Search Results (487)

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Brain-derived neurotrophic factor (BDNF) is associated with nicotine use behaviours, the intensity of nicotine cravings, and the experience of withdrawal symptoms. Given the established influence of sex, brain-derived neurotrophic factor variants, personality traits and anxiety levels on nicotine use, this study aimed to conduct a comprehensive association analysis of these factors within a cohort of women who use nicotine. The study included 239 female participants: 112 cigarette users (mean age = 29.19, SD = 13.18) and 127 never-smokers (mean age = 28.1, SD =10.65). Study participants were examined using the NEO Five-Factor Inventory and the State–Trait Anxiety Inventory. Genotyping of rs6265, rs10767664, and rs2030323 was performed by real-time PCR using an oligonucleotide assay. We did not observe significant differences in the distribution of either genotype or allele of rs6265, rs10767664 and rs2030323 between groups. However, compared to the never-smokers, cigarette users scored significantly lower on the Agreeableness (5.446 vs. 6.315; p = 0.005767; d_Cohen’s = 0.363; η² = 0.032) and the Conscientiousness (5.571 vs. 6.882; p = 0.000012; d_Cohen’s = 0.591; η²= 0.08) scales. There was significant linkage disequilibrium between all three analysed polymorphic variants—between rs6265 and rs10767664 (D′ = 0.9994962; p < 2.2204 × 10⁻¹⁶), between rs6265 and rs2030323 (D′ = 0.9994935; p < 2.2204 × 10⁻¹⁶) and between rs10767664 and rs20330323 (D′ = 0.9838157; p < 2.2204 × 10⁻¹⁶), but the haplotype association analysis revealed no significant differences. While our study did not reveal an association between the investigated brain-derived neurotrophic factor polymorphisms (rs6265, rs10767664 and rs2030323) and nicotine use, it is essential to acknowledge that nicotine dependence is a complex, multifactorial phenotype. Our study expands the current knowledge of BDNF ’s potential role in addictive behaviours by exploring the understudied variants (rs10767664 and rs2030323), offering a novel contribution to the field and paving the way for future research into their functional relevance in addiction-related phenotypes. The lower Agreeableness and Conscientiousness scores observed in women who use nicotine compared to never-smokers suggest that personality traits play a significant role in nicotine use in women. The observed relationship between personality traits and nicotine use lends support to the self-medication hypothesis, suggesting that some women may initiate or maintain nicotine use as a coping mechanism for stress and negative affect. Public health initiatives targeting women should consider personality and psychological risk factors in addition to biological risks. Full article

The film Defiance (2008) explores the true story of the Bielski Partisans in the Second World War, led by the brothers Tuvia and Zus Bielski. While the film is in many ways a conventional action-packed war story of resistance to Nazi domination during the war, the filmmakers conscientiously depicted the myriad and sundry ways Jews resisted Nazi racial hatred and the attempted extermination of European Jewry. The Bielski partisans are portrayed as violently opposing the Nazi domination and their Belorussian supporters in armed conflict; as engaging in acts of sabotage and destruction to hamper the Axis war effort; as preserving and engaging in Jewish culture and religion to ensure its propagation, even in the most extreme of circumstances; and as continually struggling for survival against Nazi efforts to murder them. Their struggle for freedom from oppression inspires these acts of resistance. Furthermore, the filmmakers explore the theological problem of understanding the Jews as God’s chosen people as they fight for their lives against Nazi policies of extermination. Full article

Masting, or the synchronous and intermittent production of seeds, can have profound consequences for Tropical Montane Cloud Forest (TMCF) tree populations and the trophic webs that depend on their mass flowering and seeds. Over the past 80 years, the importance of Fagus mexicana Martínez (Mexican beech) masting has become apparent in terms of conservation and management, promoting regeneration, and conserving endangered tree species, as well as the conscientious development of edible beechnuts as a non-timber forest product. The establishment of the relict-endemic Mexican beech is unknown, and several microenvironmental factors could influence natural regeneration. Thus, this study was conducted in two well-preserved Mexican beech forests to assess the influence of light incidence and soil moisture on the natural germination and seedling establishment of beeches. During two masting years (2017 and 2024), we assessed in situ beechnut germination and establishment. We tested the effect of the microenvironment of the oldest beeches on beechnut germination and seedling establishment. Our study highlights the complexity of the microenvironment of old beeches influencing the early stages of establishment and provides insights into possible conservation actions aimed at mitigating the impact of environmental change and humans. Full article

