Search Results (4,181)

Cancer is one of the most deadly diseases, costing millions of lives and billions of USD every year. There are different ways to identify the biomarkers that can be used to detect cancer types and subtypes. RNA sequencing is steadily taking the lead as the method of choice due to its ability to access global gene expression in biological samples and facilitate more flexible methods and robust analyses. Numerous studies have employed artificial intelligence (AI) and specifically machine learning techniques to detect cancer in its early stages. However, most of the models provided are very specific to particular cancer types and do not generalize. This paper proposes a deep learning and explainable AI (XAI) combined approach to classifying cancer subtypes and a deep learning-based approach for the classification of cancer types using BARRA:CuRDa, an RNA-seq database with 17 datasets for seven cancer types. One architecture is designed to classify cancer subtypes with around 100% accuracy, precision, recall, F1 score, and G-Mean. This architecture outperforms the previous methodologies for all individual datasets. The second architecture is designed to classify multiple cancer types; it classifies eight types within the neighborhood of 87% of validation accuracy, precision, recall, F1 score, and G-Mean. Within the same process, we employ XAI, which identifies 99 genes out of 58,735 input genes that could be potential biomarkers for different cancer types. We also perform Pathway Enrichment Analysis and Visual Analysis to establish the significance and robustness of our methodology. The proposed methodology can classify cancer types and subtypes with robust results and can be extended to other cancer types. Full article

Bacterial wilt, caused by Ralstonia solanacearum, is a major constraint to tomato production globally. To uncover resistance loci and develop efficient molecular tools for breeding, we conducted disease phenotyping over two growing seasons, which revealed consistent variation in resistance and moderate broad-sense heritability (H² = 0.22–0.28), suggesting a genetic basis. A genome-wide association study (GWAS) was performed on a diverse panel of 267 tomato accessions, evaluated against two R. solanacearum strains. A major resistance locus was identified on chromosome 12, with the strongest association observed at SNP S12_2992992, located within a gene encoding a leucine-rich repeat (LRR) receptor-like protein. Haplotype analysis indicated that the resistance-associated allele is relatively rare (~13.5%) in the population, underscoring its potential value in breeding programs. Functional validation in an F₂ population derived from a cross between the susceptible ‘Seedathip6’ and the resistant ‘Hawaii 7996’ confirmed that the TT genotype at S12_2992992 was significantly associated with enhanced resistance. A Kompetitive Allele Specific PCR (KASP) marker was developed for this SNP, facilitating cost-effective and high-throughput selection. Collectively, these findings establish S12_2992992 as a robust and functionally informative marker, offering a valuable tool for accelerating bacterial wilt resistance breeding in tomato through marker-assisted selection. Full article

The integration of Internet of Things (IoT) technologies in agriculture has facilitated real-time environmental monitoring, with low-cost IoT-enabled agrometeorological stations emerging as a valuable tool for climate-smart farming. This systematic review examines low-cost IoT-based weather stations by analyzing their hardware and software components and assessing their potential in comparison to conventional weather stations. It emphasizes their contribution to improving climate resilience, facilitating data-driven decision-making, and expanding access to weather data in resource-constrained environments. The analysis revealed widespread adoption of ESP32 microcontrollers, favored for its affordability and modularity, as well as increasing use of communication protocols like LoRa and Wi-Fi due to their balance of range, power efficiency, and scalability. Sensor integration largely focused on core parameters such as air temperature, relative humidity, soil moisture, and rainfall supporting climate-smart irrigation, disease risk modeling, and microclimate management. Studies highlighted the importance of usability and adaptability through modular hardware and open-source platforms. Additionally, scalability was demonstrated through community-level and multi-station deployments. Despite their promise, challenges persist regarding sensor calibration, data interoperability, and long-term field validation. Future research should explore the integration of edge computing, adaptive analytics, and standardization protocols to further enhance the reliability and functionality of IoT-enabled agrometeorological systems. Full article

