Search Results (811)

Exposure to Particulate Matter (PM) is linked to respiratory and cardiovascular diseases, certain types of cancer, and accounts for approximately seven million premature deaths globally. While governments and organizations have implemented various strategies for Air Quality (AQ) such as the deployment of Air Quality Monitoring Networks (AQMN), these networks often suffer from limited spatial coverage and involve high installation and maintenance costs. Consequently, the implementation of networks based on Low-Cost Sensors (LCS) has emerged as a viable alternative. Nevertheless, LCS systems have certain drawbacks, such as lower reading precision, which can be mitigated through specific calibration models and methods. This paper presents the results and conclusions derived from simultaneous PM₁₀ and PM_2.5 monitoring comparisons between LCS nodes and a T640X reference sensor. Additionally, Relative Humidity (RH), temperature, and absorption flow measurements were collected via an Automet meteorological station. The monitoring equipment was installed at the Faculty of Environment of the Universidad Distrital in Bogotá. The LCS calibration process began with data preprocessing, which involved filtering, segmentation, and the application of FastDTW. Subsequently, calibration was performed using a variety of models, including two statistical approaches, three Machine Learning algorithms, and one Deep Learning model. The findings highlight the critical importance of applying FastDTW during preprocessing and the necessity of incorporating RH, temperature, and absorption flow factors to enhance accuracy. Furthermore, the study concludes that Random Forest and XGBoost offered the highest performance among the methods evaluated. While satellites map city-wide patterns and MAX-DOAS enables hourly source attribution, our calibrated LCS network supplies continuous, street-scale data at low CAPEX/OPEX—forming a practical backbone for sustained micro-scale monitoring in Bogotá. Full article

Background and Objectives: Dermatology relies on a complex terminology encompassing lesion types, distribution patterns, colors, and specialized sites such as hair and nails, while dermoscopy adds an additional descriptive framework, making interpretation subjective and challenging. Our study aims to evaluate the ability of a chatbot (Gemini 2) to generate dermatology descriptions across multiple languages and image types, and to assess the influence of prompt language on readability, completeness, and terminology consistency. Our research is based on the concept that non-English prompts are not mere translations of the English prompts but are independently generated texts that reflect medical and dermatological knowledge learned from non-English material used in the chatbot’s training. Materials and Methods: Five macroscopic and five dermoscopic images of common skin lesions were used. Images were uploaded to Gemini 2 with language-specific prompts requesting short paragraphs describing visible features and possible diagnoses. A total of 2400 outputs were analyzed for readability using LIX score and CLEAR (comprehensiveness, accuracy, evidence-based content, appropriateness, and relevance) assessment, while terminology consistency was evaluated via SNOMED CT mapping across English, French, German, and Greek outputs. Results: English and French descriptions were found to be harder to read and more sophisticated, while SNOMED CT mapping revealed the largest terminology mismatch in German and the smallest in French. English texts and macroscopic images achieved the highest accuracy, completeness, and readability based on CLEAR assessment, whereas dermoscopic images and non-English texts presented greater challenges. Conclusions: Overall, partial terminology inconsistencies and cross-lingual variations highlighted that the language of the prompt plays a critical role in shaping AI-generated dermatology descriptions. Full article

This entry introduces an integrated model of reading that situates the Lexical Quality Hypothesis (LQH) within the Reading Systems Framework (RSF). The LQH posits that skilled reading depends on high-quality lexical representations—precise and flexible mappings of orthographic, phonological, morpho-syntactic, and semantic features—stored in the mental lexicon. These representations facilitate automatic word identification, accurate meaning retrieval, and efficient word-to-text integration (WTI), forming the foundation of text comprehension. Extending this micro-level perspective, the RSF positions lexical quality (LQ) within a macro-level cognitive architecture where the lexicon bridges word identification and reading comprehension systems. The RSF integrates multiple knowledge systems (linguistic, orthographic, and general world knowledge) with higher-order processes (sentence parsing, inference generation, comprehension monitoring, and situation model construction), emphasizing the bidirectional interactions between lower-level lexical knowledge and higher-order text comprehension. Central to this model is WTI, a dynamic mechanism through which lexical representations are incrementally incorporated into a coherent mental model of the text. This integrated model carries important implications for theory refinement, empirical investigation, and evidence-based instructional practices. Full article

