Comparison of multidimensional decoding of affect from audio, video and audiovideo recordings.
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Affect↗
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Plant cis-regulatory grammar: Decoding the multidimensional code of transcriptional regulation for programmable crop engineering.
Cis-regulatory elements (CREs) orchestrate the spatiotemporal precision of gene expression that underlies plant development, adaptation, and domestication. Decoding the cis-regulatory grammar of plant genomes remains a central challenge in modern biology, with profound implications for programmable crop engineering. Here, recent conceptual and technological advances are synthesized to reshape our understanding of plant CREs. This review first argues that CRE function is not only an intrinsic property of DNA sequence alone but also emerges from a multidimensional context, including chromatin accessibility, histone modifications, three-dimensional genome topology, and cell type-specific regulatory landscapes. Furthermore, the convergence of single-cell epigenomics, high-throughput functional assays, and CRISPR-based dissection has begun to unravel this contextual grammar, revealing the computational principles governing transcriptional regulation. Critically, we propose that artificial intelligence (AI) platforms are catalyzing an ongoing transition from descriptive discovery to predictive engineering, wherein these platforms outperform natural evolution in designing synthetic CREs. Finally, a roadmap is outlined toward a plant regulatory grammar foundation model, which will enable truly predictive engineering of gene expression when fine-tuned for specific tasks. Collectively, the integration of single-cell resolution maps, precise genome editing, AI-driven design, and regulatory-compliant delivery systems promises to transform our ability to reprogram plant gene regulation for next-generation agriculture, bridging the gap between foundational regulatory biology and tangible crop improvement.
artificial intelligence↗
Multidimensional Protein Corona Analysis Toward Predictive Nano-Bio Interface Design.
Nanoparticles entering biological fluids are rapidly coated by proteins and other biomolecules, converting their synthetic surfaces into biologically active nano-bio interfaces. These coronas regulate colloidal stability, immune recognition, cellular uptake, biodistribution, pharmacokinetics, cargo delivery, and toxicity. Yet a protein list obtained by mass spectrometry captures only part of this interface. Corona identity and function are also shaped by protein organization, binding stability, exchange dynamics, conformational changes, and molecular accessibility. Here, we discuss recent progress in protein corona isolation and analysis from a question-oriented analytical perspective, with emphasis on how centrifugation, magnetic recovery, affinity- or chemistry-enabled capture, chromatography, filtration, and field-flow fractionation (FFF) influence the fidelity, integrity, and comparability of recovered coronas. We then examine how proteomic profiling can be integrated with binding measurements, interfacial structural analysis and functional validation to distinguish descriptive corona signatures from biologically meaningful mechanisms. We further consider how biofluid composition, disease state, tissue interfaces and cellular environments remodel corona identity, presentation, and bioactivity. Finally, we argue that standardized reporting, computational modeling, and AI-enabled approaches are essential for converting protein corona datasets into reproducible and predictive knowledge that can guide the design of drug delivery systems and precision nanomedicines.
Protein Corona↗
Pharmacogenomics-based subtype decoded implications for risk stratification and immunotherapy in pancreatic adenocarcinoma.
BACKGROUND: With fatal malignant peculiarities and poor survival rate, outcomes of pancreatic adenocarcinoma (PAAD) were frustrated by non-response and even resistance to therapy due to heterogeneity across clinical patients. Nevertheless, pharmacogenomics has been developed for individualized-treatment and still maintains obscure in PAAD. METHODS: A total of 964 samples from 10 independent multi-center cohorts were enrolled in our study. With drug response data from the profiling of relative inhibition simultaneously in mixtures (PRISM) and genomics of drug sensitivity in cancer (GDSC) databases, we established and validated multidimensionally three pharmacogenomics-classified subtypes using non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms, separately. The heterogenous biological characteristics and precision medicine strategies among subtypes were further investigated. RESULTS: Three pharmacogenomics-classified subtypes after stable and reproducible validation, distinguished in six aspects of prognosis, biological peculiarities, immune landscapes, genomic variations, immunotherapy and individualized management strategies. Subtype 2 was close to immunocompetent phenotype and projected to immunotherapy; Subtype 3 held most favorable outcomes and metabolic pathways distinctively, promising to be treated with first-line agents. Subtype 1 with worst prognosis, was anticipated to chromosome instability (CIN) phenotype and resistant to chemotherapeutic agents. In addition, ITGB6 contributed to subtype 1 resistance to 5-fluorouracil, and knockdown of ITGB6 enhanced sensitivity to 5-fluorouracil in in vitro experiments. Ultimately, appropriate clinical stratified treatments were assigned to corresponding subtypes according to pharmacogenomic transcripts. Some limitations were not taken into account, thus needs to be supported by more research. CONCLUSION: A span-new molecular subtype exploited for PAAD uncovered an insight into precise medication on ground of pharmacogenomics, and highly refined multiple clinical management strategies for specific patients.
Humans↗
Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.
In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.
Multiomics↗