This review systematically outlines the technological framework and clinical applications of neural regeneration mediated by pluripotent stem cells (ESCs/iPSCs). The article first compares the fundamental differences between the two: ESCs face ethical controversies regarding their embryonic origin and challenges related to immune rejection; while iPSCs support autologous transplantation, their reprogramming process may lead to genomic abnormalities, and the presence of residual undifferentiated cells poses a tumorigenic risk. The article traces the trend of directed differentiation technologies evolving from two-dimensional culture to three-dimensional organoids and details their practical applications in diseases such as stroke, Alzheimer's disease, and spinal cord injury—particularly in spinal cord injury models, where transplanted cells have achieved long-distance axonal regeneration and circuit reconstruction. Addressing core bottlenecks currently facing the field—such as low differentiation efficiency, difficulties in functional integration, and tumorigenicity—this paper explores breakthrough pathways through cutting-edge technologies including gene editing, organoids, and AI-enabled approaches. Finally, it compares the clinical advantages and disadvantages of ESCs and iPSCs, noting that while challenges remain, the deep integration of interdisciplinary technologies is accelerating the translation of stem cell therapies into standard clinical treatments, bringing revolutionary hope to patients with neurological diseases.
Daiyi Yang· International Journal of Bio...· 0 citations
We describe here an efficient method for gene editing in mouse T cells, based on well-established, high-efficiency retroviral transduction protocols. Our platform allows analysis of mutant phenotypes in primary murine T cells in vitro and in vivo. This approach uses a single retroviral vector to simultaneously knockout an endogenous gene and ectopically express its mutant version. This knockout/re-expression vector can be used as the only plasmid to transduce Cas9-expressing T cells, or used together with a Cas9 retroviral vector to transduce T cells from any mouse strain. We validated the system for analysis of murine T cells by targeting key molecules in proximal T cell signaling, i.e. CD3γ and Zap70. We obtain high knockout and re-expression efficiencies in both Cas9-expressing and non-Cas9 T cells. Knockout efficiencies can be further improved by gRNA multiplexing. Endogenous proteins compete with their ectopically expressed mutants or tagged versions for cellular location, protein interactions and cellular functions. Here, we quantified the incorporation of CD3γ-GFP into surface T cell receptor (TCR) complexes. Our data shows that the knockout and re-expression platform improves integration of CD3γ-GFP into the TCR. Therefore, eliminating competition between endogenous and ectopic proteins benefits analyses of protein assemblies and signaling pathways in primary T cells. Furthermore, we validated advantages of our system for mutant analysis using wild-type and mutant Zap70s. Zap70 mutants deficient in TCR binding or kinase activity show their phenotypes only in the absence of endogenous protein, further validating our knockout/re-expression approach. Most importantly, this system can be used to generate gene-edited primary T cells for in vivo studies, such as the quantification of anti-tumor responses. Our knockout and re-expression platform provides a useful gene editing tool for primary T cells in fundamental research and immunotherapy development.
Vasileios Morfos, Meredith Frie, Daniel Peschkov et al.· bioRxiv (Cold Spring Harbor...· 0 citations
The widespread application of CRISPR/Cas genome editing for commercial crop improvement is currently hindered by a complex and restrictive intellectual property (IP) landscape. The recent development of OpenCRISPR-1, a fully AI-designed and open-source Cas9-like nuclease, provides a promising, IP-unencumbered alternative; however, its efficacy in dicotyledonous plants remains largely uncharacterized. Here, we report the successful adaptation of the OpenCRISPR-1 system for highly efficient targeted mutagenesis in dicots. We constructed a plant-optimized binary vector (pBSE-OpenCRISPR-1) and validated its editing capability across two species. In soybean (Glycine max), targeting the GmFAD2-1B gene via an Agrobacterium rhizogenes-mediated hairy root transformation system yielded a robust mutation rate of approximately 50%. In Nicotiana benthamiana, stable Agrobacterium-mediated transformation targeting the phytoene desaturase homologs (NbPDSa/b) achieved a 75% editing efficiency in T0 lines, with up to 13.8% of events displaying complete homozygous or biallelic mutations and the corresponding visible albino phenotypes. Deep amplicon and Sanger sequencing revealed a characteristic mutation profile dominated by 1-bp insertions and small deletions occurring two to three nucleotides upstream of the PAM. These results demonstrate that the AI-designed OpenCRISPR-1 system is a highly active and versatile nuclease for dicot genome engineering, offering a powerful, commercially unencumbered tool to accelerate global crop trait improvement.
