wide assessment of water availability in forest ecosystems. Our study further advocates for the need of tree species-specific thresholds to assess matric potential in relation to drought stress in order to better assess the impact of climate change on our forest ecosystems.
Parkinson’s Disease Detection by Using Machine Learning Method based on Local Classification on Class Boundary
Authors: Du Qiuyang , Shen Jinan , Wen Pengcheng , Chen Xinpeng 
Published: October 2024

AbstractParkinson’s disease (PD) detection has long been an important task in medical intelligence. Recognition methods based on speech signals show great potential in Parkinson’s disease diagnosis. In this paper, based on an efficient machine learning method for Parkinson’s disease detection, we take the use of test data incorporates an efficient Secure Two-Party Computing (S2PC) protocol to protect the privacy of patients. We present two key components, the secure use of data and a local classification methodology, including the description of class boundaries. We conducted experiments on two datasets to validate our proposed method, and the results show well data security protection ability compared to some more sophisticated methods. And the performance of Local Classification on Class Boundary(LCCB) and Hyperplane K-Nearest Neighbor(HKNN) is significantly better than that of both Support Vector Machines(SVM) and Random Forest(RF). When the number of selected features is from 400 to 500, HKNN and LCCB are roughly equal where the accuracy of HKNN is 95.2%, and LCCB has the rate of 94.7%. Then we use Multi-Cluster Feature Selection(MCFS) to analyze and select the important features from D2 dataset. It shows that even if only two features are selected, the boundaries of the two categories are also clear and easy to distinguish.
Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management
Authors: Oyegbile Oluwatobi , Chan Andy , Ooi Maggie , Anwar Parvez , Mohamed Abdullahi Ali , Li Li 
Published: October 2024

Abstract
In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.
Formation of concavities on the ends of parts manufactured on CNC skew rolling mills
Authors: Pater Zbigniew , Bulzak Tomasz , Tomczak Janusz , Shu Xuedao , Xia Yingxiang 
Published: October 2024

Abstract
This study investigates the problem of concavity formation on the ends of parts manufactured on CNC skew rolling mills. Numerical modeling and Taguchi method were used to determine the effects of the main parameters of skew rolling (i.e., forming angle, skew angle, reduction ratio, temperature, steel grade, dimeter ratio, velocity ratio) on the depth of concavities formed on the product ends. The simulations showed that the only parameter to have a significant impact on the concavity depth was the reduction ratio. The FEM results were then used to establish equations for calculating concavity depth and allowance for excess material with concavity. For more universality, the established equations took into account the billet diameter. The experimental validation showed high agreement between the numerical and the experimental concavity depths.
Cybercrime in Nordic countries: a scoping review on demographic, socioeconomic, and technological determinants
Authors: Padyab Mojgan , Padyab Ali , Rostami Amir , Ghazinour Mehdi 
Published: October 2024

Abstract
Knowledge of factors contributing to cybercrime threats is needed to plan effective prevention strategies to combat the increasingly common occurrence of cybercrime. This scoping review summarizes current status of the literature published on the demographic, socioeconomic, and technological determinants of cybercrime offenders and victims in Nordic countries. The review followed Arksey and O’Malley’s methodology and the PRISMA-ScR guidelines, leading to the selection of 16 articles published between 2013 and 2022 for critical appraisal and data extraction. Only two studies focused on cybercrime offenders. The majority of studies (n = 10) were conducted in Finland and the dominant types of crime were harassment and online hate. Age, gender, living alone, immigration background, income, and education played different roles in determining victimization for different types of cybercrime. Associations between victimization and technological factors such as computer skills, protective measures, and specific platforms have also been identified. This review contributes to the emerging research area of cybercrime in Nordic countries by mapping the context of cybercrime and highlighting areas where further research is needed to ensure the protection of individuals in an increasingly digitalized world.
Assessing microplastic abundances in freshwater fishes in a subtropical African reservoir
Authors: Dalu Tatenda , Themba Samkelisiwe T. , Dondofema Farai , Wu Naicheng , Munyai Linton F. 
Published: October 2024

AbstractMicroplastics are emerging pollutants of global concern, and their presence in the aquatic environment poses a serious risk for aquatic biota. While many studies have been conducted on the presence of microplastics in marine habitats, little research has been done in freshwater African reservoirs on microplastic pollution and their impacts on fish within the subtropical regions. To bridge this gap, the current study investigated microplastic abundances and distribution in freshwater fish within Nandoni reservoir, South Africa, across two seasons (i.e., hot–wet, cool–dry). Fish were randomly sampled using seine and gill nets from seven sites within the reservoir. In the laboratory, fish were then sorted according to taxa before dissecting them to remove the gills and the gastrointestinal tract (GIT). The organs were digested using hot hydrochloric acid and hydrogen peroxide, and the microplastics were classified according to their colours and shapes. Among the 94 fishes (i.e., 8 species) examined, microplastics were detected in 86.6% of the eight species caught. Microplastics were dominant in the gills and GIT during the cool–dry and hot–wet seasons, respectively. High microplastic abundances were found in the gills of Micropterus salmoides and the GIT of Coptodon rendalli, where fibres and the transparent colour were the most dominant. The results further showed high microplastic abundances in benthopelagic feeders highlighting that habitat influences fish consumption of microplastics whether directly or indirectly. Significant differences were observed in the feeding zone and season for all microplastic types. Microplastic sources in the reservoirs could be due to anthropogenic activities such as illegal dumping, fishing, and agriculture. Thus, there is a need for further investigation into the relation of fish weight, fish sex and body in relation to microplastic pollution. The highlighted ecological factors should be taken into consideration for future research and management actions aimed at mitigating and protecting the negative impacts of microplastic pollution on environmental and human health.
University forests and carbon sequestration: an untapped potential
Authors: Leal Filho Walter , Luetz Johannes M. , Dinis Maria Alzira Pimenta 
Published: October 2024

AbstractThe role of universities in addressing climate change is often discussed, but one crucial aspect tends to be overlooked: their potential contribution to carbon sequestration. Many universities around the world possess significant tracts of land, often featuring substantial forested areas. These landscapes represent an underutilised opportunity for nature-based solutions that facilitate carbon sequestration. This paper discusses the current trends and status of university-owned forests, highlighting some of the largest and most ecologically significant forests managed by academic institutions across different regions worldwide. It explores their capacity for carbon sequestration, their biodiversity value, and the potential they hold for contributing to global climate change mitigation efforts. Furthermore, the article examines how these university forests can serve as dynamic, living laboratories for research, education, and conservation, as well as spaces for community engagement. In addition to their environmental benefits, these green spaces can enhance human health and well-being. Based on this analysis, the paper offers recommendations for universities on how to optimise their forested lands as carbon sinks and enhance their role in advancing sustainable practices, research, and education.
Graphical abstract