bluuebunny/crossref_metadata_2025_split huggingface dataset:
How first- and second-language emotion words influence emotion perception in Swedish–English bilinguals
Authors: Champoux-Larsson Marie-France , Nook Erik C. 
Published: January 2024

Abstract
Emotional experiences are often dulled in one's second language. We tested whether emotion concepts are more strongly associated with first language (L1) than second language (L2) emotion words. Participants (140 L1-Swedish–L2-English bilinguals) saw a facial expression of an emotion (cue) followed by a target, which could either be another facial expression, an L1 emotion word, or an L2 emotion word. Participants indicated whether the cue and target represented the same or different emotions as fast as possible. Participants were faster and more accurate in both the L1 and L2 word conditions compared to the face condition. However, no significant differences emerged between the L1 and L2 word conditions, suggesting that emotion concepts are not more strongly associated with L1 than L2 emotion words. These results replicate prior research showing that L1 emotion words speed facial emotion perception and provide initial evidence that words (not only first language words) shape emotion perception.
Validation of the Short Dark Tetrad (SD4) in Persian
Authors: Qaderi Bagajan Kaveh , Ziegler Matthias , Soleimani Mehdi , Paulhus Delroy L. , Soleimani Zahra Asl , Kordbagheri Mohammadreza , Alavinejad Leila , Amiri Hamid , Asl Vida Yousefi , Hoseini Sepideh , Bagajan Hadi Qaderi 
Published: January 2024

Abstract: The Short Dark Tetrad of Personality (SD4) is a self-report instrument for screening individuals with dark personality traits, including narcissism, Machiavellianism, psychopathy, and sadism. In the present study, we examined the psychometric properties of the Persian version in an Iranian sample. After translating the instrument, we conducted a large online survey that included 1,696 participants (67% female), aged 18–60 years. We performed a series of confirmatory factor analyses and examined the nomological network to validate the instrument. After assessing five competing structural models, the four-factor model showed the best fit based on standard goodness-of-fit indices. The sub-scales also showed coherent links with risk-taking and pathological personality traits. We conclude that the Persian SD4 has a distinct four-factor structure with adequate reliability and validity. Therefore, it can be used to measure dark personality traits in Farsi-speaking samples.
Effects of lockdowns on neurobiological and psychometric parameters in unipolar depression during the COVID-19 pandemic
Authors: Unterholzner Jakob , Kautzky Alexander , Reed Murray Bruce , Wechsler Theresa Friederike , Popper Valentin , Spurny-Dworak Benjamin , Stöhrmann Peter , Klöbl Manfred , Varghese Nimmy , Mühlberger Andreas , Eckert Anne , Frey Richard , Rujescu Dan , Lanzenberger Rupert , Vanicek Thomas 
Published: January 2024

AbstractDefying the COVID-19 pandemic required restriction measures of unprecedented scale, that may induce and exacerbate psychiatric symptoms across the population. We aimed to assess in vivo dynamic effects of mitigation strategies on human brain neurobiology, neuroplastic as well as psychometric parameters. Three structural magnetic resonance imaging measurements, serum brain-derived neurotrophic factor (sBDNF) analyses, and psychometric assessments (Beck Depression Inventory-II and Perceived Stress Questionnaire-20) were performed in healthy individuals and patients with a recurrent major depressive disorder in the period from September 2020 to July 2021. Group differences and changes over time in structural imaging, neuroplastic and psychometric parameters were assessed with linear mixed models. Analysis of data from 18 patients with a recurrent major depressive disorder and 28 healthy individuals showed clinically relevant scores for depression and stress in the patient group as well as significant cross-sectional differences in depression scores (F = 30.89, p < 0.001) and three subscales of the Perceived Stress Questionnaire (Worries: F = 19.19, p < 0.001, Tension: F = 34.44, p < 0.001, Joy: F = 12.05, p = 0.001). Linear mixed models revealed no significant changes over time in cortical thickness of the prefrontal cortex, anterior cingulate cortex, hippocampus, and amygdala (F = 0.29, p > 0.1) and no interaction with group (F = 0.28, p > 0.1). Further, analysis revealed no main effect of time and no interaction of time x group in depressive symptoms, perceived stress subscales, and sBDNF (all p > 0.1). Despite the limited sample size, the strength of this investigation lies in the multimodal assessment of peri-pandemic lockdown effects. Nine months of varying restrictions measures did not result in observable changes in brain morphology nor impact depressive symptoms in either psychiatric patients with a recurrent major depressive disorder or healthy individuals. While these neurobiological and psychometric data stand in contrast to initial expectations about the effects of restriction measures, they might inform future investigations of longitudinal effects of restriction measures on mental health.
Representation and quantification of module activity from omics data with rROMA
Authors: Najm Matthieu , Cornet Matthieu , Albergante Luca , Zinovyev Andrei , Sermet-Gaudelus Isabelle , Stoven Véronique , Calzone Laurence , Martignetti Loredana 
Published: January 2024