AI chatbots have the potential to facilitate students’ academic progress and enhance knowledge accessibility in higher education, yet learners’ attitudes toward these technologies vary amid AI-driven disruptions, with factors influencing acceptance remaining debated. The current study constructs an integrated model based on Technology Acceptance Model 3 (TAM3), an extension of the original TAM, incorporating factors including Self-Efficacy, Perceived Enjoyment, Anxiety, Perceived Ease of Use, Perceived Usefulness, Output Quality, Social Influence, and Behavioral Intention, to explore determinants and mechanisms influencing learners’ acceptance of AI chatbots. This addresses key challenges in AI-augmented learning, such as personalization benefits versus risks like information inaccuracy and ethical concerns. Results from the questionnaire survey analysis with 265 valid responses reveal significant relationships: (1) self-efficacy significantly predicts perceived ease of use; (2) both perceived enjoyment and perceived ease of use positively influence perceived usefulness; and (3) self-efficacy, perceived usefulness, and social influence collectively exert significant effects on behavioral intention. Measurement invariance tests further indicate significant differences in acceptance between undergraduate and graduate students, suggesting academic level moderates behavioral intentions. Findings offer principled guidance for designing inclusive AI tools that mitigate accessibility barriers and promote equitable adoption in educational environments. Full article

The binary anomaly detection (classification) of ionospheric data related to Very Low Frequency (VLF) signal amplitude in prior research demonstrated the potential for development and further advancement. Further data quality improvement is integral for advancing the development of machine learning (ML)-based ionospheric data (VLF signal amplitude) anomaly detection. This paper presents the transition from binary to multi-class classification of ionospheric signal amplitude datasets. The dataset comprises 19 transmitter–receiver pairs and 383,041 manually labeled amplitude instances. The target variable was reclassified from a binary classification (normal and anomalous data points) to a six-class classification that distinguishes between daytime undisturbed signals, nighttime signals, solar flare effects, instrument errors, instrumental noise, and outlier data points. Furthermore, in addition to the dataset, we developed a freely accessible web-based tool designed to facilitate the conversion of MATLAB data files to TRAINSET-compatible formats, thereby establishing a completely free and open data pipeline from the WALDO world data repository to data labeling software. This novel dataset facilitates further research in ionospheric signal amplitude anomaly detection, concentrating on effective and efficient anomaly detection in ionospheric signal amplitude data. The potential outcomes of employing anomaly detection techniques on ionospheric signal amplitude data may be extended to other space weather parameters in the future, such as ELF/LF datasets and other relevant datasets. Full article

Background & Objective: Stroke remains a leading cause of mortality and long-term disability worldwide, demanding rapid and accurate diagnosis to improve patient outcomes. Computed tomography (CT) scans are widely used in emergency settings due to their speed, availability, and cost-effectiveness. This study proposes an artificial intelligence (AI)-based framework for multiclass stroke classification (ischemic, hemorrhagic, and no stroke) using CT scan images from the Ministry of Health of the Republic of Turkey. Methods: We adopted MaxViT, a state-of-the-art Vision Transformer (ViT)-based architecture, as the primary deep learning model for stroke classification. Additional transformer variants, including Vision Transformer (ViT), Transformer-in-Transformer (TNT), and ConvNeXt, were evaluated for comparison. To improve model generalization and handle class imbalance, classical data augmentation techniques were applied. Furthermore, explainable AI (XAI) was integrated using Grad-CAM++ to provide visual insights into model decisions. Results: The MaxViT model with augmentation achieved the highest performance, reaching an accuracy and F1-score of 98.00%, outperforming the baseline Vision Transformer and other evaluated models. Grad-CAM++ visualizations confirmed that the proposed framework effectively identified stroke-related regions, enhancing transparency and clinical trust. Conclusions: This research contributes to the development of a trustworthy AI-assisted diagnostic tool for stroke, facilitating its integration into clinical practice and improving access to timely and optimal stroke diagnosis in emergency departments. Full article

