The Microbe Directory: a centralized database for biological interpretation of microbiome data.

We conducted six metabarcoding studies from different aquatic habitats. It has been confirmed that fungal metabarcoding has the advantages of overcoming the cultivation bias, increasing the number of possible samples by high-throughput sequencing, and enabling the detection of yet uncultivated fungal taxa. However, the already well-known limitations, in particular the lack of taxonomic accuracy when using automated classification also occurred. During the manual curation of pipeline-retrieved datasets, the quality of the automatic classification, specifically phylogenetic rank and current nomenclature, was verified. The manual curation step revealed that only 10-20% of the Operational Taxonomic Units (OTUs) were identified correctly by the automatic classification. Re-blasting of the remaining OTUs resulted in taxonomic and nomenclatural correction of a further 30-40% of OTUs. Approximately 50% of the OTUs resulted in insufficient identifications, which can be attributed to shortcomings in the database, including yet unidentified species, missing reference sequences, and the disadvantages of the genetic marker used. Pipeline parameters that can be adjusted to improve the classification of taxa in metabarcoding studies include adjusting the cut-off threshold for sequence similarity, updating and refining databases, and aligning and collapsing of OTUs to species hypotheses. Here, we describe a method of manual data curation and its impact on improving the taxonomic resolution of a metabarcoding dataset. Shortcomings as well as advantages are discussed to illustrate the need for bioinformatic tools that support the process of data curation.

BackgroundRecent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.ResultsIn this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.ConclusionThis study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

An awakening in medicine: the partnership of humanity and intelligent machines

ABSTRACTA key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions.IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

Flow cytometry (FCM) allows pathologists to accurately immunophenotype hematopoietic cells by detecting the expression of surface proteins with fluorochrome-conjugated antibodies. Accordingly, FCM plays a pivotal role in the diagnostic workup of many hematologic malignancies. However, post-analytic processing and the interpretation of FCM data are primarily manual processes that impact the consistency and limit the throughput of the method. The post-analytic processing workflow is colloquially referred to as ‘gating’ and involves the identification and characterization of immune cell populations by hand through successions of biaxial plots. The gated FCM data is then used by the pathologist to make a clinical interpretation and to then determine potential diagnoses from the patterns and cell frequencies seen in bivariate plots of the data. Since gating and interpretation are manually performed, post-analytic analyses of FCM data are not only laborious from a workflow perspective but remain subjective and prone to variability based on the experience and skill of both the medical laboratory scientist and pathologist. The objective of this study was to develop a computational pipeline that leverages machine learning-based (ML) solutions to automate gating and clinical interpretation of FCM data to increase the throughput and improve the repeatability of FCM analysis. Raw FCS files from clinical samples being evaluated for the presence of T-cell lymphoproliferative disease were exported from the on-instrument database. Automated gating was performed using open-source, supervised-ML packages for flow cytometry data (flowCore and flowDensity; R). These packages implement all processing steps that would typically be done manually (e.g. applying compensation, quality control (QC), and gating). Plots of interest were then generated from the gated data and classified as normal or abnormal using the clinical interpretations that were applied during the normal clinical workflow. These binary labels were used to train an ML-based classifier (VGG-19; Python). To evaluate pipeline performance, we collected FCS files from 1,188 samples that were analyzed by our flow cytometry lab. The automated gating pipeline was used to gate for CD4+CD3+ cells and to create bivariate plots for CD7/CD26 expression. 1150 (96.8%) passed QC and were all gated correctly by visual inspection. Of the 38 (3.2%) samples that failed QC, 13 (1.1%) were lymphopenic and were gated correctly by visual inspection, and the remaining 25 (2.1%) were gated manually. Using the CD7/CD26 plots, the classifier demonstrated promising predictive performance and achieved a precision of 0.85 and recall of 0.83 (weighted average across classes). Our findings represent a novel effort to automate both the gating and interpretation of FCM data using artificial intelligence. These results suggest that ML-based tools have potential utility in aiding the processing and interpretation of FCM data and can augment the efficiency and consistency of workflows in the clinical flow cytometry laboratory.

Follow-up of Artificial Intelligence Development and its Controlled Contribution to the Article: Step to the Authorship?