AbstractThe efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package’s capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at:www.github.com/sysbio-curie/rROMA. Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.
Altered grey matter structural covariance in chronic moderate–severe traumatic brain injury
Authors: Symons Georgia F. , Gregg Matthew C. , Hicks Amelia J. , Rowe Christopher C. , Shultz Sandy R. , Ponsford Jennie L. , Spitz Gershon 
Published: January 2024

AbstractTraumatic brain injury (TBI) alters brain network connectivity. Structural covariance networks (SCNs) reflect morphological covariation between brain regions. SCNs may elucidate how altered brain network topology in TBI influences long-term outcomes. Here, we assessed whether SCN organisation is altered in individuals with chronic moderate–severe TBI (≥ 10 years post-injury) and associations with cognitive performance. This case–control study included fifty individuals with chronic moderate–severe TBI compared to 75 healthy controls recruited from an ongoing longitudinal head injury outcome study. SCNs were constructed using grey matter volume measurements from T1-weighted MRI images. Global and regional SCN organisation in relation to group membership and cognitive ability was examined using regression analyses. Globally, TBI participants had reduced small-worldness, longer characteristic path length, higher clustering, and higher modularity globally (p < 0.05). Regionally, TBI participants had greater betweenness centrality (p < 0.05) in frontal and central areas of the cortex. No significant associations were observed between global network measures and cognitive ability in participants with TBI (p > 0.05). Chronic moderate–severe TBI was associated with a shift towards a more segregated global network topology and altered organisation in frontal and central brain regions. There was no evidence that SCNs are associated with cognition.
Analyzing the impact of COVID-19 on consumption behaviors through recession and recovery patterns
Authors: Chen Rui , Li Tong , Li Yong 
Published: January 2024

AbstractThe COVID-19 outbreak has dramatically impacted the economy, particularly consumption behaviors. Studies on how consumption responses to COVID-19 can be a powerful aid for urban consumption recovery. In this paper, based on a high-frequency consumption dataset from January 6, 2020, to April 28, 2020 covering 18 sectors and dataset from the corresponding lunar period in 2021, we look at how COVID-19 changed how people spent their money by looking at patterns of recession and recovery during the pandemic. Specifically, we first explore the recession-recovery pattern of national consumption and the effects of various policies and quantify it using regression methods. Then, recession-recovery patterns across cities are widely studied. We also reveal how consumption structures change during a pandemic and the relationship between patterns of change in citizens’ consumption and the socioeconomic characteristics of cities. And the specific empirical analysis is provided through panel regression models. In general, national consumption represented a Vshaped pattern during the pandemic, experiencing a dramatic decline and a rapid rebound. Consumption is significantly inhibited by lockdown, while it is stimulated positively but gradually by easing policies. Consumption patterns at the city level are associated with socioeconomic characteristics. Cities with high-income groups experience a more significant decline, and cities with a high share of the secondary sector have a higher recovery rate in consumption. The consumption structure redistributes but does not fundamentally change. During the recession and early recovery phase, consumption related to basic living saw a significant rise, whereas leisure-related consumption dropped dramatically and recovered slowly. Our study can assist policymakers in implementing diversified market provisions and targeted lockdown policy adjustments for consumption recovery in cities with different socioeconomic backgrounds.
Enhancement of optical levitation with hyperbolic metamaterials
Authors: Paralı Ufuk , Üstün Kadir , Giden İbrahim Halil 
Published: January 2024

AbstractThe tightly focused laser beam in an optical trap has become a useful tool for many recent research areas. The momentum change in the photon-stream path of incident laser beam induces radiation force that enables trapping and manipulating mesoscopic micron-sized objects. In this study, we report the first analytical demonstration of optical trapping and levitation with radiation pressure on a transparent micron-sized spherical object made of hyperbolic metamaterial (HMM). The optical radial and axial forces acting on dielectric and HMM spherical particles are calculated using ray-optics approximation, assuming an optical levitation trapping setup. We compared the net force acting on the two objects, finding that the net radiation force exerted towards HMM particle is enhanced in the axial direction: The optical force enhancement in the HMM particle is more than ~ 8 times stronger compared to the induced force on the conventional dielectric particle with the corresponding material parameters. Besides, a better performance in the radial stabilization is observed for the HMM particle in comparison with the dielectric case, at which some oscillations and unstable saturation locations for the radial stabilization is monitored for TEM00 beam incidence. Furthermore, “zero-force” paths where radial stabilization of the HMM particle exists are also obtained for both TEM00 and $$TEM_{01}^{*}$$