Cuong Xuan Nguyen, Phat Tien, Thu M. Tran· bioRxiv (Cold Spring Harbor...· 0 citations
CRISPR-Cas technology has revolutionized functional genomics, and gene therapy by enabling precise and programmable manipulation of the genome. Early CRISPR-Cas nucleases relied on the generation of double-strand breaks (DSBs) to disrupt genes or introduce exogenous DNA. More recently, the development of base editors and prime editors has expanded the genome editing toolbox, enabling precise single-nucleotide substitutions as well as targeted insertions and deletions without requiring DSBs. By avoiding DSB-associated toxicity and unpredictable repair outcomes, these next-generation editors have substantially improved the precision and safety of therapeutic genome editors. This dissertation focuses on the therapeutic application and engineering of adenine base editors (ABEs). We first demonstrated that a compact ABE based on an evolved Neisseria meningitidis Cas9 could be packaged into a single adeno-associated virus (AAV) and achieve therapeutic genome editing in two mouse models of alpha-1 antitrypsin disease (AATD). Treatment resulted in therapeutically relevant levels of editing and significant improvements in both liver and lung pathology. We next sought to further optimize ABEs through rational engineering of the terminal regions of the TadA deaminase. This work generated a panel of base editors with altered editing windows and efficiencies, thereby expanding the repertoire of available genome editing tools. Collectively, this work advances both the therapeutic application and engineering of ABEs by establishing a compact platform for in vivo genome editing and identifying protein terminal engineering as a modulator of base editor activity.
Jenny Gao· University of Massachusetts...· 0 citations
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This is the first public release of the analysis code accompanying the manuscript "Differential expression and machine-learning classification of Parkinson's disease using AMP-PD RNA-seq data" (Hu R, Dong X, et al.). Overview Analysis pipeline for RNA-seq–based differential expression (DE) and machine-learning (ML) classification of Parkinson's disease (PD) case/control status, using AMP-PD cohorts PPMI and PDBP/BioFIND (PDBF). What's included Data preparation — build case/control and mutation-carrier sample sets and extract per-cohort RNA-seq quantification matrices (DE/src/filtration.py, DE/src/extract_quant.py) Differential expression — DESeq2-based DE analysis for genes, eRNAs, and circRNAs, adjusted for covariates (age, sex, plate, RIN, genotype PCs) (DE/src/DE_PCs.R, DE/src/eRNA/, DE/src/circRNA/) Functional enrichment — GO/KEGG/Reactome/GSEA analysis on DE results (DE/src/Enrich_Profiler.R) ML classification — logistic regression, LASSO, SVM, random forest, XGBoost, KNN, MLP, and stepwise feature-addition classifiers trained on PPMI and independently tested on PDBF (ML/src/*.py) Model evaluation — combined AUROC/PR curve and feature-importance summary plots (ML/src/31_plots_AUC_PR_inOne.py) NanoString QC — standalone endogenous/housekeeping gene QC script (DE/NanoStringData/) Recorded R/Python session info and pinned dependencies (session_info/) Requirements R ≥ 4.0 (tidyverse, DESeq2, clusterProfiler, and related Bioconductor/CRAN packages) Python ≥ 3.8 (pandas, numpy, scikit-learn, matplotlib, seaborn) Known limitations Scripts are tailored to the specific AMP-PD sample sets and covariate schemas used in this study; not general-purpose tools File paths and cohort-specific parameters are set via literals/CLI args and may need editing for new datasets Raw AMP-PD data are not distributed with this repository; access requires a separate application to the AMP-PD Knowledge Portal (https://amp-pd.org/) License MIT License
TheDongLab· Zenodo (CERN European Organi...· 0 citations
The articles collected in this Research Topic highlight the growing integration of genomics and microbial ecology in advancing plant disease management and soil health. Together, they demonstrate how molecular approaches can reveal the mechanisms that shape microbial communities, uncover the genetic basis of pathogen virulence, and support the development of innovative and durable disease-control strategies.One of the recurring themes across this collection is the critical role of soil microbial communities in maintaining agricultural productivity and ecosystem stability. Han et al. (2025) explored how continuous maize cropping influences microbial community assembly over a cultivation period extending from one to twenty-five years. Their study provides compelling evidence that long-term monoculture substantially alters both bacterial and fungal communities. As cropping duration increased, bacterial communities became increasingly dominated by specific taxa, whereas dominant fungal groups gradually