Search Results (1,062)

In the context of typhlo music therapy, personalized interventions can significantly enhance the therapeutic experience for visually impaired children. Leveraging a data-driven approach, we incorporate action-rule discovery to provide insights into the factors of music that may benefit individual children. The system utilizes a comprehensive dataset developed in collaboration with an experienced music therapist, special educator, and clinical psychologist, encompassing meta-decision attributes, decision attributes, and musical features such as tempo, rhythm, and pitch. By extracting and analyzing these features, our methodology identifies key factors that influence therapeutic outcomes. Some themes discovered through action-rule discovery include the effect of harmonic richness and loudness on expression and communication. The main findings demonstrate the system’s ability to offer personalized, impactful, and actionable insights, leading to improved therapeutic experiences for children undergoing typhlo music therapy. Our conclusions highlight the system’s potential to transform music therapy by providing therapists with precise and effective tools to support their patients’ developmental progress. This work shows the significance of integrating advanced data analysis techniques in therapeutic settings, paving the way for future enhancements in personalized music therapy interventions. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

Beach Sand Mycobiome is currently among the most important health challenges for viticulture in the world. Remarkably, the study of fungal communities in coastal beach sand and recreational waters remains underexplored despite their potential implications for human health. This research aimed to assess the prevalence of fungal species and the antifungal susceptibility profiles of fungi recovered from the beaches of the Persian Gulf and the Sea of Oman. Sand and seawater samples from 39 stations distributed within 13 beaches along the coastline were collected between May and July 2023. The grown isolates were identified at the species level based on morphological characteristics and DNA sequencing. Antifungal susceptibility testing was performed according to the Clinical Laboratory Standards Institute guidelines. Of 222 recovered isolates, 206 (92.8%) filamentous fungi and 16 (7.2%) yeast strains were identified. Sand-recovered fungi comprised 82.9%, while water-originated fungi accounted for 17.1%. The DNA sequencing technique categorized 191 isolates into 13 genera and 26 species. The most recovered genus was Aspergillus (68.9%), and Aspergillus terreus sensu stricto was the commonly identified species (26.14%). Voriconazole was the most effective antifungal drug against Aspergillus species. Research on fungal contamination levels at these locations could provide a foundation for establishing regulatory frameworks to diminish fungal risks, thereby enhancing public health protection. The ecological significance of fungal communities in sandy beaches to human infections remains to be explored, and earlier reports in the literature may motivate researchers to focus on detecting this mycobiome in natural environments where further investigation is warranted. Ultimately, our discovery serves as a reminder that much remains to be learned about pathogenic fungi and underscores the need for vigilance in areas where emerging pathogens have not yet been identified. Full article

A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like FAIRsharing offer structured metadata describing data standards, repositories, and policies aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, they do not enable seamless, programmatic access to the underlying datasets. We present FAIRFind, a system designed to bridge this accessibility gap. FAIRFind autonomously discovers, interprets, and operationalizes access paths to biological databases on the deep web, regardless of their FAIR compliance. Central to our approach is the Deep Web Communication Protocol (DWCP), a resource description language that represents web forms, HyperText Markup Language (HTML) tables, and file-based data interfaces in a machine-actionable format. Leveraging large language models (LLMs), FAIRFind combines a specialized deep web crawler and web-form comprehension engine to transform passive web metadata into executable workflows. By indexing and embedding these workflows, FAIRFind enables natural language querying over diverse biological data sources and returns structured, source-resolved results. Evaluation across multiple open-source LLMs and database types demonstrates over 90% success in structured data extraction and high semantic retrieval accuracy. FAIRFind advances existing registries by turning linked resources from static references into actionable endpoints, laying a foundation for intelligent, autonomous data discovery across scientific domains. Full article

