BASIS International (United States)

Self-deceit, defined as the concealment of inconvenient truths from one’s conscious awareness, represents a paradox central to human psychology. This paper examines self-deceit through three complementary perspectives: evolutionary, psychodynamic, and social. From an evolutionary standpoint, self-deception functions as an adaptive mechanism enhancing survival and reproductive success by preserving confidence and reducing cognitive dissonance. The psychodynamic perspective emphasizes unconscious defense mechanisms, such as repression and rationalization, which protect the self-concept from psychological threats. The social perspective highlights how interpersonal interactions and group dynamics reinforce or legitimize self-deceptive tendencies. Together, these perspectives reveal that self-deceit is neither purely irrational nor pathological; rather, it is a strategic process balancing individual stability with social adaptation. By integrating theoretical insights with empirical findings, this study contributes to a nuanced understanding of how self-deceit operates as a survival tool within the broader context of human cognition and behavior.

Abstract While kinase-substrate associations (KSAs) are fundamental to cancer signaling, their rewiring patterns and functional roles across different cancers during cancer progression remain generally poorly characterized. Current predictive tools often fail to integrate three-dimensional structural constraints and cancer-specific dynamic information, limiting their biological relevance and translational potential. To address this, the present study constructs SPARK, a machine learning framework that integrates multi-modal features within cancer-specific contexts. These include data-driven features (such as phosphoproteomic co-expression, mutual information, protein-level abundance, and phosphosite-level abundance), knowledge-driven features (e.g., STRING protein-protein interactions), and structural features (ESM-2 embeddings compressed via an autoencoder). The training of the model was done with the adoption of the XGBoost classifier on data from eight CPTAC cancer cohorts. SPARK demonstrates strong predictive performance across different cancer types, with AUROC values ranging from 0.929 to 0.957 (mean: 0.944) in normal tissues and 0.941 to 0.958 (mean: 0.948) in tumor tissues. The accuracy of these predictions was further validated against experimentally determined kinase-substrate specificities. Using SPARK’s predictions, tissue-specific phosphorylation signaling networks are systematically reconstructed. These SPARK-derived networks reveal distinct phosphorylation patterns and kinase activation landscapes across cancers while identifying several highly tissue-specific kinases as promising therapeutic targets. Abstract Figure Graphical Abstract

Nike is one of the most dominant companies in the athletic apparel and footwear industry and has achieved its dominance mainly through a combination of strategic marketing, efficient and versatile supply chain, and continuous and high-leveled technology innovations. However, this success has come with environmental challenges like carbon emissions and waste. In response, Nike has implemented different initiatives and incorporated the use of recycled materials to alleviate the stress the company puts on the environment. This study conducts a meta-analysis of Nikes different strategies and financial data to investigate what strategies and how these strategies enabled Nikes dominance. Further, this study analyzes whether environmental friendly products are also economically beneficial, comparing them to original products in terms of sales and feedback. Findings suggest that while environmental friendly products strengthen brand reputation and attract attention, they still trail behind traditional products in terms of profitability and popularity. Nonetheless, as technologies mature in the future and as consumers become more aware, the future of environmental friendly products are still very promising.

The rapid advancement of artificial intelligence (AI) technologies has revolutionized modern education, offering innovative solutions to long-standing challenges. This paper explores the transformative role of AI in education, focusing on its potential to enhance personalized learning, improve institutional efficiency, and promote accessibility. The study employs a multi-method research framework, including a systematic review of peer-reviewed literature (2010-2023), case studies of prominent AI implementations, and insights from education technology practitioners. The findings highlight the significant benefits of AI, such as adaptive learning systems and intelligent tutoring, while also addressing ethical concerns, implementation barriers, and potential negative impacts. The paper concludes with recommendations for future research and policy directions to ensure equitable and ethical AI integration in education.