declined. Importantly, the authors demonstrated that microbial community assembly shifted from being largely stochastic to being increasingly governed by deterministic processes, particularly heterogeneous environmental selection. Changes in microbial co-occurrence networks further suggested that long-term continuous cropping reshapes ecological interactions within soil microbiomes. These findings deepen our understanding of how intensive agricultural practices influence microbial succession and provide a valuable ecological framework for designing sustainable soil-management strategies.The importance of agricultural management practices in shaping soil microbial ecology is further illustrated by the work of Tang et al. (2025) who investigated the effects of different rice strawreturn methods in karst paddy fields. By combining field experiments with 16S rRNA sequencing, the authors demonstrated that straw incorporation not only improved soil fertility but also influenced bacterial community composition, assembly processes, and interaction networks. Different straw-return methods generated distinct ecological outcomes, highlighting the importance of selecting management practices according to production goals. While rotary tillage incorporation produced the highest rice yield, no-till mulching and bioreactor treatments enhanced soil organic matter and nutrient accumulation while promoting more structured microbial communities. Their results further revealed that bacterial β-diversity and total nitrogen were among the strongest determinants of rice productivity. This study emphasizes that crop performance is shaped by the combined effects of soil physicochemical properties and microbial ecological processes, reinforcing the importance of microbiome-informed management approaches for sustainable agriculture.Beyond soil microbial ecology, this Research Topic also showcases the transformative role of genomics in understanding plant pathogens and improving disease control. Duan et al. ( 2025) provide the first whole-genome sequence of Phomopsis asparagi (Diaporthe asparagi), the fungal pathogen responsible for asparagus stem blight. This work represents a significant step forward in understanding the biology of an economically important disease that affects asparagus production worldwide. Through genome annotation and comparative transcriptomic analyses, the authors identified a range of virulence-associated pathways linked to oxidative stress responses, reactive oxygen species metabolism, cell-wall degradation, and programmed cell death. Their investigation of pathogen responses under elevated temperature conditions revealed a sophisticated molecular adaptation system involving stress signaling, metabolic reprogramming, DNA repair, and enzymatic activities associated with host colonization. These genomic resources and mechanistic insights establish a valuable foundation for future research aimed at developing more effective and targeted disease-management strategies.Advances in understanding pathogen biology are closely linked to innovations in plant resistance breeding. In this regard, Senthilraja et al. ( 2025) review emerging strategies that target pathogen effector proteins to achieve durable disease resistance. Effectors play a central role in plantpathogen interactions by manipulating host cellular processes and suppressing immune responses. The authors discuss a range of promising approaches, including susceptibility-gene modification, CRISPR/Cas-based genome editing, RNA interference technologies, and the use of synthetic decoys to enhance immune recognition. By focusing on effector biology, these approaches offer opportunities to develop crop varieties with broader and more durable resistance while reducing reliance on chemical control measures. The review highlights how advances in molecular genetics and biotechnology are reshaping plant protection strategies and contributing to long-term food security.Collectively, the studies presented in this Research Topic illustrate the increasingly interconnected nature of microbial ecology, genomics, and plant pathology. Although they address different biological systems and agricultural contexts, they converge on a common objective: understanding biological processes at molecular, genomic, and community levels to support more sustainable crop production systems. The studies on microbial communities demonstrate how agricultural practices shape soil ecosystem functioning and crop performance, while the genomics-focused contributions reveal the molecular mechanisms underlying pathogen virulence and plant resistance.Several broader messages emerge from this body of work. First, sustainable disease management cannot be achieved by focusing solely on pathogens; it must also account for the broader soil microbiome and its ecological functions. Second, advances in sequencing technologies continue