Tree diseases affecting Camellia japonica have emerged as a significant threat to the health and longevity of this ornamental tree, particularly in countries where this tree species is widely distributed and cultivated. Among these, Pestalotiopsis spp. have been frequently reported and are considered one of the most impactful fungal pathogens, causing leaf blight or leaf spot, in multiple countries. Understanding the etiology and distribution of these diseases is essential for effective management and conservation of C. japonica populations. The traditional methods based on pathogen isolation and pure culture cultivation for diagnosis of tree diseases are labor intensive and time-consuming. In addition, the frequent coexistence of the major pathogens with other endophytes within a single C. japonica tree, coupled with inconsistent symptom expression and the occurrence of pathogens in asymptomatic hosts, further complicates disease diagnosis. These challenges highlight the urgent need to develop more rapid, accurate, and efficient diagnostic or monitoring tools to improve disease monitoring and management on trees, including C. japonica. To address these challenges, we applied a metagenomic approach to screen fungal communities within C. japonica trees. This method enabled comprehensive detection and characterization of fungal taxa present in symptomatic and asymptomatic tissues. By analyzing the correlation between fungal dominance and symptom expression, we identified key pathogenic taxa associated with disease manifestation. To validate the metagenomic approach, we employed a combined strategy integrating metagenomic screening and traditional fungal isolation to monitor foliar diseases in C. japonica. The correlation between dominant taxa and symptom expression was confirmed. Simultaneously, traditional isolation enabled the identification of a novel species, Pestalotiopsis, as the causal agent of leaf spot disease on C. japonica. In addition to confirming previously known pathogens, our study led to the discovery and preliminary characterization of a novel fungal taxon with pathogenic potential. Our findings provide critical insights into the fungal community of C. japonica and lay the groundwork for developing improved, rapid diagnostic tools for effective disease monitoring and management of tree diseases. Full article

International efforts are underway to implement carbon neutrality policies in rapidly changing climate conditions. This situation has strongly demanded the discovery of novel carbon sinks. The Salix genus has attracted attention as a promising carbon sink owing to its rapid growth and efficient use as a biofuel in short-rotation cultivation. The present study aims to derive an allometric equation and conduct stem analysis as fundamental tools for estimating net primary productivity (NPP) in Salix pierotii Miq. stand, which is increasingly acknowledged as an important emerging carbon sink. The allometric equations derived showed a high explanatory rate and fitness (R² ranged from 0.74 to 0.99). The allometric equations between DBH and stem volume and biomass derived in the process of stem analysis also showed a high explanatory rate and fitness (R² ranged from 0.87 to 0.94). The NPPs calculated based on the allometric equation derived and stem analysis were 11.87 tonC∙ha⁻¹∙yr⁻¹ and 15.70 tonC∙ha⁻¹∙yr⁻¹, respectively. These results show that the S. pierotii community, recognized as the representative riparian vegetation, could play an important role as a carbon sink. In this context, an assessment of the carbon absorption capacity of riparian vegetation such as willow communities could contribute significantly to achieving carbon neutrality goals. Full article

It is well established that effective management and restoration of freshwater ecosystems is often limited by the availability of reusable data. Although numerous public, private, and nonprofit organizations collect data from freshwater ecosystems, much of what is collected remains inaccessible or unusable by Rights holders and end users (including researchers, practitioners, community members, and decision-makers). In Canada, the federal government plans to improve freshwater data sharing practices through the newly formed Canada Water Agency, which is currently drafting a National Freshwater Data Strategy. Our study aimed to support these efforts by synthesizing insights from the technology sector, where data management and sharing practices are more mature. We interviewed 12 experts from the technology sector, asking them for advice on how to improve data sharing practices in the freshwater science sector. Using a Reflexive Thematic Analysis of participants’ responses to semi-structured interview questions, we identified nine broad recommendations. Recommendations centred on motivating open data sharing, promoting data reuse through data licences, training and skill building, and developing standards and digital solutions that enable data discovery, accessibility, interoperability, and reuse. These recommendations can support the numerous initiatives that are working to improve access to high-quality freshwater data and help address the pressing crisis of global freshwater ecosystem degradation. Full article