The rapid development of generative artificial intelligence (AI) systems such as ChatGPT (GPT-4) could transform teaching and learning. Yet, integrating these tools requires insight into what drives students to adopt them. Research on ChatGPT acceptance has so far focused on university settings, leaving school contexts underexplored. This study addresses the gap by surveying 506 upper secondary students in Baden-Württemberg, Germany, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Performance expectancy, habit and hedonic motivation emerged as strong predictors of behavioral intention to use ChatGPT for school purposes. Adding personality traits and personal values such as conscientiousness or preference for challenge raised the model’s explanatory power only marginally. The findings suggest that students’ readiness to employ ChatGPT reflects the anticipated learning benefits and enjoyment rather than the avoidance of effort. The original UTAUT2 is therefore sufficient to explain students’ acceptance of ChatGPT in school contexts. The results could inform educators and policy makers aiming to foster the reflective and effective use of generative AI in instruction. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

In this research, we aimed to evaluate the relationship between the main dimensions of personality (Extraversion, Maturity, Agreeableness, Conscientiousness, and Self-actualization) and mobile phone addiction, both directly and mediated by the professional context (professional status), and moderated by adaptive cognitive-emotional coping strategies. The participants, adult Romanian citizens, completed measures of personality—Big Five ABCD-M, a mobile phone addiction questionnaire, and the CERQ for adaptive coping strategies. They also responded to a question about current professional status (employed, student, etc.). Data were analyzed using Jamovi, and the findings were somewhat unexpected, though it aligned with the existing literature. Maturity emerged as a consistent inverse predictor of smartphone addiction (r = −0.45, β = −0.43, p < 0.001) across all three analyses. Extraversion showed an indirect effect mediated by professional status (β = −0.077, p < 0.05). Self-actualization was also found to predict smartphone addiction positively through full mediation by professional status (β = 0.05, p < 0.05). Agreeableness became a significant negative predictor (β = −0.13, p < 0.05) only when adaptive coping strategies were included. These findings highlight that the transition from frequent smartphone use—whether for work or personal reasons—to addiction can be subtle. This study may support both the general population in understanding smartphone use from a psycho-emotional perspective and organizations in promoting a healthy work-life balance. Full article

The Values in Action Inventory (VIA) is one of the most widely used measures for assessing character strengths. While the original version includes 240 items, shorter versions such as the VIA-72 have been developed to enhance its applicability. Psychometric studies of the VIA-72 in Spanish are still limited. This study examined the factorial structure, reliability, and convergent validity of the Spanish VIA-72 in a sample of 470 adults. Three alternative models—comprising three, five, and six factors—were tested using confirmatory factor analysis. All models showed acceptable fit, but the three-factor solution—Caring, Self-Control, and Inquisitiveness—showed the best performance in terms of parsimony, fit indices, and conceptual clarity. Internal consistency for the three-factor model was high across dimensions and comparable to previous studies. Convergent validity was supported through meaningful correlations with personality traits, particularly with conscientiousness. The factorial structure largely replicated findings obtained with both VIA-72 and VIA-240. These results support the Spanish VIA-72 as a reliable and valid instrument for assessing character strengths, offering a concise, theory-based alternative for Spanish-speaking populations. Full article

In today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personality traits from Instagram content. Motivated by the growing demand for scalable, non-intrusive methods of psychological assessment, we developed a personality prediction system combining convolutional neural networks (CNNs) and random forest (RF) algorithms. Our model is grounded in the Big Five Personality framework, which includes Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using data collected with informed consent from 941 participants, we extracted visual features from their Instagram images using two pretrained CNNs, which were then used to train five RF models, each targeting a specific trait. The proposed system achieved an average mean absolute error of 0.1867 across all traits. Compared to the PAN-2015 benchmark, our method demonstrated competitive performance. These results highlight that using social media data for personality prediction offers potential applications in personalized content delivery, mental health monitoring, and human–computer interactions. Full article