RNA polymerases are essential enzymes that catalyze DNA transcription into RNA, vital for protein synthesis, gene expression regulation, and cellular responses. Non-template-dependent RNA polymerases, which synthesize RNA without a template, are valuable in biological research due to their flexibility in producing RNA without predefined sequences. However, their substrate polymerization mechanisms are not well understood. This study examines Poly (A) polymerase (PAP), a nucleotide transferase superfamily member, to explore its substrate selectivity using computational methods. Previous research shows PAP’s polymerization efficiency for nucleoside triphosphates (NTPs) ranks ATP > GTP > CTP > UTP, though the reasons remain unclear. Using 500 ns Gaussian accelerated molecular dynamics simulations, stability analysis, secondary structure analysis, MM-PBSA calculations, and Markov state modeling, we investigate PAP’s differential polymerization efficiencies. Results show that ATP binding enhances PAP’s structural flexibility and increases solvent-accessible surface area, likely strengthening protein–substrate or protein–solvent interactions and affinity. In contrast, polymerization of other NTPs leads to a more open conformation of PAP’s two domains, facilitating substrate dissociation from the active site. Additionally, ATP binding induces a conformational shift in residues 225–230 of the active site from a loop to an α-helix, enhancing regional rigidity and protein stability. Both ATP and GTP form additional π–π stacking interactions with PAP, further stabilizing the protein structure. This theoretical study of PAP polymerase’s substrate selectivity mechanisms aims to clarify the molecular basis of substrate recognition and selectivity in its catalytic reactions. These findings offer valuable insights for the targeted engineering and optimization of polymerases and provide robust theoretical support for developing novel polymerases for applications in drug discovery and related fields. Full article

The Internet of Things (IoT) has enabled a vast network of devices to communicate over the Internet. However, the fragmentation of IoT systems continues to hinder seamless data sharing and coordinated management across platforms.However, there is currently no actual search engine for IoT data. Existing IoT search engines are considered device discovery tools, providing only metadata about devices rather than enabling access to IoT application data. While efforts such as IoTCrawler have striven to support IoT application data, they have largely failed due to the fragmentation of IoT systems and the heterogeneity of IoT data.To address this, we recently introduced SensorsConnect—a unified framework designed to facilitate interoperable content and sensor data sharing among collaborative IoT systems, inspired by how the World Wide Web (WWW) enabled shared and accessible information spaces for humans. This paper presents the IoT Agentic Search Engine (IoTASE), a real-time semantic search engine tailored specifically for IoT environments. IoTASE leverages LLMs and Retrieval-Augmented Generation (RAG) techniques to address the challenges of navigating and searching vast, heterogeneous streams of real-time IoT data. This approach enables the system to process complex natural language queries and return accurate, contextually relevant results in real time. To evaluate its effectiveness, we implemented a hypothetical deployment in the Toronto region, simulating a realistic urban environment using a dataset composed of 500 services and over 37,000 IoT-like data entries. Our evaluation shows that IoT-ASE achieved 92% accuracy in retrieving intent-aligned services and consistently generated concise, relevant, and preference-aware responses, outperforming generalized outputs produced by systems such as Gemini. These results underscore the potential of IoT-ASE to make real-time IoT data both accessible and actionable, supporting intelligent decision-making across diverse application domains. Full article

Background: Chemsex, the intentional use of psychoactive substances to enhance sexual experiences, is an emerging public health issue in Brazil, associated with increased risks of sexually transmitted infections and complex psychosocial vulnerabilities. Despite the universal coverage provided by the Unified Health System (SUS), individuals who practice chemsex often encounter barriers to healthcare, including stigma, discrimination, and a lack of specialized services. To date, no comprehensive reviews appear to synthesize evidence on how this population accesses healthcare in the Brazilian context; existing knowledge remains fragmented across individual studies. Objectives: The aim is to map and synthesize the available evidence regarding access to health services among people who engage in chemsex in Brazil, identifying health needs, professional demands, barriers, and facilitators. Methods: The protocol follows the Joanna Briggs Institute methodology for scoping reviews and PRISMA-ScR guidelines. A systematic search will be conducted in MEDLINE (PubMed), Embase, Scopus, SciELO, and LILACS for studies published between 2014 and 2024 in Portuguese, English, or Spanish. Data will be summarized using descriptive and narrative synthesis, presented in tables and thematic categories. Studies will be included if they address chemsex or sexualized drug use in Brazil and report on healthcare access, regardless of gender identity, sexual orientation, or drug type. Studies that do not address chemsex, focus on drug use outside a sexual context, or are unrelated to Brazil will be excluded. Expected results: The review is expected to identify key barriers and facilitators to healthcare access, highlight knowledge gaps for underrepresented groups, and support recommendations for research, policy, and practice to improve care for people engaging in chemsex in Brazil. By focusing on an underexplored intersection of drug use, sexuality, and healthcare access in Latin America, this study aims to provide an innovative contribution to public health literature. Full article