The ability to model the earliest stages of human embryonic development in vitro using pluripotent stem cells offers researchers new ways to understand and interrogate the intricacies of implantation. It also raises important ethical and regulatory considerations, both those common to research involving human embryos, as well as those unique to stem cell-based embryo and endometrial models. This review examines the underpinning scientific discoveries that have led to the development of this rapidly expanding area of research, and how three-dimensional embryo models could be employed in advancing assisted reproductive technologies and understanding implantation failure. Importantly, we also discuss the ethical and legal implications and explore various governance models that have been proposed to foster responsibility and innovation in this area of research. Given the heightened interest in the scientific community on this topic, we finish on the question of how and when to involve the public in the development of this technology and its regulation. Full article

Oral narratives play a crucial role in shaping the historical consciousness of Indigenous communities in Northeast India, where history writing is a relatively recent phenomenon. Among the Mizos, Nagas, Khasis, Kuki-Chins, and other Indigenous tribes of Northeast India, including the Bodos, the Garos, the Dimasas, or the Karbis of Assam, much of what is considered written history emerged during British colonial rule. Native historians later continued it in postcolonial India. However, written history, especially when based on fragmented colonial records, includes interpretive gaps. In such contexts, oral traditions provide complementary, and frequently, more authoritative frameworks rooted in cultural memory and collective transmission. Oral narratives, including ritual poetry, folk songs, myths, and folktales, serve as vital mediums for reconstructing the past. Scholars such as Jan Vansina view oral narratives as essential for understanding the histories of societies without written records, while Paul Thompson sees them as both a discovery and a recovery of cultural memory. Romila Thapar argues that narratives become indicative of perspectives and conditions in societies of the past, functioning as a palimpsest with multiple layers of meaning accruing over generations as they are recreated or reiterated over time. The folk narratives of the Mizos and Angami Nagas not only recount their origins and historical migrations, but also map significant geographical and cultural landmarks, such as Khezakheno and Lungterok in Nagaland, Rounglevaisuo in Manipur, and Chhinlung or Rih Dil on the Mizoram–Myanmar border. These narratives constitute a cultural understanding of the past, aligning with Greg Dening’s concept of “public knowledge of the past,” which is “culturally shared.” Additionally, as Linda Tuhiwai Smith posits, such stories, as embodiments of the past, and of socio-cultural practices of communities, create spaces of resistance and reappropriation of Indigenous identities even as they reiterate the marginalization of these communities. This paper deploys these ideas to examine how oral narratives can be used to decolonize grand narratives of history, enabling Indigenous peoples, such as the Mizos and the Angamis in North East India, to reaffirm their positionalities within the postcolonial nation. Full article

Maintaining a balanced skin microbiota is essential for skin health, whereas disruptions in skin microbiota composition, known as dysbiosis, can contribute to the onset and progression of various skin disorders. Microbiota dysbiosis has been associated with several inflammatory skin conditions, including atopic dermatitis, seborrheic dermatitis, acne, psoriasis, and rosacea. Recent advances in high-throughput sequencing and metagenomic analyses have provided a deeper understanding of the skin microbial communities in both health and disease. These discoveries are now being translated into novel therapeutic approaches aimed at restoring microbial balance and promoting skin health through microbiome-based interventions. Unlike conventional therapies that often disrupt the microbiota and lead to side effects or resistance, microbiome-based products offer a more targeted strategy for preventing and managing inflammatory skin diseases. These products, which include probiotics, prebiotics, postbiotics, and live biotherapeutic agents, are designed to modulate the skin ecosystem by enhancing beneficial microbial populations, suppressing pathogenic strains, and enhancing immune tolerance. As a result, they represent a promising class of products with the potential to prevent, manage, and even reverse inflammatory skin conditions. However, realizing the full therapeutic potential of microbiome-based strategies in dermatology will require continued research, robust clinical validation, and clear regulatory frameworks. Full article