Setu Bazie Tagele, Assefa Sintayehu Kassa, Shimeles Tilahun et al.· Frontiers in Microbiology· 0 citations
This is the first public release of the analysis code accompanying the manuscript "Differential expression and machine-learning classification of Parkinson's disease using AMP-PD RNA-seq data" (Hu R, Dong X, et al.). Overview Analysis pipeline for RNA-seq–based differential expression (DE) and machine-learning (ML) classification of Parkinson's disease (PD) case/control status, using AMP-PD cohorts PPMI and PDBP/BioFIND (PDBF). What's included Data preparation — build case/control and mutation-carrier sample sets and extract per-cohort RNA-seq quantification matrices (DE/src/filtration.py, DE/src/extract_quant.py) Differential expression — DESeq2-based DE analysis for genes, eRNAs, and circRNAs, adjusted for covariates (age, sex, plate, RIN, genotype PCs) (DE/src/DE_PCs.R, DE/src/eRNA/, DE/src/circRNA/) Functional enrichment — GO/KEGG/Reactome/GSEA analysis on DE results (DE/src/Enrich_Profiler.R) ML classification — logistic regression, LASSO, SVM, random forest, XGBoost, KNN, MLP, and stepwise feature-addition classifiers trained on PPMI and independently tested on PDBF (ML/src/*.py) Model evaluation — combined AUROC/PR curve and feature-importance summary plots (ML/src/31_plots_AUC_PR_inOne.py) NanoString QC — standalone endogenous/housekeeping gene QC script (DE/NanoStringData/) Recorded R/Python session info and pinned dependencies (session_info/) Requirements R ≥ 4.0 (tidyverse, DESeq2, clusterProfiler, and related Bioconductor/CRAN packages) Python ≥ 3.8 (pandas, numpy, scikit-learn, matplotlib, seaborn) Known limitations Scripts are tailored to the specific AMP-PD sample sets and covariate schemas used in this study; not general-purpose tools File paths and cohort-specific parameters are set via literals/CLI args and may need editing for new datasets Raw AMP-PD data are not distributed with this repository; access requires a separate application to the AMP-PD Knowledge Portal (https://amp-pd.org/) License MIT License
TheDongLab· Zenodo (CERN European Organi...· 0 citations
Genetic Disorders: Molecular Mechanisms, Diagnostic Evolution, Precision Therapeutics and Future Directions Description Genetic Disorders: Molecular Mechanisms, Diagnostic Evolution, Precision Therapeutics and Future Directions is a comprehensive narrative review examining the current understanding and rapidly evolving landscape of genetic disorders. The review integrates advances in molecular genetics, genomic diagnostics, precision medicine, and emerging therapeutic strategies to provide a broad perspective on the diagnosis and management of genetic diseases. The review explores the molecular mechanisms underlying genetic disorders, including pathogenic genetic variants, mutations, genomic alterations, gene regulation, and disrupted molecular pathways involved in disease development and progression. It further examines the evolution of genetic diagnosis from conventional approaches toward advanced genomic technologies, including next-generation sequencing (NGS), whole-exome sequencing (WES), whole-genome sequencing (WGS), and molecular diagnostic approaches. A major focus of this review is the development of precision therapeutics and personalized medicine. Emerging approaches such as gene therapy, genome editing, RNA-based therapies, targeted molecular treatments, and genotype-guided therapeutic strategies are discussed in the context of their potential to transform the management of genetic disorders. The review also addresses important challenges in genomic medicine, including variant interpretation, genetic counseling, ethical considerations, accessibility of advanced diagnostic technologies, clinical implementation, and the integration of genomic data into routine healthcare. Finally, the article highlights future directions in genetic medicine, including the potential contributions of artificial intelligence, bioinformatics, multi-omics technologies, genomic data analysis, and precision healthcare. These developments may facilitate earlier diagnosis, improved disease classification, individualized treatment selection, and better clinical outcomes. Overall, this narrative review provides an integrated overview of the transition from traditional genetic diagnosis and management toward genomics-driven, mechanism-based, and precision therapeutic approaches, while identifying key opportunities and challenges for future research and clinical practice. Keywords:Genetic Disorders; Molecular Genetics; Genetic Mutations; Genomic Medicine; Molecular Mechanisms; Genetic Diagnosis; Next-Generation Sequencing; Whole-Exome Sequencing; Whole-Genome Sequencing; Precision Medicine; Precision Therapeutics; Gene Therapy; Genome Editing; RNA-Based Therapy; Personalized Medicine; Bioinformatics; Artificial Intelligence; Genomic Technologies.