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laser beam incidences. Such phenomenon does not occur for particles of only dielectric and only metal material, which can be considered as another superiority of the proposed HMM particle.
Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models
Authors: Ahmad Fareed , Javed Kashif , Tahir Ahsen , Khan Muhammad Usman Ghani , Abbas Mateen , Rabbani Masood , Shabbir Muhammad Zubair 
Published: January 2024

AbstractFrancisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical–chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft’s persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.
Smart energy management in residential buildings: the impact of knowledge and behavior
Authors: Hakawati Baraa , Mousa Allam , Draidi Fadi 
Published: January 2024

AbstractA new technology called smart energy management makes use of IoT concepts to enhance energy efficiency and lower waste in structures. The goal of this study is to comprehend how household energy management knowledge affects energy usage, user behavior, related expenses, and environmental effect. Through a survey of 100 valid replies in Palestine, the research model assessed the knowledge and consumption habits of building occupants. Smart PLS software was used to analyze the research model using partial least squares structural equation modeling (PLS-SEM). Using path coefficients and behavior as a mediating variable, the structural model connected the latent variables. The mediation hypotheses were tested using the Preacher and Hayes method, and the indirect effect and confidence intervals were estimated and calculated using bootstrapping. The findings demonstrated that by lowering energy use and enhancing overall building performance, residential buildings that implement smart energy consumption management systems may move toward a more sustainable future. Furthermore, the study found that education and awareness campaigns are necessary to increase residents’ knowledge of these systems to promote energy savings. The results also indicated statistically significant indirect effects, supporting the existence of mediation of the behavior construct. Path coefficient values and P-values were presented to further support the study’s hypotheses. Such smart energy management systems represent an important innovation in building management and can help create more sustainable and efficient buildings.
The influence of adolescents essential and non-essential use of technology and Internet addiction on their physical and mental fatigues
Authors: Aziz Maryam , Chemnad Khansa , Al-Harahsheh Sanaa , Abdelmoneium Azza O. , Bagdady Ahmed , Hassan Diana Alsayed , Ali Raian 
Published: January 2024

AbstractThis study explores the impact of Internet addiction (IA), age, and essential and non-essential technology usage time on the physical and mental fatigue of adolescents. The research surveyed 477 adolescents from Qatar and employed the Internet Addiction Diagnostic Questionnaire (IADQ) and Chalder's Fatigue Scale for data collection. Multiple linear regression and Mann–Whitney U tests were utilized for analysis. The findings indicate that IA, non-essential usage time, and age are positively associated with overall fatigue among adolescents. IA and non-essential usage time are positively linked to physical fatigue, while IA, non-essential usage time, and age are positive predictors of mental fatigue. However, essential usage time is negatively associated with mental fatigue. These results highlight the importance of distinguishing technology usage based on intent and necessity, as well as differentiating between physical and mental fatigue when examining problematic technology usage. This study is among the few conducted in the Middle Eastern context.
The immunity and redox clocks in mice, markers of lifespan
Authors: Félix Judith , Martínez de Toda Irene , Díaz-Del Cerro Estefanía , Gil-Agudo Fernando , De la Fuente Mónica 
Published: January 2024