Spatial transcriptomics combines gene expression data with spatial coordinates to allow for the discovery of detailed RNA localization, study development, investigating the tumor microenvironment, and creating a tissue atlas. A large range of spatial transcriptomics software is available, with little information on which may be better suited for particular datasets or computing environments. A review was conducted to detail the useful metrics when choosing appropriate software for spatial transcriptomics analysis. Specifically, the results from benchmarking studies that compared software across four key areas of spatial transcriptomics analysis (tissue architecture identification, spatially variable gene discovery, cell–cell communication analysis, and deconvolution) were assimilated into a single review that can serve as guidance when choosing potential spatial transcriptomics analysis software. Full article

Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems. Full article

Background: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain poorly understood. In this study, we investigated the alterations in metabolic profiles due to e-waste exposure and linked these metabolites to systemic biological effects. Methods: We applied untargeted high-resolution metabolomics using dual-column LC-MS/MS and a multi-step analysis workflow combining MS1 feature detection, MS2 annotation, and chemical ontology classification, to characterize urinary metabolic alterations in 91 e-waste workers and 51 community controls associated with the Agbogbloshie site (Accra, Ghana). The impacts of heavy metal exposure in e-waste workers were assessed by establishing linear regression and four-parameter logistic (4PL) models between heavy metal levels and metabolite concentrations. Results: Significant metal-associated metabolomic changes were identified. Both linear and nonlinear models revealed distinct sets of exposure-responsive compounds, highlighting diverse biological responses. Ontology-informed annotation revealed systemic effects on lipid metabolism, oxidative stress pathways, and xenobiotic biotransformation. This study demonstrates how integrating chemical ontology and nonlinear modeling facilitates exposome interpretation in complex environments and provides a scalable template for environmental biomarker discovery. Conclusions: Integrating dose–response modeling and chemical ontology analysis enables robust interpretation of exposomics datasets when direct compound identification is limited. Our findings indicate that e-waste exposure induces systemic metabolic alterations that can underlie health risks and diseases. Full article

The rapid urbanization and technological advancements in the United Arab Emirates (UAE) have placed its modern architectural heritage from the 1970s and 1980s at increasing risk of being unrecognized and lost, particularly in Dubai following the discovery of oil. This research addresses the critical need for the documentation and heritage representation of Dubai’s modern heritage, a city undergoing rapid transformation within a globalized urban landscape. Focusing on the Nasser Rashid Lootah Building (Toyota Building), an iconic early 1970s residential high-rise representing the modern architecture of Dubai and a significant milestone in its architectural history, this study explores a replicable and cost-effective approach to digitally document and conserve urban heritage under threat. The existing building was meticulously documented and analyzed to highlight its enduring value within the fast-changing urban fabric. Through the innovative combination of drone photography, ground-based photography, and HBIM, a high-resolution 3D model and a semantically organized HBIM prototype were generated. This research demonstrates a replicable measure for identifying architectural values, understanding modernist design typologies, and raising local community awareness about Dubai’s modern heritage. Ultimately, this study contributes toward developing recognition criteria and guiding efforts in documenting modern high-rise buildings as vital heritage worthy of recognition, documentation, and future conservation in the UAE. Full article

Since the discovery of two-dimensional transition metal carbides and nitrides (MXenes), MXenes have attracted widespread research in the academic community due to their advantages, such as adjustable interlayer spacing, excellent hydrophilicity, conductivity, compositional diversity, and rich surface chemical composition. More than 100 different MXene combinations can be calculated theoretically, but only more than 40 have been successfully synthesized through experiments. Among the many synthesized and reported MXene materials, vanadium-based carbide MXenes, represented by V₂CT_x and V₄C₃T_x, show excellent application prospects in energy storage and have become the focus of researchers. In this review, we mainly discuss the structure, characteristics, and preparation methods of vanadium-based MXene precursors in the MAX phase and their applications in supercapacitors. Finally, we propose the main challenges existing at the current stage of vanadium-based materials and their heterostructures and provide a perspective on future research directions. Full article