The integration of Artificial Intelligence (AI) in non-formal, informal and formal education is an emerging area with great potential to transform the world. However, the capacity of educators, youth workers and teachers to capitalise on this potential remains limited due to a lack of well-defined frameworks for AI integration in practices. The scope of this study is to explore the needs, challenges, and perspectives of youth work professionals regarding AI integration.The research methodology employed mixt research methods. The literature review was followed by four semi-structured focus groups involving 72 youth workers and professionals with diverse backgrounds, nationalities and experience levels. A common guideline was used to ensure consistency, including facilitator instructions, discussion prompts, and reporting templates. The data collected during the focus groups were analysed using a Computer-Assisted Qualitative Data Analysis Software (CAQDAS), ATLAS.ti software dedicated to qualitative research. The authors conducted qualitative analysis, through coding, identification of main categories, thematic map for visualization of results, and interpretation of findings to uncover patterns and deeper insights. The results revealed a generally positive attitude toward AI tools and an awareness of their potential benefits in youth work, particularly in content creation. However, participants reported significant challenges regarding the use of AI for administrative tasks efficiency, for personalised learning, ethical concerns, and difficulty in interpretation of data. Cross-country similarities emerged, suggesting a shared need for capacity building and continuous training, while differences highlighted the influence of national contexts and digital infrastructure.This study provides a foundational understanding of the competence needs of youth workers in relation to AI adoption. The findings are relevant for course designers wishing to offer targeted training for youth workers. Future research is also needed into policy-level support mechanisms that can enhance AI literacy and ethical integration across the sector.

The rapid development of Artificial Intelligence (AI) has transformed educational leadership practices, including at the elementary school level, which raises distinctive pedagogical and ethical considerations. This study aims to examine AI-based educational leadership in elementary education, emphasizing the balance among data utilization, pedagogical wisdom, and humanistic values. A Systematic Literature Review (SLR) was conducted by analyzing peer-reviewed articles published between 2015 and 2025 that address educational leadership, AI, and primary education. The findings indicate that AI significantly supports data-driven leadership by enhancing responsive and evidence-based decision-making; however, it cannot replace the role of school principals as interpreters of data who consider students’ social-emotional development and the unique context of elementary education. The successful implementation of AI is also highly dependent on establishing teachers’ psychological safety, fostered by supportive, ethical leadership that encourages reflective adaptation to technology without undermining professional autonomy. Furthermore, AI has strong potential to support differentiated and inclusive learning, provided it is managed fairly and does not lead to labeling or learning inequalities. This study underscores that elementary school leadership in the AI era must be grounded in a strong ethical commitment to child protection, equity, and inclusivity, positioning AI as a means to strengthen educational practices rather than an end in itself.

During the last decade, the Human Genome Project [1], Human Proteome Project [2], Human Physiome Project [3], The International HapMap Project, Genome-Wide Associate Study (GWAS), The 1000 Genomes Project, The Human Microbiome Project, Epigenome, Metabolome, LIPID MAPS, and Next Generation Sequencing have generated large volumes of molecular data about human genes, proteins, physiology, SNPs (single nucleotide polymorphism), metabolites, and lipids etc. This has led to the fast growth of several research sub-areas: 1) high-volume molecular data mining and interpretation; 2) systems biology based on molecular features (i.e., biomarkers) extracted from high-throughput data. Examples include molecular interaction network reconstruction and pathway modeling; 3) synthetic biology, where researchers use engineering approach to make artificial cell and organism; and 4) translational bioinformatics that identifies genomic and proteomic biomarkers of disease to understand molecular mechanisms and guide clinical decision making. This field has grown so rapidly that there are a dozen bioinformatics conferences each year organized by researchers with different backgrounds such as biological or medical sciences, computing or computer science, and engineering. In 2004, the IEEE Engineering in Medicine and Biology Society (EMBS) started the theme “Bioinformatics and Computational Biology, Systems Biology, Modeling Methodologies.” It started with high throughput omic data analysis, structural bioinformatics, physiological modeling, then moved to systems biology, and recently included synthetic biology, with future movement towards translational bioinformatics in biomedical applications such as cancer prevention, therapeutics, clinical data mining and decision making. In this spotlight, we summarize key topics of importance for the EMBS community.

This scoping review examines the integration of artificial intelligence (AI) tools into scientific education practices in school settings. Following the PRISMA statement guidelines, a literature search was conducted in the Web of Science and Scopus databases, identifying 2892 articles published between 2020 and 2024. After applying the eligibility criteria, 75 primary studies with empirical data demonstrating the outcomes of school‐based scientific practices using AI tools were selected. The studies were coded based on the type of AI, the type of scientific practice, AI competencies, and other contextual and pedagogical aspects. The results indicate that the most frequently used tools by students are computer vision, natural language processing and data mining. Most studies focus on secondary education levels, combining strategies such as project‐based learning, scientific inquiry and the STEM approach. Although the evidence suggests that these tools enhance scientific skills like data interpretation and computational thinking, limitations were identified, including the use of low‐reliability or paid software and a lack of representation of ethical competencies. Finally, the review highlights the need to strengthen interdisciplinary collaboration and the design of curricular programmes that integrate AI through an ethical framework. The findings are discussed to provide guidance for pedagogy, public policies and future research. Context and implications Rationale for this study: This study was conducted to examine current evidence on how AI tools are being integrated into K‐12 scientific practices. Why the new findings matter: The findings presented are relevant for analysing current trends, identifying research gaps and exploring opportunities to update interdisciplinary pedagogical frameworks and digital literacy curriculum policies. Implications for educational practitioners, policy makers and researchers: For education practitioners, this study provides a planning model with practical examples for integrating AI into K‐12 scientific practices. For policy makers, it highlights the urgency of updating curricular frameworks and public education policies to ensure equitable access to trustworthy technologies, as well as initial and ongoing teacher training in AI, while fostering interdisciplinary collaboration, particularly between science and mathematics. For researchers, there is a clear need to expand empirical studies that examine scientific practices in virtual and hybrid modalities, in underexplored levels such as elementary education, and that take into account ethical competencies in the use of AI.