Background/Objectives: Comparative analysis of antimicrobial resistomes in hospital and community wastewater can provide valuable insights into the diversity and distribution of antimicrobial resistance genes (ARGs), contributing to the advancement of the One Health approach. This study aimed to characterize and compare the resistome profiles of wastewater sources from a hospital and community. Methods: Longitudinal metagenomic analysis was conducted on wastewater samples collected from the National Center for Global Health and Medicine (hospital) and a shopping mall (community) in Tokyo, Japan, between December 2019 and September 2023. ARG abundance was quantified using reads per kilobase per million mapped reads (RPKM) values, and comparative analyses were performed to identify the significantly enriched ARGs in the two sources. Results: A total of 46 monthly wastewater samples from the hospital yielded 825 unique ARGs, with a mean RPKM of 2.5 across all detected genes. In contrast, 333 ARGs were identified in the three shopping mall wastewater samples, with a mean RPKM of 2.1. Among the ARGs significantly enriched in the hospital samples, 23, including genes conferring resistance to aminoglycosides (nine groups) and β-lactam antibiotics (eight groups), exhibited significantly high RPKM values. No ARGs were found to be significantly enriched in the community wastewater samples. Conclusions: This study highlights the higher diversity and abundance of ARGs, particularly those conferring resistance to aminoglycosides and β-lactam antibiotics including carbapenems, in hospital wastewater than in community wastewater. These findings underscore the importance of continuous resistome monitoring of hospital wastewater as part of the integrated One Health surveillance strategy. Full article

This study presents a multimodal framework that uses smartphone motion sensors and generative AI to create audio comics from live news headlines. The system operates without direct touch or voice input, instead responding to simple hand-wave gestures. The system demonstrates potential as an alternative input method, which may benefit users who find traditional touch or voice interaction challenging. In the experiments, we investigated the generation of comics on based on the latest tech-related news headlines using Really Simple Syndication (RSS) on a simple hand wave gesture. The proposed framework demonstrates extensibility beyond comic generation, as various other tasks utilizing large language models and multimodal AI could be integrated by mapping them to different hand gestures. Our experiments with open-source models like LLaMA, LLaVA, Gemma, and Qwen revealed that LLaVA delivers superior results in generating panel-aligned stories compared to Qwen3-VL, both in terms of inference speed and output quality, relative to the source image. These large language models (LLMs) collectively contribute imaginative and conversational narrative elements that enhance diversity in storytelling within the comic format. Additionally, we implement an AI-in-the-loop mechanism to iteratively improve output quality without human intervention. Finally, AI-generated audio narration is incorporated into the comics to create an immersive, multimodal reading experience. Full article

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer and is marked by pronounced intra-tumoural heterogeneity that complicates therapeutic response. Patient-derived organoids offer a physiologically relevant model to capture this diversity and evaluate treatment effects. When integrated with spatial transcriptomics, they might enable the mapping of spatially resolved transcriptional and isoform-level changes within the tumour microenvironment. Methods: We established a robust workflow for generating patient-derived ccRCC organoids, that are not passaged and retain original cellular components. These retain key features of the original tumours, including cancer cell, stromal, and immune components. Results: Spatial transcriptomic profiling revealed multiple transcriptionally distinct regions within and across organoids, reflecting the intrinsic heterogeneity of ccRCC. Isoform-level analysis identified spatially variable expression of glutaminase (GLS) isoforms, with heterogeneous distributions of both the GAC and KGA variants. Treatment with NUC-7738, a phosphoramidate derivative of 3′-deoxyadenosine, induced marked transcriptional remodelling of organoids, including alterations in ribosomal and mitochondrial gene expression. Conclusions: This study demonstrates that combining long-read spatial transcriptomics with patient-derived organoid models provides a powerful and scalable approach for dissecting gene and isoform-level heterogeneity in ccRCC and for elucidating spatially resolved transcriptional responses to novel therapeutics. Full article