While often framed as historical or ‘post’colonial, the pervasive legacies of anti-Black racism, rooted in the afterlives of slavery and the dehumanization of African, Caribbean, and Black (ACB) voices, continues to shape the health experiences of young ACB women in Ontario, Canada. Using an intersectional framework, this qualitative study utilized focus groups (n = 24) to understand factors influencing access to sexual and reproductive health services for young ACB women in southern Ontario. The findings reveal that fostering ACB youth engagement in the design and facilitation of healthcare programs will be vital for creating more responsive spaces to fully express sexual health concerns. It also demonstrates that Eurocentric biomedical frameworks continue to obscure young ACB women’s needs, emphasizing the necessity for culturally relevant care. Lastly, the findings indicate that internalized colonial narratives around health practices perpetuate intergenerationally, further complicating young ACB women’s access to adequate sexual and reproductive healthcare. This examination illuminates the need to address the colonial legacies within healthcare systems that continue to pathologize and hypersexualize young ACB women’s bodies. The study concludes by advocating for intersectional, youth-centered, and culturally competent approaches to dismantling the barriers young ACB women face in accessing sexual and reproductive health services. Full article

Background/Objectives: Understanding the diverse neuron types within the hippocampal formation is essential for advancing our knowledge of its fundamental roles in learning and memory. Hippocampome.org serves as a comprehensive, evidence-based knowledge repository that integrates morphological, electrophysiological, and molecular features of neurons across the rodent dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. In addition to these core properties, this open access resource includes detailed information on synaptic connectivity, signal propagation, and plasticity, facilitating sophisticated modeling of hippocampal circuits. A distinguishing feature of Hippocampome.org is its emphasis on quantitative, literature-backed data that can help constrain and validate spiking neural network simulations via an interactive web interface. Methods: To assess and enhance its utility to the neuroscience community, we integrated Google Analytics (GA) into the platform to monitor user behavior, identify high-impact content, and evaluate geographic reach. Results: GA data provided valuable page view metrics, revealing usage trends, frequently accessed neuron properties, and the progressive adoption of new functionalities. Conclusions: These insights directly inform iterative development, particularly in the design of a robust Application Programming Interface (API) to support programmatic access. Ultimately, the integration of GA empowers data-driven optimization of this public resource to better serve the global neuroscience community. Full article

Various corporate groups, such as third-sector institutions in Latin America, have shown increasing interest in evaluating organizational performance as a possible strategy for increasing their effectiveness and competitiveness. From this perspective, this study analyzes the psychometric properties of a scale that assesses organizational performance in third-sector institutions in Latin America. The design was instrumental. The sample consisted of 355 workers from nine Latin American countries, recruited through non-probability sampling. A validity and reliability analysis of the scale confirmed the items and original factors. In this sense, the accessibility and use of a brief and useful tool for measuring organizational performance enriches knowledge about organizational theory by facilitating the comparison and validation of existing approaches or even by suggesting new dimensions that reflect the dynamic complexity of current organizations in Latin America. Full article

Following the 2022 Russian invasion of Ukraine and the resulting refugee crisis, this study investigates innovative and flexible forms of trans-local care among communities of Ukrainian refugees living with HIV in host countries, particularly Germany and Poland. The study adopts a community-based participatory research approach to understand how trans-local community-based organizations (CBOs) support access to HIV care for Ukrainian refugees in Germany and Poland, and what roles activists and peer networks play in overcoming barriers to healthcare in the context of forced displacement. It is based on semi-structured interviews with refugee activists, community members, healthcare professionals, social workers, and subject-matter experts—52 interviews in total conducted in 2023–2024. The research identifies emerging configurations of community networks that facilitate access to healthcare, including community-based, mixed-type, and bridge-type organizations. Activists play a central role in navigating unfamiliar healthcare systems, advocating for migrant-sensitive services, and developing grassroots solutions to both individual and structural barriers to HIV care in contexts of forced displacement. Migrant organizations are instrumental in facilitating community-based linkage to HIV care for refugees. In the case of Ukrainian transnational communities, these organizations draw on previously acquired experiences, knowledge, and skills to support their peers. The involvement of community-led initiatives is essential to reducing disparities in healthcare access and promoting the well-being of forced migrants. Full article