Dr.Zeeshan Qaiser· Zenodo (CERN European Organi...· 0 citations
Intracerebral hemorrhage (ICH) is a life-threatening subtype of stroke characterized by the acute accumulation of blood within the brain parenchyma and progressive secondary brain injury. After ICH, dysregulated neuroinflammation drives a cascade of secondary injury processes that shape neurological deterioration and long-term recovery. However, the molecular mechanisms that determine the magnitude, temporal evolution, and resolution of immune-inflammatory responses after ICH remain incompletely understood, limiting the development of targeted therapeutic strategies. Accumulating evidence indicates that epigenetic regulation constitutes a critical layer controlling neuroinflammatory programs after ICH. Hematoma-derived stimuli, including hemoglobin degradation products, iron overload, oxidative stress, and damage-associated molecular patterns, create a unique inflammatory microenvironment that reshapes gene-regulatory landscapes in resident and infiltrating cells. DNA methylation remodeling, histone modification dynamics, chromatin accessibility alterations, and non-coding RNA regulatory networks collectively orchestrate cell-type–specific transcriptional reprogramming in microglia, astrocytes, endothelial cells, neurons, and infiltrating leukocytes. Rather than serving as passive consequences of tissue injury, these epigenetic processes actively modulate innate immune activation, cytokine production, leukocyte recruitment, blood–brain barrier integrity, and the balance between neurotoxic and reparative inflammatory states. In this review, we synthesize current evidence on the immuno-epigenetic regulation of neuroinflammation after ICH, with emphasis on cell-specific mechanisms, temporal dynamics, and immune–vascular interactions. We discuss how epigenetic reprogramming contributes to inflammatory amplification, glial phenotypic transitions, endothelial dysfunction, and the potential persistence of maladaptive inflammatory memory. Finally, we highlight emerging precision strategies, including locus-specific epigenome editing and RNA-based therapeutics, that may enable targeted modulation of neuroinflammation after ICH. An immuno-epigenetic perspective may provide a conceptual framework for developing precision neurotherapeutics for ICH.
Yifan Li, Wei Jiang, Fangtian Zhong et al.· Frontiers in Immunology· 0 citations
Liver failure (LF) represents a severe clinical syndrome associated with rapid deterioration of hepatic function, high mortality, and limited therapeutic options. Despite extensive research efforts, the complex pathogenesis of LF remains incompletely understood, and effective therapeutic strategies remain limited. Animal models have therefore become indispensable platforms for elucidating disease mechanisms, dissecting pathological processes, and evaluating potential therapeutic interventions. In this review, we systematically summarize major animal models for liver failure research, covering surgical, toxin-induced, drug-induced, metabolic, immune-mediated, ethanol-associated, and gene-editing approaches. We discuss their mechanisms, modeling strategies, advantages, limitations, and translational relevance, with emphasis on their ability to recapitulate key pathological features of human LF. We also discuss recent therapeutic advances assessed in LF models and highlight future perspectives. Finally, we discuss current challenges and future directions, including humanized models, organoid-based platforms, multi-omics integration, artificial intelligence-assisted optimization, and standardized evaluation systems. By integrating current advances and unresolved challenges, this review underscores the importance of refining LF animal models to enhance mechanistic investigations and improve the clinical translation of emerging therapies.
Lu Tian, Jiale Zhang, Dongyue Jiao et al.· Stem Cell Research & Therapy· 0 citations
Agriculture stands at a critical crossroads. Escalating challenges from climate change, population growth, resource constraints, and complex biotic and abiotic stresses demand crop improvement strategies that are faster, more precise, and more sustainable than conventional breeding alone. Genetically modified (GM) and genome-edited (GEd) technologies have emerged as powerful complementary tools to enhance resilience, productivity, nutritional quality, and environmental compatibility (Chen et al., 2024;Ahmadikhah et al., 2025;Bahmankar et al., 2026). The eleven articles in this Research Topic illustrate the rapid evolution of plant biotechnology-from foundational genome-editing platforms to functional trait validation, accelerated breeding, biosafety assessment, and responsible deployment. This Editorial synthesizes key themes, highlights synergies, and charts pathways toward sustainable agricultural systems.Plant genetic engineering has advanced from random transgene integration to precise modification of endogenous genes. Ahmadikhah et al. (2025) trace the progression from RNAi and early nucleases (ZFNs, TALENs) to versatile CRISPR/Cas systems, noted for their simplicity, efficiency, and reduced off-target effects. Yu et al. (2025) show that heterologous gene expression can outperform endogenous counterparts, underscoring the importance of smart gene selection. These advances support complex trait modifications for multi-stress tolerance and quality (Chen et al., 2024), proving especially powerful in polyploid species (Li and Iqbal, 2024).Genome editing has become indispensable for functional genomics and rapid cultivar development. Jayakody et al. ( 2024 2024) exemplify both the power and complexity of this approach: knockout of StNRL1 enhanced late blight resistance but increased susceptibility to early blight, highlighting defense trade-offs and the need for multi-omics integration. 