Introduction: Low vision affects more than visual acuity; it substantially disrupts daily functioning and may contribute to long-term cognitive, emotional, and social consequences. When medical or surgical treatment options are no longer effective, structured low-vision rehabilitation becomes essential, providing strategies and tools that support functional adaptation and promote independence. This review aims to map the current outcomes of rehabilitation services, identify gaps in existing research, and highlight opportunities for further study. Methods: An article search was conducted via PubMed, Scopus, PsycInfo, and Google Scholar. Then, title, abstract, and full-text screenings for inclusion were performed by all the authors independently, and disagreements were resolved through discussion. The relevant outcomes from the eligible publications were extracted by four authors and then cross-checked by the other authors. The results are presented via the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews checklist. Results: A total of 13 studies met the inclusion criteria. Most were randomized controlled trials (n = 10,77%), with the majority conducted in the United States and the United Kingdom. Study populations consisted of adults aged 18 years and older. Across the included studies, low-vision rehabilitation interventions particularly visual training, magnification-based programs, and multidisciplinary approaches, were associated with significant improvements in visual function, activities of daily living, and vision-related quality of life. Conclusions: Low vision rehabilitation interventions demonstrate clear benefits for visual acuity, contrast sensitivity, reading speed, and functional independence. However, substantial gaps remain, including limited evidence on long-term outcomes, inconsistent assessment of psychosocial influences, and underrepresentation of diverse populations. Standardized outcome measures and long-term, inclusive research designs are needed to better understand the sustained and equitable impact of low-vision rehabilitation. Full article

Precocious-fruiting Amomum villosum Lour. is characterized by early fruit set, rapid yield formation, and shortened economic return cycles, indicating strong cultivation potential. However, the molecular mechanisms underlying its flowering transition remain unclear. To elucidate the flowering mechanism of A. villosum, we used the Illumina NovaSeq X Plus platform to compare gene expression profiles in three tissues (Rhizomes, R; Stems, S; Leaves, L) during the vegetative stage and three tissues (Rhizomes and Inflorescences, R&I; Stems, S; Leaves, L) during the flowering stage of individual plants: VS-R vs. FS-R&I, VS-S vs. FS-S, and VS-L vs. FS-L. We obtained 52.5 Gb clean data and 789 million reads, and identified 2963 novel genes. The 3061 differentially expressed genes (DEGs, FDR ≤ 0.05 and |log2FC| ≥ 1) identified in the three comparison groups included six overlapping genes. The DEGs were enriched primarily in GO terms related to cellular process, metabolic process, binding, catalytic activity, and cellular anatomical entity, as well as multiple terms associated with development and reproduction. KEGG enrichment analysis revealed enrichment primarily in metabolic pathways, including global and overview maps, energy metabolism, and carbohydrate metabolism. Moreover, the most significantly enriched core pathways included metabolic pathways, photosynthesis, and carbon assimilation. Among all alternative splicing (AS) events, skipped exons (SEs) accounted for the largest proportion (59.5%), followed by retained introns (RI, 19.4%), alternative 3′ splice sites (A3SS, 10.7%), alternative 5′ splice sites (A5SS, 6.8%), and mutually exclusive exons (MXE, 3.6%). A preliminary set of 43 key DEGs was predicted, displaying spatiotemporal expression specificity and strong interactions among certain genes. Nine genes were further selected for RT-qPCR validation to confirm the reliability of the RNA-seq results. This study established a foundational framework for elucidating the flowering mechanism of precocious-fruiting A. villosum. Full article

In recent years, the quality of genome assemblies has notably improved, primarily due to advances in third-generation sequencing technologies and bioinformatics tools. In the present study, we obtained genome assemblies for two flax (Linum usitatissimum L.) varieties, K-3018 and Svyatogor, using Oxford Nanopore Technologies (ONT) simplex R10.4.1 data and the Hifiasm algorithm optimized for ONT reads. The K-3018 genome assembly was 491.1 Mb and consisted of thirteen full-length chromosomes and two one-gap chromosomes. The Svyatogor genome assembly was 497.8 Mb and consisted of twelve full-length chromosomes and three one-gap chromosomes. All chromosomes had telomeric repeats at their ends for both varieties. Hi-C contact maps and Illumina genomic data supported the accuracy of the obtained assemblies. The K-3018 and Svyatogor genome assemblies surpassed the quality of the best currently available flax genome assembly of variety T397, which serves as a reference for L. usitatissimum in the NCBI Genome database. Comparative analysis revealed that the flax genomes are generally quite similar at the chromosome level, with only a few large-scale differences. Thus, two near-T2T (telomere-to-telomere) flax genomes were assembled from the ONT simplex R10.4.1 reads using Hifiasm ONT without involving Pacific Biosciences (PacBio) HiFi or ultra-long ONT reads as well as optical maps. High-quality flax genomes are essential for improving the efficiency of genetic research, evaluating genetic diversity at the whole-genome level, and developing breeding and genome editing approaches of this valuable multipurpose crop. Full article