Addressing climate change requires deeper insight into the psychological drivers of pro-environmental behavior. This study investigates how personality traits, climate-related emotions, and demographic factors can predict sustainable consumption and climate action participation using a Bayesian regression approach. Drawing from the HEXACO personality model and key emotional predictors—Climate Change Worry (CCW) and environmental empathy (EE)—we analyzed data from 604 adults in Greece to assess both private and public climate-related behaviors. This research is novel in its integrative approach, combining dispositional traits and affective states within a Bayesian analytical framework to simultaneously predict both sustainable consumption and climate action. Bayesian model testing highlighted education as the most powerful and reliable predictor of sustainable consumption, with increasing levels—namely Doctoral education—linked to more environmentally responsible action. CCW produced small but reliable effects, supporting hypotheses that moderate emotional concern will lead to sustainable behavior when linked to efficacy belief. The majority of HEXACO traits, e.g., Honesty–Humility and Conscientiousness, produced limited predictive power. This indicates in this case that structural and emotional considerations were stronger than dispositional personality traits. For climate action involvement, Bayesian logistic models found no considerable evidence of any predictor, corroborating the perspective that public participation in high effort action is most likely to rely on contextual enablers instead of internal sentiments or attributes. A significant interaction effect between education and gender also indicated that the sustainability effect of education is moderated by sociocultural identity. Methodologically, this research demonstrates the strengths of Bayesian analysis in sustainability science to make sensitive inference and model comparison possible. The results highlight the importance of affect-related structural variables in behavioral models and have applied implications for theory-informed and targeted climate education and communication interventions to enable different populations to act sustainably. Full article

Drought, a natural phenomenon intricately intertwined with the broader canvas of climate change, exacts a heavy toll by ushering in acute terrestrial water scarcity. Its ramifications reverberate most acutely within the agricultural heartlands, particularly those nestled in arid regions. To address this pressing issue, this study harnesses the temperature vegetation dryness index (TVDI) as a robust drought indicator, enabling a granular estimation of land water content trends. This endeavor unfolds through the sophisticated integration of geographic information systems (GISs) and remote sensing technologies (RSTs). The methodology bedrock lies in the judicious utilization of 72 high-resolution satellite images captured by the Landsat 7 and 8 platforms. These images serve as the foundational building blocks for computing TVDI values, a key metric that encapsulates the dynamic interplay between the normalized difference vegetation index (NDVI) and the land surface temperature (LST). The findings resonate with significance, unveiling a conspicuous and statistically significant uptick in the TVDI time series. This shift, observed at a confidence level of 0.05 (Z_S = 1.648), raises a crucial alarm. Remarkably, this notable surge in the TVDI exists in tandem with relatively insignificant upticks in short-term precipitation rates and LST, at statistically comparable significance levels. The implications are both pivotal and starkly clear: this profound upswing in the TVDI within agricultural domains harbors tangible environmental threats, particularly to groundwater resources, which form the lifeblood of these regions. The call to action resounds strongly, imploring judicious water management practices and a conscientious reduction in water withdrawal from reservoirs. These measures, embraced in unison, represent the imperative steps needed to defuse the looming crisis. Full article

