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324 papers
Findings reveal that FA9 interacts with VAP to promote lipid droplet biogenesis and lipid transport, thereby driving fatty acid accumulation in soybean seeds.
ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.
The present study addresses optimization of in-vitro regeneration via direct organogenesis and Agrobacterium-mediated genetic transformation, enabling efficient multiplex CRISPR/Cas9-based genome editing of the phytoene desaturase (PsPDS) gene in pea. Pea (Pisum sativum L.) is an important legume crop valued for food, plant-based protein, vegetable, and green manure. Although genome editing offers a precise and rapid strategy for crop improvement, its application in pea remains challenging due to inherent recalcitrance to in-vitro regeneration and genotype-dependent transformation. The regeneration and Agrobacterium-mediated transformation systems were optimized, and the dicotyledonary node (DCN) was identified as the preferred explant for multiplex CRISPR/Cas9-based genome editing in pea. Among three explant types (embryonic axis, DCN and nodal segment), DCN showed the highest regeneration efficiency, producing 100% shoot bud induction and 39.70 shoots per explant on MS medium augmented with 6-benzylaminopurine (BAP; 6.00 mg/L) and kinetin (1.00 mg/L). Shoot elongation and rooting efficiencies were improved using GA3 (1.00 mg/L), BAP (1.00 mg/L), IAA (0.10 mg/L), and NAA (0.5 mg/L), respectively. Manipulating explant type, Agrobacterium optical density, vacuum infiltration, acetosyringone concentration, infection time, and co-cultivation duration improved the transient transformation efficiency. We noted efficiency from 23.33% to 90.00% in DCN and from 6.66% to 93.33% in embryonic axis explants across 10 pea cultivars. Stable transformed lines generated from the DCN of cultivar Kashi Samridhi were confirmed by GUS staining and PCR. The optimized regeneration and transformation system facilitated targeted editing of phytoene desaturase (PsPDS) in pea, achieving ICE-estimated mutation frequencies of upto 97% in independent lines. The study provides a robust platform for functional genomics and accelerates the deployment of genome-editing technologies for pea improvement.
Rice (Oryza sativa L.) is highly vulnerable to drought during the reproductive phase, with yield losses exceeding 50% due to spikelet sterility, pollen abortion, and impaired grain filling. Progress through conventional breeding has been constrained by the polygenic nature of drought tolerance and by strong genotype × environment (G × E) interactions. This review proposes a systems breeding strategy integrating five complementary approaches rice pangenomics, genome-wide association studies (GWAS), genomic selection (GS), high-throughput phenomics, and precision genome editing to strengthen drought resilience at the reproductive stage. Structural variants identified through pangenome analyses across diverse Oryza accessions have been implicated in abscisic acid (ABA) signalling, osmolyte biosynthesis, antioxidant defence, and root system architecture pathways central to reproductive-stage drought adaptation. Multi-omics-informed GWAS, combined with co-localisation of eQTLs and protein QTLs in drought-stressed reproductive tissues, highlights high-confidence candidate genes including OsNAC14, OsbZIP23, and DRO1 that help explain the physiological basis of water-deficit adaptation. Incorporating envirotyping data into GS models has been shown to improve predictive accuracy across diverse rainfed environments. Alongside marker-assisted selection, base editing and prime editing enable targeted allelic refinement with minimal off-target effects. We present a proposed tiered candidate prioritisation pipeline that advances loci supported by convergent genomic, transcriptomic, proteomic, and field-level evidence toward practical breeding deployment. Translating these discoveries into climate-resilient varieties will require FAIR data sharing, coordinated phenotyping networks, and multi-environment validation platforms linking genomic discovery to scalable breeding pipelines for drought-resistant, high-yielding rice in rainfed systems.
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Overall, despite existing challenges, neoantigen-targeted immunotherapies hold promise for advancing precision oncology and improving patient outcomes.
It is demonstrated that porcine expanded potential stem cells, derived from preimplantation embryos, provide a robust and versatile platform for generating donor cells for xenotransplantation and for functionally evaluating genetic modifications, thereby advancing the prospects of xenotransplantation.
This review summarizes the trajectory of iPSC reprogramming technologies and identifies the core “translational triltrilas”, namely, the inherent tradeoffs between security, homogeneity, and scalability, and proposes a comprehensive strategy to overcome these bottlenecks.
A charge-altering releasable transporter (CART) that delivers RNA selectively to the corneal endothelium, a non-regenerative cell layer whose dysfunction underlies several blinding conditions and establishes CARTs as a platform for non-viral gene delivery to the eye, with the potential to treat corneal dystrophies and other vision disorders.
These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.
The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios.
A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.
Human-Computer Interaction and Visualization
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
Generative AI
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.
In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value — and why innovation resists simple formulas.
General Science