This article offers a comparative study of two closely linked constellations of early Ṣūfī thought: the ascetic–mystical program of al-Fuḍayl ibn ʿIyāḍ (d. 187/803) and that of his renowned disciple Bishr al-Ḥāfī (d. 227/841). Moving beyond hagiographic anecdote, the study advances the thesis that the pair articulate two complementary modalities of tawba (repentance) that generate distinct ascetic habitus and pedagogical lineages: al-Fudayl’s “ethic of awe” (fear, juridical redress, and renunciation of patronage) and Bishr’s “aesthetics of reverence” (beauty-induced modesty, evident humility, and fame avoidance). Drawing on primary sources (Ḥilyat al-Awliyāʾ, al-Sulamī’s Ṭabaqāt al-Ṣūfiyya, al-Qushayrī’s Risāla, al-Sarrāj’s Lumaʿ), the article reconstructs each thinker’s core concepts, practices (e.g., returning wrongs, ḥafāʾ/barefoot humility), and teaching styles and maps how the teacher–disciple nexus transmits, adapts, and ritualizes these ethics into durable Ṣūfī dispositions. Methodologically, the article combines close textual analysis with practice theory to show how emotions—such as fear and modesty (ḥayāʾ)—are choreographed into public, socially legible acts, thus reframing repentance as embodied discipline rather than interior feeling alone. A prosopographic appendix traces transmission from al-Fudayl to Bishr to Sarī al-Saqaṭī and al-Junayd, clarifying how each modality survives in later Baghdad sobriety and Malāmatī self-effacement. The contribution is twofold: first, it supplies a granular typology of early Ṣūfī repentance that explains divergent stances toward money, publicity, and power; second, it models how to read early Ṣūfī biography as anthropology of practice, recovering the lived grammar by which “conversion stories” become social programs. In doing so, the article nuances standard narratives of early Ṣūfism, showing that Bishr is not merely al-Fuḍayl’s echo but a creative reframer whose “reverential” path complements—rather than imitates—the awe-driven ethic associated with al-Fuḍayl. Full article

Floods are among the most destructive natural disasters, posing major challenges to human safety, property, and urban resilience. Effective communication of flood risk is therefore crucial for disaster preparedness and the sustainable management of smart cities. This study explores how interface design elements of flood hazard maps, including interaction modes and legend color schemes, influence users’ risk perception, decision support, and usability. An online questionnaire survey (N = 776) and a controlled 2 × 2 experiment (N = 40) were conducted to assess user comprehension, cognitive load, and behavioral responses when interacting with different visualization formats. Results show that slider-based interaction significantly reduces task completion and map-reading times compared with drop-down menus, enhancing usability and information efficiency. Multicolor legends, although requiring higher cognitive effort, improve users’ risk perception, engagement, and memory of flood-related information. These findings suggest that integrating cognitive principles into interactive design can enhance the effectiveness of digital disaster communication tools. By combining human–computer interaction, visual cognition, and smart governance, this study provides evidence-based design strategies for developing intelligent and user-centered flood hazard mapping systems. The proposed framework contributes to the advancement of smart urban resilience and supports the broader goal of building safer and more sustainable cities. Full article

This paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i) Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii) Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate Human–Computer Interaction (HCI) module, each bounded to a workflow-aligned role (e.g., empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains. Full article