Background: Personality traits, particularly Conscientiousness, are recognised as crucial psychological factors contributing to success in elite-level athletes. Emerging evidence suggests that individual differences in these traits are influenced by environmental exposure and genetic variation, especially within the dopaminergic system. The DRD2 promoter polymorphism rs1799732, which affects dopamine D2 receptor expression, may modulate goal-directed behaviour and self-regulation traits. Methods: This study included 323 participants (141 elite mixed martial arts (MMA) athletes and 182 non-athlete controls). Participants completed the NEO Five-Factor Inventory (NEO-FFI). Genotyping for the DRD2 rs1799732 polymorphism was conducted using real-time PCR. Group comparisons and two-way ANOVA were used to assess genotype–phenotype associations and gene × environment interactions. Results: Athletes scored significantly higher on Conscientiousness than controls. A significant main effect of the DRD2 rs1799732 genotype and a genotype × group interaction were observed for Conscientiousness. Specifically, athletes with the ins/ins genotype exhibited the highest levels of Conscientiousness, whereas individuals with the del/del genotype showed the lowest scores. No significant associations were found for other personality traits. Conclusions: These findings suggest that the DRD2 promoter polymorphism rs1799732 moderates the expression of Conscientiousness, particularly under the structured and demanding conditions experienced by elite athletes. Our results support a gene × environment interaction model, highlighting the importance of considering genetic predispositions in high-performance environments. These insights may inform personalised psychological support strategies tailored to athletes’ genetic profiles, enhancing motivation, self-regulation and long-term athletic development. Full article

Previous research has linked Big Five personality traits—agreeableness, openness to experience, neuroticism, extraversion, and conscientiousness—to relationship initiation in speed dating and relationship quality in long-term relationships. However, little is known about their role in matchmaking, where a matchmaker selects potential partners based on individual preferences and guides the dating process. This study examined whether the Big Five personality traits predict relationship formation in a matchmaking context. The sample included 1704 participants (48.5% men, 51.5% women; mean age = 62.5 years) living in the Netherlands. Personality was measured at baseline using the NEO-FFI questionnaire. Relationship formation success was defined as being in a relationship for at least three months. None of the Big Five traits predicted matchmaking success. Personality traits did not significantly predict relationship success in professionally guided matchmaking, suggesting that personality may play a limited role in this particular dating context. Full article

Background/Objectives: Students with specific learning disorders (SLDs) are at increased risk for emotional and academic difficulties. While teacher characteristics can influence student outcomes, few studies have examined the role of teacher personality in supporting students with SLDs. This study investigated whether teacher personality traits predicted student emotional well-being and academic achievement in a school-based intervention context. Methods: Participants were 64 students with SLDs (Mage = 13.28) nested within 21 teachers. Students completed measures of emotional well-being at baseline and post-intervention, and grade point average (GPA) was obtained from school records at the end of the school year. Teachers completed the Big Five Inventory mid-intervention. Two-level multilevel models were conducted in Mplus using maximum likelihood estimation with robust standard errors (MLR). The models controlled for student and teacher demographics, baseline emotional well-being, and the intervention group. Missing data were addressed using full information maximum likelihood (FIML). Results: Teacher female sex, higher neuroticism, and lower teaching experience were associated with higher student emotional well-being post-intervention. Follow-up analyses confirmed that teacher sex, neuroticism, and conscientiousness each explained substantial between-teacher variance. In the GPA model, student sex and teacher openness were significant predictors, with female students and students taught by more open teachers earning higher GPAs. Conclusions: Teacher personality traits, specifically neuroticism, conscientiousness, and openness, were associated with emotional and academic outcomes among students with SLDs. The findings highlight the importance of considering teacher characteristics in designing school-based interventions to support the development of learners with SLDs or other neurodevelopmental disorders. Full article

While static conscientiousness is known to predict academic success, personality can be particularly dynamic during adolescence. This study adopted a unique change-oriented perspective to examine the longitudinal relationship between within-person changes in conscientiousness and changes in academic performance among Chinese high school students, while also exploring the moderating role of changes in parental academic expectations. Four waves of longitudinal data were collected from 453 students (265 males, Mage = 15.42, SD = 0.76), with each wave spaced 6 months apart. Results indicated that the changes in conscientiousness (T2-T1) predicted the changes in academic performance (T4-T3) through the changes in academic engagement (T3-T2). However, the moderating effect of changes in parental academic expectations on the relationship between changes in conscientiousness and academic engagement was not significant. These findings go beyond static trait approaches by illustrating how dynamic changes in personality relate to evolving academic outcomes via engagement during the crucial high school years. The study highlights the importance of a dynamic perspective on personality, particularly within the developmental context of adolescence, and offers implications for interventions targeting both student traits and parental support in the Chinese educational context. Full article