Search Results (818)

The Sharpchin Slickhead, Bajacalifornia burragei, is a rarely collected bathypelagic fish endemic to the eastern tropical Pacific Ocean, and its genetic diversity remains undocumented. This study characterizes mitochondrial diversity in a localized deep-basin collection from the Carmen Basin of the Gulf of California by sequencing complete mitochondrial genomes from four individuals collected simultaneously at 1300 m in a single Tucker trawl. A high-quality reference mitogenome was assembled using PacBio HiFi long reads, and three additional mitogenomes were generated from Illumina PE150 libraries mapped to this reference. The mitogenome of B. burragei exhibits the canonical 37 gene architecture and conserved gene order typical of teleost mitogenomes. Overall mitogenome divergence was low (Range: 0.21–0.29%), with most protein-coding and rRNA genes exceeding 99.5% identity. Slightly elevated variation occurred in atp8, nad6, and several tRNA genes. This study provides the first genetic characterization of B. burragei and establishes a baseline for evaluating mitochondrial diversity within a localized collection of individuals and provides a point of comparison for future studies assessing connectivity among deep basins. Full article

Background/Objectives: Flea beetles (Coleoptera, Chrysomelidae: Alticinae) show extensive karyotypic diversity, yet cytogenetic and genomic data remain scarce for many taxa. Species of the genus Podagrica are characterized by unusually high chromosome numbers compared with the modal condition in Alticinae, suggesting a history of chromosomal fissions. This study aimed to characterize the karyotype and repetitive DNA composition of Podagrica fuscicornis, with special emphasis on the satellitome and its contribution to chromosome organization. Methods: Male specimens of P. fuscicornis collected in southern Spain were analyzed using conventional cytogenetic techniques, including Giemsa staining, DAPI staining, and C-banding. Fluorescence in situ hybridization was employed to map nucleolar organizer regions (NORs), telomeric repeats, and major satellite DNA (satDNA) families. The satellitome was characterized using Illumina short-read sequencing and analyzed with the RepeatExplorer2/TAREAN pipeline to identify satDNA families and estimate their genomic abundance and divergence. Results: The male karyotype of P. fuscicornis was 2n = 40 (38 + XY), with an Xy_p sex chromosome system. Constitutive heterochromatin was mainly pericentromeric, and the Y chromosome was largely heterochromatic. NORs were located on a single autosomal pair, and the ancestral insect telomeric motif (TTAGG)_n was detected at chromosome ends. The satellitome comprised at least 70 different satDNA families, representing 9.51% of the genome, some of them related to transposable elements. Ten of these 70 satDNAs are shared in other Alticinae species. The most abundant families were primarily localized in pericentromeric regions and showed differential distribution between autosomes and sex chromosomes. Conclusions: These results indicate that extensive chromosomal fissions and high satDNA dynamics could drive the high chromosome number and heterogeneous genome organization in P. fuscicornis, highlighting the role of repetitive DNA in karyotype evolution within Chrysomelidae. Full article

Over the last three decades, architecture has undergone a sustained digital transformation that has progressively displaced the ontology of the geometric generator, understood here as the primary artefact through which form is produced, controlled, and legitimized. This paper argues that, within one extended digital epoch, three successive regimes have reconfigured architectural agency. First, a digital model regime, in which computer-generated 3D models become the main generators of geometry. Second, a parametric code regime, in which scripted relations and numerical parameters supersede the individual model as the core design object, defining a space of possibilities rather than a single instance. Third, an emerging latent regime, in which diffusion and transformer systems produce high plausibility synthetic images as image-first generators and subsequently impose a post hoc image-to-geometry translation requirement. To make this shifting paradigm comparable across time, the paper uses the blob as a stable morphological reference and develops a comparative reading of four blobs, Kiesler’s Endless House, Greg Lynn’s Embryological House, Marc Fornes’ Vaulted Willow, and an author-generated GenAI blob curated from a traceable AI image archive, to show how the geometric generator migrates from object, to model, to code, to latent image-space. As a pre-digital hinge case, Kiesler is selected not only for anticipating blob-like continuity, but for clarifying a recurrent disciplinary tension, “ form first generators” that precede tectonic and programmatic rationalization. The central hypothesis is that GenAI introduces an ontological shift not primarily at the level of style, but at the level of architectural judgement and evidentiary legitimacy. The project can begin with a predictive image that is visually convincing yet tectonically underdetermined. To name this condition, the paper proposes the plausibility gap, the mismatch between visual plausibility and tectonic intelligibility, as an operational criterion for evaluating image-first workflows, and for specifying the verification tasks required to stabilize them as architecture. Selection establishes evidentiary legitimacy, while a friction map and Gap Index externalize the translation pressure required to turn predictive imagery into accountable geometry, making the plausibility gap operational rather than merely asserted. The paper concludes by outlining implications for authorship, pedagogy, and disciplinary judgement in emerging multi-agent design ecologies. Full article