(1) Background: With the continuous development of intelligent education, classroom behavior recognition has become increasingly important in teaching evaluation and learning analytics. In response to challenges such as occlusion, scale differences, and fine-grained behavior recognition in complex classroom environments, this paper proposes an improved YOLOv11-ASV detection framework; (2) Methods: This framework introduces the Adaptive Spatial Pyramid Network (ASPN) based on YOLOv11, enhancing contextual modeling capabilities through block-level channel partitioning and multi-scale feature fusion mechanisms. Additionally, VanillaNet is adopted as the backbone network to improve the global semantic feature representation; (3) Conclusions: Experimental results show that on our self-built classroom behavior dataset (ClassroomDatasets), YOLOv11-ASV achieves 81.5% mAP₅₀ and 62.1% mAP_50–95, improving by 1.6% and 2.9%, respectively, compared to the baseline model. Notably, performance shows significant improvement in recognizing behavior classes such as “reading” and “writing” which are often confused. The experimental results validate the effectiveness of the YOLOv11-ASV model in improving behavior recognition accuracy and robustness in complex classroom scenarios, providing reliable technical support for the practical application of smart classroom systems. Full article

The identification of mRNA isoforms in biological samples is crucial for studying tissue- and cell-specific isoform expression, activity of tissue-specific promoters, alternative splicing events, and alternative polyadenylation signals in genes. For single or several genes, expressed mRNA isoforms can be found using RT-PCR and RT-qPCR. Available transcriptome short-read archives deposited in GenBank or as laboratory data can be used to identify mRNA isoforms instead of or prior to wet analysis by other methods in eukaryotic organisms with annotated genomes. However, isoform expression analysis requires advanced bioinformatics skills and may be time-consuming. In addition, this analysis generates a large amount of unnecessary data. To detect mRNA isoforms encoded by one gene of interest, screening of expressed mRNAs in NGS data can be simplified by mapping NGS short reads to a single-gene or transcript sequence. Using single-gene/transcript mapping, we analyzed the expression of the Otx gene at the mRNA isoform level in some embryonic and adult tissue mRNA libraries of the sea urchin Strongylocentrotus purpuratus available in GenBank. The presence of expressed Otx mRNA isoforms was confirmed by RT-qPCR in the same tissues and at the same developmental stages of the closely related species Strongylocentrotus intermedius. We showed that single-gene/transcript mapping is a suitable approach for qualitative evaluation of the expression of mRNA isoforms and recognition of at least two expressed isoforms in the same biological sample. Full article

Background: Duchenne muscular dystrophy (DMD) results from pathogenic variants in the DMD gene, one of the most significant and most mutation-prone genes in the human genome. Although global mutation registries are well developed, genetic data from Central Asian populations remain extremely limited, leaving essential gaps in regional epidemiology and in the understanding of genotype–phenotype patterns. Methods: We conducted a retrospective analysis of patients with genetically confirmed dystrophinopathy in Kazakhstan. Variants were identified using multiplex ligation-dependent probe amplification (MLPA) for exon-level copy number alterations and next-generation sequencing (NGS) with Sanger confirmation for sequence-level changes. All variants were classified under ACMG guidelines. Statistical modelling incorporated mutation-class grouping, exon-hotspot mapping, reading-frame status, CPK stratification, chi-squared association testing, Spearman correlations, Kaplan–Meier ambulation survival curves, and multivariable logistic and Cox regression. Results: multi-exon deletions were the predominant mutation class, with a marked concentration within the canonical hotspot spanning exons 44–55. Recurrent deletions affecting exons 46–50 and 45–50 appeared in several unrelated patients. NGS confirmed severe protein-truncating variants, including p. Lys1049* and p. Ser861Ilefs*7. Phenotypic severity followed a consistent hierarchy: hotspot-associated deletions and early truncating variants showed the earliest loss of ambulation, whereas splice-site variants and duplications demonstrated the mildest courses. CPK levels correlated with the extent of genomic involvement, though extreme elevations did not consistently predict early functional decline. Regression models identified hotspot localization and out-of-frame effect as independent predictors of ambulation loss. Conclusions: This study provides the first statistically modelled characterisation of DMD gene mutations in Kazakhstan. While the mutational landscape largely mirrors global patterns, notable variability in clinical severity suggests the presence of population-specific modifiers. Integrating comprehensive molecular diagnostics with statistical-genetics approaches enhances prognostic accuracy and supports the development of mutation-targeted therapeutic strategies in Central Asia. Full article