IntroductionSocial behavior assessment in female mice has been historically challenged by inconsistent results from the classic three-chamber test, which reliably detects social preferences in males but fails to capture female specific social dynamics.MethodsWe developed a modified three-chamber paradigm by replacing standard social stimuli with familiar cagemates (co-housed for 2 weeks, 1 week or 24 hours) to better assess sociability and novelty preference in female mice.ResultsIn the sociability phase, female mice showed a significant preference for interacting with cagemates compared to empty chambers. Crucially, during the social preference phase, test females demonstrated robust novelty seeking behavior, spending significantly more time exploring novel conspecifics compared to 2-week cagemates or 1-week cagemates. This preference trended similarly, though non significantly, with 24-hour cagemates. Notably, our paradigm enhanced social preference indices without altering total interaction time, confirming its specificity for detecting novelty driven exploration.DiscussionThese findings overcome the limitations of traditional paradigms and establish a validated framework for studying female social behavior, with critical implications for modeling neurodevelopmental disorders like autism spectrum disorder (ASD) in female preclinical research.

This paper reviews the research on the use and development of educational technologies for children and teenagers with Downs Syndrome. Many believe in the effectiveness of this approach due to the inability of conventional methods to support this population. Given that conventional educational methods often do not adequately support this population, we hypothesize that integrating educational technologies could offer more effective learning solutions. To verify this hypothesis, we screened 132 studies available on EBSCO, and finally got 31 studies is fully supported by our findings. The results of these studies show that there are many benefits of using technological tools to support the learning of children with Downs Syndrome. However, our findings illustrate almost all the existing educational technology studies (85%) investigating youth with Downs Syndrome investigate human-to-human relationships that include a technological component rather than studying with technology alone. This finding supports the credibility of Lev Vygotskys Sociocultural Theory of Learning in real-world educational settings. Indeed, this review finds that many studies systematically underestimate the positive impact of integrating technology and relationship-based interventions, indicating the need for further research. Ultimately, it is the combination with human capital that can unleash the full potential of educational technologies and yield the most positive outcomes for children with Downs Syndrome.

This study is to set up a scientific evaluation system for choosing high school football central defenders. The Delphi method is used to judge 55 preliminary indicators by experts. After verification for consistency (Kendall’s W = 0.638, p < 0.05) and comprehensive analysis, a three-dimensional evaluation framework was constructed with a percentage of physical fitness (37.10%), specialized technical skills (25.03%), and tactical awareness (37.87%) based on weights determined by the Analytic Hierarchy Process. It is advisable that tactical awareness and physical fitness are placed before technical skills. Innovatively, the present study adapted the traditional eleven-a-side match evaluations into 7-a-side small-field matches, which accord with the developmental characteristics of adolescents. Practical testing showed excellent discriminative validity (SD = 8.47) and criterion-related validity (correlation coefficient with coaches’ evaluations: r = 0.82, p < 0.01), which findings should reveal the trend in modern football towards comprehensive defensive aptitudes and therefore are valuable for practical use in youth talent identification.

This research examines the connection between subjective interoceptive accuracy and the tendency to maintain or alter opinions when individuals were confronted with opposing views. We proposed that individuals exhibiting higher interoceptive accuracy would be less inclined to change their opinions. Surprisingly, our findings did not reveal a significant association between interoceptive accuracy and the extent of opinion change. Notably, however, we identified a moderate association between an increased sensitivity to internal bodily signals-as indexed by the Noticing subscale of the Multidimensional Assessment of Interoceptive Awareness (MAIA-N)-and a higher likelihood of opinion change. This observation implies that individuals who are more attuned to their internal bodily signals might also be more adaptable in revising their perspectives when exposed to contradictory viewpoints. Overall, these results enhance our understanding of interoceptive accuracy and attitude shifting, highlighting the intricate interplay between bodily sensations and higher-order cognitive processes.

Spam email detection is still a considerable and ongoing challenge in today’s online environment, as the number of unsolicited emails keeps growing exponentially. Various algorithms such as the tree-based model, support vector machine Algorithm, and Convolutional Neural Network have been explored in prior research to tackle this challenge. This research specifically examines the effectiveness of the Naïve Bayes classifier for identifying and filtering spam emails. By delving into the fundamental principles of this classifier, its practical implementation, and the comprehensive evaluation of its performance on a combined dataset, its strengths and limitations in distinguishing spam from ham messages are revealed. The result of the study demonstrates an overall accuracy of 97.82%, showcasing the Naïve Bayes classifier's high efficiency and stability in identifying spam. With consistently high metrics score throughout both classes, the Naïve Bayes classifier has proven to be an exceptionally reliable tool for spam email detection, underscoring its suitability for numerous real-world applications.