This research investigates how artificial intelligence (AI) is changing microprocessor design and industrial competitiveness using a mixed-methods approach that combines systematic literature research (861 Scopus-indexed articles spanning 2015-2025) and patent analysis. The study goals emphasize (1) assessing architectural changes toward AI specialization, (2) examining vendor tactics, and (3) spotting sector-specific adoption obstacles. The study assessed trends methodologically using PRISMA-guided document screening, NVivo-based theme coding (Krippendorff's α=0.82), and statistical analysis (χ² tests, linear regression). While small and medium enterprise (SME) adoption is behind 3.2× because of $540 M+ 5 nm development expenses, key findings show that 58.88% of current designs are AI-optimized (14.7× increase since 2015) with Google's Tensor Processing Units attaining 2.8× greater energy efficiency than graphics processing units. AI has significantly, in our opinion, significantly changed microprocessor invention paths, hence producing different vendor tactics (NVIDIA's acceleration vs. Intel's hybrid approach) and new market obstacles. Strategic advice calls for unifying AI processor benchmarks and building modular architectures for SMEs. Future studies should examine 3 nm/2 nm node economics, photonic/neuromorphic substitutes, and ethical consequences of AI hardware in sensitive uses. With implications for semiconductor policy and research and development priorities, this study offers a verified methodology for evaluating AI's contribution to upholding Moore's law under physical scaling restrictions. Received: 5 March 2025 | Revised: 27 April 2025 | Accepted: 3 June 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Gabriel Silva-Atencio: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.

This comprehensive review explores the historical progression and influence of artificial intelligence (AI) in the domain of microbiology. John McCarthy's groundbreaking 1956 idea is followed as it explores the development of AI and how it has become a part of everyday life, especially with the advent of machine learning (ML) and deep learning (DL). The significance of Arthur Samuel's founding of Machine Learning (ML) as a subsection of Artificial Intelligence (AI) is underscored, particularly in relation to its impact on clinical decision-making and the identification of antibiotics. The review highlights AI's critical role in combating antibiotic resistance worldwide by employing machine learning to forecast novel antibiotic structures and monitor soil microbes. A major focus is on the application of AI to medical diagnostics, including in radiological scans, pathology evaluations, and the emerging field of clinical microbiology informatics. The implementation of AI in the clinical microbiology laboratory is examined, focusing on image analysis, automation, and the possibility of standardizing microbiological interpretation. The discussion concerns the challenges associated with integrating AI, including data accuracy, interpretability, bias, and compliance with regulatory norms. The review also explores the potential of AI to expedite the analysis of parasite morphology, namely in the field of malaria diagnostics, as well as its ability to analyze mass spectral data for predicting antibiotic resistance. The study identifies challenges like data quality, interpretability, and ethical concerns, even as it acknowledges AI's transformative potential in microbiology. To fully leverage the potential of AI in scientific research and healthcare, it is crucial to prioritize interdisciplinary collaboration, effective data management, ongoing learning, and ethical behaviours. In the study's conclusion, a future with AI-assisted personalized healthcare, enhanced prevention of infectious diseases, and environmentally sound solutions is envisioned. Although, AI in its infancy, the study concludes that responsible technology use has the potential to enhance sustainability and global health significantly.