Background: Although most amputations caused by diabetes and peripheral artery disease (PAD) are preventable, current limb preservation efforts in the United States remain poorly understood. This study aims to identify key barriers and facilitators to limb preservation from the primary care provider (PCP) perspective. We plan to use the insights from this work to promote targeted intervention strategies. Methods: Using a mixed-methods design, an online 5–10 min survey was distributed to Oklahoma primary care providers who could elect to participate further in a semi-structured, audio-recorded interview. Descriptive analysis was used to summarize survey results. Interviews were transcribed and qualitatively analyzed using grounded theory. Donabedian’s structure, process, and outcome framework was used to categorize how each identified barrier and facilitator increases or reduces the risk of limb loss for at-risk patients at the practice level. Finally, we compared and contrasted survey and interview findings. Results: Thirty surveys were completed (approximately 14% response rate), and seven interviews were conducted with PCPs geographically dispersed across Oklahoma. Most clinicians reported in the survey that they see at-risk limbs at least once every 1–2 months (n = 29, 96.7%). Half of clinicians were satisfied or very satisfied with access to vascular surgery (n = 15, 50.0%), interventional specialists (n = 13, 43.3%), and endocrinologists (n = 12, 40.0%). Finally, survey respondents reported that social needs most often affecting their patients with a limb at risk of amputation include income, health education, transportation, and health insurance. Interviews confirmed PCPs frequently see at-risk limbs. We identified thematic barriers to limb preservation that included limited access to specialty care, limited PCP and patient amputation prevention education, and patient social struggles surrounding transportation, finances, and insurance. Patient advocates (community, clinical, or personal), affordable medications, and more time with patients were reported as facilitators in amputation prevention. Conclusions: Oklahoma PCPs frequently see at-risk feet, realize poor access to care, and desire structural change to support excellent preventive care in diabetes and PAD. Limb preservation in Oklahoma is contingent upon shifting from disempowerment to engagement that requires systemic reform, clinical innovation, and community engagement. We identified several intervention strategies, including increasing education for PCPs to empower them to initiate early prevention, improving early identification and preventive therapy for patients at risk for limb loss, and cultivating specialty care access via networking and policy change. Full article

Background: Currently, population aging and the growing incorporation of digital technologies into everyday life highlight the need to ensure the digital inclusion of older adults. This is due to the existence of a significant digital divide that affects this population group, limiting not only their access to services and opportunities but also their emotional well-being and quality of life. The lack of digital skills can generate feelings of exclusion, frustration, and dependence, negatively impacting their mental health and autonomy. Methods: The objective of this study is to assess the level of basic digital competence in 404 older adults using the Scale of Basic Digital Competence in Older Adults (DigCompB_PM) in order to identify existing digital divides and provide empirical evidence for the design of educational interventions that promote the digital inclusion of this population group. To this end, we start with the following research question: Are older adults prepared to face the digital and knowledge society, taking into account personal variables such as age, gender, geographical location, place of residence, and type of cohabitation? Results: The findings reveal that participants scored highest in the dimension related to safety and digital device usage while scoring lowest in online collaboration, indicating a disparity between basic digital skills and collaborative competencies. Cluster analysis further demonstrates that age and previous occupational experience significantly influence digital literacy levels. These results highlight the heterogeneity of digital competence among older adults. Conclusions: The study concludes by emphasising the importance of implementing tailored policies that enhance digital literacy in this population. Key factors such as accessibility, training, and motivation should guide such efforts. Additionally, intergenerational learning emerges as a promising strategy, facilitating the development of digital skills through knowledge exchange and sustained support from younger cohorts. Full article