2024) provide biosafety insights, showing that transgenic maize and glyphosate influence rhizosphere microbial communities in ways modulated by genotype and management.Successful adoption requires effective co-existence with conventional and organic systems. As Caradus (2025) emphasizes, coexistence is achievable through science-based stewardship, including appropriate isolation distances, temporal and physical barriers, and robust supply chain segregation. Countries with extensive organic and GM cultivation demonstrate that transparent governance and farmer communication minimize adventitious presence while preserving market choice. Advances in detection, traceability, and transgene-free editing (Ahmed et al., 2024) support proportionate oversight that balances innovation with safety. Public acceptance depends on transparent risk communication, stakeholder engagement, and equitable access-particularly for smallholder farmers.This Research Topic opens exciting horizons: enhanced editing efficiency in complex genomes, strategic stacking of beneficial alleles, integration with synthetic biology and AI-driven design, and accelerated use of crop wild relatives for climate resilience. Challenges remain, including mitigation of pleiotropic effects, long-term ecological stability, regulatory harmonization, and equitable scaling. Overcoming these requires deepened interdisciplinary collaboration and sustained investment in contained trials, post-release monitoring, and knowledge-sharing platforms.The studies in this Research Topic demonstrate that GM and GEd technologies have matured into indispensable pillars of sustainable agriculture. By integrating precision editing, accelerated breeding, functional genomics, and rigorous biosafety assessment, we can develop crops that are more resilient, productive, and environmentally harmonious. Realizing these potential demands adaptive regulation, transparent stakeholder engagement, equitable access, and global collaboration. As pressures on food systems intensify, these innovations offer genuine hope for nourishing a growing population while safeguarding planetary boundaries. The future lies not in choosing between technologies, but in responsibly harnessing them to build resilient, sustainable, and inclusive food systems for generations to come.
Moslem Bahmankar, Maryam Salehi, Mohammad Reza Naghavi et al.· Frontiers in Sustainable Foo...· 0 citations
A-to-I RNA editing is increasingly recognized as a regulator of fungal development and pathogenesis, yet its extent and lifecycle dependence remain poorly characterized in basidiomycetes. Microbotryum superbum (MvSup), M. intermedium (MI), and M. lychnidis-dioicae (MVLG) are members of the M. violaceum fungal complex. Each species infects specific host plant species, resulting in commonly anther-smut disease. The lifecycle of these basidiomycete fungi includes the haploid, mating, and infection stages. RNA editing is a post-transcriptional process where adenosine (A) is converted to inosine (I) by adenosine deaminase enzymes, and such modifications to RNAs may lead to synonymous and nonsynonymous codon changes, thereby altering protein function. Here, we compared A-to-I editing across the haploid, mating, and infection stages of these three related species to determine how editing patterns vary with lifecycle stage and among closely related fungal pathogens. The a2 haploid strain of MI had fewer editing sites compared to other haploid strains. The predicted codon/amino acid changes in each haploid strain across the three species indicated three primary types of resulting amino acid substitutions that were common to both of the mating-type strains across the three species: threonine to alanine, lysine to glutamic acid, and valine to alanine. During the mating stage of MvSup, a synonymous codon change was found in a mitogen-activated protein kinase domain-containing protein within the protein’s conserved region. Gene expression analysis revealed that certain genes, uniquely edited during the mating stage of MvSup, tend to be upregulated in the haploid stage but downregulated during mating, and vice versa. Research on RNA editing in basidiomycetes is relatively new. RNA editing mechanisms in fungi have been implicated in fungal pathogenesis, although the exact roles and implications remain unclear. Additional research will help us understand the functional significance of this apparently ubiquitous process in several members of the Microbotryum fungal complex, with possible ramifications more generally in fungi.
Shikhi Baruri, Nasara Shamsudeen, Alycia C. R. Lackey et al.· Journal of Fungi· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
MIT News · Artificial Intelligence· news.mit.eduAug 17, 2026