In this study, a decision-level detection framework is presented and evaluated; it integrates sensor data (e.g., temperature, humidity, gas readings) with machine learning (ML) models and computer vision-based smoke and fire detection systems, in an effort to increase overall robustness, as well as false-alarm reduction. To this end, sixteen (16) ML and deep learning (DL) models are employed on an Internet of Things (IoT) sensor dataset. Moreover, a range of YOLO (You Only Look Once) models, such as older versions (YOLOv5n, YOLOv8n), as well as newer versions (YOLOv10n, YOLOv11n, YOLOv12n), are employed on an image-label-based dataset. Model selection initially prioritizes lightweight architectures that are suitable for resource-constrained edge devices. Afterwards, the selected models are evaluated via well-known metrics, such as parameter count, F1-score/mean average precision (mAP) and real-time inference latency. In the same context, explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) for ML models and LIME (Local Interpretable Model-agnostic Explanations) for the YOLO detectors, are integrated into the platform as well. According to the presented results, the Explainable Sensor Fusion (ESF) framework demonstrates a high level of internal consistency and logical reliability through its decision-level fusion paradigm in a controlled environment. Full article

The mitochondrial (mt) genomes of animals, including humans, are typically a single circular chromosome containing all mt genes. In several animal lineages, however, mt genomes have become fragmented, with genes distributed on multiple minichromosomes. How fragmented mt genomes are transcribed is still poorly understood. In this study, we investigated the transcription of the extensively fragmented mt genomes of the human head louse (Pediculus humanus capitis) and the human body louse (Pediculus humanus corporis). RNA-seq reads of both subspecies were retrieved from the NCBI Sequence Read Archive database and mapped to their mt genomes. The transcription level of each mt gene, minichromosome, motif, coding region and non-coding region, measured by RPKM (Reads Per Kilobase of transcript per Million mapped reads), TPM (Transcripts Per Million) or read coverage, was analysed statistically. In both subspecies, mt minichromosomes were transcribed entirely, with coding regions transcribed at much higher levels than non-coding regions. The 37 mt genes are transcribed unevenly, with rrnL, cox1, cox2, cox3 and atp6 transcribed at significantly higher levels than several other genes. Many transcription events terminate near a GC-rich motif in the non-coding regions; however, some transcription events pass this motif, leading to the transcription of entire non-coding regions. Despite the drastic difference in mt genome organisation, the human lice share several transcriptional features with humans, but also have unique features related to their fragmented mt genome organisation. The current study represents the first effort into the transcription of fragmented mt genomes. As more RNA-seq data become available, further studies on other animals with fragmented mt genomes are necessary to fully understand how genome fragmentation affects transcription. Full article

Microfluidics has emerged as a powerful platform for the analysis of minute sample volumes, driving its widespread adoption in biosensing applications. Optical imaging and electrochemical sensing are two typical integration strategies, each offering distinct advantages. The optical methods provide detailed spatial mapping of chemical processes, while electrochemical techniques enable selective detection that is unhindered by optical scattering from impurities. Here, we introduce a novel optical imaging–electrochemical sensor for integrated microfluidic analysis. This approach employs an electrochemical workstation to modulate optical signals, enabling the simultaneous acquisition of decoupled optical images and electrochemical readings. Consequently, it delivers complementary information, revealing both the spatial distribution of analytes and their intrinsic electrochemical properties. We detail the system design and imaging principle, demonstrate its utility through the analysis of noble metal nanoparticles, which are commonly used for signal amplification in biosensors, and finally apply it to monitor biological processes on live cells. We believe this integrated methodology will develop into a powerful tool for operando analysis in microfluidics, significantly expanding its application in the biosensing of complex biological fluids. Full article

Between 1940 and 1980, large scale social housing construction in Milan reshaped the city’s modern urban identity, yet how these postwar estates are perceived today remains insufficiently documented. This study analyzes user-generated Google Maps reviews to examine how these neighborhoods are evaluated in terms of sentiment and recurring narratives. We develop a replicable Natural Language Processing (NLP) workflow that combines automated translation, sentiment scoring, thematic keyword extraction, and unsupervised clustering to compare perception patterns across construction decades and architectural typologies, and we synthesize multi dimensional signals into a Perceived Space Quality Index (PSQI). Results show clear differentiation by typology and period: earlier courtyard-based estates are more frequently associated with positive evaluations, while later open block modernist developments exhibit more polarized discourse. Across clusters, positive evaluations most often co-occur with references to well-maintained shared spaces and active everyday life, while negative discourse concentrates on neglect and insecurity. Review narratives also increasingly foreground social experience and reputation over formal description. Overall, the workflow supports comparative reading of public narratives across estates and highlights which themes most shape favorable or unfavorable perception. Full article

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