AbstractImmune function and redox markers are used for estimating the aging rate, namely biological age (BA). However, it is unknown if this BA and its changes can be reflected in longevity. Thus, we must quantify BA in experimental animals. In peritoneal immune cells of 202 female mice (ICR/CD1) in different ages, 10 immune and 6 redox parameters were evaluated to construct two mathematical models for BA quantification in mice by multiple linear regression. Immune and redox parameters were selected as independent variables and chronological age as dependent, developing two models: the Immunity and the Redox Clocks, reaching both an adjusted R2 of 80.9% and a standard error of 6.38 and 8.57 weeks, respectively. Both models were validated in a different group of healthy mice obtaining a Pearson’s correlation coefficient of 0.844 and 0.800 (p < 0.001) between chronological and BA. Furthermore, they were applied to adult prematurely aging mice, which showed a higher BA than non-prematurely aging mice. Moreover, after positive and negative lifestyle interventions, mice showed a lower and higher BA, respectively, than their age-matched controls. In conclusion, the Immunity and Redox Clocks allow BA quantification in mice and both the ImmunolAge and RedoxAge in mice relate to lifespan.
Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19
Authors: Scardua-Silva Lucas , Amorim da Costa Beatriz , Karmann Aventurato Ítalo , Batista Joao Rafael , Machado de Campos Brunno , Rabelo de Brito Mariana , Bechelli José Flávio , Santos Silva Leila Camila , Ferreira dos Santos Alan , Koutsodontis Machado Alvim Marina , Vieira Nunes Ludwig Guilherme , Rocha Cristiane , Kaue Alves Silva Souza Thierry , Mendes Maria Julia , Waku Takeshi , de Oliveira Boldrini Vinicius , Silva Brunetti Natália , Nora Baptista Sophia , da Silva Schmitt Gabriel , Duarte de Sousa Jhulia Gabriela , Marchiori de Oliveira Cardoso Tânia Aparecida , Schwambach Vieira André , Barbosa Santos Leonilda Maria , dos Santos Farias Alessandro , Nogueira Mateus Henrique , Cendes Fernando , Lin Yasuda Clarissa 
Published: January 2024

AbstractAlthough some studies have shown neuroimaging and neuropsychological alterations in post-COVID-19 patients, fewer combined neuroimaging and neuropsychology evaluations of individuals who presented a mild acute infection. Here we investigated cognitive dysfunction and brain changes in a group of mildly infected individuals. We conducted a cross-sectional study of 97 consecutive subjects (median age of 41 years) without current or history of psychiatric symptoms (including anxiety and depression) after a mild infection, with a median of 79 days (and mean of 97 days) after diagnosis of COVID-19. We performed semi-structured interviews, neurological examinations, 3T-MRI scans, and neuropsychological assessments. For MRI analyses, we included a group of non-infected 77 controls. The MRI study included white matter (WM) investigation with diffusion tensor images (DTI) and functional connectivity with resting-state functional MRI (RS-fMRI). The patients reported memory loss (36%), fatigue (31%) and headache (29%). The quantitative analyses confirmed symptoms of fatigue (83% of participants), excessive somnolence (35%), impaired phonemic verbal fluency (21%), impaired verbal categorical fluency (13%) and impaired logical memory immediate recall (16%). The WM analyses with DTI revealed higher axial diffusivity values in post-infected patients compared to controls. Compared to controls, there were no significant differences in the functional connectivity of the posterior cingulum cortex. There were no significant correlations between neuropsychological scores and neuroimaging features (including DTI and RS-fMRI). Our results suggest persistent cognitive impairment and subtle white matter abnormalities in individuals mildly infected without anxiety or depression symptoms. The longitudinal analyses will clarify whether these alterations are temporary or permanent.
On-chip Raman spectroscopy of live single cells for the staging of oesophageal adenocarcinoma progression
Authors: Farooq Alisha , Wood Christopher D. , Ladbury John E. , Evans Stephen D. 
Published: January 2024

AbstractThe absence of early diagnosis contributes to oesophageal cancer being the sixth most common cause of global cancer-associated deaths, with a 5-year survival rate of < 20%. Barrett’s oesophagus is the main pre-cancerous condition to adenocarcinoma development, characterised by the morphological transition of oesophageal squamous epithelium to metaplastic columnar epithelium. Early tracking and treatment of oesophageal adenocarcinoma could dramatically improve with diagnosis and monitoring of patients with Barrett’s Oesophagus. Current diagnostic methods involve invasive techniques such as endoscopies and, with only a few identified biomarkers of disease progression, the detection of oesophageal adenocarcinoma is costly and challenging. In this work, single-cell Raman spectroscopy was combined with microfluidic techniques to characterise the development of oesophageal adenocarcinoma through the progression of healthy epithelial, Barrett’s oesophagus and oesophageal adenocarcinoma cell lines. Principal component analysis and linear discriminant analysis were used to classify the different stages of cancer progression. with the ability to differentiate between healthy and cancerous cells with an accuracy of 97%. Whilst the approach could also separate the dysplastic stages from healthy or cancer with high accuracy—the intra-class separation was approximately 68%. Overall, these results highlight the potential for rapid and reliable diagnostic/prognostic screening of Barrett’s Oesophagus patients.