Purpose Artificial intelligence (AI) plays a crucial role in executing marketing tasks such as identifying risks, targeting customers, advertising brands and optimizing pricing for maximum profit. In today's digital age, AI's presence in communication and marketing channels has had a substantial impact. Experts and scholars contend that the growing use of AI in analyzing big data, employing machine learning, analyzing social media, making algorithmic decisions, modeling and simulating has significantly influenced brand presentation in the global market. As a result, AI greatly changes brand preferences, marketing strategies and customer attitudes. This research primarily aims to explore the persuasive effects, methods, advancements and challenges of implementing AI in digital branding. Design/methodology/approach To address the research questions, a two-phase qualitative study has been conducted. The study's first phase involved a systematic literature review. The databases Scopus and Web of Science were used, resulting in an initial collection of 3,983 studies. These studies were then filtered through Preferred Reporting Items for Systematic Reviews and Meta-Analyses, including an evaluation of titles and abstracts, quality assessment and the application of ten specific criteria, ultimately narrowing the selection to 78 articles. In the second phase, 17 experts in AI and digital branding were selected for semi-structured interviews. The data from both phases were analyzed by thematic analysis. Findings Key themes related to the application of AI in branding include scalable computational algorithms, machine learning, reinforcement learning algorithms, search algorithms, operational automation, recommendation algorithms, data interpretation and processing, human–computer interaction algorithms and AI-based platforms. The transformations due to AI in branding involve dynamic digital marketing, changes in distribution channels, personalized brand communications, dynamic pricing, adaptive business strategies, enhanced cybersecurity, development of internal and external databases, improved customer experiences, strengthened brand positioning and status, advancements in organizational processes and systems, improved decision-making quality, brand globalization, the creation of digital business models and added value creation. Originality/value This is the first study to use a multi-method qualitative approach to explore the functionalities of AI in branding.

High-throughput sequencing technologies have greatly facilitated omics research in the past two decades. An enormous volume of omics data have been generated, with the potential to answer various key biological questions. Unsupervised learning technologies such as cluster analysis serve as a powerful tool to uncover hidden patterns and associations in omics data. However, considering the complex natural structure and special characteristics of omics data, it is usually inappropriate or inefficient to directly apply clustering methods designed for general purposes. To address these issues, we develop several clustering and biclustering algorithms specifically designed for omics data. %In this dissertation, we tried to address these concerns from multiple different aspects: The first project is about Poisson hurdle model-based clustering, which aims to deal with extra sparsity in microbiome count data. Extra sparsity is a huge problem when dealing with microbiome data and some other omics data. In this study, we present a novel model-based clustering framework based on Poisson hurdle models, designed specifically for the intricacies of sparse microbiome data. We describe an expectation-maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Additionally, we offer algorithms for intuitive initialization and determining the optimal number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. A practical application of our method to a sorghum rhizosphere microbiome dataset yielded intriguing biological insights. The second project is a Dirichlet-Multinomial biclustering algorithm for compositional microbiome data. Similar to (one-way) cluster analysis, biclustering is a popular tool to study the interactions between microbial sub-communities and environmental samples. In this project, we introduce a Dirichlet-Multinomial (DM) model-based checkerboard biclustering technique tailored for the simultaneous clustering of microbiome features and samples. This approach assumes the presence of a DM distribution mixture within each microbiome sample, and utilizes a combination of Expectation-Maximization and coordinate descent algorithm to solve for optimal parameters. Compared to conventional, general-purpose biclustering algorithms, our method consistently demonstrated superior clustering accuracy and enriched biological interpretation across diverse simulation settings and multiple real datasets. The third project studied and compared the effect of several feature selection techniques implemented in the biclustering framework. When dealing with biological data, usually tens of thousands of features are present in a single dataset, but most of them are likely non-informative and do not benefit our analysis. Including them in the analysis can be computationally inefficient and lead to overfitting issues. This project gives a review of implementing penalization methods in the biclustering framework to do feature selection. Penalization/regularization are broadly used in regression analysis, and have also been applied to one-way clustering. This project focuses on extending and applying penalization methods to bicluster analysis of biological data. We show two different classes of penalization methods, one on the with-cluster sum of squares (WCSS) and the other on the feature weights. By introducing sparsity into the feature space, we are able to simultaneously find feature groups/sample subtypes and identify non-informative features. The fourth project focused on clustering analysis using an over-clustering and merging structure. This idea originates from a problem we encountered in the third project: if the true feature classes show a highly imbalanced structure, K-means type of clustering or biclustering methods usually lose power. This is due to the spherical nature of K-means clusters, which lets it perform poorly when the class sizes differ a lot. Motivated by this drawback of K-means, and also by the simple fact that K-means can only produce convex clusters, we developed a novel K-means merging algorithm \emph{CavMerge} that is suitable for producing general-sized and imbalanced clustering results. This method is intuitive, free of parameter tuning, and computationally efficient. It starts from an over-clustered K-means result, and utilizes a property called log-concavity to perform merging. Operating under minimal local distributional assumptions, our algorithm demonstrates strong consistency and guarantees rapid convergence. Empirical studies on various simulated and real datasets demonstrate that our method yields more reliable clusters in comparison to current state-of-the-art algorithms.